National Institute on Alcohol Abuse and Alcoholism

Project MATCH Monograph Series

Volume 8

Project MATCH

Hypotheses:

Results and

Causal Chain

Analyses

Editors:

Richard Longabaugh, Ed.D. 

Brown University

Philip W. Wirtz, Ph.D.

George Washington University

Project MATCH Monograph Series Editors:

Margaret E. Mattson, Ph.D. 

(Project MATCH staff collaborator)

Jane K. Myers

U.S. Department of Health and Human Services

Public Health Service

National Institutes of Health

National Institute on Alcohol Abuse and Alcoholism 

6000 Executive Boulevard

Rockville, Maryland 20892–7003
Project MATCH was supported by grants under a cooperative 

agreement funded by the National Institute on Alcohol Abuse and 

Alcoholism (NIAAA) and implemented by nine clinical research 

units and a coordinating center. The project was initiated and 

administered by the Treatment Research Branch, NIAAA. Questions 

or comments regarding this monograph should be directed to 

Richard Longabaugh, Ed.D., Center for Alcohol and Addiction 

Studies, Brown University, School of Medicine, 800 Butler Drive, 

Potter Building, Room 204, Providence, RI 02906.

For further information on Project MATCH and a list of the 

study’s publications, see http://www.commed.uchc.edu/match. 

The NIAAA website is http://www.niaaa.nih.gov.

The views and opinions expressed on the following pages are solely 

the responsibility of the authors and do not necessarily represent 

the official views of the Institute or the U.S. Department of Health 

and Human Services.

The figures appearing on pages 35, 88, 140, and 213 are 

copyrighted and are reproduced herein with permission of 

the copyright holder. Further reproduction of these copyrighted materials is prohibited without specific permission of the copyright holder. All other material contained 

in this volume is in the public domain and may be used 

or reproduced without permission from the Institute or 

the authors. Citation of the source is appreciated.

NIH Publication No. 01–4238 

Printed 2001
iii

Acknowledgments

As in any such mammoth effort as this one, the people to whom we owe 

gratitude are legion. They have been thanked in other Project MATCH 

publications. In producing this monograph, we want to give special 

thanks to a special few. First, we thank the authors of the matching hypothesis chapters, who have worked with us through a decade 

of research: initially through the hypothesis development phase, and 

more recently through the numerous iterations of chapter development. 

We hope their patience and perseverance is well rewarded. Secondly, 

we wish to thank three administrative assistants at the Brown Center 

for Alcohol and Addiction Studies: Isabel Vieira, Deborah Mendes, and 

Julia Wolin. Each assisted mightily in keeping this project on track. 

Third, we wish to thank Jane K. Myers, publisher, who took extra special care to make this monograph a quality production. Finally, we want 

to express our gratitude to Dr. Margaret E. Mattson, Project MATCH 

Monograph Series Editor. Her assistance in editing the chapters was 

exceptional. Her help in guiding the development of the overall MATCH 

story was critical. As always, the monograph editors accept full responsibility for the finished product, which we hope is equal to more than 

the sum of its parts.

Project MATCH Research Group and

Other Contributors

William Miller, Ph.D.*

J. Scott Tonigan, Ph.D.

Center on Alcoholism, Substance Abuse and Addictions 

University of New Mexico

Albuquerque, NM

Gerard Connors, Ph.D.*

Robert Rychtarik, Ph.D.

Research Institute on Alcoholism

Buffalo, NY

Carrie Randall, Ph.D.*

Raymond Anton, M.D.

Medical University of South Carolina and

Veterans Affairs Medical Center

Charleston, SC

Clinical 

Research Units 

Principal and 

Coinvestigators

* Principal Investigator/Steering Committee member
iv

Project MATCH

Ronald Kadden, Ph.D.*

Mark Litt, Ph.D.

University of Connecticut School of Medicine

Farmington, CT

Ned Cooney, Ph.D.*

West Haven Veterans Affairs Medical Center and 

Yale University School of Medicine

New Haven, CT

Carlo DiClemente, Ph.D.*

Joseph Carbonari, Ed.D. 

University of Houston

Houston, TX

Allen Zweben, D.S.W.*

University of Wisconsin—Milwaukee

Milwaukee, WI

Richard Longabaugh, Ed.D.*

Robert Stout, Ph.D.

Brown University

Providence, RI

Dennis Donovan, Ph.D.*

University of Washington

Seattle, WA

Coordinating 

Center

Principal and 

Coinvestigators

Thomas Babor, Ph.D.*

Frances Del Boca, Ph.D.

University of Connecticut

Farmington, CT

Kathleen Carroll, Ph.D.

Bruce Rounsaville, M.D.

Yale University

New Haven, CT

NIAAA Staff Richard K. Fuller, M.D.

John P. Allen, Ph.D.

Project Officers for Project MATCH

Margaret E. Mattson, Ph.D.*

Staff Collaborator for Project MATCH

Treatment Research Branch

Diana O’Donovan

Information Specialist

Scientific Communications Branch

* Principal Investigator/Steering Committee member
v

Acknowledgements

Consultant Philip Wirtz, Ph.D.

George Washington University

Washington, DC

Contractor Jane K. Myers

Janus Associates

Bethesda, MD
vi

Project MATCH Monograph Series

The following publications are available from the National Institute on 

Alcohol Abuse and Alcoholism, Publications Distribution Center, P.O. 

Box 10686, Rockville, MD, 20849–0686.

Volume 1 Twelve Step Facilitation Therapy Manual: A Clinical Research Guide for 

Therapists Treating Individuals With Alcohol Abuse and Dependence, by 

J. Nowinski, S. Baker, and K.M. Carroll. NIH Publication No. 94–3722.

Volume 2 Motivational Enhancement Therapy Manual: A Clinical Research Guide 

for Therapists Treating Individuals With Alcohol Abuse and Dependence, 

by W.R. Miller, A. Zweben, C.C. DiClemente, and R.G. Rychtarik. NIH 

Publication No. 94–3723.

Volume 3 Cognitive-Behavioral Coping Skills Therapy Manual: A Clinical Research 

Guide for Therapists Treating Individuals With Alcohol Abuse and 

Dependence, by R. Kadden, K.M. Carroll, D. Donovan, N. Cooney, P. 

Monti, D. Abrams, M. Litt, and R. Hester. NIH Publication No. 94–3724

Volume 4 The Drinker Inventory of Consequences (DrInC): An Instrument for 

Assessing Adverse Consequences of Alcohol Abuse, by W.R. Miller, 

J.S. Tonigan, and R. Longabaugh. NIH Publication No. 95–3911.

Volume 5 Form 90: A Structured Assessment Interview for Drinking and Related 

Behaviors. Test Manual. by W.R. Miller. NIH Publication No. 96–4004.

Volume 6 Improving Compliance With Alcoholism Treatment, K.M. Carroll, editor. 

NIH Publication No. 97–4143.

Volume 7 Strategies for Facilitating Protocol Compliance in Alcoholism Treatment 

Research, A. Zweben, D. Barrett, K. Carty, B. McRee, P. Morse, and C. 

Rice, editors. NIH Publication No. 98–4144.

Volume 8 Project MATCH: Hypotheses, Results, and Causal Chain Analyses, R. 

Longabaugh and P.W. Wirtz, editors. NIH Publication No. 01–4238.
vii

Foreword

The editors and authors are to be warmly applauded for their work in 

developing this unique monograph. Although Project MATCH officially 

concluded in 1997, not all analyses and interpretations of this rich 

data set were complete at that time. In this work, Drs. Longabaugh 

and Wirtz and their authors make particularly insightful and rigorous 

contributions not previously published.

This volume is the first systematic and comprehensive treatment of 

causal chain analysis of interventions for alcohol problems. Causal 

chains are theory-based models of the change process underlying 

intervention effects. Causal chains specify a testable sequence of steps 

postulated to be necessary and sufficient occurrences leading to an 

intervention’s effects. The Project MATCH design included causal chain 

testing for all of the tested hypotheses.

In the initial chapters, the editors lay the methodological groundwork 

developed in Project MATCH for testing the causal chains associated 

with the matching hypotheses. The subsequent 18 topical chapters 

examine each of the hypotheses tested in Project MATCH and provide a 

rich array of approaches to conceptualizing and testing the associated 

causal chains. In the final two chapters, the editors provide a comprehensive and thoughtful critique of the preceding topical chapters 

and bring in new analytic approaches not available at the time of the 

original MATCH analyses. Their discussions shed light on why MATCH 

produced the findings it did and what this implies for future matching 

research and treatment research in general.

The primary audience for this volume is treatment researchers engaged 

in testing the efficacy of interventions. It offers a systematic guide to 

specify, classify, and test causal chains. The message to the field is that 

as we test the efficacy of interventions, we would do well to assure that 

we also specify a theory-grounded basis for our hypotheses and make 

use of methodology to test the causal chains underlying intervention 

effects. We recognize that the field has far to go in understanding the 

mechanisms by which behavioral interventions for alcohol problems 

exert their effects and how this knowledge might ultimately be used to 

improve outcomes. This pioneering volume is to be recommended to 

the research community as important guidance in this endeavor.

Enoch Gordis, M.D.

Director

National Institute on Alcohol Abuse and Alcoholism
viii

Project MATCH
ix

Preface

In this, the final volume of the Project MATCH Monograph series, the 

editors and chapter authors address aspects of the Project MATCH 

findings absent from previous publications. Matching was a promising approach for alcoholism treatment from the 1970s through the 

mid 1990s but, after MATCH reported no compelling support for the 

concept, many puzzling issues were raised. Why were relatively few significant matches found? Why were the effects of these matches small 

in size and inconsistent across time, outcome measure, and setting? 

How well developed were the theories underlying the matching hypotheses? Did the design and analytic methods used in Project MATCH 

permit a fair test of the hypothesis? Does the dearth of compelling 

matching findings imply that matching is no longer a relevant topic for 

treatment research?

Whereas previous MATCH publications have reported on what works, 

the emphasis here is on the why (or why not) behind the observed 

effects. In focusing on testing treatment matching theory, the editors 

and authors undertake several daunting tasks: a review of the rationale of the hypotheses; a summary of all reported and unreported 

matches from the enormous Project MATCH data base; a description 

of the causal chains postulated for the hypotheses; and the analytic 

methods employed to test the causal chains.

The term “causal chains” may be new to many. It refers to the sequence 

of steps (or pathway) postulated to lead from the intervention to its 

outcome(s). It specifies aspects of treatment, patient characteristics, or 

other factors linking the operative components of the intervention to 

intermediary processes that, in turn, lead to changed drinking behavior. This mechanism of action concept is fundamental in medicine and 

is analogous to, for example, the steps in a pharmacologic pathway, 

which may involve the metabolism of a drug to its active species, its 

binding to a particular enzymatic site, and its alteration of a biochemical reaction that leads to a change in the physiological state of the 

organism. In this volume, we see a systematic application of the concept of causal mechanisms and their testing in the realm of behavioral 

sciences.

An understanding of the causal sequence of events can inform us how 

and why the interventions were or were not effective. This constitutes 

a step beyond efficacy testing which determines if an intervention 

works. The pathway idea is built around the concepts of mediators 
x

Project MATCH

and moderators, variables that change the relationship between the 

independent variable (i.e., treatment) and the outcome variable (i.e., 

drinking).

Mediators explain the “why and how” of the effect, whereas moderators 

influence the strength of the association between independent and outcome variables. Given the many behavioral and environmental factors 

shaping alcohol addiction, it is not surprising that the picture grows 

more complicated in Project MATCH, which examined the mediation of 

moderator effects.

The authors summarize the work done by Project MATCH investigators 

using the analytic models extant at that time, that is, the late 1980s 

and early 1990s. The editors reflect on that effort and also bring to bear 

thinking developed in other fields since that time. They offer a critique 

on why Project MATCH yielded the results it did and go on to provide 

a theoretical and analytic model for an approach that could fruitfully 

become a standard aspect of treatment research in the future.

This important work has the potential to significantly advance alcoholism treatment research by stimulating other theory-driven work on 

the causal mechanisms of treatments for alcohol abuse and dependence. Improved understanding of the active ingredients of treatment 

might well contribute to: (1) more accurate models of treatment and 

placebo effects; (2) more efficient and parsimonious interventions; (3) 

a more informed justification for combining interventions; and (4) better facilitation of the transfer of treatment research findings to practice 

settings.

Margaret E. Mattson, Ph.D.

Editor, Project MATCH Monograph Series

Staff Collaborator, Project MATCH

Treatment Research Branch

National Institute on Alcohol Abuse and Alcoholism
xi

Contents

Acknowledgments .......................................................................................... iii

Project MATCH Monograph Series.................................................................... vi

Foreword ....................................................................................................... vii

Preface ...........................................................................................................ix

Purpose of the Monograph ...............................................................................1

Richard Longabaugh and Philip W. Wirtz

Part I: Design and Methodology

Matching Hypotheses .................................................................................4

Richard Longabaugh and Philip W. Wirtz

Causal Chain Analysis .............................................................................18

Richard Longabaugh and Philip W. Wirtz

Part II: Alcohol Dysfunction

Matching Clients to Alcoholism Treatment Based on Severity of

Alcohol Dependence.............................................................................30 

Ned L. Cooney, Thomas F. Babor, and Mark D. LW

The Alcohol Involvement Matching Hypothesis and Findings .....................44

Robert G. Rychtarik, William R. Miller, and J. Scott Tonigan

Part III: Psychological Dysfunction

Cognitive Impairment as a Client-Treatment Matching Hypothesis.............62

Dennis M Donovan, Daniel R. Kivlahan, Ronald M. Kadden, 

and Dina Hill

Matching Clients to Alcoholism Treatment Based on Psychopathology .......82

Ned Cooney, Ray Anton, Joe Carbonari, Kathleen Carroll, 

Carrie Randall, and James Roberts

Sociopathy as a Client-Treatment Matching Variable ................................98

Ronald Kadden, Mark Litt, Ned Cooney, Dennis Donovan, 

Robert Stout, and Richard Longabaugh

Alcoholic Typology as an Attribute for Matching Clients to Treatment ...... 118

Mark D. Litt and Thomas F. Babor

Part IV: Personality Variables

Client Anger as a Predictor of Differential Response to Treatment ............ 134

Holly Barrett Waldron, William R. Miller, and J. Scott Tonigan
xii

Project MATCH

Prospects for Matching Clients to Alcoholism Treatments Based on 

Conceptual Level................................................................................ 149 

John P. Allen

The Search for Meaning in Life as a Predictor of Alcoholism Treatment 

Outcome ............................................................................................ 154 

J. Scott Tonigan, William R. Miller, and Gerard J. Connors

Religiosity and Responsiveness to Alcoholism Treatments........................ 166

Gerard J. Connors, J. Scott Tonigan, and William R. Miller

The Interpersonal Dependency Matching Hypothesis ............................... 176

Robert G. Rychtarik

The Gender Matching Hypothesis ............................................................ 186

Frances K Del Boca and Margaret E. Mattson

Part V: Motivation and Self-Efficacy

Motivation Hypothesis Causal Chain Analysis ......................................... 206

Carlo C. DiClemente, Joseph Carbonari, Allen Zweben, 

Tanya Morrel, and Rebecca E. Lee

Alcohol Problem Recognition and Treatment Outcomes............................ 223

William R. Miller and J. Scott Tonigan

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis ............... 239

Carlo C. DiClemente, Joseph P. Carbonari, Jill Walker Daniels, 

Dennis M. Donovan, Lori E. Bellino, and Tara M. Neavins

Part VI: Interpersonal Functioning and Support

Network Support for Drinking ................................................................. 260

Richard Longabaugh, Philip W. Wirtz, Alan Zweben, and 

Robert Stout

Prior Alcoholics Anonymous Involvement and Treatment Outcome .......... 276

J. Scott Tonigan, William R. Miller, and Gerard J. Connors

Social Functioning................................................................................... 285

Richard Longabaugh, Philip W. Wirtz, and Christopher Rice

Part VII: Conclusions and Implications

Methodological Assessment and Critique ................................................. 296

Philip W. Wirtz and Richard Longabaugh

Substantive Review and Critique ............................................................. 305

Richard Longabaugh and Philip W. Wirtz
1

Purpose of the Monograph

Richard Longabaugh, Ed.D., and Philip W. Wirtz, Ph.D.

Project MATCH was conducted to provide a rigorous, large-scale study of the 

client-treatment matching hypothesis, a 

topic of keen interest to the alcohol treatment 

community. The generic matching hypothesis 

states that client outcomes can be improved 

by matching clients of known characteristics 

to specified treatments. The result of successful matching is that clients in the “matched” 

condition have better outcomes than similar clients assigned to an alternative treatment. The 

results of testing the matching hypothesis have 

been widely disseminated in several key Project 

MATCH publications (Project MATCH Research 

Group 1997a, b, 1998a, b, 1999) and a book 

that summarizes the study and its numerous 

contributions to the knowledge base (Babor and 

Del Boca, in press).

As is widely acknowledged (Project MATCH 

Research Group 1998b), evidence for matching 

effects was disappointing. Despite the promise 

of earlier matching studies (Mattson et al. 1994), 

the intuitively appealing notion that matching 

can appreciably enhance treatment effectiveness has been severely challenged. Why were so 

few successful matches found (Project MATCH 

1997b, p. 1690)? It may be that matching clients based on single attributes is simply not an 

effective strategy to alter drinking outcomes, 

that is, acceptance of the null hypothesis of no 

effect. Or perhaps the study design was flawed 

in some essential way that prevented an adequate test of the hypothesis. Aless obvious but 

critical issue is that per-haps our understanding of matching processes was inadequate, 

leading to flawed assumptions about the operatives involved and unsupportable hypotheses.

The purpose of the present volume is to 

examine this latter alternative in detail. It is 

important to conduct this exercise for the following reason. If the theory underlying each 

of the 40 predictions was adequate and yet 

the matches were not supported by the study 

results, then one would be justified in concluding that the tested matches are not important 

in assigning treatment, and that the theories 

underlying those matches are also invalid. 

Indeed, matching as a generic concept would 

be severely challenged. If, on the other hand, 

the matching predictions were based on inadequately developed theory, then perhaps Project 

MATCH did not give the generic hypothesis of 

client-treatment matching a fair test.

This volume focuses on what has been 

learned from examination of the theories underlying each of the matching hypotheses. The 

rationale for each a priori matching prediction 

is presented, as well as a complete description 

of the results of testing each prediction. Thus, 

our objective is to present the results of testing 

the theories from which each matching prediction was derived in greater depth than has been 

provided in other publications.

Preview of the Monograph

Part I describes the design and methodology 

used in Project MATCH. The first chapter provides a detailed introduction to the development 

of the matching hypotheses and discusses key 

design and statistical decisions made by Project 

MATCH to guide the testing of these hypotheses. The second chapter presents a detailed 

discussion of the causal chain analyses used to 

examine the theories underlying these hypotheses. It also describes a typology for organizing 

the voluminous data resulting from testing 

the matching hypotheses and their underlying 

theoretical frameworks. This provides the context for the chapters presenting the individual 

matching hypotheses, results, and causal chain 

analyses.
2

Project MATCH

Each of the 18 topical chapters follows the 

same general organization. First, the empirical and theoretical rationale is provided for the 

matching predictions, as well as the predictions 

themselves. Each matching variable is operationally defined, as are other variables to be 

included in the analyses. Next, a causal chain 

is provided to test the linkages hypothesized to 

underlie the anticipated matching effect. The 

data analysis plan is summarized, followed by 

a presentation of the results of tests of each 

matching prediction and its underlying causal 

chain. Each matching hypothesis and causal 

chain was tested twice, once with outpatients 

and once with aftercare clients. Usually, these 

results are presented separately. Finally, each 

chapter concludes with a discussion of the 

results of testing predictions involving this particular matching variable.

We have loosely grouped these chapters by 

sections. Part II has two chapters devoted to 

constructs pertaining to the severity of alcohol 

dysfunction, namely, alcohol dependence and 

alcohol involvement. Part III includes matching 

variables having to do with psychological dysfunction. The first chapter focuses on cognitive 

impairment. The next chapter, psychopathology, reports the results of testing predictions 

from two correlated matching variables, psychiatric severity and axis I psychiatric diagnoses. 

The third chapter in this section also focuses 

on two correlated matching variables, sociopathy and antisocial personality disorder. The last 

chapter in this section is concerned with the A 

versus B alcohol typology.

Part IV presents variables in the domain 

of person trait variables: anger, conceptual 

level, meaning seeking, prior religious beliefs 

and behaviors, interpersonal dependency, and 

gender.

Part V includes person variables that are 

conceptualized as more state- rather than traitlike in their nature. Two chapters are devoted to 

measures of motivational readiness: readiness 

to change and alcohol problem recognition. The 

last chapter in this section addresses two correlated measures of self-efficacy: temptation, and 

temptation minus confidence.

In Part VI, the focus changes to constructs 

which address the clients’ relationships to their 

interpersonal environment: network support 

for drinking, prior involvement with Alcoholics 

Anonymous, and client social functioning.

As each of these chapters has a major content 

focus in its own right, we recommend that the 

reader first approach them selectively, according to specific interest.

Finally, the concluding chapters of this 

mono-graph again approach the subject of 

matching as the end point in its own right. Here 

we attempt to summarize and critique what we 

have learned from Project MATCH’s decade-long 

quest to contribute to client-treatment matching theory.

References

Babor, T., and Del Boca, F., eds. Treatment 

Matching in Alcoholism. United Kingdom: 

Cambridge University Press, in press.

Mattson, M.E.; Allen, J.P.; Longabaugh, R.; 

Nick-less, C.; Connors, G.J.; and Kadden, 

R.M. A chronological review of empirical 

studies matching alcoholic clients to treatment. Journal of Studies on Alcohol Suppl. 

12:16–29, 1994.

Project MATCH Research Group. Matching alcoholism treatments to clients heterogeneity: 

Project MATCH posttreatment drinking 

outcomes. Journal of Studies on Alcohol 

58(1):7–29, 1997a.

Project MATCH Research Group. Matching 

Alcoholism treatments to client heterogeneity: Tests of the secondary a priori 

hypotheses. Addiction 92(12):1671–1698, 

1997b.

Project MATCH Research Group. Matching 

alcoholism treatments to client heterogeneity: Treatment main effects and 

matching effects on drinking during 

treatment. Journal of Studies on Alcohol 

59(6):631–639, 1998a.

Project MATCH Research Group. Matching 

alcoholism treatments to client heterogeneity: Project MATCH three-year 

drinking outcomes. Alcoholism: Clinical 

and Experimental Research 22(6a):1300–

1311, 1998b.

Project MATCH Research Group. Matching 

patients with alcohol disorders to treatments: Clinical implications from Project 

MATCH. Journal of Mental Health 7(6):589–

602, 1999.
3

Part I: Design and Methodology

Matching Hypotheses

Causal Chain Analysis
4

Matching Hypotheses

Richard Longabaugh, Ed.D., and Philip W. Wirtz, Ph.D.

Project MATCH was the largest randomized clinical trial of a psychosocial treatment 

for alcoholism ever undertaken, involving 1726 clients, 10 universities, and 10 clinical 

research units in a collaborative study with the National Institute on Alcohol Abuse 

and Alcoholism. Three treatment modalities were compared, and 21 client characteristics were tested as matching variables. For each matching variable, one or more 

hypotheses were developed to predict which treatments would be most and least helpful to clients who differed on that variable. The client population studied had a current 

DSM-III-R diagnosis of alcohol abuse or dependence. Clients had to be at least 18 

years old and could not be currently dependent on sedative/hypnotic drugs, stimulants, cocaine, or opiates nor could they have used any intravenous drug in the prior 6 

months. Clients were comprehensively assessed (Connors et al. 1994) prior to random 

assignment to one of the three treatments and then were followed at 3-month intervals 

for a year after treatment completion. In the outpatient arm of study, clients were again 

interviewed at 3 years after treatment completion.

The design chosen to test the matching 

hypotheses in Project MATCH was only 

one of several viable alternatives. While 

scientific considerations were primary in the 

eventual design selected, other factors came into 

play once scientific criteria were met. Examples 

of these factors are described below.

Treatments

A matching study might well have started 

by selecting a client variable to which to match 

(e.g., gender) and then embarked upon developing a treatment that would be tailored to gender 

needs, perhaps maximizing the chances of finding matching effects. We however selected our 

treatment modalities first and then chose the 

client attributes that might be differentially 

affected by assignments to these treatments. 

Thus, matching hypotheses were developed 

after treatments had been decided upon. We 

might have increased the robustness of matching effects had we first developed the hypotheses 

and then designed treatment modalities to maximize the differences between the treatments by 

embodying active ingredients thought to be differentially effective in interaction with the client 

attribute.

The treatments chosen were CognitiveBehavioral Coping Skills Therapy (CBT; Kadden 

et al. 1992), Motivational Enhancement 

Therapy (MET; Miller et al. 1992), and Twelve 

The author listing of this chapter is alphabetical. Both authors contributed equally.

Richard Longabaugh, Ed.D.

Center for Alcohol and Addiction Studies 

Brown University, School of Medicine

800 Butler Drive, Potter Building, Room 204 

Providence, RI 02906

E-mail: Richard_Longabaugh@Brown.edu

Philip W. Wirtz, Ph.D.

Department of Management Science 

George Washington University

2115 G Street NW, #403

Washington, DC 20052

Email: pww@gwu.edu
5

Matching Hypotheses

Step Facilitation (TSF; Nowinski et al. 1992). 

They were selected on the basis of several criteria (Donovan et al. 1994) :

■ Demonstrated clinical effectiveness

■ Potential for revealing matching effects

■ Applicability to existing treatment programs and client populations

■ Distinctiveness from the other MATCH 

treatments selected

■ Feasibility of implementation within the 

constraints of a research trial.

Cognitive-behavioral therapy had been shown 

to be effective in a great number of studies, was 

widely used in academic settings, had been 

delivered to alcohol dependent populations in 

a great many studies (Holder et al. 1991), and 

could be delivered within a short period. Several 

of the principal investigators had previously 

conducted randomized clinical trials involving 

variations of CBT.

While we had considered a no-treatment 

control group, ethical and practical considerations precluded this. Our search for a 

minimum treatment comparison group led to 

the selection of MET, a specific application of 

motivational interviewing (Miller and Rollnick 

1991). Motivationally based brief intervention 

had been shown to be effective in a number 

of studies (Holder et al. 1991). Because of its 

philosophy of maximizing utilization of client 

resources, it was thought to provide a significant contrast from CBT, which assumed that 

the road to recovery was through teaching new 

skills to the client. Consequently, many of the 

matching predictions developed were predicated 

in whole or in part on the expectation that four 

sessions of MET would be insufficient for clients 

with severe problems in various areas.

TSF was selected because of the popularity 

of the Minnesota Model in the treatment field. 

Fundamental to the Minnesota model are the 

Twelve Steps of Alcoholics Anonymous and 

the integration of treatment with the client’s 

involvement in AA. Despite the popularity of 

this treatment approach, its effectiveness had 

not received adequate rigorous testing (Miller 

and Hester 1986). The opportunity to provide 

a rigorous test of the model in the context of 

client-treatment matching was compelling. It 

was expected that its treatment philosophy, 

which included a reliance on support groups 

and a higher power, would differ markedly from 

the MET focus on utilization of client resources 

and CBT’s focus on individual skill development 

guided and taught by the CBT therapist.

Other treatment modalities were seriously 

considered, for example, the Community 

Reinforcement Approach (CRA). It was decided 

not to select CRA in view of perceived difficulties 

in implementing it across 10 clinical research 

units (CRUs). One pharmacological intervention, naltrexone, was also considered but was 

judged to not have sufficient evidence for its 

effectiveness at that time to warrant its inclusion in a major multisite test of matching.

The study design compared treatment modalities within two distinct treatment settings (or 

arms), aftercare and outpatient. The CRUs successful in the competition for the cooperative 

agreement grants were located at sites that had 

access to either inpatient or freestanding outpatient programs. The outpatient arm involved 

clients who had not had an inpatient treatment 

immediately preceding their involvement in the 

MATCH trial. In the case of CRUs attached to 

inpatient units, an aftercare study was conducted because it was deemed infeasible to 

superimpose the MATCH treatment on the inpatient program. The study was not conducted as 

an aftercare versus outpatient matching study 

because clients could not be randomly assigned 

to treatment setting.

Dependent Variables

As the study was of alcohol treatment effectiveness, one or more measures of alcohol 

consumption were to be included as primary 

dependent variables. Among a large number 

considered, percentage of days abstinent (PDA) 

and average drinks per drinking day (DDD) 

were chosen. PDA was an easy selection as it 

had high usage in prior studies and provided a 

relatively straightforward measure of drinking 

frequency (Babor et al. 1994). The selection of 

DDD was much more difficult. The goal was to 

index drinking intensity, a dimension of drinking typology that was empirically associated 

with, but conceptually independent of drinking 

frequency.
6

Part I: Design and Methodology

Several candidates were considered, the 

leading contender among them being percentage of heavy drinking days. This measure was 

not selected, however, because of difficulty in 

designating a “heavy drinking day”. While earlier research had frequently used six or more 

drinks as a cutoff, changes in cultural practices 

in the United States suggested that this figure 

was now too high. Furthermore, it was becoming clear that males and females differed in the 

effects of consuming the same amount of alcohol. Also, body weight and other factors were 

refining the conception of risky alcohol consumption. We concluded that what constituted 

a heavy drinking day was a moving target. DDD 

was selected because it represented an index 

of absolute amount of alcohol consumed (measured in standard drinks), independent of what 

might eventually be determined to constitute 

hazardous drinking intensity.

DDD had the major disadvantage that clients who had no drinking days during a period 

would have no data point. As this was likely to 

be so for a sizable percentage of clients within 

any followup period, this was unsatisfactory. 

Instead we opted to include in the analyses clients who had zero drinks on a “drinking day”. 

This retained all clients in the analysis who were 

successfully followed up but also ensured that 

there would be a hefty correlation between PDA 

and DDD during the posttreatment period. The 

decision to include “zero drink drinking days” 

also resulted in a heavy concentration of observations at zero drinks per drinking day. This led 

to a methodologically determined lack of independence between the two primary dependent 

variables.

Primary dependent variables were limited 

to two in order to preserve the power to detect 

credible differences. Using the same two made 

it possible to preserve a standard metric for 

comparing matching effects across different 

matching variables.

Another promising construct for a primary 

dependent variable was believed to be negative 

consequences of alcohol consumption. However 

a disadvantage was that, at the time, there was 

no standard measure of negative consequences 

(Zweben and Cissler 1996). Alcohol dependence 

was also considered but discarded because it 

was an ambiguous construct that was difficult 

to quantify. Total abstinence was excluded 

as a primary dependent variable because of 

its insensitivity to major changes in drinking 

patterns that did not involve total abstinence 

and because of the relatively low percentage 

of clients who would be likely to achieve total 

abstinence throughout the entire recorded followup period.

Hypothesis Selection

The development of client-treatment matching 

hypotheses in Project MATCH was a significant 

departure from previous research on matching 

which was accomplished via single-site, smaller 

scale studies (Mattson et al. 1994). Because of 

the nature of the multisite collaborative study, 

a Steering Committee (SC) was authorized to 

develop the research hypotheses and the design 

to test these hypotheses as well as to assume 

collective responsibility for conducting the 

study and reporting the findings. The 11 principal investigators and other senior investigators 

involved in the process published main findings 

papers under the corporate authorship identified as the Project MATCH Research Group, 

PMRG (Project MATCH 1997a).

At the outset, the SC decided that rather than 

testing one or two matching hypotheses, several 

would be tested. Members of the PMRG formed 

self-selected groups called matching hypothesis 

teams (MHTs). Typically each team was composed of three to five members, including at 

least one statistician/methodologist and one or 

more clinical scientists who were well versed in 

the substance of the matching variable. Each 

team was charged to develop predictions about 

matching a single client attribute to one or 

more of the three study treatments. These were 

then presented to the entire SC, which had the 

responsibility for deciding which matching variables and a priori hypotheses would be selected 

for testing in the trial.

So that the entire SC would be fully informed 

in making these decisions, a procedure and 

review process was put into place. Each MHT 

developed a 10–20 page document presenting the rationale for selection of a given client 

variable and rationale for each of the matching 

predictions proposed. The rationale included a 

review of published empirical support for use of 
7

Matching Hypotheses

the variable in matching research and a theoretical justification for the matching predictions 

made. Central to the theoretical justification was 

a “causal chain” (discussed in detail in the next 

chapter) that would provide a description of the 

underlying process (or processes) postulated to 

be necessary for the matching prediction to be 

supported. The procedures for operationalizing 

the matching variable were specified as well as 

analytic methods to test the predictions,

Each MHT document was submitted to a 

matching hypothesis review team composed 

of the editors of this volume, RL and PWW, 

whose primary expertise was, respectively, substantive and methodological/statistical. This 

review team critiqued the document and determined if it was ready for review by the full SC. 

Eventually each matching hypothesis was discussed, critiqued, and voted upon by the full SC 

to determine whether it would be included as 

one of the matching hypotheses.

This process was intensive and lengthy, 

spanning approximately 2 years during the 

planning phase. First, the document developed by the MHT had to pass the team’s own 

review. Then, critique by the review team was 

followed by one and usually several iterations 

with the MHT before the document was ready 

for consideration by the full SC. Typically, on 

first presentation, the SC did not vote acceptance but raised questions and returned the 

document to the MHT for further revisions. The 

revised manuscript would then again undergo 

one or more iterations between the review team 

and the MHT before being resubmitted to the 

full SC for further deliberation. At that point, 

the matching hypothesis would be voted in or 

out. This last step was not perfunctory. In total, 

28 matching variables were proposed by MHTs 

to the SC. Of these, 21 were tested in matching 

predictions.

Many of the variables selected for matching had 

been previously researched in single-site studies by members of the SC: Alcohol Involvement 

(Miller, Rychtarik,), Alcohol Dependence 

(Babor, Cooney), Psychiatric Severity 

(Cooney, Kadden), Sociopathy and Antisocial 

Personality Disorder (Cooney, Kadden, Litt, 

Longabaugh), Cognitive Impairment (Cooney, 

Donovan, Kadden, Longabaugh), Motivation 

(Carbonari, DiClemente, Miller, Tonigan), 

Self-Efficacy (DiClemente), Typology (Babor, 

Litt), Social Support (Longabaugh, Stout), Social 

Functioning (Cooney, Kadden), Interpersonal 

Dependency (Longabaugh), and Alcoholics 

Anonymous (Tonigan).

Given the treatments selected and the demonstrated potential of most of the matching 

variables, a primary focus of the matching 

hypothesis teams was development of the 

rationale for making client-treatment matching predictions, that is, why one or the other of 

the three already selected treatments would be 

likely to have a differential effect on the client 

matching variable.

How many treatments were to be included 

in the individual matching hypotheses was 

left up to the matching hypothesis teams. 

Some teams chose to develop predictions that 

involved contrasting matching effects for all 

three treatments, while other teams selected 

just two of the three treatments in their contrasts. Still others included all three treatments 

in the matching predictions but aggregated two 

together and compared the aggregate against 

the third. Any such aggregation was based on 

theory; for example, one treatment included 

an active ingredient thought likely to especially 

effect clients with a given attribute, whereas the 

other two treatments lacked or deemphasized 

this active ingredient. Another factor was the 

MHT’s belief in the likely strength of the predicted matching effect. Teams believing that 

the matching effect was likely to be robust were 

more apt to include more predictions.

The SC decided during this review process 

that not all matching hypotheses would have 

equal status. One group was designated as primary matching hypotheses. They were given a 

higher priority because the SC felt they had a 

more compelling rationale because either their 

prior empirical support was stronger or the theory underlying the expected matching effects 

was more persuasive. 

A second group of client variables was designated to test secondary matching hypotheses. 

Matching predictions involving these variables 

were also developed a priori but were deemed 

of lesser priority because their empirical 

and/or theoretical rationale was seen as less 

persuasive. By the end of the review process, 

9 matching variables were accepted by the SC 
8

Part I: Design and Methodology

as primary and the remaining 12 as secondary. 

Had our results supported this designation of 

primary versus secondary hypotheses, much 

more would have been made of the distinction. While earlier publications reported the 

results of primary (Project MATCH Research 

Group 1997a) and secondary (Project MATCH 

Research Group 1997b) matching hypotheses, 

that distinction has proved unnecessary and is 

not used in this monograph. Table 1 lists all of 

the Project MATCH matching hypotheses.

Client-Treatment 

Interactions

For our purposes, an interaction is said to 

occur when a differential response to two treatments occurs as a function of the degree to 

which a client possesses a particular characteristic. Project MATCH allowed matching effects to 

involve interactions that were either ordinal or 

disordinal. A disordinal interaction was judged 

to occur when the two slopes were observed 

to cross one another at some point along the 

measurable client attribute continuum (figure 1), such that clients at one interval on the 

continuum were found to have better drinking 

outcomes when assigned to one treatment, but 

Good

Poor

Low High

Treatment A

Treatment B

Drinking

outcome

Client attribute

Figure 1. Disordinal interaction

clients at another interval on the continuum 

were found to have better drinking outcomes 

when assigned to the contrasting treatment.

If, in contrast, at one interval along the client attribute continuum, clients were found to 

have better drinking outcomes when assigned 

to one treatment but clients elsewhere on the 

continuum were found to have neither better 

nor worse drinking outcomes, then the interaction was designated as ordinal (figure 2).

Good

Poor

Low High

Treatment A

Treatment B

Drinking

outcome

Client attribute

Figure 2. Ordinal interaction

In Project MATCH, we were interested in 

detecting both disordinal and ordinal interactions, even though disordinal interactions 

were more likely to have immediate wide-ranging implications for the field. Because of their 

theoretical value as well as their more limited 

immediate clinical value, interactions hypothesized to be ordinal were also approved by the 

SC.

All attribute-treatment interaction effects 

were hypothesized to be linear. Therefore, it was 

expected that the further up or down the range 

of the client attribute scale the actual value is, 

the more likely the treatments are to lead to different outcomes. Had a nonlinear interaction 

been hypothesized (or observed), the detection procedure would have become much more 

complex.

Ordinal Interactions at the 

Nonpredicted End of the Continuum

We did not specify as essential evidence for 

matching that the observed difference between 

the treatments occur at the end of the continuum where we expected the matching effect to 

be apparent. Thus, it was possible that some 

interactions would be observed at the “wrong 

end” of the variable continuum; this may be a 

somewhat difficult point to grasp and therefore 

can perhaps be best understood by an example.
9

Matching Hypotheses

Table 1. Summary of hypothesized contrasts for each matching variable

Matching variable Hypothesized contrast of slopes or differences between meansa

CBT vs. MET TSF vs. MET CBT vs. TSF

Alcohol Dependence TSF > CBT

Alcohol Involvementb CBTc

 > MET TSFc > MET

Cognitive Impairment CBT > MET TSF > MET TSF > CBT

Psychopathology and 

Severity CBT > MET CBT > TSF

Sociopathy and ASPD CBT > MET TSF > MET CBT > TSF

A versus B Typologyb CBTc

 > MET TSFc > MET

Angerb MET > CBTc MET > TSFc

Conceptual Level MET > TSF

Meaning Seekingb TSF > METd TSF > CBTd

Religiosityb TSF > METd TSF > CBTd

Interpersonal Dependencyb TSF > METd TSF > CBTd

Gender CBT > TSF

Motivational Readiness CBT > MET

Problem Recognitionb CBTc

 > MET TSFc > MET

Self-Efficacy confidence MET > CBT MET > TSF

Temptation minus 

confidence TSF > MET

Network Support for 

Drinking CBT > MET TSF > MET

Prior AAb TSF > METd TSF > CBTd

Poor Social Functioning CBT > MET CBT > TSF

a The hypothesized contrasts predict differences in slopes of the regression lines for each treatment on outcome as 

a function of client attribute. With the exception of the gender and typology attributes (which take on only discrete 

values), all contrasts take the form: The difference between the first treatment and the second becomes more positive (or less negative) with increasing values on the attribute. The gender and typology attributes take the form: The 

difference in means between the treatments is greater at one level of the attribute than at the other. Hypotheses did 

not test whether interactions were ordinal or disordinal.

b The rationale underlying the alcohol involvement, meaning seeking, typology, anger, religiosity, interpersonal 

dependency, problem recognition, and prior AA hypotheses assumes that, pertinent to the putative active ingredients involved in the hypothesized matching effect, two treatmenTSFcre not different in their effect. Therefore, they 

were combined into a single condition which was then contrasted with the third treatment.

c

 Combined TSF and CBT treatment groups 

d Combined MET and CBT treatment groups
10

Part I: Design and Methodology

In Project MATCH, the matching hypothesis 

team predicted that CBT would be more effective than TSF or MET for clients with higher 

psychopathology. This assertion was made with 

the knowledge that some components of the 

CBT treatment are designed to treat psychopathology while this is not so for either TSF or 

MET. Thus, the attribute-treatment interaction 

was predicted to occur because the CBT slope 

would be more positive than would the TSF or 

MET slopes, that is, as client psychopathology 

increased, it would be expected that CBT clients 

would have increasingly more favorable drinking outcomes than would TSF or MET clients. 

However, we instead found that with decreasing

psychopathology, TSF clients had increasingly 

better outcomes than did CBT clients. The 

treatment triaging implication is to assign low 

psychopathology clients to TSF and not to CBT, 

rather than to assign high psychopathology clients to CBT and not to TSF.

But does this triage implication mean that 

the matching hypothesis is unsupported? 

Examination of the attribute-treatment interaction (see figure 2, page 88) reveals that the 

relative position of the two slopes is as predicted. 

As psychopathology increases, the outcomes of 

CBT clients are relatively better than at lower 

levels of psychopathology In contrast, as psychopathology of TSF clients increases, their 

outcomes are not relatively better than at lower 

levels of psychopathology. As a result, the CBT 

slope is more positive than is the TSF slope, as 

was predicted from the rationale leading to the 

matching prediction.

Thus, the matching effect indicates that CBT 

is worse than TSF for clients without psychopathology Clearly, evidence for and against the 

matching hypothesis is incomplete. It may be 

that TSF is a more effective treatment than CBT 

for the average alcohol dependent client because 

TSF has more of a particular active ingredient 

that would help all alcohol dependent clients 

irrespective of their psychopathology. However, 

because of CBT’s unique effectiveness for clients with greater psychopathology, it is able to 

close the gap of effectiveness between TSF for 

clients with high psychopathology. Other explanations are also conceivable.

What is critical for the purpose of making the 

present point is that, as predicted, (1) the CBT 

slope was more positive than the TSF slope in 

the direction predicted at the required level of 

statistical significance and (2) at some interval along the client attribute continuum clients 

assigned to one of the two treatments would 

have better drinking outcomes than clients 

assigned to the other treatment, while this was 

not so for the remainder of the client sample.

In this example, all of the criteria specified 

for meeting the MATCH requirements for a 

matching effect were met. However, whether the 

theory underlying the matching effect is supported cannot be determined from outcome data 

alone. The causal chain analyses presented in 

this volume contribute to the interpretation of 

the matching results by examining further pertinent information.

Clinical Significance

If a disordinal interaction occurs, the clinical 

implications are obvious. The clinician would 

assign clients to the treatment that is best for 

clients with similar characteristics. All other 

factors being equal, those having a low score on 

the attribute should be assigned to treatment 

A, while those with a high score on the attribute 

should be assigned to treatment B.

Had Project MATCH found several disordinal 

interactions, the value to the treatment field 

would have likely been considerable. However, 

disordinal interactions were found for only three 

client attributes—client anger and network support for drinking in the outpatient arm of study 

(although ordinal interactions were hypothesized in each instance) and client dependence 

on alcohol in the aftercare arm of study.

The clinical benefits to the treatment field of 

an ordinal interaction are less obvious. A first 

reaction is, if clients with one score on the attribute do better in one treatment, but there is no 

difference between treatments on the outcome 

for clients with other scores on the attribute, 

then assign them all to the treatment that has 

been found to be more effective for at least some 

of them, and no worse for the others.

However, the decision tree may become more 

complex when other considerations are taken 

into account. Two examples of considerations 

that may influence treatment choice involve 

practical issues. For example, if the treatment 

that is more effective for some requires more 
11

Matching Hypotheses

resources to deliver than the other, then this 

treatment might be reserved for only those clients who are likely to incrementally benefit. This 

is the rationale for assigning clients to different 

levels of treatment intensity, such as inpatient 

versus outpatient treatment.

Another consideration is the availability 

of the treatments contrasted. If a given program has only a limited number of therapists 

who have been trained to deliver the treatment 

that is more effective for some, but has other 

therapists available who can deliver the other 

treatment that is not less effective for the others, then the program might well assign clients 

with nondiscriminating scores to this other 

treatment.

Matching Hypotheses

As operationalized in Project MATCH, a 

client-treatment matching hypothesis is a prediction about the occurrence of a statistically 

significant interaction between a client attribute and a treatment modality, such that the 

predicted regression slopes of the contrasted 

treatments over the range of the client attribute 

are different from one another in a direction 

consistent with the theory. Graphically, this is 

evident when the slopes of the regression lines 

of the compared treatments on client outcomes 

are significantly different from one another in 

the hypothesized direction across the range of 

values of the client attribute. The criterion for 

statistical significance was achieved when a 

one-tailed prediction had an observed probability value equal to or less than 0.05, corrected for 

the number of contrasts tested for that matching variable.

It is important to note a criterion that was 

not included in the specification of our matching hypotheses. We did not require that the 

matching prediction specify the directionality 

of the individual slopes; rather the requirement 

was that the difference in slopes be consistent 

with theory-based expectations.

This decision was based on the absence of 

an untreated control group in Project MATCH. 

Thus, we were unable to tell whether a given client attribute would have prognostic value in the 

absence of treatment. Without this knowledge, 

we would be unable to say whether a slope that 

was negative was an indication that the treatment-client attribute combination was having 

an adverse affect on drinking outcome as compared to no treatment. Thus, if the slope of 

the line is descending for clients higher on the 

attribute scale, the only meaningful comparison 

possible is relative to the slope of the line for 

the comparison treatment. If both are descending, then the slope with the lesser decline would 

still be considered a relative match for that 

treatment. It is theoretically possible that both 

treatments may have been mismatches for this 

set of clients relative to no treatment at all. It 

must therefore be remembered that throughout this volume we are examining only client 

attribute-treatment combinations relative to 

other combinations of the attribute with different treatments.

Matching Effects

If the observed attribute by treatment interaction met these two criteria (statistically 

significant different slopes in a specified direction), then we tested whether the observed 

interaction had any clinical significance. An 

interaction that also had clinical significance 

was deemed a “matching effect”. Clinical significance was asserted when we could identify one 

or more intervals along the client attribute axis 

in which we were at least 95-percent confident 

that a client having an attribute score within 

this interval would have an average drinking 

outcome superior to the contrasting treatment 

condition.

However, in order for the effect to be a 

matching effect it was also required that not all 

clients assigned to the one treatment condition 

would have better outcomes than if assigned 

to the other condition. In other words, at some 

point along the client attribute continuum, 

the two slopes also had to either cross or be 

close enough to each other that we could not be 

95-percent confident that clients with attribute 

scores at this point would have different drinking outcomes. If this latter criterion were not 

met, then we did not designate the attributetreatment interaction to be a matching effect.
12

Part I: Design and Methodology

Clinical Versus Theoretical 

Value

In one respect, the criterion of clinical usefulness sets a higher standard for results to 

be judged of value than does the criterion of 

theoretical importance. For a client-treatment 

interaction to be clinically useful, it is not only 

necessary that attribute-treatment interactions 

be found, it is also minimally necessary that 

one subset of clients, but not another, be identified for whom the likelihood of a good outcome 

is enhanced by assignment to one or another 

treatment. The amount of clinical improvement 

resulting from differential treatment assignment is indicative of the clinical value to be 

attached to the matching effect. If the outcomes 

of a large number of clients could be enhanced, 

this result translates into clinical usefulness. 

Similarly, if the treatment outcomes of a smaller 

number of clients would be greatly improved by 

matching, this also would have greater clinical 

significance.

In contrast, for an attribute-treatment interaction to be theoretically interesting, it is only 

necessary that the slopes of the contrasting 

treatment by attribute lines be statistically significantly different from one another. This would 

include the case in which there was no matching effect discerned, even though there was a 

statistically significant interaction. This possible scenario is illustrated in figure 3, where 

it can be seen that at no level of the matching 

variable was the one treatment less effective 

than the other, yet an attribute-treatment interaction was observed, nested within a main effect 

of treatment.

Good

Poor

Low High

Treatment A

Treatment B

Drinking

outcome

Client attribute

Figure 3. A client-treatment interaction 

without a matching effect.

Thus, it is not even necessary that the interaction account for clinically meaningful variance; 

what is important is that a lawful relationship 

has been established.

If a client attribute-treatment interaction is 

statistically significant, but we are unable to 

identify the mechanisms producing this interaction, two effects result. First, it undermines the 

credibility of the effect, that is, was the relationship observed by chance or because of a lawful 

relation that exists between the variables. In 

other words, is it really true? Second, it also 

substantially limits the information available 

when attempting to replicate and/or generalize 

the effect with new populations. The scientific 

aim is to identify the set of conditions under 

which a client attribute-treatment interaction occurs, irrespective of whether a clinically 

important matching effect is observed.

Data Analysis

A Design Committee)1 was constituted for 

developing the plan for data analysis. Upon 

completion, the Project MATCH data analysis 

plan consisted of six major phases: (1) a preliminary phase in which descriptive and other 

analyses were performed to provide an overview 

of the data and to test assumptions for the main 

hypothesis tests; (2) confirmatory testing of the 

primary a priori matching hypotheses; supplementary analyses addressing (3) the secondary 

a priori matching hypotheses and (4) alternative analytical approaches for testing matches; 

(5) analyses directed at major issues other than 

patient-treatment matching; and (6) exploratory analyses aimed at detecting matching 

that was not predicted a priori. As the focus of 

this monograph is testing the a priori matching 

hypotheses, only the first four of these phases 

is reviewed.

1 Design Committee members included Joseph 

Carbonari, Fran Del Boca, Mark Litt, Richard 

Longabaugh, Larry Muenz, Robert Rychtarik, Robert 

Stout (Chair), Scott Tonigan, and Philip Wirtz.
13

Matching Hypotheses

Preliminary Analyses

Extensive analyses were performed to pro-vide 

an overview of the data and test assumptions 

for the main hypothesis tests. Of primary concern at this stage were:

■ Compliance

■ Missing data

■ Psychometric and distributional properties of variables that were critical to the 

primary objectives of the trial

■ The effectiveness of urn randomization in 

producing equivalent treatment groups

■ Detection of any site-based effects

■ The relationships among the matching 

factors and among the primary dependent measures

■ Assessment of the adequacy of the 

potential covariate candidates.

A number of key decisions resulted from 

these preliminary analyses. The distributional 

properties of the primary outcome variables 

suggested the need for an arcsin transformation 

of the frequency measure (percentage of days 

abstinent) and for a square root transformation 

of the intensity measure (drinks per drinking 

day). The two key outcome variables were found 

to be moderately correlated, but enough differences were found to merit separate analysis of 

each of them in the main analytical phase of 

the trial. Based on extensive analysis of missing 

data patterns, a “two-thirds” rule was adopted 

for aggregating daily data into summary weekly 

and monthly outcome indices, such that an 

individual’s outcome indicators were recorded 

as “missing” on any week (during treatment) or 

month (following treatment) in which less than 

two-thirds of the drinking data were available 

for that individual.

Testing the Matching Hypothesis

For the two principal classes of a priori 

hypotheses addressed in Project MATCH (primary and secondary matching hypotheses), 

individual differences in response to alcohol 

treatment were modeled as a latent growth process (see, for example, Bryk and Raudenbush 

1992; Laird and Ware 1982; Goldstein 1986; 

Strenio et al. 1983). Conceptually, this is 

roughly equivalent to creating a separate plot 

for each subject, with up to 12 points plotted (one for each followup period where data 

are available for the individual subject), with 

the outcome variable on the vertical axis and 

time on the horizontal axis. For each subject, a 

smooth curve (a latent growth curve) is then fit 

to these data so as to come as close as possible 

to the plotted points. Depending on investigator-specified constraints, this curve can be as 

simple as a straight line or as complex as an 

nth order polynomial and is of the same level of 

complexity for all subjects.

Review of the matching hypotheses led to the 

decision that testing for quadratic polynomials 

would be sufficient. First, it could be argued that 

a matching effect would result from treatment 

that would persist in strength across the one 

year of posttreatment followup. Certainly this 

would be the hope of clinical investigators. On 

the other hand, given the oftentimes observed 

short-lived main effects of treatment, it was also 

quite plausible that matching effects present at 

the completion of treatment might subside with 

increasing time from treatment.

Still a third possibility considered was that 

matching effects might take time to emerge after 

treatment. This would be especially likely if 

ceiling effects were operative at treatment completion because all clients were doing very well. 

Only with time at risk from treatment completion would the differential effects of matching 

emerge. Finally, it was conceived as possible 

that it might take time for an effect to emerge, 

but that this effect would dissipate with longer 

intervals from treatment completion. A quadratic time variable would capture any of these 

possible scenarios. No greater levels of complexity were considered to be interpretable.

A further decision was made to split the 

period of observation into two distinct periods, within treatment and posttreatment. The 

primary window of observation was the period 

following scheduled treatment completion. 

(Treatment was scheduled to occur over 12 

weeks). The Steering Committee believed that 

the posttreatment window was by far the most 

important time to consider. A matching effect 

that did not persist after the completion of 

treatment was considered to have little practical 

importance. Thus, the primary outcome period 

was the posttreatment period extending from 
14

Part I: Design and Methodology

the beginning of month 4 to the end of month 

15. However, within-treatment matching effects 

were also to be investigated for their theoretical 

importance. If it were to be found that a matching effect occurred during treatment, this would 

support the theoretical rationale underlying the 

matching prediction, even though the observed 

matching effect would not substantially help 

clients.

It is interesting to note that this emphasis 

on posttreatment effects is not shared in randomized clinical trials of pharmacotherapeutic 

effects. In such studies, the end point for analysis is usually when the client is taken off the 

medication. For a parallel analysis in psychosocial treatment studies, the end point would 

also be at the completion of the therapy administration. Pharmacotherapy studies are more 

likely to focus on efficacy whereas psychosocial 

treatment outcome studies have traditionally 

focused more on effectiveness.

Model Parameters

The data analysis plan had no provision for 

systematically testing for nonlinear interaction effects between treatment modality and 

client attribute. This was because none of the 

matching hypotheses to be tested anticipated 

nonlinear matching effects. However, such relationships are not inconceivable. For example, it 

could be hypothesized that CBT would be more 

effective with clients who had moderate social 

skills than with clients who had either very 

good social skills or were markedly deficient. 

Those with extreme skills deficits might be 

unable to benefit sufficiently from social skills 

acquisition, whereas those with exceptionally 

good skills would not be in need of any further 

skills acquisition.

In contrast, TSF might be especially beneficial both for clients with especially poor social 

and exceptionally good social skills. Those with 

poor social skills might benefit from the group 

support offered to AA members, irrespective 

of their coping skills. Those with exceptionally good social skills might attain high status 

within AA because of these skills, which would 

help them to serve as role models for those 

less fortunate. These two nonlinear functions 

between a social skills matching variable and 

CBT and social skills and TSF could produce a 

nonlinear matching effect.

Some of the matching hypothesis teams did 

give consideration to the possibility of nonlinear matching effects, but it was decided that 

adding this complexity to an already seemingly 

very complex data analysis plan was unnecessary. Consequently, no comprehensive test of 

nonlinear matching effects was ever conducted, 

although some of the individual matching 

hypothesis teams did conduct exploratory 

analyses for such effects when linear matching 

effects were not observed.

For each matching hypothesis, each analysis was conducted twice (once for each of the 

two principal dependent measures) in each arm 

(outpatient, aftercare) and for each time period 

(within treatment and posttreatment) using the 

PROC MIXED procedure in SAS. In each analysis, the intercept, time, and time squared terms 

were entered as random factors in a linear 

model which also included the baseline value 

of the outcome variable, the matching variable, 

the treatment variable, the matching-by-treatment interaction, and the interactions of each 

of these with time and with time squared.

Analyses involving these time effects were 

centered at the midpoint of the associated 

period (within treatment, posttreatment) for two 

reasons. First, midpoint centering can reduce 

the implicit colinearity between an interaction 

term and the constituent variables of which it 

is a product (see Aiken and West 1991), thereby 

improving, the power of the design to identify matching effects when they exist. Second, 

centering facilitates the interpretation of any 

significant attribute-treatment interaction 

which does not change across time. Specifically, 

when time is centered at the midpoint of the 

period under investigation (and in the absence 

of a significant interaction of linear or quadratic 

time with the attribute-treatment effect), the 

coefficient of a significant attribute-treatment 

interaction represents the difference in slopes 

at the midpoint of the period.

Analyses involving discrete matching variables (e.g., gender, typology) were conducted 

in a manner similar to those associated with 

continuous matching variables, although the 

interpretation of the results varied slightly. 

Instead of focusing on between-treatment differences in the slope of the relationship between 
15

Matching Hypotheses

the dependent variable and the (continuous) 

matching variable, these analyses focused on 

between-treatment differences in the mean

of the dependent variable at the two levels of 

the matching variable. Thus, the question 

became, for example, whether the difference in 

mean outcome between two treatments varied 

as a function of gender (possibly in interaction with linear or quadratic time). Similar 

to the analyses involving continuous matching variables, the hypotheses associated with 

the discrete matching variables were tested 

using Bonferroni-adjusted a priori directional 

contrasts.

Two sets of covariates were examined. The 

first set included the baseline value of the criterion drinking measure and its interaction with 

time. The second added a site-effect term. The 

objective of these analyses with the site-effect 

term added was to minimize the possibility that 

any discovered interactions were merely spurious reflections of differences between sites (i.e., 

to control for one potential source of internal 

invalidity). Conducting the analysis both ways—

with and without the site in the model—was 

intended to facilitate discovery of hypothesized 

interaction relationships while at the same time 

allowing any such discovered relationships to 

be conservatively interpreted if they dissipated 

as a result of including site in the model.

Formal Testing Procedure

Each of the primary and secondary matching 

hypotheses was formally tested using a series 

of hypothesis-specific directional contrasts. Of 

primary interest was whether a given contrast 

was statistically significant (in the hypothesized direction), either by itself (directionally) 

or in interaction with the linear or quadratic 

time terms (nondirectionally). Contrasts for 

which a significant linear time and/or quadratic term appeared were then subjected to 

further testing on a month-by-month (posttreatment) or week-by-week (within treatment) 

basis in order to more adequately capture the 

timing of the increasing or decreasing matching 

effect. For any matching effect that interacted 

with time, each individual period was tested to 

judge whether the matching effect was present 

during that period. For examining these changing matching effects, p<.05 was judged to be a 

sufficient criterion for deciding whether matching was present within that single time period.

Although in theory only three data points 

are needed to estimate the parameters of any 

individual’s latent growth curve under a quadratic polynomial specification, additional data 

points provide a much better assessment of the 

validity of the polynomial specification selected 

and of the error terms employed in the statistical tests. The flexibility offered by the Time 

Line Follow-Back procedure (Miller and Del 

Boca 1994) for assessing drinking behavior 

provided Project MATCH investigators with the 

opportunity to analyze drinking behavior across 

any time interval desired (including daily, if 

warranted). Recognizing that computational 

restrictions prohibited latent growth analysis 

of daily data across an 18-month period, 12 

summary data points were felt to be sufficient 

to capture the essence of an individual’s drinking behavior. For this reason, the latent growth 

analyses were based on the 12 weekly assessments of each participant’s drinking behavior 

during treatment and on the 12 monthly assessments of each participant’s drinking behavior 

following treatment.

For the a priori matching analyses, there was 

concern that strict application of a Bonferroni 

adjustment (accounting for both the primary 

and the secondary hypotheses)—including 

all of the hypotheses, two outcome variables 

per hypothesis, and multiple contrasts within 

some of the hypotheses—would result in an 

excessively conservative cutoff level that would 

unduly enhance the likelihood of a type 2 error. 

Since the matching hypotheses were conceived 

of as conceptually independent of one another, 

it was decided to apply a trialwide Bonferroni 

correction to tests of significance within each 

hypothesis family, taking into account the two 

outcome variables and the number of proposed 

contrasts for that hypotheses, irrespective of 

whether the matching hypothesis was considered to be primary or secondary. If, for example, 

there were three hypotheses relating to a single 

matching variable, then those hypotheses were 

tested at a Bonferroni-corrected alpha level 

of 0.05/3. Because there were two dependent 

variables, the alpha level was further corrected 

by a factor of 2.
16

Part I: Design and Methodology

Latent Growth Analysis Versus 

Fixed-Effects MANOVA

At the time that Project MATCH was being 

designed, latent growth modeling (LGM) was 

largely unknown in alcohol research, although 

it was in widespread use in certain social 

science disciplines (notably educational psychology). The decision was made to focus 

on this analytical technique as the primary 

methodology for several reasons. First, there 

is extensive evidence of idiographic patters of 

drinking among those dependent upon alcohol, and LGM permits individuals to manifest 

their unique patterns (within the confines of the 

selected polynomial form). Second, unlike classical general linear model formulations, LGM 

permits individual variation across time to be 

modeled as a random (rather than fixed) effect, 

thereby providing (1) estimation of the extent of 

between-individual variation across time, and 

as a result, (2) better estimation of the interaction effects and the error terms which form 

the bases for the statistical tests. Third, unlike 

multivariate formulations of the general linear model, individual subjects are permitted to 

have missing data at one or more time points 

and still be included in the analysis.

For these reasons, LGM was selected as 

the primary analytical approach for testing 

the Project MATCH primary and secondary 

matching hypotheses. However, because latent 

growth modeling was relatively new to the alcohol field, all major analyses were conducted a 

second time using the more widely understood 

fixed-effects general linear modeling procedure. 

Results of the two approaches consistently 

converged, with small differences due primarily to the differential assumptions, the slightly 

different sample size (classical general linear 

modeling requires no missing data), and crossclient variance in parameters.

In summary, the data analysis plan was 

quite elegant and appeared well suited to the 

hypotheses that it was developed to test. If 

matching was an active process in treatment, 

it seemed likely that this powerful design was 

well equipped to identify these effects, at least 

within the scope of possible effects envisioned.

Acknowledgements

This project was supported by a series of 

grants from the National Institute on Alcohol 

Abuse and Alcoholism as part of the cooperative 

agreement on matching patients to alcoholism 

treatments (U10AA 08443). We particularly 

thank Dr. Margaret E. Mattson and Jane K. 

Myers for their careful and thoughtful review 

and editing of this chapter. We also thank Julia 

Wolin for her assistance in the preparation of 

this chapter.

References

Aiden, L.G., and West, S.G. Multiple Regression: 

Testing and Interpreting Interactions.

Newbury Park: Sage, 1991.

Babor, T.F.; Longabaugh, R.; Zweben, A.; Fuller, 

R.; Stout, R.L.; Anton, R.F.; and Randall, 

C.L. Issues in the definition and measurement of drinking outcomes in alcoholism 

treatment research. In: Donovan, D., and 

Mattson, M.E. Alcoholism Treatment 

Matching Research: Methodological and 

Clinical Approaches. Journal of Studies 

on Alcohol Suppl. No. 12 (Dec.): 101–111, 

1994.

Bryk, A.S., and Raudenbush, S.W. Hierarchical 

Linear Models: Applications and Data 

Analysis Methods. Newbury Park, CA: 

Sage Publications, 1992.

Connors, G.J.; Alen, J.P.; Cooney, N.L.; 

DiClemente, C.C.; Tonigan, J.S.; and 

Anton, R.F. Assessment issues and strategies in alcoholism treatment matching 

research. In: Donovan, D., and Mattson, 

M.E. Alcoholism Treatment Matching 

Research: Methodological and Clinical 

Approaches. Journal of Studies on Alcohol

Suppl. No. 12 (Dec.): 92–100, 1994.

Donovan, D.M.; Kadden, R.M.; DiClemente, 

C.C.; Carroll, K.M.; Longabaugh, R.; 

Zweben, A.; and Rychtarik, R. Issues in the 

selection and development of therapies in 

alcoholism treatment matching research. 

In: Donovan, D., and Mattson, M.E. 

Alcoholism Treatment Matching Research: 

Methodological and Clinical Approaches. 

Journal of Studies on Alcohol Suppl. No. 12 

(Dec.): 138–148, 1994.
17

Matching Hypotheses

Goldstein, H.I. Multilevel mixed linear model 

analysis using iterative generalized least 

squares. Biometrika 73(1):43–56, 1986.

Holder, H.D.; Longabaugh, R.; Miller, W.R.; 

and Robonis, A.V. The cost effectiveness of 

treatment for alcoholism: A first approximation. Journal of Studies on Alcohol

52:517–540, 1991.

Kadden, R.; Carroll, K.M.; Donovan, D.; Cooney, 

N.; Monti, P.; Abrams, D.; Litt, M.; and 

Hester, R. Cognitive-Behavioral Coping 

Skills Therapy Manual: A Clinical Research 

Guide for Therapists Treating Individuals 

With Alcohol Abuse and Dependence. 

Project MATCH Monograph Series. Vol. 3 

DHHS Pub. No. (ADM) 92–1895. Rockville, 

MD: National Institute on Alcohol Abuse 

and Alcoholism, 1992.

Laird, N.M., and Ware, H. Random-effects 

models for longitudinal data. Biometrics

38:963–974, 1982.

Mattson, M.E.; Allen, J.P.; Longabaugh, R.; 

Nickless, C.; Connors, G.J.; and Kadden, 

R.M. A chronological review of empirical 

studies matching alcoholic clients to treatment. Journal of Studies on Alcohol Suppl. 

12:16–29, 1994.

Miller, W.R., and Del Boca, F.K. Measurement 

of drinking behavior using the Form 90 

family of instruments. Journal of Studies 

on Alcohol Suppl. 12:112–118, 1994.

Miller, W.R., and Hester, R.K. The effectiveness 

of alcoholism treatment: What research 

reveals. In: Miller, W.R., and Heather, N., 

eds. Treating Addictive Behaviors: Process 

of Change. New York: Plenum Press, 1986. 

pp. 121–174.

Miller, W.R., and Rollnick, S. Motivational 

Interviewing. New York: Guilford Press, 

1991.

Miller, W.R.; Zweben, A.; DiClemente, C.C.; and 

Rychtarik, R.G. Motivational Enhancement 

Therapy Manual: A Clinical Research Guide 

for Therapists Treating Individuals With 

Alcohol Abuse and Dependence. Project 

MATCH Monograph Series Vol. 2. DHHS 

Pub No. (ADM) 92-1894. Rockville, MD: 

National Institute on Alcohol Abuse and 

Alcoholism, 1992.

Nowinski, J.; Baker, S.; and Carroll, K. Twelve 

Step Facilitation Therapy Manual: A Clinical 

Research Guide for Therapists Treating 

Individuals With Alcohol Abuse and 

Dependence. Project MATCH Monograph 

Series Vol. 1. DHHS Pub No. (ADM) 

94–3722. Rockville, MD: National Institute 

on Alcohol Abuse and Alcoholism, 1992.

Donovan, D.M., and Mattson, M.E. 

Alcoholism treatment matching research: 

Methodological and clinical issues. Journal 

of Studies on Alcohol Supplement 12:5–14, 

1994.

Sobell, L.C., and Sobell, M.B. Timeline follow-back: A technique for assessing 

self-reported alcohol consumption. In 

Litten, R.Z., and Allen, J.P., eds. Measuring 

Alcohol Consumption: Psychosocial and 

Biomedical Methods. Totowa, NJ: Humana 

Press, 1992. pp. 41–72.

Strenio, J.L.F.; Weisberg, H.I.; and Bryk, A.A. 

Empirical Bayes estimation of individual growth curve parameters and their 

relationship to covariates. Biometrics

39:71–86, 1983.

Zweben, A., and Cisler, R. Composite outcome measures in alcoholism treatment 

research: Problems and potentialities. 

Substance Use and Misuse 31:1783-1805, 

1996.
18

Causal Chain Analysis

Richard Longabaugh, Ed.D., and Philip W. Wirtz, Ph.D.

ABSTRACT

The analytic strategies used to assess the theories underlying the a priori matching 

hypotheses share a core element in that they all employ a “causal chain analysis” to 

test their underlying theory. The aim of causal chain analysis is to identify the influence of the variables thought to mediate the effects of treatment on client outcomes. 

Thus, causal chain analysis is mediator analysis applied to understanding how treatment works. When the search for mediators is applied to treatment outcome studies, 

it involves subsets of potential variables that are descriptive of treatment and of client 

response to treatment. This chapter describes mediation analysis and its application 

to testing mediation of interaction effects. Three models for testing are identified and 

matching hypotheses are classified according to which models they employed in each 

causal chain analysis. A typology for classifying results of testing the matching hypotheses in subsequent chapters is offered. This typology includes the paradoxical result of 

finding a supportive causal chain in the absence of a matching effect.

The purposes of the causal chain analyses presented in this volume are to (1) 

provide further information that would 

either support the observed matching effect or 

challenge its credibility and (2) identify the variables contributing to an observed interaction or 

discover why the hypothesized attribute-treatment interaction did not occur. Thus, where 

matching hypotheses are supported, the aim 

of the causal chain analysis is to identify the 

process through which the interaction occurs. 

Delineation of the causal mechanism increases 

the credibility of the interaction, and the active 

ingredients of treatment so identified can be 

exported to other treatment interventions. When 

an attribute-treatment interaction hypothesis 

is not supported, the causal chain analysis permits identification-of the source or sources of 

its failure, revealing unsupported assumptions 

about treatment processes.

Mediators Versus 

Moderators

By definition, a “mediated relationship” is one 

which is “dependent on, acting by, or connected 

through some intervening agency” (Webster’s 

1986). In its simplest form, if variable A (e.g., 

treatment modality) affects variable C (drinking 

outcome) indirectly (through the effect of variable A on variable B [e.g., treatment structure] 

The author listing of this chapter is alphabetical. Both authors contributed equally.

Richard Longabaugh, Ed.D.

Center for Alcohol and Addiction Studies 

Brown University, School of Medicine

800 Butler Drive, Potter Building, Room 204 

Providence, RI 02906

E-mail: Richard_Longabaugh@Brown.edu

Philip W. Wirtz, Ph.D.

Department of Management Science 

George Washington University

2115 G Street NW, #403

Washington, DC 20052

Email: pww@gwu.edu
19

Causal Chain Analysis

and the consequent effect of variable B on variable C), then variable B (structure) is said to 

mediate the relationship between variables A 

(treatment modality) and C (drinking outcome). 

The term “mediator analysis” defines the formal process of empirically testing whether, for 

example, B mediates (or explains) the relationship between A and C.

The term “moderator” is intended to convey 

a quite different—but also in matching studies, 

quite important—phenomenon. Generically, 

if the magnitude of the relationship between 

variable A and variable C differs depending on 

the level of variable B, then B is said to moderate the relationship between A and C. If, for 

example, the effect of treatment (variable A) on 

percentage of days abstinent (variable C) differs 

depending on the level of anger manifested by 

the client at baseline (variable B), then anger 

is said to moderate the relationship between 

treatment and percentage of days abstinent.

The conjunction of mediators and moderators 

lies at the heart of the Project MATCH causal 

chain analysis. The matching hypotheses each 

represented a hypothesized moderator relationship. The objective of the causal chain analysis 

was, for each such hypothesis, to scrutinize the 

logic underlying the purported moderator relationship, that is, to test the purported mediators 

of each hypothesized moderator relationship (i.e., 

each of the matching hypotheses). In so doing, it 

is possible to both determine if the matching 

effects observed experimentally were supported 

for the hypothesized reasons and locate the 

locus of the failure in the causal chains of those 

matching hypotheses which were not supported.

In general, there are three common analytical approaches:

■ An informal comparison of treatmentgroup slopes

■ A formal test using the general linear 

model

■ A formal test using a structural equation 

modeling approach

All three approaches involve comparing the 

models which include a purported mediator 

to models which do not include the purported 

mediator; the approaches differ in the rigor with 

which mediation can be tested and in the comprehensiveness of the underlying model.

The informal approach, which involves 

comparing the significance level of the attributeby-treatment interaction before versus after 

controlling for one or more purported mediators, is the simplest to perform but provides no 

formal basis for concluding that mediation has 

occurred. The general linear model approach is 

elegantly suited to causal models characterized 

by a single attribute-by-treatment of interest; 

most of the Project MATCH hypotheses fall into 

this category. A structural modeling approach, 

which involves comparing constrained to 

unconstrained models, is ideal when testing for 

the simultaneous effect of several mediators (although it can be employed in simpler models as 

well).

Testing Mediation of 

Treatment Main Effects

Although Project MATCH focused on interaction effects, it is instructive to understand the 

procedure for testing mediation of main effects 

before attempting to broaden the scope to the 

more complex models involving mediation of 

interaction effects. Readers already familiar 

with the formal testing for mediation of main 

effects may safely skip this section.

Irrespective of the analytic strategy developed 

for testing mediation, the conceptual model for 

a full causal chain analysis involves the following steps (figure 1).

First, a defined treatment needs to be discriminated from a comparison treatment on the 

basis of measured treatment ingredients (link 1 

in the diagram). For example, it may be hypothesized and observed that Twelve Step Facilitation 

(TSF) and Motivational Enhancement Therapy 

(MET) differ from one another in their emphasis 

on abstinence as a goal for treatment.

Second, this identified difference in treatment is related to a difference in client response 

to treatment. For example, clients receiving an 

abstinence focus are observed to achieve greater 

posttreatment abstinence than those receiving 

less of an abstinence focus (link 2 in the causal 

chain). If it were also found that TSF was more 

effective than MET in producing client abstinence (link 3), then the next step in the causal 

chain analysis would be to examine whether the 

focus on abstinence accounts in whole, or in 
20

Part I: Design and Methodology

Treatment 

Ingredient

Treatment A

vs. B

1 2

= Significant relationship

= Significantly reduced relationship

Client Response

To Treatment

3

4

Figure 1. A generic causal chain analysis

part, for TSF’s incremental effectiveness (link 4). 

This would be demonstrated if it were observed 

that the significance of the main effect of TSF 

versus MET on abstinence was significantly 

reduced when the effect of focus on abstinence 

was partialed out from the relationship of treatment modality to client drinking outcome.

While this evidence is necessary for strong 

support for mediation, it is not sufficient evidence for causation. This is so because the 

entire causal chain analysis is conducted 

through an analysis of associations among the 

identified variables. The only variable that has 

been experimentally manipulated is treatment 

assignment. For unequivocal evidence of mediation, it would be necessary to experimentally 

manipulate each variable intervening between 

treatment assignment and drinking outcome 

to show that each in turn was dependent (or 

partially dependent) upon its predecessor in 

the causal chain. Such experimental manipulations cannot ordinarily be done in typical 

treatment outcome studies in which it is practical to manipulate only one variable, assignment 

to treatment condition. In a similar vein, some 

client variables of interest (age, gender, etc.) 

cannot be experimentally varied.

The case for causation can be strengthened 

by examination of competing mediator variable candidates. For maximum credibility, it 

is necessary to demonstrate that the identified 

mediating effect persists even after the effects 

of competing candidate variables have been 

removed. In our TSF versus MET example, this 

could be done analytically by showing that the 

mediator effects of focus on abstinence on the 

relationship between treatment and abstinence 

persisted even after the effects of competing candidates for mediation were removed. 

Because mediator analysis is correlational, and 

not experimental, the disciplined researcher 

can only conclude that the results obtained are 

consistent with the hypothesized mediation and 

the hypothesis has not been refuted.

Causal chain analyses in alcohol treatment 

outcome studies may quickly become complex, 

especially when a broad spectrum treatment 

such as Cognitive-Behavioral Therapy (CET) 

is considered. CBT is often theorized to work 

indirectly to improve a client’s drinking outcome (Longabaugh and Morgenstern 2000; 

Morgenstern and Longabaugh 2000). For 

example, CBT is intended to involve more time 

devoted to skills training than does MET and is 

expected to be predictive of differences in coping 

skills between CBT and MET clients at treatment completion. It is further hypothesized that 

improved coping skills will result in reduced 

drinking. Thus, for mediator analysis to support the theory, the causal chain analysis must 

test the links between: (1) treatment modality 

(CBT versus MET) and focus on skills training, 

(2) skills training and end of treatment improvement in client skills, and (3) improvement in 

client skills and drinking outcome. If it were 

found that (4) CBT was more effective than MET 

in reducing client drinking posttreatment, it 

would be necessary to show that (5) this greater 

effectiveness could be attributable to the causal 

chain, that is, when the effects of the linkages 

involved in the causal chain are removed from 

the relationship of treatment to drinking, this 

relationship is significantly reduced (figure 2). 

Finally, the evidence for mediation is strengthened if it can be shown that the mediated effect 

persists after ruling out alternative candidates 

for mediation, for example, the number of treatment sessions received in CBT versus MET.

Another complexity that frequently arises in 

causal chain analysis of treatment outcomes 

occurs because treatment effects are measured 

at intervals distally removed from the end of 

treatment, for example, clients are assessed a 

year after treatment completion. To be credible, a theory of treatment effectiveness is 

likely to postulate and test that client changes 

at the end of treatment are predictive of longer term drinking outcomes. Thus, the causal 

chain sequence specifies that assignment to 
21

Causal Chain Analysis

Skills

Training

Improvement in Client

Coping Skills

CBT vs.

MET

1 +

+

+

2

= Significant relationship

= Association consistant with the predicted direction

= Significantly reduced relationship

Drinking

Outcome

4

+

+

3

5

Figure 2. A hypothetical mediation analysis: 

CBT versus MET

alternative treatments leads to end-of-treatment 

differences in clients, themselves predictive 

of longer term drinking outcomes. In the TSF 

versus MET example described above, the 

causal chain would predict that the abstinence 

achieved by the client by the end of treatment 

would be predictive of reduced drinking during 

the year following treatment.

Not necessary for testing mediation, but 

highly desirable for demonstrating the generalizability of the mediator effects, would be 

demonstration that the effects of the putative 

mediator variable could be replicated in a new 

population. In Project MATCH, this opportunity 

was seemingly provided by the conduct of two 

independent studies—the aftercare treatment 

arm and the outpatient treatment arm. If it 

could be shown that the same mediator variables were operative in both arms of the study 

despite the difference in client populations and 

treatment context (i.e., standalone outpatient 

treatment versus aftercare following a more 

intensive inpatient or partial hospital experience), this would strongly support the belief 

that the mediator effects could be generalized 

across a broad spectrum of treatment settings.

Testing Mediation of 

Interaction Effects

While this approach has been widely adopted 

for testing simple mediation hypotheses, the formal test of a matching causal chain is one level 

more sophisticated, in that it requires testing 

for mediation of a moderator (i.e., interaction) 

effect rather than of main effect. The additional 

challenge imposed by a matching hypothesis is 

further heightened under a structural equation 

modeling approach, where detection of interactions involving latent constructs has proven 

particularly daunting.

When testing for a causal chain underlying main effects, it is necessary to show that 

the treatments compared differ in their implementation, for example, there was greater 

emphasis on an abstinence goal in TSF than in 

MET. When testing for causal chains to explain 

interactions, there is no single analogous step. 

Rather, for the causal chain to be supported, 

it is necessary that an interaction be observed 

in at least one step of the causal chain analysis and that the other links in the chain (which 

may involve either main or interaction effects) 

are also sustained.

Canonical Models

We describe three basic models which we 

believe are sufficient to account for the ways 

in which a causal chain can support a hypothesized client attribute-treatment interaction. 

Each of the models can be (and is in the following chapters) elaborated to involve considerably 

more complex causal chains. Nevertheless, at 

core they can be reduced to one of these three 

generic models, which we will refer to as canonical models. It should be emphasized that the 

value of distinguishing between canonical models is primarily descriptive.

Canonical Model 1

An element of the treatment process interacts 

with the client attribute.

The first model hypothesizes that treatments 

will differ in delivery of a specified treatment 

ingredient (figure 3). For example, TSF is 

hypothesized to have a greater focus on abstinence as a treatment goal than is CBT. To test 

this hypothesis, a measure of emphasis on 

abstinence is included in the causal chain to be 

tested. This difference in focus on abstinence 

is hypothesized to interact with a client attribute, alcohol dependence, called the matching 

variable, so that a differential drinking outcome 

is observed as a function of the interaction of 

emphasis on abstinence. Clients with greater 

alcohol dependence benefit more from an 

emphasis on abstinence than clients with less 
22

Part I: Design and Methodology

Treatment

Modality Optional

Additional

Intervening

Paths

Drinking

Outcomes

Matching

Variable

Treatment 

Process

Figure 3. Canonical Model 1

alcohol dependence. Because TSF has a greater 

emphasis on abstinence than CBT, it is expected 

that TSF will be more effective with clients having greater alcohol dependence. Because CBT 

has a lesser focus on abstinence, it is expected 

that CBT will be more effective with clients with 

less alcohol dependence. In order for this causal 

chain to support an observed alcohol dependence by TSF versus CBT treatment interaction, 

at least three steps are necessary.

First, it needs to be established that TSF did 

involve a greater focus on an abstinence goal 

than CBT. Second, the amount of a client’s 

dependence must be shown to interact with 

focus on abstinence to affect drinking outcomes, 

such that those with higher dependence had 

better drinking outcomes when their treatment 

had a greater emphasis on abstinence and/or 

that clients with low dependence had better 

drinking outcomes when their treatment had a 

lesser focus on abstinence. Third, it is necessary 

to demonstrate that this abstinence emphasis-dependence interaction accounted for the 

observed treatment modality-dependence interaction by showing that the significance of the 

latter interaction is significantly reduced when 

the effect of the former interaction is removed. 

Several of the hypothesized causal chains were 

predicated on this canonical model.

Canonical Model 2

The client matching variable produces a behavior which interacts differentially with treatment.

A second causal model (figure 4) hypothesizes that a client matching variable will lead to 

a certain kind of client behavior in treatment. 

This client behavior will interact with treatment modality to affect client outcome. MATCH 

hypothesized that clients differing in their anger 

prior to treatment would differ in their resistance to treatment, in that high anger clients 

would respond with greater resistance to treatment than would clients with low anger. To test 

this causal chain, indices of client resistance in 

treatment needed to be operationalized.

The next link in the causal chain was the 

hypothesis that this client resistance would 

interact with the type of treatment received, so 

that high anger clients assigned to MET would 

show less resistant behaviors than would high 

anger clients in CBT/TSF. The differential 

resistance to treatment would in turn result 

in different drinking outcomes, with those less 

resistant to treatment having better drinking 

outcomes than those more resistant.

In order for this causal chain to be supportive 

of an observed treatment modality-client attribute interaction, it is first necessary to show 

that clients high in anger differ from those low 

Figure 4. Canonical Model 2

Matching

Variable

Optional

Additional

Intervening

Paths

Drinking

Outcomes

Clincial Behavior

During Treatment

Treatment 

Modality
23

Causal Chain Analysis

in anger in their behavioral resistance. Then, 

it is necessary to show an interaction of resistance with treatment modality such that high 

resistance clients in MET show greater reductions in drinking severity than do clients in any 

of the other treatment conditions. Finally, it is 

necessary to show that the observed relationship between the combination of treatment 

modality-client anger and drinking outcome 

is significantly reduced when the effect of the 

treatment modality-client resistance interaction 

is removed.

Canonical Model 3A

Treatment modality interacts with a client 

matching variable to affect an intervening variable that is predictive of drinking outcome.

A third model postulates that a hypothesized 

treatment modality-client attribute interaction 

leads to a measured client response that in turn 

is predictive of drinking outcome (figure 5). For 

example, it was hypothesized that TSF would 

lead to greater AA participation by high meaning-seeking clients (but not by low meaning 

seekers) than would MET or CBT. AA participation in turn was expected to be predictive of 

better drinking outcomes. In this model, the 

combination of treatment modality and client 

attribute leads the client to engage in behavior otherwise unpredictable from treatment 

modality or client attribute by themselves. This 

changed behavior is in turn related to drinking 

outcome.

To test this causal chain, AA participation 

would be regressed on the product term of TSF 

versus MET and/or CBT by meaning seeking, 

and the product term would be observed to 

account for variance in AA participation over 

and above that accounted for by the two main 

effect variables, treatment assignment and 

meaning seeking. To validate the chain, AA participation would have to be shown predictive of 

drinking outcome. Finally, by partialing out the 

effect of AA participation on the initial meaning 

seeking by treatment assignment interaction, it 

would be expected that the latter effect would 

be significantly reduced.

Canonical Model 3B

The client matching variable modifies the 

delivery of the treatment modality to affect an 

intervening variable that is predictive of drinking 

outcome.

While it could be reasonably expected that 

one or the other of the three models described 

so far should be at the core of all of the causal 

chains hypothesized and tested, a second variation of Model 3 emerges. In this model, it is 

hypothesized that the client matching attribute 

will modify one of the treatments, such that 

the treatment actually changes as a function 

of the client attribute. This interaction of client 

attribute and treatment modality is predictive 

of a subsequent client behavior, which itself is 

associated with subsequent drinking outcome 

(figure 6).

As treatments are intended to be standardized across clients and delivered uniformly 

irrespective of client attributes, causal chain 

data supporting such a model would appear to 

challenge the integrity of the three treatments. 

However, this is not necessarily so, as there is 

room for such variability explicitly built into the 

CBT manual. The CBT treatment manual prescribes the delivery of eight core modules, but 

after completion of these modules, there is the 

option to select further modules from a menu 

of alternatives available. Thus, once the core 

Intervening

Client Variables

Affected by

Matching Treatment

Variable

Treatment 

Modality Optional

Additional

Intervening

Paths

Drinking

Outcomes

Figure 5. Canonical Model 3A, Treatment modality interacting with client matching variable 

to affect an intervening variable predictive of drinking outcome.
24

Part I: Design and Methodology

Intervening

Treatment

Process

Variables

Affected by

Matching

Variable

Matching

Variable

Treatment 

Modality Optional

Additional

Intervening

Paths

Drinking

Outcomes

Figure 6. Canonical Model 3B. The client matching variable modifies the treatment modality 

to affect an intervening variable that is predictive of drinking outcome.

sessions have been completed, the client and 

therapist can select from the remaining modules, the selection being dependent upon client 

choice or need. For example, a client might or 

might not be exposed to a mood management 

module.

In MATCH, it was hypothesized that CBT 

would be more effective then either TSF or 

MET for clients with greater psychopathology 

because CBT would provide high psychopathology clients with a greater exposure to treatment 

ingredients that reduce psychopathology. It was 

expected that this intervention would lead to a 

greater reduction in drinking than it would for 

low psychopathology clients.

In this scenario, CBT could be demonstrated 

to be different from the comparison treatment 

when delivered to a subset of clients, though not 

necessarily all (i.e., there would be an interaction 

observed in this first step of the causal chain). 

For example, if the mood management module 

in CET was selected more often for (or by) high 

psychopathology clients, then this group would 

actually receive a- somewhat greater exposure 

to CBT than would low psychopathology clients. Consequently, the CBT treatment for high 

psychopathology clients would differ from the 

CBT treatment provided for low psychopathology clients as a function of the psychopathology 

matching variable. This would establish a differential treatment experience for the subset 

of clients for whom it was hypothesized that 

CBT would be more effective. For this reason, 

it might be expected that a treatment-modalityby-client-attribute interaction would arise as a 

consequence of the matching variable affecting 

the treatment delivered. (This is in contrast to 

Model 1 in which the standard delivery of the 

treatment has a differential effect on clients 

varying in the matching variable.) If it were then 

shown that: (1) focus on mood management was 

predictive of reduced psychopathology for either 

all clients or just those psychopathologically 

impaired and (2) improved mood management 

was associated with better drinking outcomes 

either for all clients or just those psychopathologically impaired, this would provide evidence 

for the causal chain.

Summary

One or more of these canonical models are 

embedded in each of the hypothesized causal 

chains (table 1). Usually the causal chain is more 

complex and involves other linkages as well. In 

order for a causal chain to be supported, it is 

necessary that at least one interaction occur 

somewhere in the causal chain (which might be 

the original interaction affecting the mediator 

variable) and that connecting effects be carried 

through the causal chain from treatment implementation to drinking outcome.

In the full causal chain underlying a matching hypothesis, differences in treatment delivery 

should not be assumed but should be tested. If 

this step is not conducted, one would not know 

if the lack of a differential client response is due 

to incorrect theory or if the clients in the treatments compared did not receive different doses 

of the putative active ingredients. Furthermore, 

some theories underlying the hypothesized 

client-treatment interactions make use of 

intervening client variables, such as increased 

motivation, as variables mediating the effect 

of treatment on drinking outcome. These need 

to be measured and their mediating influence 

tested as well. Finally, drinking outcomes are 

measured over one year following treatment 

completion. If a client-treatment interaction 

is observed to change over time (as some did), 

then the choice of time period for measuring 
25

Causal Chain Analysis

Table 1. Hypothesized mediator variables and Canonical Models for each causal chain

Matching variable Hypothesized mediator Canonical model*

Alcohol Dependence Emphasis on abstinence 1

AA involvement/Attendance 1 

Alcohol Involvement Within-Treatment Drinking 1&2

Within-Treatment Self-Efficacy 1

Negative Consequences of drinking 1 

Cognitive Impairment Amount of Therapy 1/3B

Amount of Therapeutic structure 1

AA involvement/Attendance in Treatment 1 

Psychopathology and Psychiatric 

Severity

Emphasis on psychopathology 1/3A/3B

Reduction in psychiatric symptoms 1/3A/3B 

Sociopathy/ASPD Amount of Therapeutic structure 1

AA attendance 2

Working Alliance 2

Anger reduction 2,1

A versus B Typology Amount of Therapeutic structure and cognitive change 1 

Working Alliance 3A

Change in psychopathology 3A

Anger Taking Steps 2

Problem recognition 2

Working Alliance 3

Conceptual Level Amount of Therapeutic structure 1

Meaning Seeking Working Alliance 3A

Client satisfaction with Treatment 3A

Percentage of Treatment sessions attended 3A

AA involvement/attendance 3A

Religiosity Working Alliance 3A

Interpersonal Dependency Satisfaction with Treatment 3A, 3A

Percentage of Treatment sessions attended 3A, 3A

Gender Role Demands 1 & 2

External Stresses 1 & 2

Psychiatric severity 1 & 2

Depression 1 & 2

Self-esteem 1 & 2

Motivational Readiness Working Alliance 3A

Compliance 3A

Change Processes 3A

Increased Readiness to change 3A

Self-efficacy for abstinence 3A

Problem Recognition Working Alliance 3A

Change in Problem Recognition 3A

Self-Efficacy Change in Self-Efficacy 3A

Temptation minus Confidence Change in Temptation minus Confidence 3A

Network Support for drinking Coping with social pressure to drink 1

Change in network support for drinking 1

AA attendance 1

Prior AA Working Alliance 3A

Social Functioning Change in Social Functioning 3A

*Canonical models separated by a comma show that one model precedes the other in the causal chain. Models separated by 

an & were conceptualized as occurring simultaneously. A / between canonical classifications indicates that the mediational 

model allowed alternative canonical pathways through which the mediating effect could occur. Consensus classifications were 

made by the two authors on the basis of discussion following independent review and classification. Some causal chains may 

be misclassified due to a lack of total explicitness in their descriptions.
26

Part I: Design and Methodology

drinking outcome becomes less obvious. As 

treatment effects have generally been found to 

be transient, the case can be made for measuring the drinking variable immediately following 

the end of treatment, rather than at a time further removed, when the effects of treatment may 

have been obscured by posttreatment events.

To conclude this section, we hypothesize that 

a moderating effect will be explained by a causal 

chain only when at least one of the connecting 

links in the causal chain involves an interaction and the remaining links involve either 

main effects or interactions. However, even if 

these conditions are met, only a complete test 

for mediation will establish whether the causal 

linkages are explanatory.

Paradoxical Findings

Under limited circumstances, causal chain 

linkages in the absence of client-treatment 

interactions may be observed. While the 

hypothesized client attribute-treatment modality inter-action may not be detected, it may still 

be possible to find evidence for the causal chain 

developed to explain the expected but unobserved interaction. One explanation for this 

seemingly paradoxical finding is that different 

treatment modalities provide different pathways 

for clients with the same attribute to achieve 

equally good drinking outcomes.

A MATCH example may help to make this 

point clear (Kadden et al., this volume). It is 

hypothesized that CBT will be more helpful than 

TSF in reducing drinking for clients with antisocial personality disorders (ASPD). The logic 

behind this client-treatment modality interaction is as follows: ASPD clients are frequently 

lacking in coping skills. Drinking is their way of 

coping in situations where they lack the needed 

coping skills. Therefore, ASPD clients need 

training in coping to achieve their goals and 

avoid drinking. CBT trains clients to acquire and 

use these skills. When such skills are enacted 

instead of drinking, the ASPD client will have a 

good drinking outcome. Thus, the causal chain 

is: CBT involves coping skills training. Coping 

skills training leads to the acquisition of coping 

skills. Coping skills acquisition for ASPD clients 

will produce an interaction such that ASPD clients will have better drinking outcomes than 

non-ASPD clients.

Hypothetically, however, it could also have 

been hypothesized that TSF will be more helpful than CBT for ASPD clients, resulting from 

a different causal chain. TSF exhorts clients 

to attend AA. AA participation leads to better 

drinking outcomes. Generally, ASPD clients 

would be less likely to attend AA than nonASPD clients. Nevertheless, because of the TSF 

emphasis on attending AA, more do so when 

assigned to TSF. ASPD clients are unlikely to 

receive acceptance and esteem from people in 

their natural social network because of the lack 

of consideration by the ASPD of the feelings and 

needs of others. However, in AA this behavior would not be cause for rejection because 

alcoholism is considered to be at the core of 

all such antisocial behaviors. The client may 

thus find acceptance in AA that would not be 

available outside of AA. This acceptance could 

lead to an increase in the ASPD client’s concern and feelings for others. The reciprocated 

concern brings about increased self-esteem; the 

mutual concern increases social cohesiveness. 

The acceptance that ASPD clients find in AA, 

conditional upon their wanting to quit drinking, results in their increasing abstinence. In 

this scenario, TSF leads to AA participation, AA 

participation for the ASPD leads to greater selfacceptance conditional upon sobriety, which in 

turn leads to better drinking outcomes.

Both of these causal chains could be equally 

operative—the former in CBT and the latter in 

TSF. However, if the only causal chain developed 

to support the hypothesized ASPD-treatment 

modality interaction was for CBT, the hypothesized client attribute-treatment interaction 

might not appear. This would not be because the 

underlying theory regarding the effect on drinking of ASPD client’s acquiring coping skills was 

wrong, but because the causal chain underlying TSF’s affect on ASPD clients was also right. 

In this scenario, TSF would have affected ASPD 

clients through an alternative (but unidentified) 

causal chain process that led to equally good 

drinking outcomes for ASPD clients treated in 

TSF. If this were the case, then a causal chain 

analysis could support coping skills training as 

a correlate of better drinking outcomes in CBT, 

despite the observation that the hypothesized 

interaction of CBT by ASPD did not materialize. 

Thus, ASPD clients would have equally good 
27

Alternate Forms

drinking outcomes in CBT and TSF, but for different reasons.

In such an event it would be instructive to 

re-examine the treatment modality initially 

believed to be inferior for clients with this attribute to assess whether an alternative causal 

chain was responsible for the unexpected effectiveness of the TSF treatment for the ASPD 

client. Such a discovery would not undermine 

the theory underlying the unobserved CBT–

ASPD interaction, but it would provide one 

explanation as to why the interaction did not 

emerge. Such a discovery would be a considerable achievement for theories trying to explain 

treatment effectiveness.

A Classification Typology 

for MATCH Results

The joint examination of both attributetreatment interaction effects and causal chain 

analysis leads to one of four outcomes (table 2).

First, in the best of circumstances, the 

hypothesized attribute-treatment interaction 

has occurred and a supporting causal chain has 

been identified. In this case, the theory underlying the matching hypothesis is supported and 

the credibility of the matching effect is strengthened. Not only are we able to explain why the 

treatment is especially effective for a defined 

client, we are also able to extract the identified treatment ingredient for incorporation into 

another treatment, hypothesizing that the active 

ingredient will increase the effectiveness for this 

type of client in this other treatment as well.

Second, it may be observed that the hypothesized attribute-treatment interaction has not 

occurred nor has the hypothesized causal chain 

been supported. In this circumstance the breakdown in the linkage in the causal chain will 

indicate whether the failure may be attributable 

to a failure in treatment implementation (e.g., 

where the treatments do not differ in their putative active ingredients) or a failure in the theory 

as to how treatment affects drinking (e.g., where 

the observed differences in treatments do not 

affect the clients as expected) (Finney and Moos 

1992). Depending upon which of these is the 

case, future research may seek to strengthen 

the treatment or develop a better theory regarding treatment effects.

Table 2. A fourfold typology for classifying 

MATCH results

Matching 

hypothesis

Supporting causal chain

Identified Not identified

Matching 

prediction 

supported

yes/yes yes/no

Matching 

prediction not 

supported

no/yes no/no

Third, it may be observed that the hypothesized attribute-treatment interaction has 

occurred, but the causal chain has not been 

sup-ported, that is, the prediction is supported 

but not for the reasons we postulated. In this 

instance, the credibility of the observed interaction is undermined, with the belief that it may be 

the result of a type 1 error. Moreover, in terms 

of theory development, nothing has been added 

to the existing knowledge base. We may believe 

that we have produced an attribute-treatment 

interaction but we know not how. We are unable 

to identify the active ingredients of treatment to 

be included in subsequent research.

Finally, it may be that a hypothesized attribute-treatment interaction does not occur even 

though the causal chain analysis suggests that 

it should have materialized. In this case, it is 

likely that alternative paths for reaching the 

same end are present in the comparison treatment but are unidentified. The next step for 

research would be to seek out these alternative 

pathways to better outcomes. This would permit the implementation of different treatment 

modalities with equal effectiveness by making 

within-modality adjustments to address the 

salient characteristics of these clients.

Acknowledgment

This project was supported by a series of 

grants from the National Institute on Alcohol 

Abuse and Alcoholism as part of the Cooperative 

Agreement on Matching Patients to Alcoholism 

Treatments.

References

Baron, R.M, and Kenny, D.A. The moderatormediator variable distinction in social 

psychological research: Conceptual, 
28

Part I: Design and Methodology

strategic, and statistical considerations. Journal of Personality and Social 

Psychology 51(6):1173–1182, 1986.

Finney, J.W., and Moos, R.H. Four types of 

theory that can guide treatment evaluations. In: Chen, H.-T., and Rossi, P.H., 

eds. Using Theory to Improve Program and 

Policy Evaluations. New York: Greenwood 

Press, 1992. pp. 15–27.

Holmbeck, G.N. Toward terminological, conceptual, and statistical clarity in the study of 

mediators and moderators: Examples from 

the child-clinical and pediatric psychology literatures. Journal of Consulting and 

Clinical Psychology 65:599–610, 1997.

Longabaugh, R., and Morgenstern, J. CognitiveBehavioral Coping-Skills Therapy for 

Alcohol Dependence: Current Status and 

Future Directions. Alcohol Research and 

Health 23(2):78–86, 2000.

Morgenstern, J., and Longabaugh, R. 

Cognitive behavioral treatment for alcohol dependence: A review of evidence for 

its hypothesized mechanisms of action. 

Addiction 95(10):1475–1490, 2000.

Webster’s New World Dictionary, 2nd ed. 

Englewood Cliffs, NJ: Prentice-Hall, 1986.
29

Part II: Alcohol Dysfunction

Matching Clients to Alcoholism Treatment 

Based on Severity of Alcohol Dependence

The Alcohol Involvement Matching Hypothesis 

and Findings
30

Matching Clients to Alcoholism 

Treatment Based on Severity of Alcohol 

Dependence

Ned L. Cooney, Ph.D., Thomas F. Babor, Ph.D., and Mark D. Litt, Ph.D.

ABSTRACT

Several studies have reported significant matching effects based on individual differences in severity of alcohol dependence. An alcohol dependence matching hypothesis 

developed for Project MATCH predicted that clients who have greater alcohol dependence 

would have better outcomes following Twelve Step Facilitation (TSF) than following Cognitive-Behavioral Coping Skills Therapy (CBT) or Motivational Enhancement 

Therapy (MET), relative to clients who have less alcohol dependence. The CBT versus 

TSF matching contrast was supported in the aftercare arm. However, this matching 

contrast was not supported in the outpatient arm, and the MET versus TSF matching 

contrast was not supported in either arm. The significant aftercare CBT versus TSF 

matching effects were found in the year after treatment, and it is not known whether 

these matching effects were maintained beyond 1 year because the aftercare sites did 

not participate in the 3-year followups. Aftercare clients with severe dependence had 

better drinking outcomes (higher percentage of days abstinent (PDA) and lower drinks 

per drinking day (DDD)) when assigned to TSF, while those with low levels of dependence had better outcomes (higher PDA and lower DDD) when assigned to CBT. In a 

post hoc analysis, subjects in the aftercare sample were divided into groups; those 

with scores on the Ethanol Dependence Syndrome Scale less than or equal to 35 were 

classified low dependence, and those with scores greater than 35 were classified high 

dependence. Matched clients had a 10-percent higher success rate than mismatched 

clients and a 5-percent higher success rate than unmatched (randomly assigned) clients. These effects are not large, so clinicians should expect only modest gains from 

using this matching strategy. Process analyses supported the hypothesis that the alcohol dependence matching effect in the aftercare arm was mediated by differences in 

the degree of therapist emphasis on abstinence. Only partial support was found for the 

other causal chain hypothesis that Alcoholics Anonymous attendance mediated the 

dependence-treatment matching effect.

I

n part to address the perceived need for 

improved nomenclature, consistent classification criteria, and more objective 

measurement procedures, several investigators 

have postulated a construct termed the alcohol 

dependence syndrome (ADS). First introduced 

in a seminal paper by Edwards and Gross 

(1976), the construct was given its most formal 

description in 1977 by a group of investigators 

convened by the World Health Organization 

(Edwards et al. 1977) and has recently provided the conceptual underpinnings for the 

definition of dependence criteria in DSM—III—R 

(Rounsaville et al. 1986; American Psychiatric 

Ned L. Cooney, Ph.D.

VA Connecticut Healthcare System (116B) 

950 Campbell Avenue

West Haven, CT 06516

E-mail: ned.cooney@yale.edu
31

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

Association 1987), ICD-10 (World Health 

Organization 1992), and DSM—IV (American 

Psychiatric Association 1995).

As an empirical clustering of cognitive, behavioral, and physiological phenomena, the ADS 

includes the following seven elements (Edwards 

and Gross 1976; Edwards et al. 1977):

■ Narrowing of the drinking repertoire, 

that is, a tendency for the drinking pattern to become stereotyped around a 

regular schedule of almost continuous 

daily alcohol consumption.

■ Increased salience of drink-seeking 

behavior, such that alcohol is given 

greater priority than its adverse consequences or inconveniences.

■ Increased tolerance to alcohol, reflected 

in a capacity to sustain high levels of 

consumption and the need to drink more 

alcohol than previously to achieve a constant level of effect.

■ Repeated withdrawal symptoms, including affective disturbance, tremor, 

nausea, and sweating.

■ Relief or avoidance of withdrawal by further drinking.

■ Subjective awareness of compulsion to 

drink, including such phenomena as 

craving for alcohol, impaired control of 

alcohol intake once drinking has commenced, and difficulty in abstaining 

from drinking.

■ Rapid reinstatement of symptoms if 

drinking is resumed after a period of 

abstinence. 

The last element is no longer considered as a 

diagnostic criterion because it describes a characteristic of the syndrome as a whole.

There is now good evidence from interview 

and questionnaire studies to show that the 

main components of dependence can be reliably assessed (Stockwell et al. 1983; Babor 

1996) and that many of them cluster together 

to form a core syndrome (Edwards 1986; Babor, 

Lauerman, and Cooney 1987). Factor analytic 

studies have not only provided support for the 

construct validity of a single alcohol dependence factor (Kosten et al. 1987; Skinner and 

Allen 1982) but have also demonstrated that 

the construct has similar characteristics in 

alcoholic clients and heavy drinkers in different 

cultures (Babor, Lauerman, and Cooney 1987; 

Hall et al. 1993; Allen et al. 1993). Finally, one 

study has shown that dependence severity 

predicts the rapidity of reinstatement of dependence symptoms after a period of abstinence 

(Babor, Cooney, and Lauerman 1987).

Evidence for Matching

In addition to research on the construct 

validity and predictive utility of the ADS concept, several studies suggest that the severity 

of alcohol dependence may have implications 

for treatment matching. Polich and associates 

(1981), for example, looked at higher order 

interaction effects between alcohol dependence, 

marital status, age, and relapse to drinking 

at 18 months and 4 years after treatment. 

Older men with a high severity of dependence 

at admission to treatment had a lower risk of 

relapse if they abstained rather than engaged 

in nonproblem drinking at 18 months, regardless of marital status. The reverse was true for 

younger men with low dependence severity. But 

for older men with low dependence and younger 

men with high dependence, marital status 

played a moderating role. Those who were married had lower relapse rates if they abstained, 

whereas non-married men had lower relapse 

rates if they were nonproblem drinkers. Other 

studies have also shown that severity of dependence is a strong predictor of the alcoholic’s 

ability to engage in nonproblem drinking over 

time (Sanchez-Craig et al. 1984; Foy et al. 1984; 

Taylor et al. 1986; Vaillant 1983).

Only a few studies have evaluated severity of dependence in relation to treatment in a 

randomized trial. Orford and colleagues (1976) 

concluded that clients who had been classified 

as severely dependent (i.e., gamma alcoholics) 

were more likely to have better 2-year outcomes 

if they had received treatment (outpatient plus 

inpatient) rather than one session of advice. 

Conversely, nongamma alcoholics fared better 

if they received advice rather than treatment. A 

10-year followup study of these clients indicated 

that the better outcome of the more dependent 

clients was associated with more treatment and 

Alcoholics Anonymous (AA) involvement (Taylor 

et al. 1986). However, a more recent reanalysis 
32

Part II: Alcohol Dysfunction

of the 1-year outcomes of the Orford group’s 

(1976) study found no evidence for a treatmentmatching effect and called into question the 

conclusions of the original report (Edwards and 

Taylor 1994).

Given these conflicting findings, there is 

reason to evaluate the alcohol dependence 

hypothesis in a more precise and systematic 

way. The original study by Orford and associates (1976) and the recent secondary analysis 

of the 1-year outcome data (Edwards and Taylor 

1994) were based on measures of dependence 

and treatment outcome that were not designed 

to explore the treatment-matching implications 

of dependence. The matching hypothesis was 

tested in an entirely post hoc manner. It was 

based on a rather crude classification of clients 

as gamma and nongamma alcoholics, and the 

predictions dealt with the relationship between 

dependence and intensity of therapy, rather 

than dependence and type of treatment. Given 

these limitations, and the wide acceptance of 

the original findings, it seemed important to 

conduct a more systematic test of the dependence severity hypothesis in Project MATCH.

Statement of Hypothesis

This hypothesis is based on research and 

speculation suggesting that the alcohol dependence syndrome has important implications for 

treatment matching. It differs from the hypothesis on alcohol involvement discussed elsewhere 

in this volume in that the matching variable is 

more specific (alcohol dependence versus alcohol 

involvement) and the predicted interactions are 

different. For example, the alcohol involvement 

hypothesis predicted that Cognitive-Behavioral 

Coping Skills Therapy (CBT; Kadden et al. 1992) 

and Twelve Step Facilitation (TSF; Nowinski et 

al. 1992) would have similar impacts on clients with higher dependence, while the present 

hypothesis predicted that these treatments 

would have a differential impact on clients as 

severity of dependence increased.

All a priori matching hypotheses in Project 

MATCH were specified as predictions that the 

slopes of the regression lines of the matching variable on the primary outcome variables 

would significantly differ across specified treatments. The hypotheses identified the directional 

nature of the difference in slopes, but they did 

not specify whether or where the regression 

lines intersected. Thus, the alcohol dependence 

matching hypothesis was specified as follows: 

Clients who have greater alcohol dependence will 

have better outcomes following TSF than following CBT or Motivational Enhancement Therapy 

(MET; Miller et al. 1992), relative to clients who 

have less alcohol dependence.

We tested this hypothesis as two a priori 

contrasts, one comparing TSF and CBT and the 

other comparing TSF and MET. As illustrated 

in figure 1, we predicted that the slope of the 

TSF regression line would be more positive than 

the slopes of the CBT or MET regression lines. 

No prediction was made regarding whether 

or where the regression lines intersect. The 

alpha level was divided evenly between the two 

contrasts.

Good

Poor

Low High

(TSF)

(CBT, MET)

Drinking

outcome

Severity of dependence

Figure 1. Predicted relationships between 

severity of dependence and drinking outcome 

for clients treated with TSF, CBT, and MET.

Measurement of Matching 

Variable

Matching Variable

Dependence severity was measured by 

means of the Ethanol Dependence Syndrome 

Scale (EDSS), a set of 16 self-report items developed and validated by Babor and colleagues 

(Hesselbrock et al. 1983; Hall et al. 1993; Babor 

1996). This scale has been demonstrated to 

predict intensity of reinstatement of drinking 

among individuals who relapsed after inpatient 

treatment (Babor, Cooney, and Lauerman 1987). 

An analysis of the baseline Project MATCH data 

(Babor 1996) showed that the scale has a normal distribution (skewness=0.35, n=1702) and 

good test-retest as well as internal consistency 
33

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

reliability (alpha=0.90, n=1543). The total 

dependence score on the EDSS is highly correlated with the Alcohol Use Inventory G scale 

(r= 0.69, n=1590).

Dependent Variables

The primary dependent measures used to 

evaluate the dependence hypothesis were drinks 

per drinking day (DDD) and percentage of days 

abstinent (PDA). These were derived from data 

obtained in Form 90 interviews (Miller and Del 

Boca 1994) scheduled every 3 months in the 

year following treatment. Data from these followups were also used to compute a composite 

outcome variable (Zweben and Cisler 1996) that 

was used as a secondary outcome measure.

Causal Chain Hypotheses

Two causal chain hypotheses were developed prior to examination of outcome data. The 

dependence-matching effect was thought to be 

mediated by (a) the degree of emphasis that 

the therapist placed on abstinence as a goal of 

treatment and (b) differences in the client’s frequency of AA attendance. Figure 2 illustrates 

these hypothesized causal models.

The first causal chain hypothesis predicted a 

mediating role for the degree of emphasis that 

the therapist placed on abstinence as a goal. 

TSF therapists were expected to give a strong 

message to clients that complete abstinence is 

necessary because any alcohol consumption is 

likely to lead to loss of control; CBT and MET 

therapists were not expected to emphasize 

abstinence and loss of control as strongly or as 

explicitly. CBT provided explicit instruction in 

how to cope after a “lapse.”

Highly dependent clients were expected to 

do poorly with CBT because their coping skills 

would be inadequate to deal with lapses. In these 

clients, a high level of dependence is thought 

to produce rapid reinstatement of dependence 

symptoms after the reinitiation of drinking. 

MET used an empathic, client-centered, gently persuasive approach to help clients who 

are ambivalent about abstinence to consider 

their options. Severely dependent clients were 

expected to do poorly with MET because it was 

thought they might attempt to pursue a moderate drinking goal or they may need more than 

client-generated strategies.

On the other hand, clients 

with mild dependence were 

expected to do well with CBT 

because they could effectively utilize the lapse-coping 

skills, and B they would also 

do well with MET because 

they might have a reasonable chance of success if 

Drinking outcome they chose 

a moderate drinking goal. In 

contrast, clients with low 

levels of dependence were 

expected to do poorly with 

TSF because the notion of 

loss of control that is strongly 

emphasized in AA would not 

be consistent with their personal experience of their own 

drinking history.

A second causal chain 

hypothesis predicted a mediating role for AA attendance. 

First, it was predicted that 

TSF would result in more 

Emphasis on

abstinence Treatment

Drinking outcome

Dependence

Emphasis

by

dependence

A

Treatment AA attendance

Drinking outcome

Dependence

AA attendance

by

dependence

B

Figure 2. Hypothetical causal chains for matching based on 

alcohol dependence. (A) Causal cahin in relation to therapist’s 

emphasis on abstinence. (B) Causal chain in relation to AA 

attendance.
34

Part II: Alcohol Dysfunction

AA meeting attendance across all types of clients compared with CBT or MET. Second, it 

was predicted that severely dependent clients 

would attend more AA meetings than mildly 

dependent clients. This prediction was based 

on the expectation that clients who attended 

AA meetings would be likely to receive the message that any drinking would result in loss 

of control. This loss of control message was 

expected to resonate in the highly dependent 

subjects because they have frequently experienced such loss of control. On the other hand, 

the loss of control message was expected to be 

dissonant to clients with mild dependence. The 

degree of concordance between the loss of control message and previous experience of clients 

was expected to affect AA meeting attendance.

Frequent AA attendance was expected to be 

associated with reduced drinking. However, to 

produce a matching effect, the impact of AA 

attendance on drinking should be different for 

clients with severe or mild dependence. Among 

severely dependent clients, greater AA attendance was predicted to be associated with less 

drinking, and this AA-drinking correlation was 

expected to be lower in mild or moderately 

dependent clients (i.e., AA attendance really 

helps severely dependent clients while an 

absence of AA predicts failure in these clients).

Results

The prognostic and matching effects involving 

alcohol dependence were previously reported 

in Project MATCH Research Group (1997b). 

These findings are summarized below.

Prognostic Effects of Severity of 

Dependence

The prognostic effects of pretreatment 

dependence on posttreatment alcohol consumption were examined first. Prognostic 

analyses were conducted using the same 

latent growth approach that was utilized in 

the previously reported analysis of matching 

effects (Longabaugh and Wirtz, this volume, p. 

4; ProjectMATCH Research Group 1997a). The 

prognostic model included a backward elimination adjustment for the other significant a 

priori matching attributes and their matching 

interaction effects. As previously reported, 

dependence was a significant predictor of PDA 

in the outpatient arm (p<.01), with more severe 

dependence associated with a lower frequency 

of drinking. Dependence did not predict DDD 

in the outpatient arm, nor did it predict PDA or 

DDD in the aftercare arm.

Dependence by Treatment Interaction 

Effects

Interaction effects were modeled as a “latent 

growth process” as described in the chapter by Longabaugh and Wirtz (this volume, 

p. 4) and the Project MATCH Research Group 

(1997a). Table 1 shows F and nondirectional 

p values for all possible dependence by treatment interaction effects: Our a priori matching 

hypotheses predicted better outcomes for 

high-dependence clients in. TSF and better 

outcomes for low-dependence clients in CBT 

or MET. The predicted interaction effects contrasting CBT versus TSF treatments reached 

a Bonferroni-corrected level of significance for 

PDA (directional p<.01) and for DDD (directional 

p<.01) outcomes in the posttreatment period in 

the aftercare arm. Figure 3 shows that these 

were disordinal interactions. Aftercare clients 

with severe dependence had better drinking 

outcomes (higher PDA and lower DDD) when 

assigned to TSF, while those with low levels of 

dependence had better outcomes (higher PDA 

and lower DDD) when assigned to CBT.

To evaluate the clinical relevance of these 

matching effects, we estimated the PDA and 

DDD outcomes for clients with pretreatment 

dependence scores in the highest and lowest 

decile for the aftercare sample. Matched clients 

drank on average approximately 7 to 10 percent 

fewer days or about 2.5 fewer days per month. 

It is more difficult to interpret differences in 

DDD because clients who were abstinent for 

a given posttreatment month were assigned a 

DDD score of zero for those months.

As shown in table 1, CBT versus TSF 

treatment by dependence interaction effects 

were significant predictors of outcome in the 

aftercare arm in the posttreatment period. 

However, this matching contrast was not significant during the active treatment period, nor 

was it significant in the outpatient arm in the 

within-treatment or post-treatment periods. 
35

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

Table 1. F statistics and nondirectional p values for all dependence by treatment condition 

interaction effects

Treatment contrast

Within treatment Posttreatment

MVxTx MVxTxxT MVxTx MVxTxxT

PDA DDD PDA DDD PDA DDD PDA DDD

Outpatient arm

CBT vs MET F 0.02 0.40 1.99 1.21 0.41 0.72 1.19 0.00

p .89 .53 .16 .27 .52 .40 .28 .96

CBT vs. TSF F 0.40 0.03 0.12 0.25 0.18 0.15 0.56 1.23

p .53 .87 .72 .62 .67 .70 .45 .27

MET vs. TSF F 0.59 0.67 1.23 0.42 1.14 0.25 0.15 1.28

p .44 .41 .27 .52 .28 .62 .70 .26

MV x Tx F 0.35 0.36 1.08 0.60 0.58 0.36 0.62 0.86

p .71 .70 .34 .55 .56 .70 .54 .42

Aftercare arm

CBT vs. MET F 1.44 1.04 2.53 4.20 2.86 2.13 0.01 0.31

p .23 .31 .11 .04 .09 .15 .91 .57

CBT vs. TSF F 1.30 0.34 0.88 2.46 6.30 5.34 1.37 0.12

p .26 .56 .35 .12 .01 .02 .24 .73

MET vs. TSF F 0.00 0.17 0.35 0.18 0.62 0.69 1.00 0.74

p .97 .68 .55 .68 .43 .41 .32 .39

MVxTx 0.95 0.53 1.30 2.37 3.37 2.79 0.78 0.38

p .39 .59 .27 .09 .03 .06 .46 .69

NOTE: MV=matching variable, Alcohol Dependence; Tx=treatment; T=time; PDA=percentage of days abstinent; 

DDD= drinks per drinking day

100

90

80

70

60

50

0

10 20 30 40 50 60 70

CBT

Percent of Days Abstinent

Drinks per Drinking Day

Alcohol Dependency

6

5

4

3

2

1

0

10 20 30 40 50 60 70

Alcohol Dependency

TSF

Figure 3. Posttreatment plot of percentage of days abstinent and drinks 

per drinking day in the aftercare arm showing the interaction between 

CBT and TSF treatments and baseline alcohol dependence. The vertical axes represent predicted outcome scores, and the horizontal axes 

represent baseline alcohol dependence, with higher scores indicating 

higher dependence. The triangles on the horizontal axes indicate the 

10th and 90th percentiles for alcohol dependence in the aftercare arm. 

(Reprinted with permission from Journal of Studies on Alcohol, Vol. 58, 

pp. 7–29, 1997. Copyright by Alcohol Research Documentation, Inc., 

Rutgers Center of Alcohol Studies, Piscataway, NJ 08854.) 

In addition, none of the 

expected interaction 

effects contrasting TSF 

and MET treatments 

was significant.

In order to gain 

another perspective on 

the magnitude of the 

alcohol dependencematching effects, we 

examined the impact of 

applying a dependence 

matching strategy on a 

composite outcome variable. The entire sample 

of aftercare subjects was 

divided into matched or 

mismatched groups. 

High-dependence subjects were considered 

matched when they 
36

Part II: Alcohol Dysfunction

were randomly assigned to CBT, while lowdependence subjects were considered matched 

when assigned to CBT and mismatched when 

assigned to TSF. Subjects in the aftercare 

sample were divided into low- and high-dependence groups with the cut point based on the 

EDSS score at the intersection point in Figure 

3. Scores less than or equal to 35 were classified low dependence and scores greater than 35 

were classified high dependence.

Outcome was examined using a composite 

outcome variable based on drinking measures 

from the Form 90 (Miller 1996) and alcoholrelated negative consequences from the DrInC 

questionnaire (Miller et al. 1995; see Zweben 

and Cisler 1996 for a description of the composite outcome variable). Outcome was classified 

as a “success” when a subject reported no 

heavy drinking or alcohol-related negative 

consequences in the preceding 3 months. 

Outcome was classified as a “failure” when a 

subject reported any heavy drinking and/or 

consequences in the 3-month window. Figure 

4 reveals that the maximum matching effect in 

the aftercare arm on the composite outcome 

measure occurred during the 4 to 9 months 

after termination of treatment. Matched clients had an approximately 10-percent higher 

success rate than mismatched clients and an 

approximately 5-percent higher success rate 

than unmatched (randomly assigned) clients.

Causal Chain Results for Emphasis on 

Abstinence

The first causal chain analysis tested for the 

possible mediating role of differential emphasis on abstinence versus moderation and loss 

of control versus lapse coping. The assessment of treatment process in Project MATCH 

used methodology adapted from the National 

Institute of Mental Health Collaborative Study 

on Treatment of Depression (Elkins et al. 1985). 

Every Project MATCH session was videotaped, 

and Likert-type items for rating these tapes were 

generated from treatment manuals. After the 

MATCH Tape Rating Scale (MTRS) was developed, all session tapes from the second week 

of treatment and a randomly selected subsample of 150 week-6 week tapes were rated (see 

Carroll et al. 1998 for a detailed description of 

the MTRS). The following items from the MTRS 

were analyzed as potential mediating variables. 

■ Item 40. To what extent did the therapist 

discuss or address the patient’s commitment to abstinence? (Commitment to 

Abstinence)

■ Item 46. To what extent did the therapist explicitly discuss the rationale 

for/advantages of a treatment goal of 

abstinence OR the disadvantages of a 

treatment goal of reduction rather than 

cessation of alcohol use? (Abstinence as 

a Goal)

■ Item 47. To what extent 

did the therapist convey to 

the patient that a slip does 

not necessarily mean that 

the patient will experience 

a full-blown relapse? (Slip 

Is Not Relapse)

Is there a greater emphasis on abstinence as the goal 

of treatment in TSF than in 

CBT or MET? The mean ratings on items measuring 

commitment to abstinence, 

abstinence as a treatment 

goal, and the belief that a 

slip is not a relapse were 

examined by treatment 

condition for the outpatient and aftercare samples. 

Percent successful outcome

70

65

60

55

50

45

40

During Tx 1–3 Mo 4–6 Mo 7–9 Mo 10–12 Mo

Time period

Matched

Mismatched

Unmatched (randome)

All CBT

All TSF

Figure 4. Alcohol dependence matching effects on composite 

outcome across time. Matching to CBT versus TSF, aftercare arm. 

Matching strategy: EDSS < 35 to CBT; EDSS > 35 assigned to RSF.
37

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

Mutlivariate tests indicated main effects of 

treatment condition on these MTRS items in 

both the outpatient arm (Wilk’s Lambda=0.89; 

F(6, 1462)=15.02; p<.001) and the aftercare 

arm (Wilk’s Lambda=0.84; F(6, 1214)=18.68; 

p<.001).

In the outpatient arm, unvariate tests suggested that the items measuring Commitment 

to Abstinence (F(2, 733)=14.08; p<.001) and 

Abstinence as a Goal (F(2, 733)=16.76; p<.001) 

were responsible for the effect. There were no 

significant differences among treatments on the 

Slip Is Not Relapse item. Scheffé multiple comparisons, contrary to predictions, indicated the 

MET sessions were rated higher than CBT or SF 

sessions on Commitment to Abstinence. On the 

other hand, as predicted, TSF was rated higher 

than CBT or MET on the Abstinence as a Goal 

item (table 2).

In the aftercare arm, univariate tests again 

found differences on Commitment to Abstinence 

(F(2, 609)=29.38; p<.001), and on Abstinence 

(F(2, 609)=7.99; p<.001) but not on Slip Is Not 

Relapse. Scheffé multiple comparisons, contrary 

to predictions, indicated that MET was higher 

than TSF, but consistent with predictions , 

TSF was higher than CBT on Commitment to 

Abstinence. Also consistent with predictions, 

TSF was higher than CBT or MET on Abstinence 

as a Goal.

The answer to the causal chain question 

posed above is somewhat mixed. In both the outpatient and aftercare arms, there was a greater 

emphases on abstinence as a goal of treatment 

in the TSF sessions. High scores for the MET 

condition on Commitment to Abstinence may 

be the result of MET’s explicit focus on encouraging commitment to change. The absence of 

high scores on Abstinence as a Goal in the MET 

condition suggests that the MET focus on commitment to change was not always a focus on 

commitment to abstinence. With this pattern 

of tape-rating results in mind, we decided to 

conduct further causal chain analyses on the 

Abstinence as a Goal item, believing that it had 

the best potential to reflect the process underlying dependence matching effects.

Do differences in emphasis on abstinence 

have a differential impact on clients with high 

and low dependence? We expected that a greater 

degree of emphasis on abstinence would result 

in less drinking among clients with high dependence, while a lack of emphasis on abstinence 

would result in less drinking among clients with 

low dependence. To test this, we examined the 

dependence by emphasis on abstinence interaction effect in a series of repeated-measures 

analyses of variance (ANOVA) models containing the following terms: baseline drinking, site, 

main effect of dependence, and main effect 

of emphasis on abstinence. Dependent variables were monthly PDA and DDD in separate 

analyses.

In the outpatient arm, the dependence by 

emphasis on abstinence interaction effect was 

not significant for either PDA or DDD. On the 

other hand, this interaction effect was significant in the aftercare arm for both PDA (F(1, 

351)=5.60; p<.05) and for DDD (F(1, 351)=4.78; 

p<.05). Figure 5 shows this interaction effect, 

revealing that low-dependent aftercare clients 

had better PDA outcomes when assigned to 

therapists who did not give an abstinence message. High-dependent aftercare clients had 

equivalent outcomes regardless of the degree of 

emphasis on abstinence.

Table 2. Means and (SDs) for Emphasis on Abstinence items from the MATCH Tape Rating Scale

Outpatient Aftercare

CBT MET TSF CBT MET TSF

n=233 n=249 n=264 n=210 n=209 n=193

Commitment to 

Abstinence

1.76 2.22 1.88 1.41 2.15 1.65

(.93) (1.07) (1.02) (.79) (1.09) (.86)

Abstinence as a Goal 1.37 1.54 1.88 1.18 1.27 1.56

(.71) (.86) (1.10) (.57) (.59) (.89)

Slip Is Not Relapse 1.28 1.17 1.25 1.23 1.22 1.25

(.64) (.51) (.57) (.75) (.69) (.59)

NOTE: MATCH Tape Rating Scale items range from 1 to 5.
38

Part II: Alcohol Dysfunction

No Abstinence Message

Some Abstinence Message

Predicted percent days abstinent (months 4 to 15)

18

19

20

17

16

15

14

13

12

0 2 10 0 30 40 50 60 70 80

Alcohol dependence

Figure 5. Interaction of therapy emphasis on 

abstinence with alcohol dependence in the 

aftercare arm, CBT and TSF clients only.

The ultimate test of whether or not emphasis 

on abstinence mediated the dependence matching effect is to examine a repeated-measures 

ANOVA model containing both a treatment by 

dependence term and an emphasis on abstinence 

by dependence term along with their associated 

main effects. When this was done, the treatment 

by dependence term was made nonsignificant 

for PDA outcome (F(1, 344)=1.85; p>.1) and for 

DDD outcome (F(1,344)=3.40; p>.05). This supports the conclusion that, particularly for PDA 

outcome, the alcohol dependence matching 

effect in the aftercare arm was mediated by differences in the degree of therapist emphasis on 

abstinence.

AA Attendance Causal Chain Results

Do severely dependent clients attend more AA 

meetings than less dependent clients? Do TSF clients attend more AA meetings than CBT or MET 

clients? Frequency of AA meeting attendance 

was assessed by research interviews conducted 

at posttreatment and at 3, 6, 9, and 12 months 

following treatment. Repeated-measures analysis of covariance (ANCOVA) using type-1 sum 

of squares (i.e., a hierarchical procedure) was 

used to determine the effects of pretreatment 

dependence and treatment assignment on the 

five repeated measures of AA meeting attendance, adjusting for site differences. Factors 

were entered in the following order: site, treatment assignment, baseline dependence, and 

the interaction of treatment with dependence.

In the outpatient arm, AA meeting attendance was associate with higher dependence 

(F(1, 831)=24.89; p<.001), with treatment 

assignment (F(2, 831)=20.48), and with a significant dependence by treatment interaction (F(2, 

831)=5.31; p>.01). Although dependence was 

analyzed as a continuous variable, it was trichotomized to illustrate these effects. As shown, 

in Figure 6, the high-dependence outpatient 

clients assigned to TSF treatment attended 

approximately twice as many AA meetings as 

did clients assigned to CBT or MET.

Among aftercare clients, AA meeting 

attendance was also associated with higher 

dependence (F(1, 673)=16.31; p<.001) and with 

treatment assignment (F(2, 673)=6.36; p<.001). 

However, the treatment by dependence interaction effect was nonsignificant. Thus it is 

concluded that in both the outpatient and aftercare arms, TSF resulted in more AA meeting 

attendance than CBT or MET, and more severely 

dependent clients were more likely to attend AA 

meetings. The relationship between dependence 

and AA attendance is particularly strong in the 

outpatient TSF treatment condition.

Is AA attendance predictive of less alcohol 

consumption? Does AA attendance have a differential impact on clients with high and low 

dependence? Repeated-measures ANCOVA 

using type-1 sum of squares was used to determine the predictive effects of within-treatment 

AA attendance and treatment assignment on 

12 monthly measures of drinking, adjusting 

for site differences. In these analyses, baseline 

drinking was the covariate. Factors were then 

entered in the following order: AA attendance 

during treatment, baseline dependence score, 

and the interaction of AA attendance with baseline dependence.

In the outpatient arm, more frequent AA 

attendance within treatment was positively 

associated with PDA in the year after treatment (F(1, 843)=47.13; p<.001), and the AA 

attendance by dependence interaction was not 

significant. DDD outcome results were similar, 

with a significant prognostic effect found for 

AA attendance (F(1, 843)=41.07; p<.001) and 

a nonsignificant AA attendance by dependence 

interaction.

Aftercare arm results mirrored those in the 

outpatient arm. AA attendance within treatment 
39

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

Mean AA meetings attended

140

120

100

80

60

20

0

Low Moderate High

40

Severity of dependence

CBT (n=269)

MET (n=270)

TSF (n=302)

A

Mean AA meetings attended

140

120

100

80

60

20

0

Low Moderate High

40

Severity of dependence

CBT (n=239)

MET (n=229)

TSF (n=216)

B

Figure 6. AA attendance by dependence score for each treatment condition. (A) Outpatient arm 

(N=841). (B) Aftercare arm (N=684).
40

Part II: Alcohol Dysfunction

was predictive of PDA outcome (F(1, 670)= 

40.51; p<.001), with a nonsignificant AA attendance by dependence interaction. Similarly, AA 

attendance predicted DDD in the aftercare arm, 

with a nonsignificant AA attendance by dependence interaction.

In summary, in both the outpatient and 

aftercare arms, TSF clients attended more AA 

meetings than CBT or MET clients, severity 

of alcohol dependence was a strong predictor 

of AA attendance, and AA attendance was a 

strong predictor of drinking outcome. However, 

no evidence was found in either arm for an AA 

attendance by dependence interaction effect. 

This interaction effect also was not significant 

when the analyses were repeated using only clients assigned to the CBT and TSF conditions. 

Thus, we do not have evidence to support the 

hypothesis that differences in AA attendance 

mediated the dependence treatment-matching effect involving CBT and TSF treatments. 

Although elements of the AA attendance causal 

chain were supported, the overall chain does 

not explain the dependence-matching effects.

Discussion

The degree of alcohol dependence has long 

been considered an important variable in planning treatment (Lindstrom 1992). In the present 

study, it was hypothesized that TSF, a treatment that was thought to put greater emphasis 

on the need for total abstinence (because of 

the alcoholic’s assumed powerlessness over 

alcohol), would be more effective with highly dependent clients than either CBT, which teaches 

relapse prevention skills, or MET, which focuses 

on motivating clients to develop their own strategy to achieve abstinence. In addition, it was 

hypothesized that CBT would be more effective 

with clients at low levels of dependence because 

they could make greater use of coping skills. 

Finally, low-dependence clients were expected 

to respond well to MET because they might prefer its greater flexibility regarding abstinence 

versus nonabstinence treatment goals.

In contrast, low-dependence clients treated 

in TSF were expected to do poorly because 

the disease concept promoted by AA, with its 

emphasis on loss of control, would be inconsistent with their experience. To the extent that a 

major feature of severe dependence is impaired 

control over drinking, TSF is likely to be more 

effective with severely dependent alcoholics 

because of its assumption that loss of control 

occurs immediately with alcohol ingestion and 

its emphasis on the need to obtain support from 

the AA Fellowship.

The results from the aftercare treatment 

arm of the trial were consistent with the outcome predictions involving the TSF versus CBT 

contrast. Clients low in dependence had better outcomes when treated in CBT than when 

treated in TSF, while those high in dependence 

had better outcomes in TSF than in CBT. When 

clients were matched to CBT or TSF treatments using a cut point of 35 on the Ethanol 

Dependence Syndrome Scale, the success rate 

for matched clients was approximately 10 percent higher than for mismatched clients and 

approximately 5 percent higher than for randomly assigned clients. These effects are not 

large, so clinicians should expect only modest 

gains from using this matching strategy.

Although there was support for the alcohol 

dependence-matching hypothesis in the aftercare arm, there was no evidence for matching in 

the outpatient arm. There also was no evidence 

of matching effects involving the TSF versus 

MET contrast. TSF versus CBT matching effects 

observed in the aftercare arm were not evident 

during the treatment phase. These effects only 

emerged in the year following termination of 

treatment.

There are several possible explanations for 

the finding of some significant dependence 

matching only in the aftercare arm. First, the 

distribution of dependence scores in the aftercare sample contained many more clients with 

high dependence than did the outpatient sample. Sixty-four percent of the aftercare sample 

met the definition of high dependence, compared 

with only 37 percent of the outpatient sample. 

It is therefore possible that there was not a sufficient number of high-dependence clients in 

the outpatient sample to find a dependence 

matching effect. Recent changes in treatment 

practices might result in a greater number 

of severely dependent clients being admitted 

directly to low-intensity outpatient treatment 

than was the case when Project MATCH was 

conducted. If the difference in matching effects 

across Project MATCH arms is due to the 
41

Matching Clients to Alcoholism Treatment Based on Severity of Alcohol Dependence

baseline dependence distribution, then outpatient settings with greater numbers of highly 

dependent clients might also find dependencematching effects.

A second possible explanation for the lack 

of matching effects in the outpatient arm is 

that aftercare clients probably received more 

encouragement to totally abstain from drinking 

and to attend AA meetings in the context of the 

intensive treatment delivered prior to randomization to Project MATCH therapies. This may 

have provided highly dependent aftercare clients assigned to TSF with a greater overall dose 

of matched treatment, while aftercare clients 

with low dependence assigned to TSF received 

an overall greater dose of mismatched treatment. On the other hand, outpatient clients in 

TSF may not have received enough of a 12-step 

dose to produce dependence-matching effects. 

This explanation would imply that a greater 

intensity or duration of TSF may be needed to 

produce matching effects among clients directly 

admitted to outpatient treatment.

A third potential reason for the differences 

among study arms is that aftercare clients 

were more likely to have “hit bottom,” suffering 

severe negative consequences of drinking. This 

could have made it more likely that they would 

recognize their need for the support of AA.

The expected TSF versus MET matching 

effect did not emerge. This hypothesized matching contrast was in part based on the idea that 

severely dependent clients needed more than the 

client-generated strategies employed in MET. 

However, severely dependent clients assigned 

to MET in the aftercare arm actually received 

a substantial amount of other therapy prior to 

the start of their MET aftercare therapy. This 

prior treatment may have diluted the difference 

between aftercare MET and TSF in intensity of 

treatment, washing out any potential MET versus TSF matching effect.

Causal chain analyses shed some light on 

the processes underlying the aftercare CBT versus TSF matching effects. Ratings of videotapes 

obtained from therapy sessions confirmed the 

expectation that TSF treatment had a greater 

emphasis on abstinence as a goal of treatment 

than CBT treatment. Research on controlled 

drinking (Sobell and Sobell 1995) suggests that 

the recovery of severely dependent individuals 

predominantly involves abstinence, while the 

recovery of those who are not severely dependent predominantly involves moderate drinking. 

The association between dependence severity 

and outcome type appears to be independent 

of advice provided in treatment. Thus, dependence-matching effects may have occurred in 

Project MATCH because clients preferred and 

participated more actively in treatments that 

emphasized recovery outcomes that they had 

an inherent likelihood of achieving.

Another causal chain model that was not 

fully supported was the idea that the matching effects were mediated by differences in AA 

attendance. We did find that severely dependent clients were more likely to attend and 

get involved in AA meetings than were clients 

without severe dependence. These results are 

consistent with the findings of a meta-analysis 

(Emrick et al. 1993) that reported that AA affiliation was modestly associated with variables 

indicative of dependence (greater loss of control 

over drinking, higher daily quantity of alcohol 

consumption, more physical dependence, more 

severity of dependence, and more obsessivecompulsive involvement with drinking). We also 

found that greater AA attendance during treatment was predictive of better posttreatment 

outcomes. What was missing from this causal 

chain was evidence that AA attendance had a 

differential impact on clients with high and low 

dependence.

Conclusions

■ On intake, clients were administered the 

16-item Ethanol Dependence Syndrome 

Scale (EDSS). After an intensive treatment episode, outcomes were improved 

by matching those with high dependence 

(scoring greater than 35 on the EDSS) 

to TSF and by matching those with low 

dependence (less than or equal to 35 

on the EDSS) to CBT. Matched clients 

had an approximately 10-percent higher 

success rate than mismatched clients.

■ Matching effects were not observed 

when clients were directly admitted 

to the low-intensity 12-session outpatient treatments without prior intensive 

treatment.
42

Part II: Alcohol Dysfunction

■ Process analyses supported the 

hypothesis that the alcohol dependencematching effect in the aftercare arm was 

mediated by differences in the degree of 

therapist emphasis on abstinence. Only 

partial support was found for another 

causal chain hypothesis that differences 

in AA attendance mediated the dependence treatment matching effect.

Acknowledgments

This work was supported in part by grant 

number U10–AA10170–08 from the National 

Institute on Alcohol Abuse and Alcoholism.

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Taylor, J.R.; Helzer, J.E.; and Robbins, L.N. 

Moderate drinking in ex-alcoholics: Recent 

studies. Journal of Studies on Alcohol 47:115–

121, 1986.

Vaillant, G.E. The Natural History of Alcoholism: 

Causes, Patterns, and Paths to Recovery. 

Cambridge, MA: Harvard University Press, 

1983.

World Health Organization. WHO Brief Intervention 

Study Group. A cross-national trial of brief 

interventions with heavy drinkers. American 

Journal of Public Health 86:948–955, 1992.

Zweben, A., and Cisler, R. Composite outcome 

measures in alcoholism treatment research: 

Problems and potentialities. Substance Use 

& Misuse 31:1783–1805, 1996.
44

The Alcohol Involvement Matching 

Hypothesis and Findings

Robert G. Rychtarik, Ph.D., William R. Miller, Ph.D., and 

J. Scott Tonigan, Ph.D.

ABSTRACT

Individuals high in alcohol involvement were predicted to have better outcomes 

in Cognitive-Behavioral Coping Skills Therapy and Twelve Step Facilitation relative 

to Motivational Enhancement Treatment. It was hypothesized that differences in 

the intensity of these treatments moderated this alcohol involvement by treatment 

interaction through indirect effects on within-treatment drinking, within-treatment 

consequences, and post-treatment self-efficacy. However, results failed to show a significant interaction between alcohol involvement and treatment. Closer examination 

revealed that, contrary to predictions, alcohol involvement was more positively associated with outcome among outpatient clients as the followup period progressed. This 

positive association with outcome appeared mediated by the greater motivation of high 

alcohol-involved individuals and their increased likelihood of attending Alcoholics 

Anonymous (AA). Among aftercare clients, alcohol involvement was associated with 

poorer overall outcomes. However, alcohol involvement’s effects were not moderated 

by treatment intensity. The results suggest that alcohol involvement’s influence on 

outcome is complex and varies with the treatment population, client motivation, and 

posttreatment AA attendance.

Alcohol involvement was conceptualized 

as a broad, multifaceted construct representing the severity of an individual’s 

alcohol problem. The construct was meant to 

include but extend beyond the narrower concept 

of alcohol dependence (i.e., psychoperceptual 

and psychophysical withdrawal symptoms). As 

such, alcohol involvement also reflects the extent 

to which an individual’s lifestyle is directed and 

influenced by drinking. These additional facets 

include the extent to which an individual drinks 

in a sustained fashion, is obsessed with drinking, and has experienced social consequences 

as a result of drinking (e.g., unemployment, solitary lifestyle, detention by authorities). 

The Rationale for the 

Hypothesis

A basic assumption providing the foundation 

for the current hypothesis is that as alcohol 

involvement increases there are fewer abstinent days and more drinks per drinking day 

after treatment. Although specific measures 

of alcohol involvement have varied across 

studies, there exists some empirical support 

for this assumption. (e.g., Horm et al. 1990); 

Rounsaville et al. 1987). The presumed relationship between alcohol involvement and drinking 

outcomes also suggests that individuals at different alcohol involvement levels may respond 

Robert G. Rychtarik, Ph.D.

Research Institute on Addictions

1021 Main Street

Buffalo, NY 14203

E-mail: rychtari@ria.buffalo.edu
45

The Alcohol Involvement Matching Hypothesis and Findings

differentially to different treatment types. In 

particular, it is a common belief that individuals with more severe drinking problems benefit 

from more intense treatments.

Unfortunately, there has been no direct test 

of the assumption that high alcohol-involved 

individuals benefit from treatments of higher 

intensity. There is however, some evidence in 

the treatment outcome literature that treatment 

intensity may interact with problem severity to 

influence outcome. Most relevant to the current 

discussion is the study of Orford and colleagues 

(1976). They differentiated 100 married alcoholics into gamma and nongamma types and 

found at a 2-year follow up that among gammas, all successful outcomes occurred in the 

more intensive outpatient condition, whereas 

among nongammas, successes were predominant in the minimal advice condition. So, as 

a cross-over effect was found, with outcome 

status influenced by the interaction between 

treatment type and problem severity. It should 

be noted that recent findings failed to replicate 

these results in the same sample at the 1-year 

followup (Edwards and Taylor 1994). Still, the 

Orford group’s results have had wide impact 

and were extended to develop the present 

hypothesis for the MATCH trial.

Advice in the study by Orford and associates consisted of a half day’s assessment that 

included a summary evaluation informing the 

client of the alcohol problem and of the need to 

do something about it. This condition bears a 

generic similarity to Motivational Enhancement 

Therapy (MET; Miller et al. 1992), which in total 

amounts to several hours of assessment followed by four therapy sessions over 12 weeks. 

The procedure of providing important feedback 

to the client as a motivator for change of lifestyle also is share by Brief Advice and MET.

In like fashion, the MATCH CognitiveBehavioral Coping Skills Therapy (CBT; Kadden 

et a;. 1992) and Twelve Step Facilitation (TSF; 

Nowinski et al 1992) conditions share with 

the Orford team’s intensive outpatient condition a greater amount of therapeutic contact 

as a more intensive level. The presumed active 

ingredients of differential intensity and amount 

of treatment in the Orford group’s study would 

also differentiate CBT and TSF from MET in the 

MATCH study.

While there is no direct correspondence 

between gamma and nongamma alcoholism in 

the Orford study and alcohol involvement as 

measured here, the constructs do share common attributes. The gamma alcoholic is a more 

sever type characterized by both major symptoms and physical dependence. Nongamma 

alcoholics are less severe and do not have signs 

of both symptoms and physical dependence. In 

the MATCH study, alcohol involvement, characterized as a continuous variable, included 

level of alcohol dependence and symptoms/

consequences as part of its definition. Thus, the 

extrapolation of an interaction between alcohol 

involvement and treatment intensity, as much 

as Orford and colleagues found between alcoholic type and intensity, appears justified.

The Alcohol Involvement 

Prognostic Model

Figure 1a provides a general model for conceptualizing the effect of alcohol involvement on 

posttreatment outcome. In this model, alcohol 

involvement is expected to have a direct negative 

effect on outcome. In addition, a series of indirect 

effects of alcohol involvement on outcome are 

predicted. These indirect effects occur through 

alcohol involvement’s presumed negative effect 

on pretreatment self-efficacy, within-treatment 

outcomes, and posttreatment self-efficacy. In 

addition, alcohol involvement is expected to 

have a positive association with within-treatment drinking consequences. Pretreatment 

self-efficacy, posttreatment self-efficacy, and 

within-treatment drinking outcomes are predicted to be positively associated with followup 

outcomes. Drinking consequences, on the other 

hand, are expected to be negatively associated 

with outcome.

The model assumes that individuals high in 

alcohol involvement, by definition, have a history of failed treatment attempts which will 

increase the likelihood of poorer overall outcome 

in the future. Similarly, this history of treatment 

failure is presumed to result in lower self-efficacy both initially and at the end of treatment. 

Self-efficacy at the end of treatment also us 

expected to be influenced positively by outcome 

experiences during treatment and negatively 

by within-treatment drinking consequences. 

Based on the prior literature (Rychtarik et al. 
46

Part II: Alcohol Dysfunction

1992), self-efficacy is assumed to be positively 

associated with both within-treatment and followup outcome measures. It is also anticipated 

that within-treatment outcomes will be positively associated with outcomes at followup.

The Moderating Effects of Treatment 

Intensity

Figure lb presents the alcohol involvement 

model, including the moderating effects of 

treatment intensity. Treatment intensity, as 

measured by the number of treatment sessions 

attended over the period, was hypothesized to 

buffer or moderate the direct or indirect effects 

of alcohol involvement on within-treatment 

variables. It was hypothesized that more intense treatments, by definition, provide the 

opportunity for more frequent monitoring of 

the individual’s efforts to remain abstinent. So, 

frequent sessions are more likely to detect and 

address a return to drinking in the 12-week 

treatment period than would be the case if 

fewer sessions were attended. Since high 

alcohol-involved clients, by definition, may be 

more susceptible to more frequent and heavier 

drinking during treatment, the opportunity to 

Figure 1. Proposed causal model of (a) alcohol involvement and (b) treatment intensity on outcome. Parentheses in (b) represent alcohol involvement effects moderated by intensity.
47

The Alcohol Involvement Matching Hypothesis and Findings

monitor drinking status more frequently offers 

the possibility of increasing the number of abstinent days postintake, subsequently reducing 

the number of drinking-related consequences 

during treatment, and in turn, increasing an 

individual’s confidence in resisting further 

drinking (i.e., self-efficacy). So, while alcohol 

involvement would tend to lower the percentage of abstinent days within treatment, more 

and frequent sessions would buffer this effect 

and diminish the negative within-treatment 

relationship between alcohol involvement and 

percentage of days abstinent.

Several features of the model presented in 

figure 1b should be noted. First, we used the 

treatment intensity measure (i.e., number of 

treatment sessions attended) rather than a 

treatment condition variable to distinguish the 

low intensity Project MATCH MET from the 

CBT and TSF. We chose this approach for two 

reasons. First, although the three treatment 

conditions varied in philosophy and approach, 

the current hypothesis assumes that intensity 

alone moderates the alcohol involvement effect. 

Clear differences in the average number of sessions attended by MET versus CBT and TSF 

clients were observed. So, the intensity measure provides a way to discriminate between 

MET and the CBT and TSF treatments.

Second, inclusion of a treatment condition 

variable in the model would add error to the 

intensity measure since the actual number of 

sessions attended in CBT and TSF varied among 

clients. Clients attending only a few sessions of 

CBT and TSF would be expected to benefit no 

more than individuals in MET. Finally, the model 

assumes that treatment intensity positively 

influences outcome through its indirect effects 

on within-treatment drinking, within-treatment 

drinking consequences, and posttreatment selfefficacy. No direct effect of treatment intensity 

on outcome is hypothesized.

Statement of the Matching 

Hypothesis

On the basis of the above models, it was 

hypothesized that the greater the client’s alcohol involvement prior to treatment, the higher the 

percentage of days abstinent (PDA) and the lower 

the drinks per drinking day (DDD) among clients 

treated in the TSF and CBT conditions (i.e., the 

intense conditions) relative to those treated in 

MET. In statistical terms, it was predicted that 

the slope of the relationship between alcohol 

involvement and outcome variables would differ significantly between the intense (CBT/TSF) 

and less intense (MET) treatment conditions.

Operationalization of the 

Matching Variable

The raw score of the broad alcohol involvement scale (ALCINVOL) of the Alcohol Use 

Inventory (AUI; Horn et al. 1990) was used as 

the matching variable. The AUI and its subscales are widely recognized as among the best 

developed measures in this area. The AUI also 

assumes a contemporary, multifaceted view of 

the alcohol involvement concept which is consistent with that of the current hypothesis. 

The ALCINVOL scale is a third-order scale that 

measures a cluster of symptoms representative of the construct of alcohol involvement (i.e., 

obsession with drinking, sustained drinking, 

perceptual withdrawal, somatic withdrawal, 

social role maladaptation, loss of control of 

behavior). The internal consistency of this scale 

is good (0.93). In addition, the scale has shown 

reasonable construct and criterion validity 

(Horn et al. 1990; Skinner and Allen 1983).

Results

Outpatient Arm

Prognostic Effect of Alcohol Involvement

Contrary to predictions, no significant alcohol involvement effect or alcohol involvement by 

time interaction was observed for either PDA or 

DDD during the treatment period. There was, 

however, a significant (p<.05) main effect for 

alcohol involvement on abstinent days during followup, F(1, 9809)=6.37, p=.01, and a 

significant alcohol involvement by linear time 

interaction, F(1, 9809)=4.92, p=.03. On the 

DDD measure, there was no significant main 

effect of alcohol involvement, but a significant 

alcohol involvement by linear time interaction 

again emerged, F(1, 9809)=4.73, p=.03.

The nature of these alcohol involvement by 

time interactions was explored by examining the 
48

Part II: Alcohol Dysfunction

partial correlations between alcohol involvement 

and each of the primary dependent measures 

for each followup month, controlling for pretreatment abstinent days, clinical research 

unit, and treatment assignment. Figure 2 presents the partial correlations between alcohol 

involvement and PDA and DDD across followup 

months. As shown, the relationship between 

alcohol involvement and PDA increased linearly 

from nonsignificant levels in the first few months 

to a small significant association by month 15. 

For DDD, the relationship was reversed, with 

the association between alcohol involvement 

and DDD decreasing from a nonsignificant 

level in month 4 to a significant negative association at month 15. In sum, greater severity of 

alcohol involvement was associated with better

outcomes (i.e., more abstinent days and fewer 

DDD) as the followup progressed. This unanticipated positive association between alcohol 

involvement and outcome was maintained at 

a 39-month followup. At 39 months, alcohol 

involvement was correlated positively with PDA 

(r=0.15, p<.001) and negatively with DDD (r=-

0.08, p<.05).

Interaction of Alcohol Involvement With 

Treatment

Table 1 presents the results of the tests of 

the overall alcohol involvement by treatment 

interaction terms for PDA and DDD for both 

treatment and followup periods. Also included 

are tests of the effects of the interaction over 

linear and quadratic time as well as tests of 

the individual predicted contrasts. No overall 

significant treatment by alcohol involvement 

interaction was found. Individual contrasts 

also showed no significant difference in slope 

between CBT or TSF and MET conditions for 

either PDA or DDD either during treatment 

or in the 4–15 month followup period. These 

contrasts also did not vary with time in either 

period. At the 39-month followup, there was no 

significant alcohol involvement by treatment 

interaction.

Examination of the A Priori Alcohol 

Involvement Model

The reversal of the predicted relationship 

between alcohol involvement and outcome indicated a significant failure of the proposed causal 

(a)

(b)

Figure 2. Partial correlations between pretreatment alcohol involvement and (a) percentage of 

days abstinent (PDA) and (b) drinks per drinking day (DDD) across followup months 4–15. 

Pretreatment PDA/DDD, clinical research unit, 

and treatment type are controlled for.

models, which had predicted a negative association. In itself, a positive association between 

alcohol involvement and PDA would be sufficient to obviate the predicted matching effect. 

Path analyses using EQS (Bentler 1992) subsequently were conducted to examine both direct 

and indirect effects of alcohol involvement on 

outcome in the alcohol involvement and treatment intensity models presented in figure 1. 
49

The Alcohol Involvement Matching Hypothesis and Findings

Table 1. Alcohol involvement, outpatient

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT vs. MET F -1.03 1.19 0.30 0.49 1.61 -0.76 -1.63 1.20 0.89 -0.70 1.70 0.26

p .30 .23 .77 .62 .11 .45 .10 .23 .38 .48 .09 .79

CBT vs. TSF F -0.53 0.85 -1.10 0.80 -0.10 -0.34 -0.96 1.17 1.96 -1.78 0.75 -0.46

p .60 .40 .27 .43 .92 .74 .33 .24 .05 .08 .46 .64

MET vs. TSF F 0.56 -0.41 -1.40 0.27 -1.79 0.46 0.76 -0.10 1.00 -1.02 -1,05 -0.73

p .58 .68 .16 .78 .07 .64 .44 .92 .32 .31 .30 .46

Mv × Tx F 0.53 0.75 1.12 0.32 1.89 0.29 1.34 0.92 1.93 0.37 1.45 0.58

p .59 .47 .33 .73 .15 .75 .26 .40 .14 .94 .23 .79

NOTE: MV=matching variable, Alcohol Involvement; T=linear time ; T2=quadratic time; Tx =treatment ; PDA=percentage of days 

abstinent; DDD=drinks per drinking day

Since these models deviated from multivariate 

normality, the significance of individual parameter coefficients was determined using robust 

estimation methods (Bentler 1992). For similar reasons, we used the Satorra-Bentler chi 

square statistic as an indication of model fit. 

We refrained from using model fit indexes since 

model fit already is reflected in the model chi 

square. Also, model fit indexes can be deceptive given the high specificity of the models (i.e., 

nearly all potential pathways are included in the 

model). Standardized values for direct effects 

are used in the remainder of this chapter.

In the models evaluated, the confidence minus 

temptation score of the Alcohol Abstinence SelfEfficacy Scale (DiClemente et al. 1994) was 

used as a measure of self-efficacy both at pretreatment and at the end of treatment. The total 

consequence score of the Drinker Inventory of 

Consequences (Miller et al. 1995) was used as 

the measure of drinking consequences within 

treatment. Within-treatment PDA and DDD 

each were computed by averaging across treatment weeks. Followup PDA and DDD similarly 

were averaged across followup months. Arcsine 

and square root transformations subsequently 

were applied, respectively, to average PDA and 

DDD measures.

Results of tests of the alcohol involvement 

models for PDA and DDD are presented in figure 3. A decomposition of alcohol involvement 

effects is presented in table 2. Consistent with 

the results presented above, but contrary to our 

hypothesized causal model, alcohol involvement 

had a small, significant positive direct effect on 

both within-treatment and posttreatment PDA. 

No significant overall indirect effect of alcohol 

involvement on followup PDA was found.

Nevertheless, several indirect pathways were 

at least partially consistent with our model. For 

example, alcohol involvement was positively 

associated with drinking-related consequences 

during treatment. However, within-treatment 

drinking consequences were associated with 

better outcomes during followup (i.e., a greater 

PDA) rather than worse outcomes, as had been 

predicted.

Other indirect pathways were more consistent 

with the proposed model. Alcohol involvement 

was negatively associated with pretreatment 

self-efficacy. Pretreatment self-efficacy also 

was positively and significantly related to both 

within-treatment PDA and posttreatment selfefficacy. Pretreatment self-efficacy was not 

directly associated with posttreatment outcome. Posttreatment self-efficacy, however, was 

positively associated with posttreatment PDA. 

Moreover, as predicted, within-treatment PDA 

was negatively associated with within-treatment consequences, and both within-treatment 

drinking and consequences were associated 

with posttreatment self-efficacy in the predicted 

directions.

The alcohol involvement model applied to the 

DDD variable resulted in no significant direct 

effect of alcohol involvement. A significant 
50

Part II: Alcohol Dysfunction

Figure 3. Outpatient alcohol involvement model applied to (a) percentage of days abstinent (PDA) 

and (b) drinks per drinking day (DDD).

positive indirect effect of alcohol involvement 

was found that was accounted for largely 

through alcohol involvement’s negative effect 

on pretreatment self-efficacy, pretreatment selfefficacy’s negative effect on within-treatment 

DDD, and the positive effect of within-treatment DDD on followup DDD. So, at least for 

the DDD variable, we found some support for 

our hypothesized indirect positive effect on outcome. It should be cautioned, however, that the 

causal model results vary somewhat from the 

main findings, which revealed no significant 

main effect of alcohol involvement on DDD. The 

different findings may reflect the absence of 

pretreatment DDD in the model. The pretreatment value of DDD was controlled for in the 

main trial analyses.

To summarize, our model appeared to fail in 

the outpatient arm because alcohol involvement 

was positively associated with PDA both within 

treatment and during the followup period. 

Also, negative consequences during treatment, 
51

The Alcohol Involvement Matching Hypothesis and Findings

Table 2. Decomposition of outpatient total and 

indirect alcohol involvement and treatment 

intensity effects for the alcohol involvement 

and treatment intensity models

Effect

Alcohol 

involvement 

model

Treatment 

intensity 

model

PDA DDD PDA DDD

Total AI .08* .10* .08* .10*

Direct AI .09* .03 .09* .03

Indirect AI -.00 .07* -.00 .07*

Al-SE1-SE2 .02 .02

AI-SE1-

PDA/DDD

.04 .04

AI-Cons.-SE2 .01 .01

AI-SE1-

PDAMDD-SE2

.00 .00

AI-SE1-

PDAJDDDCons.-SE2

.00 .00

Al-INTPDATDDD-SE2NA .00

AI-INT-PDA/

DDD-Cons.-SE2

NA .00

AI-INTCons.-SE2NA .00

Total treatment 

intensity

.22* -.23*

Direct treatment 

intensity

NA NA

Indirect 

treatment 

intensity

.22* -.23*

INT-PDA/DDD .23 -.21

INT-PDA/

DDD-Cons.

-.03 ns

INT-PDA/

DDD-Cons.-SE2

.01 -.01

INT-Cons. -.02 ns

INT-Cons.-SE2 .01 -.00

INT-PDA/

DDD-SE2

.03 -.02

NOTE: PDA=percentage of days abstinent; DDD=drinks per 

drinking day; Al=alcohol involvement; SE1=pretreatment 

self-efficacy; SE2=end of treatment self-efficacy; Cons.=withintreatment drinking consequences; INT= number of treatment 

sessions; ns=not significant; NA=not applicable (not included 

in the model). For significant indirect effects, only individual 

pathways in which all segments are significant are shown. 

Due to rounding and elimination of pathways with at least 

one nonsignificant segment, individual indirect pathway 

effects may not add up to total indirect effects. Significance 

level of individual pathways not computed. *p<.01

contrary to our prediction, were positively associated with PDA posttreatment. Finally, the 

predicted indirect effects of alcohol involvement 

on PDA were not found. Some support for an 

indirect effect of alcohol involvement on DDD 

was found, but the observed effects occurred 

only when pretreatment DDD were not controlled for.

The Moderating Effects of Treatment 

Intensity

We subsequently examined the predicted 

moderating effects of treatment intensity 

on the alcohol involvement model (see figure 4). The direct and indirect effects of both 

alcohol involvement and number of sessions 

attended in this model are summarized in 

table 2. Consistent with our prediction, treatment intensity had a significant indirect effect 

on both followup PDA and DDD. This indirect 

effect was largely accounted for by the positive 

and negative effects of intensity, respectively, 

on PDA and DDD.

It is noteworthy, however, that two indirect 

pathways actually lowered the total overall 

effect of treatment intensity on followup PDA. 

Specifically, treatment intensity lowered withintreatment drinking consequences both directly 

and through its positive effect on within-treatment abstinent days. However, as noted earlier, 

consequences within treatment were associated 

positively with followup abstinent days. So, 

lowering consequences through more intense 

treatment caused a small decrement in the 

overall association between treatment intensity 

and abstinent days. In this case, within-treatment consequences appeared to moderate the 

treatment intensity effect.

No support was found for the predicted direct 

effect of treatment intensity on posttreatment 

self-efficacy. Also noteworthy is the support 

for the notion that treatment intensity influences outcome predominantly through indirect 

effects on within-treatment factors. This conclusion is exemplified in the fact that adequate 

model fit was obtained without a hypothesized 

direct effect of intensity on followup outcomes. 

Despite support for the role of treatment intensity in affecting outcome, there was no evidence 

of a moderating effect on alcohol involvement. 

Note in table 2, for example, that addition of 
52

Part II: Alcohol Dysfunction

Figure 4. Outpatient treatment intensity model applied to (a) percentage of days abstinent (PDA) 

and (b) drinks per drinking day (DDD).

treatment intensity effects to the alcohol involvement model did not alter either direct or indirect 

effects of alcohol involvement on outcome.

Moreover, as shown in figure 4, alcohol 

involvement was found to have a small negative 

effect on treatment intensity. Individuals high 

in alcohol involvement attended fewer sessions. 

This finding was not predicted and could have 

contributed further to our failure to find any 

moderating effect of treatment intensity.

Overall, except for a small negative effect of 

alcohol involvement on the number of sessions 

at-tended, alcohol involvement and number 

of sessions appear to function quite independently in influencing posttreatment drinking 

outcomes.

Exploratory Analyses

The models tested were averaged across 

the followup months to obtain followup drinking outcome measures. They did not take into 

account the changing relationship between 

alcohol involvement and outcome observed in 

the alcohol involvement by time interactions 
53

The Alcohol Involvement Matching Hypothesis and Findings

depicted in figure 2. As noted above, the 

increasing positive association between alcohol 

involvement and outcome across the followup 

period itself negated the proposed pathways 

hypothesized.

Two alternate pathways to account for the 

observed positive alcohol involvement-PDA relationship and its change over time subsequently 

were studied. It should be recalled that associations between alcohol involvement and outcome 

became more positive as the followup proceeded. 

To account for this finding, we first speculated 

that high alcohol-involved participants may 

have sought out more treatment within the first 

6 months following the MATCH treatment and/

or sought more affiliation with AA. We also speculated that high alcohol-involved clients may 

have been hospitalized or incarcerated more 

over this period. This higher level of involuntary 

abstinence and/or treatment-seeking behavior during followup could 

possibly account for the observed 

increase in the association between 

alcohol involvement and PDA as the 

followup period progressed. In other 

words, high alcohol-involved clients 

may have received more treatment 

over the period immediately following treatment to account for their 

better outcome later in the followup 

period.

The second potential pathway 

hypothesized that high alcoholinvolved participants, because by 

definition they had experienced 

more negative consequences before

treatment, may have been more 

motivated to change to begin with. 

This higher motivation also could 

counterbalance any negative effect 

of the severity of their problem on 

outcome.

To examine these two post hoc 

hypotheses, an exploratory path 

model, including both alternative 

pathways, was examined (figure 

5). In this model, we studied the 

relationship between alcohol involvement and pretreatment readiness to 

change. In addition, the effects of 

alcohol involvement and readiness 

to change on subsequent treatment/

incarceration and AA attendance in followup 

months 4–9 were examined. Finally, the direct 

and indirect effects of alcohol involvement, 

motivation, month 4–9 treatment/ incarceration, and months 4–9 AA attendance on PDA 

during the subsequent 10- to 12-month period 

were assessed.

In this model, motivation to change was 

measured using the Readiness score derived 

from the University of Rhode Island Change 

Assessment Scale (DiClemente and Hughes 

1990). AA involvement was a categorical variable (coded 1=yes, 0=no) indicating whether 

the individual had reported attending an AA 

meeting over the assessed period. Finally, since 

individual rates of subsequent treatment/incarceration episodes were relatively low across the 

MATCH sample and highly skewed, a composite measure of treatment/incarceration was 

Figure 5. Outpatient exploratory model applied to (a) 

percentage of days (PDA) and (b) drinks per drinking day 

(DDD).
54

Part II: Alcohol Dysfunction

used. This measure formed a dichotomous variable (coded 1=yes, 0=no) indicating whether an 

individual had been hospitalized, detoxified, or 

incarcerated; admitted to residential alcohol, 

drug, or psychiatric treatment; or received outpatient alcohol or drug treatment during the 

4- to 9-month followup period.

To summarize, we hypothesized that the 

positive effect of alcohol involvement on 10- to 

12-month drinking outcomes would be mediated by pretreatment motivation, AA attendance, 

and/ or episodes of treatment/incarceration in 

months 4–9 immediately following treatment. 

Total, direct, and indirect effects are summarized in table 3.

Support was found for both pathways. 

Alcohol involvement had a significant indirect 

positive effect on both PDA and DDD. Moreover, 

this indirect relationship appeared mediated 

in large part by alcohol involvement’s positive 

associations with both AA and motivation to 

change. Importantly, these pathways appeared 

to operate independently since there was no significant association between motivation and AA 

attendance. Alcohol involvement also was positively associated with subsequent treatment/

incarceration, but the latter was not associated 

with subsequent outcome. Treatment/incarceration was associated positively with more AA 

attendance, but this pathway contributed only 

a small amount to the alcohol involvement indirect effect.

Particularly noteworthy is that once the indirect effect of alcohol involvement on subsequent 

outcome was accounted for, its direct relationship with PDA became negative, consistent with 

our initial hypothesis. So, the positive relationship between alcohol involvement and PDA at 

followup may be at least partially mediated by 

alcohol involvement’s positive associations with 

both pretreatment motivation and AA attendance in the first few months following the 

MATCH treatments.

A similar pattern was observed when the 

model was applied to the DDD variable, although here, as with the full model, alcohol 

involvement continued to have a significant 

direct effect on DDD during months 10–12. 

Again, however, the positive association between 

alcohol involvement and AA attendance and 

readiness to change appeared to buffer this 

Table 3. Decomposition of total, direct, and 

indirect effects of exploratory model for 

outpatients

PDA DDD

Total .08* .06

Direct -.01 .12*

Indirect .09* -.06*

AI-Treatment-AA .01 -.00

AI-AA .05 -.03

AI-Motivation .03 -.04

AI-Treatment .01

NOTE: PDA=percentage of days abstinent; 

DDD=drinks per drinking day; Al=alcohol involvement; Treatment=inpatient or outpatient treatment or 

incarceration. Only pathways with all significant sections are shown; significance of individual component 

indirect effects not shown. *p<.01

effect, as noted in the reduced total effect on 

DDD.

Summary of Outpatient Arm Analyses

No significant alcohol by treatment interaction was observed in the outpatient arm. The a 

priori hypothesis failed, in part, because alcohol 

involvement was unexpectedly associated with 

better overall outcome as the followup period 

progressed. The cause of this positive association with outcome appears to result from 

a tendency for high alcohol-involved clients 

to be more motivated initially and more likely 

to attend AA posttreatment. Together, these 

account for most of the observed positive total 

effect of alcohol involvement on PDA outcomes. 

Finally, although intensity of treatment did not 

moderate the effects of alcohol involvement, 

results suggested that its observed positive 

effect on outcome was mediated largely by its 

positive indirect effect on within-treatment 

outcome.

Aftercare Arm

Prognostic Effect of Alcohol Involvement

In the aftercare arm, alcohol involvement 

again showed no main effect or interaction with 
55

The Alcohol Involvement Matching Hypothesis and Findings

time on either dependent measure during the 

treatment period. There was, however, a significant main effect for alcohol involvement on 

both PDA, F(1, 7678)=5.25, p=.02, and DDD, 

F(1, 7678)=6.71, p=.01. In each case, the relationship was in the predicted direction, with 

alcohol involvement negatively associated with 

PDA and positively associated with DDD. For 

PDA, however, there was a significant alcohol 

involvement by quadratic time interaction, F(1, 

7678)=4.87, p=.03.

This change in the relationship between alcohol involvement and PDA across followup is 

depicted in figure 6. From month 4 thru month 

7, the relationship between alcohol involvement 

and PDA showed a near linear decrease from nonsignificant to significant negative association. 

The relationship then stabilized before becoming 

Figure 6. Partial correlations between pretreatment alcohol involvement and percentage of 

days abstinent (PDA) across followup months 

4–15. Pretreatment PDA, clinical research unit, 

and treatment type are controlled for.

Table 4. Alcohol involvement, aftercare

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT vs. MET F -.84 .50 -1.02 1.09 .27 -1.02 -.86 -.23 -.19 .55 -.93 .70

p .40 .62 .31 .28 .78 .31 .39 .82 .85 .58 .35 .49

CBT vs. TSF F .08 -1.24 -.13 .87 -.27 1.51 .02 -.36 .61 -.59 .40 .17

p .94 .22 .90 .39 .78 .13 .98 .72 .54 .55 .67 .86

MET vs. TSF F .85 -1.65 .81 -.15 -.52 2.39 .81 -.14 .76 -1.07 1.26 -.47

p .40 .10 .42 .88 .61 .02 .42 .89 .45 .28 .21 .64

Mv × Tx F .48 1.43 .65 .69 .93 2.88 .47 .07 .31 .58 .85 .25

p .62 .24 .77 .50 .51 .06 .62 .93 .73 .56 .43 .78

NOTE: MV=matching variable, Alcohol Involvement; T=linear time ; T2=quadratic time; Tx =treatment ; PDA=percentage of days 

abstinent; DDD=drinks per drinking day

Table 5. Decomposition of aftercare total and indirect alcohol involvement and treatment 

intensity effects for the alcohol involvement and treatment intensity models

Effect

Alcohol 

involvement 

model

Treatment 

intensity model

Effect

Treatment 

intensity model

PDA DDD PDA DDD PDA DDD

Total AI -.10* .28* -.10* .28* Total treatment intensity .20* -.20*

Direct A -.06 .16* -.06 .16* Direct treatment intensity NA NA

Indirect AI -.04 .13* - .04 .13* Indirect treatment intensity .20* -.20*

Al-SE1-SE2 .02 .02 INT-PDA/DDD .17 -.13

AI-PDA/DDD .05 .05 Al-PDA/DDD-Cons.-SE2 .01 -.01

INT-PDA/DDD-Cons.-SE2 .00 .00 INT-SE2 .02 -.03

AI-SE2 .02 .02 INT-Cons.-SE2 .00 -.00

AI-PDA/DDD-SE2 .01 .01 INT-PDA/DDD-SE2 .01 -.02

AI-Cons.-SE2 .01 .01

NOTE: PDA=percentage of days abstinent; DDD=drinks per drinking day; AI=alcohol involvement; SEI=pretreatment self-efficacy; SE2=end of treatment self-efficacy; Cons.= within-treatment drinking consequences; NA= not applicable (not included in 

the model). For significant indirect effects, only individual pathway effects in which all segments are significant are shown. Due 

to rounding and the elimination of pathways with AI-PDA/DDD-Consnsignificant segment, individual indirect pathway effects 

may not add up to total indirect effects. Significance level of individual pathways not computed. *p<.01
56

Part II: Alcohol Dysfunction

less negative in months 11–15. Although significant, it should be noted that these changes 

in relationship are small and remain negative 

throughout the period. The clinical relevance of 

this change over time is not clear.

Interaction of Alcohol Involvement With 

Treatment

Results of overall and individual contrast 

tests in the aftercare arm are presented in 

table 4. As in the outpatient arm, no significant 

interaction or individual contrast effects were 

observed.

Examination of the Alcohol Involvement 

Causal Model

Figure 7 presents path analytic results for the 

alcohol involvement model. Table 5 summarizes 

alcohol involvement direct and indirect effects. 

There was no significant direct or indirect effect 

Figure 7. Aftercare alcohol involvement model applied to (a) percentage of days abstinent (PDA) and 

(b) drinks per drinking day (DDD).
57

The Alcohol Involvement Matching Hypothesis and Findings

of alcohol involvement on PDA, although the 

effects were in the predicted direction. Contrary 

to our prediction, alcohol involvement was not 

significantly related to either within-treatment 

or followup PDA. Involvement was significantly 

and positively related to within-treatment consequences, but within-treatment consequences 

were not related directly to posttreatment 

drinking. Alcohol involvement at pretreatment did have a small but significant negative 

relationship with posttreatment self-efficacy, 

as had been predicted. Within-treatment PDA 

and posttreatment self-efficacy significantly 

predicted posttreatment PDA in the expected 

direction.

The model applied to DDD was in accord with 

predictions. Pretreatment alcohol involvement 

had both significant direct and indirect effects 

on followup outcomes. Much of the effect was 

accounted for by the direct positive effect on 

Figure 8. Aftercare treatment intensity model applied to (a) percentage of days abstinent (PDA) and 

(b) drinks per drinking day (DDD).
58

Part II: Alcohol Dysfunction

DDD that was predicted. Only alcohol involvement’s positive effect on within-treatment DDD 

appeared to contribute substantially to the positive indirect effect found on this measure.

The Moderating Effects of Treatment 

Intensity

Results of the path analysis on the treatment intensity model are presented in figure 

8, and direct and indirect alcohol involvement 

and intensity effects are summarized in table 

5. As in the outpatient arm, the addition of 

treatment sessions attended did not show any 

evidence of moderating the direct or indirect 

effects of alcohol involvement. Treatment intensity, however, did have a significant indirect 

effect. As with the outpatient arm, this effect 

was largely accounted for by the positive and 

negative effects, respectively, on PDA and DDD 

outcomes. Also, the model showed adequate 

overall fit without inclusion of a direct effect 

of intensity on outcome. As in the outpatient 

arm, the total effect of treatment intensity on 

outcome appears largely accounted for by its 

indirect effects.

Summary of Aftercare Arm Findings

The alcohol involvement causal model 

applied to the aftercare sample more closely 

approximated that predicted than did the 

model applied to outpatients. No direct or indirect effect of alcohol involvement on PDA was 

found, possibly due to the curvilinear relationship between alcohol involvement and PDA 

across followup. Still, this relationship was in 

the predicted direction. As with the outpatient 

arm, however, there was no evidence of treatment intensity moderating alcohol involvement 

effects. Treatment intensity, however, did have a 

predicted indirect effect on followup outcomes, 

largely through its positive effect on outcomes 

during treatment.

Discussion

The common belief that outpatients with 

high levels of alcohol involvement benefit most 

from more intense treatments was not supported in this study. The causal model analyses 

suggest a number of reasons for failure to support this predicted interaction. Foremost among 

these was the unanticipated finding that high 

alcohol-involved clients had relatively better 

outcomes as the followup period progressed. In 

this population, at least, it appears that individuals with high levels of alcohol involvement 

also are more ready to change and seek out AA 

more. These pathways appear to counteract any 

direct negative influence of alcohol involvement 

on outcome. The relationship between alcohol involvement and outcome in the aftercare 

arm was more consistent with our predictions. 

However, here too, the relationship between 

alcohol involvement and abstinent days was 

complex and varied in a curvilinear manner 

over the follow-up months.

Overall, the results suggest that the relation-ship between alcohol involvement and 

outcome as measured by abstinent days and 

DDD is complex, influenced by the tendency 

for high alcohol-involved subjects to seek out 

AA more in the ensuing months and to be more 

motivated for change in the first place. The AA 

findings highlight the importance of studying 

posttreatment factors and their influence on 

overall outcome (Moos et al. 1990).

It is important to note here that others also 

have failed to find a significant association 

between alcohol dependence/involvement measures and posttreatment abstinent days (Cooney 

et al. 1986). A more reliable finding was the 

consistent negative relationship between alcohol involvement and DDD in both the aftercare 

arm and, when other factors were not partialed 

out, in the outpatient arm. It would appear that 

alcohol involvement is more reliably associated 

with drinking intensity than drinking frequency 

measures.

The direction of other pathways in the causal 

model also were unanticipated and may have 

contributed to the failure of the proposed model, 

even in the aftercare arm where more predictions 

were confirmed. Specifically, we had not anticipated the negative association between alcohol 

involvement and number of sessions attended 

in the outpatient arm. While increased number 

of sessions is associated with better outcomes, 

the negative effect of alcohol involvement on sessions appears to attenuate this effect. Similarly, 

increased drinking consequences during treatment was associated with more abstinent days 

during followup among outpatients. In the 

aftercare arm, alcohol involvement continued 
59

The Alcohol Involvement Matching Hypothesis and Findings

to be associated negatively with posttreatment 

self-efficacy, also attenuating any positive effect 

of number of sessions on this measure.

In sum, the relationship between alcohol 

involvement and outcome appears to be influenced by the outcome measure assessed, client 

motivation, posttreatment help-seeking, and 

the population under study. In hindsight, the 

proposed hypothesis was too simple. Further 

exploratory analyses of secondary outcome 

measures and multiple predictor variables 

may elucidate the role of alcohol involvement, 

either alone or in conjunction with other measures, in predicting outcome and interacting 

with treatment type. It also may be that alcohol 

involvement interacts with the content and not 

the in-tensity of treatment to influence outcome.

In the preceding chapter (Cooney et al.), 

alcohol dependence was found to interact with 

treatment in a different manner than that predicted here. Unfortunately, although the alcohol 

involvement measure used in this analysis was 

highly correlated with that of Cooney and associates (0.69), we were unable to replicate their 

findings using the alcohol involvement measure 

or those portions of the measure more directly 

related to physical dependence symptoms.

Finally, it should be cautioned that the intensity of CBT and TSF treatments in MATCH was 

considerably less than that seen in intense outpatient or inpatient programs in the community. 

The possibility remains that the predicted interaction exists only when particularly high levels 

of treatment are involved. Similarly, intensity 

and content of treatment were confounded in 

Project MATCH, such that content and intensity 

may have interacted in complex ways to negate 

the hypothesized interaction. Further exploration of these issues is needed.

Acknowledgments

This research was supported by grants U10–

AA08431 and U10–AA08435 from the National 

Institute on Alcohol Abuse and Alcoholism. 

We gratefully acknowledge the collaboration 

of the Project MATCH Research Group and, in 

particular, the helpful comments of Richard 

Longabaugh and Philip W. Wirtz on the conceptualization of this hypothesis and on its writeup.

References

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Program Manual. Encino, CA: Multivariate 

Software, Inc., 1995.

Cooney, N.L.; Meyer, R.E.; Kaplan, R.G.; and 

Baker, L.H. A validation study of four scales 

measuring severity of alcohol dependence. 

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1986.

DiClemente, C.C.; Carbonari, J.; Montgomery, 

R.; and Hughes, S. The Alcohol Abstinence 

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DiClemente, C.C., and Hughes, S.O. Stages of 

change profiles in outpatient alcoholism 

treatment. Journal of Substance Abuse

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Edwards, G., and Taylor, C. A test of the matching hypothesis: Alcohol dependence, 

intensity of treatment, and 12-month outcome. Addiction 89:553–561, 1994.

Horn, J.L.; Wanberg, K.W.; and Foster, F.M. 

Guide to the Alcohol Use Inventory. 

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Kadden, R.; Carroll, K.M.; Donovan, D.; Cooney, 

N.; Monti, P.; Abrams, D.; Litt, M.; and 

Hester, R. Cognitive-Behavioral Coping 

Skills Therapy Manual: A Clinical Research 

Guide for Therapists Treating Individuals 

With Alcohol Abuse and Dependence. 

Project MATCH Monograph Series. Vol. 3. 

DHHS Pub. No. (ADM) 92–1895. Rockville, 

MD: National Institute on Alcohol Abuse 

and Alcoholism, 1992.

Miller, W.R.; Tonigan, J.S.; and Longabaugh, 

R. The Drinker Inventory of Consequences 

(DrInC): An Instrument for Assessing 

Adverse Consequences of Alcohol Abuse. 

Project MATCH Monograph Series. Vol. 

4. NIH Pub. No. 95–3911. Rockville, MD: 

National Institute on Alcohol and Abuse, 

1995.

Miller, W.R.; Zweben, A.; DiClemente, C.C.; and 

Rychtarik, R.G. Motivational Enhancement 

Therapy Manual: A Clinical Research Guide 

for Therapists Treating Individuals With 
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Alcohol Abuse and Dependence. Project 

MATCH Monograph Series. Vol. 2. DHHS 

Pub. No. (ADM) 92–1894. Rockville, MD: 

National Institute on Alcohol Abuse and 

Alcoholism, 1992.

Moos, R.H.; Finney, J.W.; and Cronkite, R.C. 

Alcoholism Treatment: Context, Process, 

and Outcome. New York: Oxford University 

Press, 1990.

Nowinski, J.; Baker, S.; and Carroll, K. Twelve 

Step Facilitation Therapy Manual: A Clinical 

Research Guide for Therapists Treating 

Individuals With Alcohol Abuse and 

Dependence. Project MATCH Monograph 

Series. Vol. 1. DHHS Pub. No. (ADM) 

92–1893. Rockville, MD: National Institute 

on Alcohol Abuse and Alcoholism, 1992.

Orford, J.; Oppenheimer, E.; and Edwards, G. 

Abstinence or control: The outcome for 

excessive drinkers two years after consultation. Behaviour Research and Therapy

14:409–418, 1976.

Rounsaville, B.; Dolinsky, Z.S.; Bab or, T.F.; 

and Meyer, R.E. Psychopathology as a predictor of treatment outcome in alcoholics. 

Archives of General Psychiatry 44:505–

513, 1987.

Rychtarik, R.G.; Prue, D.M.; Rapp, S.R.; and 

King, A.C. Self-efficacy, aftercare and 

relapse in a treatment program for alcoholics. Journal of Studies on Alcohol 

53:435–440, 1992.

Skinner, HA., and Allen, B.A. Differential 

assessment of alcoholism—Evaluation 

of the Alcohol Use Inventory. Journal of 

Studies on Alcohol 44:852–862, 1983.
61

Part III: Psychological Dysfunction

Cognitive Impairment as a Client-Treatment 

Matching Hypothesis

Matching Clients to Alcoholism Treatment 

Based on Psychopathology

Sociopathy as a Client-Treatment Matching 

Variable

Alcoholic Typology as an Attribute for Matching 

Clients to Treatment
62

Cognitive Impairment as a ClientTreatment Matching Hypothesis

Dennis M. Donovan, Ph.D., Daniel R. Kivlahan, Ph.D., 

Ronald M. Kadden, Ph.D., and Dina Hill, Ph.D.

ABSTRACT

The present study investigated the role of cognitive impairment in drinking behavior 

during and following the three Project MATCH therapies. It was hypothesized that (1) 

more impaired clients would have poorer outcomes, (2) impaired clients in CognitiveBehavioral Coping Skills Therapy (CBT) or Twelve Step Facilitation (TSF) would have 

better outcomes than those treated in Motivation Enhancement Therapy (MET), and 

(3) more impaired clients would have better outcomes in TSF than in CBT. The latter 

two hypothesized relationships represented anticipated matching effects between type 

of therapy and level of cognitive impairment. Level of impairment was not related to 

measures of drinking frequency or intensity in the outpatient arm of the trial as either 

a main effect or as an interaction with type of treatment. However, indirect evidence 

based on analyses of therapy attendance and completion, therapist-rated working alliance, and Alcoholics Anonymous (AA) involvement suggested that CBT may be less 

acceptable to more cognitively impaired individuals. Higher levels of AA involvement 

by the more impaired individuals were related to reduced drinking intensity during 

the active phase of therapy. Examination of the first causal chain suggested that the 

hypothesized therapy by impairment matching effect may have failed because the 

anticipated difference in therapeutic structure between MET and both CBT and TSF 

was not obtained. The second hypothesized causal chain was partially supported in 

that impairment level was related to dose of therapy received. In the aftercare arm, 

there was evidence of matching during the therapy delivery phase, with more impaired 

clients doing somewhat less well with respect to drinking behaviors in CBT than in 

TSF. However, these effects were relatively transitory, had disappeared by the end of 

treatment, and were not evidenced across the 1-year followup period. Examination of 

the causal chains indicated that the primary elements thought to underlie the observed 

interaction did not obtain; namely, the three therapies did not differ in their level of 

structure, and therapy dose did not interact with level of impairment. The results 

suggest that cognitive impairment, as measured in Project MATCH, is not related to 

treatment either as a prognostic variable or as a measure that would be useful for 

assigning individuals to one of the three Project MATCH therapies.

There is clear evidence that cognitive functioning is negatively affected by long-term 

abusive drinking. Cognitive functioning refers to those intellectual capacities, both 

learned and innate, that allow individuals to 

understand and interact adaptively with their 

Dennis M. Donovan, Ph.D.

Alcohol and Drug Abuse Institute 

University of Washington

3937 15th Avenue, N.E.

Seattle, Washington 98105-6696 

E-mail: ddonovan@u.washington.edu
63

Cognitive Impairment as a Client-Treatment Matching Hypothesis

environment. There is a high prevalence of 

impairment of such adaptive abilities among 

detoxified alcoholics, suggesting that the 

observed deficits are more than just the aftereffects of acute intoxication. From 50 to 80 

percent of alcoholics have been found to be 

significantly more impaired than appropriately 

matched nonalcoholic control clients (Parsons 

1986). It is assumed that such impairments 

affect treatment process and outcome among 

alcoholics.

Rationale for the Matching 

Hypothesis

Cognitive Impairment and the 

Treatment Process

Alcoholics early in treatment show significant deficits in learning ability (e.g., Goldman 

1987) and have difficulty acquiring and remembering treatment-relevant information (Becker 

and Jaffe 1984; Godding et al. 1992; Roehrich 

and Goldman 1993). The degree of difficulty in 

handling information appears to be related to 

the level of recent heavy drinking and possibly 

to the level of cognitive impairment experienced 

by the individual (Alterman et al. 1989; Godding et al. 1992; Sanchez-Craig et al. 1987).

Alcoholics with higher levels of assessed 

impairment in problem-solving and adaptive 

abilities have been rated by therapists (blind to 

the clients’ neuropsychological test scores) as 

having higher levels of psychological distress; 

as being less able to generalize information, 

reason, and generate future plans; as participating less in therapy groups; as having 

functioned less adaptively in treatment; and 

as showing less clinical improvement over the 

course of treatment (Fals-Stewart et al. 1995; 

Kupke and O’Brien 1985; Leber et al. 1985; 

O’Leary et al. 1979; Parsons 1987). Smith and 

McCrady (1991) also found that alcoholics who 

had more impaired abstracting and conceptual abilities had more difficulties in acquiring 

drink-refusal skills than did those with higher 

levels of functioning. Alcoholics with greater 

levels of impairment are significantly less likely 

to complete in-patient treatment (O’Leary et 

al. 1979) and are predicted by inpatient therapists as likely to have poor prognoses at 1-year 

posttreatment (Leber et al. 1985).

In order to benefit from alcoholism rehabilitation, one must be capable of receiving 

new information, integrating it with existing 

stores, and translating this input into behavioral changes (Goldman 1983). The prerequisite 

cognitive competencies involved in this therapeutic process, particularly attention capacity, 

cognitive flexibility, and abstract reasoning, 

are often found to be impaired in alcoholics 

(McCrady and Smith 1986). It has been suggested that standard treatment programs are 

too fast paced, require attentional and memory 

skills that are often beyond the capabilities of 

the patients, and employ materials that are too 

abstract conceptually for the patient to absorb, 

process, generalize, and apply (McCrady 1987).

Treatment programs may need to be modified 

to deal more effectively with cognitively impaired 

alcoholics (Donovan and Chaney 1985; Gordon 

et al. 1988; McCrady 1987; McCrady and Smith 

1986). Rather than insight-oriented therapy that 

may require more verbally mediated abstracting and conceptual ability than the individual is 

capable of, more structured interventions focusing on the development and rehearsal of social 

and behavioral coping skills have been recommended (Clifford 1986; Donovan and Chaney 

1985). Such a therapeutic approach would 

focus on the development of coping skills and 

the prevention of relapse (Chaney 1989; Monti 

et al. 1989; Marlatt and Gordon 1985), with an 

emphasis on the use of modeling, role playing, 

behavioral rehearsal of, and performance feedback about coping attempts.

Despite the strong intuitive appeal of structured treatment approaches for cognitively 

impaired alcohol abusers, this hypothesis has 

not been directly tested. Smith and McCrady 

(1991) and Roehrich and Goldman (1993) demonstrated that cognitively impaired alcoholics 

have difficulty acquiring knowledge and behavioral skills that would help prevent relapse. 

Sanchez-Craig and associates (1987) found 

that alcoholic clients tended to forget coping 

strategies within a month of completing treatment, despite evidence that they had mastered 

these strategies during treatment. It was suggested that impairment of the cognitive abilities 

needed to learn, remember, and use such new 

skills may mitigate against the use of cognitively 
64

Part III: Psychological Dysfunction

mediated treatment strategies among impaired 

individuals. Thus, questions re-main about 

the appropriateness and relative efficacy of 

cognitive-behavioral approaches with impaired 

alcoholics.

Cognitive Impairment and Treatment 

Outcome

The level of cognitive impairment also appears 

to be predictive of treatment outcome among 

alcoholics. Alcoholics who were more impaired at 

the beginning of treatment had poorer outcomes 

with respect to subsequent drinking behaviors 

and other aspects of psychosocial adjustment 

such as employment status (e.g., Donovan et 

al. 1985; Walker et al. 1983). Parsons, Schaffer, 

and Glenn (1990) found that alcoholics who 

had relapsed during a 14-month posttreatment 

followup period performed significantly more 

poorly at the beginning of treatment on a global 

index of neuropsychological performance than 

did those who had abstained during followup.

Similarly, Sussman and associates (1986) 

found that nearly 75 percent of alcoholics who 

recalled less than half the items on an ecologically relevant memory test (i.e., one that 

involved the recall of familiar stimuli such as 

products found in markets) at intake to treatment had relapsed by 3 months posttreatment; 

only a third of clients who had recalled more 

than half the items on this test had relapsed.

While these results appear promising, there 

are exceptions to this pattern (e.g., Lennane 

1988; Macciocchi et al. 1989), and there is 

considerable variability in the relative strength 

of the association between cognitive impairment and treatment outcome when it is found 

(e.g., Donovan et al. 1984; Eckardt et al. 1988; 

Glenn and Parsons 1991; Parsons et al. 1990; 

Wilkinson and Sanchez-Craig 1981).

Cognitive Impairment and Matching 

With Treatment

Kadden and associates (1989) found a suggestive interaction effect on treatment outcome 

between level of cognitive impairment among 

alcoholics and the type of treatment received as 

aftercare following an inpatient alcohol rehabilitation program. Cognitively impaired alcoholics 

had better drinking outcomes following involvement in supportive interactional group therapy, 

while unimpaired clients had better outcomes 

in a cognitive-behavioral coping skills group. It 

was suggested that the focus on training a broad 

range of skills and the reliance on homework 

assignments in the coping skills group may 

have overwhelmed the impaired clients; such 

individuals may have felt more supported and 

less cognitively taxed in the interactional groups 

which emphasized interpersonal relationships.

Jaffe and colleagues (1996) provided additional findings about the role of treatment 

structure versus support, with cognitively 

impaired alcoholics receiving one of two types 

of outpatient psychotherapy. Those with lower 

levels of verbal learning had poorer drinking 

outcomes when receiving relapse prevention 

versus supportive therapy. Higher levels of verbal learning, on the other hand, were associated 

with better outcomes in relapse prevention than 

in supportive therapy.

Statement of the 

Hypotheses

Hypothesized Main Effect

Previous results suggest that those individuals with greater cognitive impairment will have 

poorer outcomes across a variety of outcome 

measures than those with less impairment.

Thus, it was hypothesized that individuals 

with greater cognitive impairment, measured 

along a continuum, will have poorer outcomes, 

regardless of type of treatment, than those with 

less impairment.

Hypothesized Interaction Effects

Structure, Intensity, and Duration

The first hypothesized interaction effects 

were based on the apparent differences in the 

level of structure, intensity, and duration of the 

MATCH treatments. Some findings suggest that 

more cognitively impaired alcoholics should do 

better in more structured/intensive or longer 

treatments than in therapies with less structure or shorter durations. Those with minimal 

impairment would be expected to do comparably in either form of treatment. Twelve Step 

Facilitation (TSF; Nowinski et al. 1992) and 

Cognitive-Behavioral Coping Skills Therapy 
65

Cognitive Impairment as a Client-Treatment Matching Hypothesis

(CBT; Kadden et al. 1992) were 

assumed to be more structured 

and were scheduled to have 

more sessions than Motivational 

Enhancement Therapy (MET; 

Miller et al. 1992) and were 

thus expected to lead to better 

outcomes for more cognitively 

impaired individuals. 

Thus, it was hypothesized that 

alcoholics who are more cognitively impaired will have better 

outcomes, defined by percentage 

of days abstinent (PDA) and drinks 

per drinking day (DDD), following 

either CBT or TSF than following 

MET relative to clients who are 

less cognitively impaired.

Two measures were used to 

investigate this hypothesis. The 

first, the degree of therapeutic structure of each 

of the interventions, was operationalized using 

ratings of the Project MATCH Tape Rating Scale. 

This scale (Carroll et al. 1998) involved raters 

blind to the therapy condition rating videotapes 

of therapy sessions on a number of dimensions. 

These included dimensions, based on ratings of therapists’ activities and interventions, 

that were defined by factor analysis as being 

relatively specific to each of the three Project 

MATCH therapies. The therapeutic structure 

scale appeared to cut across therapies and was 

defined by items such as therapists’ level of 

verbal activity, the consistency of therapeutic 

focus over the course of a session, continuity of 

therapeutic theme from previous session, and a 

global rating of structure.

The second set of measures involved the intensity of treatments received (versus scheduled) 

as defined by indices of therapy attendance. 

These indices included the number of sessions 

attended, the percentage of scheduled sessions 

attended, the percentage of clients who attended 

three or more therapy sessions, and the percentage of clients who attended all scheduled 

sessions (e.g., therapy completers). These two 

causal chains are presented graphically in figures 1 and 2.

Type of

Therapy

Drinking

Behavior

Congitive

Impairment

Therapuetic

Structure

Type of

Therapy

Drinking

Behavior

Congitive

Impairment

Therapy

Attendance or

“Dose”

Figure 1. Congitive impairment hypothesis: CBT versus MET 

as a function of therapuetic structure.

Figure 2. Congitive impairment hypothesis: CBT versus MET 

as a function of therapy attendance or dose.

Content, Support, and Cognitive 

Complexity

The second anticipated interaction was 

based on differences in the content of therapies, level of support, and cognitive complexity 

between therapies having comparable levels of 

intensity and duration. More impaired clients 

could be predicted to derive greater benefit from 

either CBT therapy, with its emphasis on training of concrete skills and behavioral rehearsal 

of them, or TSF, with its straightforward, often 

repeated messages (e.g., Alcoholics Anonymous 

(AA) “slogans”) that appear to provide simpler, 

more concrete advice to compensate for cognitive impairment (see Gordon et al. 1988), and 

the social support of AA meetings which clients 

are encouraged to attend.

However, the treatment outcome results of 

Kadden and associates (1989) and Jaffe and 

colleagues (1996) suggested that more impaired 

alcoholics have poorer outcomes in cognitivebehavioral treatments, and additional treatment 

process evidence indicated that more impaired 

alcoholics have greater difficulty acquiring 

knowledge and behavioral skills associated with 

coping skills (Roehrich and Goldman 1993; 

Smith and McCrady 1991).

Thus, it was hypothesized that cognitively 

impaired alcoholics will have better outcomes, 

defined by PDA and DDD, following TSF than 
66

Part III: Psychological Dysfunction

following CBT relative to clients 

who are less cognitively impaired.

The CBT manual states that 

the role of the client is one of 

active participation in the learning of skills for coping with 

high-risk drinking situations, 

managing thoughts about alcohol 

and drinking, problem-solving, 

refusing drinks, planning for 

emergencies, coping with lapses, 

and managing interpersonal and 

intrapersonal discomfort. These 

skills are quite complex and typically unfamiliar to many clients. 

In contrast, the TSF message is less complex, 

focused on the AA ideas of acceptance, surrender, and getting active. The latter involves social 

activities of the AA Fellowship through which 

one might gain additional support.

Given the added assumption about the 

importance of participation in AA activities for 

the 12-step clients, the number of AA meetings 

attended for both CBT and TSF was tracked. 

Unfortunately, the ratings of videotaped sessions did not include measures that would 

directly assess the purported differential levels of cognitive complexity associated with the 

CBT and TSF conditions. In the absence of 

ratings of the complexity of the therapy sessions, complexity of treatment was indexed by 

proxy through AA meeting attendance and AA 

involvement as an indication of additional low 

complexity treatment. This hypothesized causal 

chain is presented graphically in figure 3.

Operationalization of the 

Matching Variable

Three relatively brief measures of cognitive function and impairment were included in 

the Project MATCH assessment protocol: the 

Shipley Institute of Living Scale (Shipley 1940), 

the Trail Making Test, Parts A and B (Reitan 

1958), and the Symbol Digit Modalities Test 

(Smith 1973). These Measures have been found 

to be useful in differentiating alcoholics who 

learn and retain treatment-relevant information from those who do not; have been shown 

to be related to therapists’ ratings of clinical 

Type of

Therapy

Drinking

Behavior

AA Attendance

and Involvement

Cognitive

Impairment

Perceived Cognitive

Complexity of

Therapy

Figure 3. Congitive impairment hypothesis: CBT versus MET 

as a function of perceived complexity of therapy, with AA attendance and involvement serivng as proxies.

treatment process, progress, and prognosis; 

and have demonstrated some utility in predicting outcome.

A principal components analysis was conducted using the Trails B, the Symbol Digit 

Modalities Test, and the Shipley Abstraction 

T-scores from the 1726 clients from Project 

MATCH. A single principal component emerged 

(eigenvalue=1.94), accounting for 64.6 percent 

of the variance. The component matrix suggested that all three measures loaded highly on 

this factor (Abstraction T-score=0.76; Trails B 

total time=-0.83; Symbol Digit=0.82).

Based on these preliminary analyses, it 

was decided to use a single composite measure, the Cognitive Impairment Index, derived 

by summing the unit-weighted standardized 

scores for the Trails B total time, Symbol Digit 

Modalities Test (number of correct responses in 

90 seconds), and the T-score from the Shipley 

Abstraction scale (Trails B – Abstraction –

Symbol Digit). Higher positive scores on this 

index indicate higher levels of impairment; lower 

and in the overall Project MATCH data analysis negative scores indicate less impairment. 

Information concerning the distributional properties of impairment (based on a trichotomy of 

the distribution for the entire sample) to other 

measures of cognitive function for the total 

sample and the outpatient and aftercare arms 

of the trial are presented in table 1. Based upon 

the scores on other measures of cognitive function, the derived levels of cognitive impairment 

appear better at accurately classifying clients in 

the outpatient arm than in the aftercare arm of 

the trial. 
67

Cognitive Impairment as a Client-Treatment Matching Hypothesis

Table 1. Characteristics of the Cognitive Impairment Index and its relationship to other 

measures of cognitive function

Measure

Outpatient Aftercare

Low Medium High Total Low Medium High Total

Cognitive Impairment Index

Mean -2.42 -0.32 2.37 -0.47 -2.31 -.34 2.90 .56

SD .90 .59 1.49 2.13 .81 .57 1.92 2.57

Shipley Abstraction T Scorea 60 55 46 55 54 53 52 54

Shipley Vocabulary T-Scorea 54 51 46 51 50 49 49 49

Shipley Total T-Scorea 58 53 45 53 52 51 50 51

Shipley Abstraction Quotient 108 102 93 102 101 102 100 101

Shipley Conceptual Quotient 101 91 79 92 88 89 86 88

WAIS Equivalent IQ 108 100 90 101 99 98 96 97

Trails A (time in seconds) 27 30 37 31 36 35 39 37

Percent impairedb 9.7% 14.0% 38.0% 18.2% 29.1% 29.5 36.8% 32.6%

Trails B (time in seconds) 57 70 102 73 87 85 91 88

Percent impairedc 5.4% 14.3% 47.1% 19.0% 30.7% 31.8% 37.1 35.4%

Symbol Digit Substitution 

(number correct in 90 sec)

56 49 43 51 46 47 45 46

Percent impairedd 5.6% 18.5% 52.9% 22.1% 36.7% 37.2% 42.9% 37.6%

NOTE: a T-Scores are age adjusted and have a mean of 50 and standard deviation of 10; b 41 seconds or more is in the impaired 

range; c 91 seconds or more is in the impaired range; d Equal to or less than 1.5 SD from the age-adjusted normative mean is in 

the impaired range (Lezak 1983).

Results: Outpatient Arm

Tests for Prognostic and Matching 

Effects

The primary analyses investigating prognostic main effects and matching interaction 

effects and matching interaction effects 

employed latent growth procedures as outlined 

in the overall Project MATCH data analysis 

plan (Project MATCH Research Group 1997; 

Longabaugh and Wirtz, this volume, p. 4–17). 

Cognitive impairment index scores, the three 

treatment conditions (CBT, MET, and TSF), and 

the impairment by treatment interaction effects 

were the independent variables, and PDA and 

DDD across time were the dependent variables. 

These analyses also included covariate adjustments to control for extraneous variables. The 

covariate set included the baseline measure of 

the drinking outcome criterion being used in 

an analysis, terms for site main effects and site 

by treatment interaction effects, terms for site 

by matching variable interactions, and interaction terms for both linear and quadratic time for 

each of these covariates.

Analyses were conducted to determine the 

prognostic effects of the cognitive impairment 

index with respect to PDA and DDD in the outpatient arm of the trial. Cognitive impairment 

was not predictive of treatment outcome (no 

main effect for cognitive impairment) during 

months 1–3 when the treatments were being 

delivered or across the 1-year followup period 

(months 4–15). Similarly, no statistically significant interactions were found between degree of 

impairment and type of therapy to suggest a 

matching effect on either the PDA or DDD outcomes during the 1–3 or 4–15 month periods 

(table 2). Subsequent analyses of outpatients 

followed at months 37 to 39 similarly found no 

evidence of prognostic main effects for cognitive impairment or matching interaction effects 

between level of impairment and type of therapy 

(Project MATCH Research Group 1998).
68

Part III: Psychological Dysfunction

CBT Versus MET and TSF Versus MET 

Contrasts

Therapy Structure

An initial question in relation to the hypothesized causal chain is whether the TSF and 

CBT therapies were comparable in their level of 

structure within sessions and had greater structure than MET. A second and related question 

is whether the levels of structure of the three 

therapies differ for clients having different levels 

of cognitive impairment. That is, do individuals 

who are impaired seem to “pull” for more structure in therapy than do less impaired clients? To 

address these questions, a factorial analysis of 

variance (ANOVA) was conducted for each arm 

of the trial, with the three therapy conditions 

and three levels of cognitive impairment (based 

on trichotomization of the clinical impairment 

index distribution) as the independent variables 

and level of structure derived from the Project 

MATCH Tape Rating Scale as the dependent 

variable.

No differences were found in rated structure 

across level of cognitive impairment (F=0.176, 

p=.84), nor was there a significant interaction between cognitive impairment and type of 

therapy (F=0.788, p=.533). However, a significant difference (F=3.024, p=.049) was found in 

the level of structure across the three types of 

therapy. Subsequent Duncan’s paired comparisons (p=.05) indicated that the level of structure 

of CBT (16.35) was significantly less than MET 

(17.07); TSF (16.74) was comparable to both 

CBT and MET in level of structure. Thus, a major 

underlying assumption of the first hypothesis 

and causal chain failed to be supported in the 

outpatient arm of the trial; the level of structure 

within MET sessions was equal to or greater 

than that found for the TSF and CBT therapies.

Therapy Attendance and Treatment Dose

Sessions Attended. A second question was 

whether the three therapies differed with respect 

to the dose of treatment clients received. A factorial ANOVA was conducted within each arm 

of the trial, with number of treatment sessions 

attended serving as the dependent variable 

and the type of therapies and level of cognitive impairment serving as the independent 

variables. In the outpatient arm, as predicted, 

clients in MET (3.26 sessions) attended significantly fewer therapy sessions (F=193.21, 

p=.000) than those in either CBT (8.12 sessions) 

or TSF (7.51 sessions). However, CBT and TSF 

also differed significantly. A result that was not 

consistent with the causal chain assumption of 

these two therapies being equal in attendance. 

Although the overall differences across cognitive 

impairment levels failed to reach significance 

(F=2.533, p=.08), subsequent post hoc analyses 

indicated that the low impairment group (6.58 

sessions) differed (p=.05) from the high impairment group (6.00 sessions).

There was also a significant therapy by 

cognitive impairment interaction (F=3.283, 

p=.011). This appears to be due to a significant difference within the CB condition in the 

attendance among the high impairment group 

(6.97 sessions) and the level of attendance by 

the low (8.88 sessions) and medium (8.52 sessions) impairment groups, and in comparison 

to the pattern of attendance across the MET 

and TSF conditions. Thus, as anticipated, 

Table 2. Cognitive Impairment, outpatient

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT vs. MET F -.21 .89 .15 .09 .10 -.40 .20 -.33 1.00 -.91 .20 .24

p .83 .37 .88 .93 .92 .69 .84 .74 .32 .37 .84 .81

CBT vs. TSF F -.16 1.41 .19 -.21 -.27 1.20 .26 .16 .61 -1.25 1.37 -1.08

p .87 .16 .85 .83 .79 .23 .79 .87 .54 .21 .17 .28

MET vs. TSF F .06 .52 .34 -.30 -.36 1.59 .06 .48 -.38 -.33 1.14 -1.30

p .95 .60 .74 .76 .72 .11 .95 .63 .70 .74 .26 .19

Mv × Tx F .03 1.02 .05 .05 .07 1.36 .04 .12 .51 .84 1.07 .96

p .98 .36 .96 .95 .93 .26 .96 .89 .60 .43 .34 .38

NOTE: MV=matching variable, Cognitive Impairment; Tx=therapy condition; T=time; T2=quadratic time; PDA=percentage of days 

abstinent; DDD=drinks per drinking day
69

Cognitive Impairment as a Client-Treatment Matching Hypothesis

therapies differed in session attendance/treatment dose. However, TSF was found to involve 

nearly one session less than CBT, although the 

causal chain was predicated on these two being 

equivalent. Also, outpatient clients in the high 

impairment group attended fewer CBT sessions 

than did those in the medium and low impairment group (figure 4). 

Figure 4. Number of outpatient therapy sessions attended as a function of type of therapy 

and level of cognitive impairment.

Percentage of Sessions Attended. The 

relationship of cognitive impairment to attendance was explored further by examining the 

percentage of sessions attended across levels of 

impairment. Factorial ANOVAs were conducted 

for each arm of the study with percentage 

of sessions attended as the dependent variable and type of therapy and level of cognitive 

impairment as the independent variables. In the 

outpatient arm, a significant effect was for the 

style of therapy, with MET (81.49 percent) having a higher percentage of sessions completed 

than either CBT (67.7 percent) or TSF (62.59 

percent) (F=28.64. p=.000).

There were no differences in percentage of 

sessions attended as a function of level of cognitive impairment (F=2.158, p=.116); however, 

the interaction between type of therapy and 

impairment approached significance (F=2.369, 

p=.051). This appears related to the large falloff in percentage of sessions attended by highly 

impaired clients in the CBT condition (58.1 

percent) compared with those in the medium 

impairment (71.0 percent) or low impairment 

(74.0 percent) group, and in comparison to the 

consistent pattern of attendance found across 

levels of impairment in the MET and TSF groups 

(figure 5).

Figure 5. Percentage of outpatient sessions 

attended as a function of type of therapy and 

level of cognitive impairment.

Categorical Attendance Indices. A final 

method to analyze the relation of impairment 

to attendance was to examine two variables 

categorizing therapy attendance. The first classified clients having attended 0–2 sessions 

as low attendees (17.4 percent in outpatient) 

and those having attended 3 or more sessions 

as high attendees. This cutoff coincides with 

the fact that all three interventions presented 

their first two therapy sessions in the same 

timeframe, namely during the first 2 weeks of 

treatment. The second variable classified those 

having attended 100 percent of scheduled therapy sessions as therapy completers (37 percent 

of total outpatient sample and 44.8 percent of 

those attending 3 or more outpatient sessions), 

while those with less than 100-percent attendance were classified as noncompleters.

These data were analyzed using a 2×3 chisquare analysis. No significant relationship 

was found in the outpatient arm on the level of 

attendance; however, the chi square for complete/noncomplete approached significance 

(X2=5.763, p=.056). This latter value suggested 

a trend in which only 32.8 percent of clients in 

the high impairment group completed therapy 

compared to 41.6 percent and 34.9 percent, 

respectively, for the low and medium impairment groups. 

When the type of therapy was factored into 

multilevel chi-square analyses, a significant 

relationship was found for the CBT condition in the outpatient arm for both the level of 

attendance (X2=6.970, p=.031) and therapy 

completion status (X2=8.585, p=.014). These 

relationships appear to attributable to the higher 

percentage of clients in the high impairment 
70

Part III: Psychological Dysfunction

group for classified as low therapy attendees 

(22.1 percent) and noncompleters (83.1 percent) 

compared to the low (9.6 percent low attendees, 

64.5 percent noncompleters) and medium (11.1 

percent low attendees, 75 percent noncompleters) impairment groups. The relationship of 

attendance and impairment within CBT for the 

outpatient sample is depicted in figure 6.

Figure 6. Percentage of outpatients classified as 

low attenders (≤2 sessions) as a function of type 

of therapy and level of cognitive impairment. 

Working Alliance. Although not part of the 

original causal chain, another possible process 

measure with which cognitive impairment might 

interact is the development of a working alliance 

during the course of treatment. The working 

alliance inventory (WAI; Horvath and Greenberg 

1986) was completed by both clients and therapists after the second therapy session. Univariate 

factorial ANOVAs with the therapist- and clientrated WAI total scores, with level of impairment 

and type of therapy as independent variables, 

were conducted in each arm of the study.

No differences or interactions were found on 

the client-rated WAI total score. A significant 

effect was found on therapist-rated WAI total 

scores as a function of type of therapy (F=3.777, 

p=.023) and level of impairment (F=3.176, 

p=.0542); however, there was no significant 

therapy by cognitive impairment interaction 

effect. The therapy group difference is related 

to the TSF having a significantly higher WAItherapist total score (199.35) than the CBT group 

(193.43); no differences were found between in 

MET (196.94) and either the TSF or CBT groups. 

The impairment main effect is accounted for by 

the high impairment group (193.48) being significantly lower than the low impairment group 

(199.43); there were no differences between the 

medium impairment group (195.79) and either 

the low or high impairment groups.

The Bond, Goals, and Task subscale scores 

from the WAI were entered into multivariate analyses of variance (MANOVA) as the 

dependent variables, with level of cognitive 

impairment and type of therapy as the independent variables. There were no multivariate 

differences among outpatients on the clientrated WAI subscales as a function of level of 

cognitive impairment (Pillai’s=0.002, F=0.232, 

p=.966) or its interaction with type of therapy 

(Pillai’s=0.022, F=1.433, p=.143); there was 

however, a significant difference as a function of 

type of therapy (Pillai’s=0.02, F=2.577, p=.017). 

This appears to be accounted for by differences 

across therapy conditions on the Bond subscale 

(F=4.818, p=.008), in which MET (72.08) had a 

significant;y higher score than either the CBT 

(69.65) or TSF (70.34) groups, which did not 

differ from one another.

There were no significant differences in the 

outpatient arm on the therapist-rated WAI subscale scores as a function of the type of therapy 

or its interaction with level of impairment; 

while not significantly different (Pillai’s=0.014, 

F=1.811, p=.093), the level of impairment 

approached this level. Univariate ANOVAs conducted as part of the MANOVA process to test 

between-client effects found a significant difference as a function of level of impairment on the 

Task subscale (F=3.528, p=.03) and an interaction effect on the Bond scale (F=2.50, p=.041). 

The interaction appears to be accounted for by 

the high impairment group’s Bond score (63.34) 

in the CBT condition in contrast to that of the 

low (67.68) and medium (66.68) impairment 

groups and in comparison to the pattern of 

scores in the other therapy conditions (figure 7).

Figure 7. Therapist ratings of therapeutic bond 

in outpatient therapies as a function of level of 

cognitive impairment.
71

Cognitive Impairment as a Client-Treatment Matching Hypothesis

CBT Versus TSF Contrast

AA Meeting Attendance and Involvement

The number of AA meetings attended and 

scores on the AA Involvement Scale (AAIS, 

Tonigan et al. 1996) reflecting involvement in 

other 12-step activities (e.g., reading the Big 

Book, having a sponsor, doing steps) were collected across the followup from the Form 90 

(Miller 1996). Analyses were conducted on each 

of these sets of variables within each arm; only 

those results that involve level of impairment 

and its interaction with treatment are reported.

AA Attendance. A repeated-measures 

ANOVA was conducted with therapy type and 

level of impairment as the independent variables 

and AA meeting attendance across the followup 

period as the repeated measure. No main effects 

for level of impairment, 2-way interactions 

of impairment by time, or 3-way interactions 

involving therapy condition, impairment level, 

AA meeting attendance across followup points 

were found in any of the analyses.

Two summary variables were derived from 

the AA meeting attendance and involvement 

measures, representing the average of these 

variables collapsed across the 3- through 

15-month followup period. These were the 

trichotomized based on their respective distributions to provide for high, medium, and low 

AA attendance ad AA involvement categories. No 

significant relationship was found between level 

of impairment, type of therapy, and frequency 

of AA meeting attendance in the outpatient arm.

AA Involvement. Similar analyses were 

conducted with the AA Involvement Scale. A 

repeated measures ANOVA was conducted 

with therapy type and level of impairment as 

the independent variables and AA involvement 

scores across the followup period as the repeated 

measure and dependent variable. A significant 

effect was found for the interaction between 

level of impairment and AA involvement in the 

outpatient sample (Pillai’s=0.033, F=2.438, 

p=.024, within-clients F=2.753, p=.012). This 

difference appears attributable to the differential increase in the level of AA involvement 

among clients in the high impairment group relative to that in the other impairment categories 

(figure 8). Subsequent post hoc comparisons 

indicate that the high impairment group, while 

Figure 8. AA involvement score as a function 

of level of cognitive impairment: Outpatient 

sample.

not differing at baseline form the other conditions, had higher AA involvement scores a the 

1–3- and 4–9-month periods than the other two 

impairment groups, which did not differ from 

one another.

A significant chi square was found between 

level of impairment and AA involvement category for the outpatient sample, collapsing 

across type of therapy (X2=10.260, p=.036). 

This appears to be related to the relatively large 

number of individuals in the high impairment 

group who had a high level of AA involvement, 

particularly when compared to those in the low 

impairment range who were low in their AA 

involvement (figure 9).

Figure 9. Relationship between level of AA 

involvement and level of cognitive impatient: 

Outpatient sample.

The relationship between AA involvement 

and level of impairment on drinking and abstinence during the therapy period was examined. 

Two variables were included as indices. The first 

was whether abstinence or drinking occurred 

in a given week; the second indicated whether 

a heavy drinking occasion (five or more drinks 

on a drinking day) occurred in a given week. 
72

Part III: Psychological Dysfunction

Figure 10. Proportion of weeks with a heavy drinking 

episode during outpatient therapy as a function of level 

of cognitive impairment.

Repeated-measures ANOVAs were conducted 

for each variable, withe the level of AA involvement during months 1 to 3 (trichotomized) and 

level of cognitive impairment as the independent variables.

In the outpatient arm, a significant interaction was found between weeks including heavy 

drinking across time, level of impairment, and 

AA involvement (F=1.422, p=.029). The interaction, depicted in figure 10, appears to be 

attributable to the difference between clients in 

the high impairment group who are either low or 

high on their AA involvement. Highly impaired 

clients who were highly involved in AA had considerably fewer weeks involving heavy drinking 

episodes. There was no relationship between 

level of AA involvement, impairment, and abstinent weeks in the outpatient arm of the trial.

Examination of the trichotomized classification of AA meeting attendance during months 

1 to 3 indicated that it may be related differentially to abstinent weeks during this period 

for individuals differing their level of cognitive 

impairment. In the outpatient arm, a significant 

multivariate interaction was found involving 

these three variables (Pillai’s=0.076, F=1.387, 

p=.04); the within-client interaction approached 

significance (F=1.318, p=.072). The interaction 

appears to be attributable to the relatively larger 

proportion of abstinent weeks for those in the 

high impairment category who were also in the 

high frequency of AA meeting attendance group 

during moths 1 to 3, particularly in comparison 

to highly impaired clients who were in the lowest AA attendance category.

Summary of Outpatient 

Findings

The results of analyses in the outpatient arm suggest the following:

■ Cognitive impairment doe snot 

appear to be prognostic of drinkingrelated outcomes during the therapy 

delivery period, across the 1-year 

posttreatment followup.

■ There appear to be no matching 

effects between cognitive impairment and type of therapy at any of 

these time points.

■ MET was found to have significantly 

more structure than CBT, while 

CBT is comparable to TSF, and TSF 

and MET are comparable.

■ MET clients attended fewer therapy sessions than either CBT or TSF clients, 

while those receiving TSF attended significantly fewer sessions than did CBT 

clients.

■ Individuals with a high degree of cognitive 

impairment who received CBT attended 

fewer therapy sessions, were more likely 

to be classified as low therapy attendees (≤2 sessions), and were less likely to 

complete therapy than those with low 

levels of impairment.

■ Those with high levels of impairment 

appear to have a less positive working 

alliance with their therapists overall 

than those who are in the low impairment category.

■ Those with high levels of impairment 

who were in CBT appear to have developed less positive interpersonal bonds 

with their therapists than those with 

low impairment; clients with high levels 

of impairment had significantly greater 

AA involvement than those with low 

impairment.

■ Those in the high impairment group who 

had a high level of AA involvement had 

significantly fewer weeks in which heavy 

drinking occurred during the therapy 

delivery period than those who were low 

in AA involvement.
73

Cognitive Impairment as a Client-Treatment Matching Hypothesis

Results: Aftercare 

Arm

The analyses conducted in the aftercare arm of the study paralleled those 

in the outpatient arm. This section 

presents the results of these analyses 

for the aftercare arm of the trial.

Tests for Prognostic and 

Matching Effects

Level of cognitive impairment was 

not predictive of treatment outcome 

(no main effect for cognitive impairment) either during the treatments 

(months 1–3) or across the 1-year 

followup period (months 4–15) for 

aftercare clients. However, matching 

effects were apparent within treatment (table 3). A time by treatment by 

attribute effect was observed for both 

frequency (PDA, p<.02) and intensity 

(DDD, p<.02) of drinking. Examination 

of the weekly p values suggests that as 

a treatment progressed, clients with 

greater cognitive impairment did progressively better in CBT than MET, as 

hypothesized, relative to those with 

less cognitive impairment. However, 

these relative gains disappeared by 

the end of the active treatment phase, 

and no differences were found in the 

months 4–15 data. The within-treatment effects are depicted in figures 11 

and 12.

Figure 11. Drinks per drinking day (DDD) in aftercare 

during months 1–3 as a function of level of cognitive 

impairment: CBT versus MET contrast. Predicted contrasts are represented by heavy lines.

Figure 12. Percentage of days abstinent (PDA) in aftercare during months 1–3 as a function of level of cognitive 

impairment: CBT versus MET contrast. Predicted contrasts are represented by heavy lines.

Table 3. Cognitive Impairment, aftercare

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT vs. MET t .94 -.42 2.39 -2.34 -.29 .23 .57 -.25 -.06 -.25 .06 -.27

p .39 .67 .02 .02 .78 .82 .57 .81 .95 .80 .95 .79

CBT vs. TSF t -1.10 .27 1.71 -2.16 .41 -.16 .38 -.24 1.18 .08 -.58 -.25

p .27 .79 .09 .03 .68 .87 .70 .81 .24 .93 .57 .80

MET vs. TSF t -1.81 .62 -.71 .27 .62 -.35 1.19 .02 1.07 .29 -.55 .03

p .07 .54 .48 .79 .54 .73 .85 .99 .29 .77 .59 .97

Mv × Tx F 1.65 .19 3.25 3.70 .19 .06 .18 .04 .83 .05 .21 .05

p .19 .82 .04 .02 .82 .94 .84 .96 .44 .95 .81 .95

NOTE: MV=matching variable, Cognitive Impairment; Tx=therapy condition; T=time; T2=quadratic time; PDA=percentage of days 

abstinent; DDD=drinks per drinking day
74

Part III: Psychological Dysfunction

It was also hypothesized that clients high in 

cognitive impairment would have better drinking outcomes when treated in TSF than when 

treated in MET, relative to those with less 

cognitive impairment. This hypothesis was 

also supported through the treatment period 

(months 1–3) but also disappeared during the 

subsequent 12-month followup period.

Lastly, it was hypothesized that TSF would 

be more helpful than CBT for aftercare clients 

high in cognitive impairment. Examination of 

the attribute by treatment by time interaction 

on DDD (p<.03) at the weekly level suggests that 

highly impaired clients treated in TSF tended 

to drink less on drinking days during the first 

week or two of treatment than if treated in CBT. 

However, as the weeks of treatment went on, 

this effect disappeared.

CBT Versus MET and TSF Versus MET 

Contrasts

Therapy Structure

No differences were found in the level of structure in the aftercare arm as a function of level 

of cognitive impairment (F=0.767, p=.465), type 

of therapy (F=0.289, p=.749), or the interaction 

between these two variables (F=.822, p=.512). 

Thus, as in the outpatient arm, a major component of the putative causal chain, that CBT and 

TSF would be rated as having greater structure 

than MET, was not supported.

Therapy Attendance and Treatment Dose

Sessions Attended. A significant effect for 

type of therapy was found with respect to the 

number of therapy sessions attended (CBT, 

8.12; MET, 3.15; TSF, 7.36) (F=125.31, p=.000). 

However, there were no significant differences 

in therapy attendance as a function of level 

of cognitive impairment or its interaction with 

type of treatment.

Percentage of Sessions Attended.

Differences were found in the percentage of sessions attended across the CBT (67.6 percent), 

MET (78.8 percent), and TSF (61.4 percent) 

groups. No significant differences were found 

as a function of cognitive impairment (F=2.360, 

p=.095); however, a trend was found for highly 

impaired clients (65 percent) having lower attendance than those in medium or low impairment 

groups (71 percent for each). No interaction was 

found between level of impairment and type of 

aftercare therapy (F=0.273, p=.896).

Categorical Attendance Indices. Nearly a 

quarter (24.2 percent) of the aftercare clients 

were classified as low therapy attendees (≤2 

sessions); 47.8 percent of the after clients (63.2 

percent of those attending 3 or more sessions) 

were classified as therapy completers. A significant relationship was found between cognitive 

impairment and aftercare therapy attendance 

(0–2 sessions versus 3 or more sessions) 

(X2=6.123, p=.047); 28.4 percent of clients in 

the high impairment category were low therapy attendees compared to 19.6 percent and 

22.1 percent for the low and medium impairment groups, respectively. However, there was 

no relationship between level of impairment 

and therapy completion status in the aftercare 

arm. When the type of therapy was factored into 

multilevel chi-square analyses, no differences 

were found on either of these two measures as 

a function of type of therapy or within a given 

therapy as a function of level of impairment.

Working Alliance. There were no differences 

as a function of type of therapy, level of impairment, or their interaction in the aftercare arm 

on the total WAI score or on any of the three WAI 

subscales rated by either therapists or clients.

CBT Versus TSF Contrast

AA Meeting Attendance and Involvement

AA Attendance. A repeated-measures 

ANOVA was conducted with therapy type and 

level of impairment as the independent variables 

and AA meeting attendance across the followup 

period as the repeated measure. No main effects 

for level of impairment, 2-way interactions 

of impairment by time, or 3-way interactions 

involving therapy condition, impairment level, 

and AA meeting attendance across followup 

points were found in any of the analyses.

AA Involvement. No relationship was found 

among AA involvement, measured as either a 

continuous variable in repeated-measures analyses or as a categorization based on the average 

level of involvement, level of impairment in chisquare analyses, and type of treatment in the 

aftercare arm.

The relationship between AA involvement 

during the therapy delivery period and level of 

impairment on drinking and abstinence during 
75

Cognitive Impairment as a Client-Treatment Matching Hypothesis

that same period was examined using the number of abstinent weeks and the number of 

weeks including a heavy drinking occasion as 

the dependent variables. Repeated-measures 

ANOVAs were conducted for each variable, 

with the level of AA involvement during months 

1 to 3 (trichotomized) and the level of cognitive impairment as the independent variables. 

There were no differences across time in either 

the number of abstinent weeks or weeks including heavy drinking as a function of the level of 

AA involvement, cognitive impairment, or their 

interaction among aftercare clients. Similarly, 

the trichotomized classification of AA meeting attendance was not related to the number 

of abstinent weeks or weeks including heavy 

drinking among aftercare clients.

Summary of Aftercare Findings

The results of analyses in the aftercare arm 

suggest the following:

■ Cognitive impairment does not appear 

to be prognostic of drinking-related outcomes during the therapy delivery period 

or across the 1-year posttreatment 

followup.

■ There appeared to be some support for a 

matching effect between level of cognitive 

impairment and type of therapy during 

months 1–3 while treatment was being 

delivered, with clients higher in cognitive impairment having somewhat better 

within-treatment drinking outcomes 

when treated in either CBT or TSF compared to MET, and those in TSF doing 

somewhat better early in treatment compared to those in CBT.

■ These apparent matching effects dissipated over time and had disappeared by 

the end of the active treatment phase. 

■ There were no differences across the 

three aftercare therapies with respect to 

their levels of therapeutic structure.

■ No differences or interactions with type 

of treatment were found for level of cognitive impairment and the number of 

therapy sessions attended, the percentage of sessions attended, or therapy 

completion rates; however, those with 

high levels of impairment were more 

likely to be classified as low therapy 

attendees than those with low levels of 

impairment.

■ There appeared to be no relationship 

between level of impairment and the 

level of therapeutic alliance as viewed 

from either the client’s or therapists’ 

perspectives.

■ No relationships were found between the 

level of cognitive impairment and the AA 

attendance or level of AA involvement.

Discussion

Treatment Outcomes

The present findings raise questions concerning the utility of measures of cognitive 

impairment in matching clients to the three 

treatments used in Project MATCH. I the outpatient setting, the cognitive impairment index was 

found to have neither a prognostic main effect 

nor an interaction with treatments on percentage of days abstinent or drinks per drinking day 

during the 3 months of active therapy, across 

the 12-month followup period, or at followup 

approximately 3 years after treatment. While 

there was some suggestive evidence of matching in the aftercare setting during the active 

treatment phase (months 1–3), these effects 

occurred in only a brief window of time, dissipated rapidly, and were not observed in months 

4–15. Thus, the results fail to support the 

hypothesized main effects, which predicted that 

more cognitively impaired clients would have 

poorer outcomes. This portion of the results are 

consistent with others that have raised questions about the clinical utility of measures of 

cognitive function in predicting treatment outcomes among alcoholics (Donovan et al. 1984). 

The results also fail to support the hypothesized 

interactions that would have indicated a matching effect between level of impairment and type 

of therapy.

Causal Chain Analyses and Process 

Measures

Outpatient Arm

Given the lack of prognostic main effects or 

matching interaction effects between cognitive 

impairment and type of therapy, the aim of the 

causal chain analyses for the outpatient arm 
76

Part III: Psychological Dysfunction

of the trial was to determine where the causal 

chain broke down. The first causal chain, 

involving the contrasts between MET and CBT 

and TSF, seems to have failed in part because 

MET appeared to have higher levels of therapeutic structure (as rated after Session 2) than 

either CBT or TSF. This finding is contrary to 

the hypothesis that CBT and TSF would be more 

structured. Further, outpatients high in cognitive impairment did not differ from those low in 

impairment on the degree of structure observed 

in their therapy sessions nor was there an interaction between level of impairment and therapy 

with respect to observed structure.

The second causal chain was partially supported in that cognitive impairment was related 

in the expected direction with therapy does. 

High impairment was associated with fewer sessions attended, suggesting that those who were 

more impaired found therapy less helpful than 

those who were less impaired. More importantly, cognitive impairment interacted with the 

type of therapy to influence attendance. Clients 

with high levels of impairment who received 

CBT attended fewer sessions than those having 

low levels of impairment or than their counterparts in the other therapy conditions.

This differential therapeutic does was 

also supported by the percentage of sessions 

attended, classification as low or high therapy 

attendees, and categorization as therapy completers. Thus, there appears to be cumulative 

evidence that level of impairment interacts with 

CBT to reduce various indices of treatment 

dose. However, it is not clear whether these 

differences in attendance as a function of type 

of therapy and impairment level are related to 

longer term drinking-related outcomes. This 

relationship should be tested in future analyses.

In a search for the process factors that might 

be a function of the interaction between type 

of therapy and cognitive impairment, working 

alliance, AA attendance, and AA involvement 

were explored. A number of interesting indirect 

lines of evidence emerged from these analyses, 

when combined with the results concerning 

therapy does, to support the possibility that 

CBT-oriented outpatient programs may be less 

acceptable to individuals who are more cognitively impaired. First, high impairment clients 

in CBT had a lower level of overall working alliance and a less positive interpersonal bond 

with their therapists than did clients in the low 

impairment category.

Second more highly impaired outpatients 

treated in CBT, but not those in TSF or MET, 

were classified as low therapy attendees and 

were less likely to complete therapy. Thus, 

it appears that outpatients who were more 

impaired were more likely to be premature 

dropouts who thereby received a smaller does 

of the active ingredients of the CBT therapy.

Third, more impaired outpatients tended 

to have higher levels of AA involvement than 

less impaired clients. Finally, interactions 

were found suggesting that higher levels of AA 

involvement during the period when the therapies were being delivered were associated with 

fewer weeks in which heavy drinking episodes 

occurred among highly impaired clients relative to less impaired ones. Similarly, higher 

frequency of AA meeting attendance during 

months 1 to 3 was associated with more weeks 

of abstinence among more highly impaired clients than among less impaired ones.

Aftercare Arm

Given the findings suggesting interaction 

effects between cognitive impairment and type 

of therapy during the active phase of treatments, 

although these interactions dissipated over the 

3 months and were not observed over the 1-year 

followup period, the testing of the causal chain 

had two main goals: (1) to test whether any of 

the causal chain elements were supportive of 

these initial matching effects and (2) to identify 

where and when the causal chain broke down.

The first causal chain (MET versus TSF or 

CBT) failed to be supported in that the level 

of observed structure was comparable across 

therapies, although it was hypothesized that 

MET would have less structure than the other 

two therapies. The second causal chain (TSF 

versus CBT) was not supported either. Session 

attendance was unrelated to the cognitive 

impairment by therapy interaction. This was 

the case whether attendance was measure by 

the number or percentage of sessions attended.

Nor was session attendance affected by the 

level of impairment. While use of therapy completion as a criterion supported the expectation 

that more highly impaired aftercare clients 

would be less likely to complete therapy than 
77

Cognitive Impairment as a Client-Treatment Matching Hypothesis

those low in impairment, neither this measure 

nor the classification as low versus high therapy attendance supported the hypothesized 

interaction between impairment level and type 

of therapy. Thus, there was no support overall for the causal chain indicating that therapy 

does interacted with level of impairment. The 

absence of this causal chain might underlie the 

lack of the hypothesized matching effect.

Again, as in the outpatient arm, working alliance, AA attendance, and AA involvement were 

explored as process factors that might vary as a 

function of the interaction between type of therapy and cognitive impairment. However, unlike 

the outpatient results, the results of analyses with these variables were less informative. 

Neither the therapist nor the client Working 

Alliance Inventory total or subscale scores were 

found to be affected by level of impairment, type 

of therapy, or their interaction.

Similarly, neither AA meeting attendance 

nor AA involvement was influenced by level of 

impairment, type of therapy, or their interaction. When AA involvement was included as a 

factor in predicting heavy drinking weeks and 

weeks of abstinence during the active treatment 

phase, it was found that these drinking-related 

measures were not affected by the combination 

of AA involvement and level of impairment.

Implications

A number of issues are raised by the present 

findings and their failure to support cognitive 

impairment as either a prognostic or matching 

variable. First if one looks carefully at the recommendations in the literature for more structured 

approaches (such as CBT) with impaired alcoholics (e.g., Donovan et al. 1987; McCrady 

1987), there is a strong emphasis on the use of 

repeated exposure to the same material, behavioral rehearsal, and demonstrated mastery of 

the targeted skills. Such an approach, if implemented in the manner recommended, may well 

be the most appropriate strategy for cognitively 

impaired clients.

However, in examining the implementation of 

the CBT protocol in Project MATCH, it may have 

been too fast paced and too cognitively focused, 

with much less emphasis on the more concrete 

and repetitive behavioral rehearsal strategies 

thought to be appropriate for this subgroup. It 

may be that rather than skills training, the CBT 

protocol provided skills exposure. That is, given 

that only one session focused on a given topic 

area, clients may have been exposed to the general concepts of CBT but had insufficient time 

to consolidate these concepts and rehearse 

them to the point of mastery. As such, the more 

impaired clients may not have gained sufficient behavioral training to accommodate their 

deficits. However, no tests of behavioral skills 

acquisition or generalization were included in 

the trial.

Second, while cognitive impairment was found 

to be related to a number of process measures, 

it may be that its role in predicting drinkingrelated treatment outcome has been overrated. 

While the level of impairment has been found 

to predict treatment outcome among alcoholics, the results have been equivocal and, where 

relationships have been found, the amount of 

variance accounted for by cognitive function has 

often been relatively small (Donovan et al. 1984; 

Eckardt et al. 1988; Wilkinson and SanchezCraig 1981). Cognitive function has been found 

to be most consistently related to outcome in 

those studies in which extreme groups (e.g., 

unimpaired versus markedly impaired) have 

been compared (Donovan et al. 1984; Knight 

and Longmore 1994). The sample recruited into 

Project MATCH, while demonstrating considerable range in cognitive function, did not include 

clients with the marked level of impairment that 

is often associated with poor drinking-related 

treatment outcomes.

A number of researchers (e.g., Donovan et al. 

1984; Eckardt et al. 1988; Goldman 1995) have 

suggested that while important in the prediction of outcome, cognitive impairment is only 

one of a number of variables that contribute to 

the determination of treatment outcome. Knight 

and Longmore (1994) have noted that while most 

research in this area presumes a causal role for 

cognitive impairment in poor treatment outcomes, such deficits may simply be correlates 

of other factors, such as age, chronicity, severity of dependence, or low social stability, that 

actually determine the response to treatment.

Also, the posttreatment environment may or 

may not pose a challenge for the individual’s 

cognitive abilities (Goldman 1995). The effects 

of cognitive impairment are most likely to be 

observed in novel situations or those that pose 
78

Part III: Psychological Dysfunction

a challenge to individuals and their cognitive 

problem-solving abilities. Also, performance on 

cognitive measures of problem-solving are not 

necessarily reflective of the individual’s interpersonal problem-solving abilities (Nixon et al. 

1992), which may be more important in dealing 

with posttreatment relapse precipitants.

Gass (1991) has suggested that alcoholrelated impairment can affect mood judgement, 

and self-esteem, which influence the motivation one needs in order to achieve and maintain 

abstinence. She argued that neuropsychological 

function should be viewed as part of the total 

and more complex picture in which variables 

such as motivation to change, social stability, 

personality, and comorbid psychological problems may also influence treatment process 

and outcome. This view is consistent with the 

results of Donovan and associates (1986), who 

found that clinically meaningful client subtypes, defined by level of cognitive impairment, 

psychopathology, alcohol dependence, and 

demographic characteristics, provided a better 

predictor of outcome than did cognitive impairment alone.

A third issue deals with the assessment of 

cognitive function. In order to minimize client 

burden from an already lengthy assessment 

process in Project MATCH (see Connors et al. 

1994), only a few measures were used to assess 

cognitive function. It may be that other tests 

assessing different areas of function or assessing in greater depth would have been more 

sensitive to deficits and possibly would have 

provided more positive results with respect to 

outcome or treatment matching. Similarly, a 

number of alternative strategies for combining 

the measures of cognitive impairment, rather 

than a factor-analytically derived continuous 

index of impairment, might be more effective as 

predictors of outcome. As an example, Kadden 

and colleagues (1989) assigned score of 0 or 1 

(unimpaired/impaired) for each of three cognitive tests administered to aftercare clients; 

individuals were classified as impaired if they 

scored in the impaired range on two out of the 

three tests.

The choice of measures raises two related 

issues. The first is that currently available measures of cognitive function may be inadequate 

in their ability to assess or predict those skills 

and abilities involved in the treatment process 

and those needed to deal with the posttreatment 

environment. Knight and Longmore (1994) indicated that it is difficult to determine which tests 

best assess the cognitive skills needed to consolidate and apply treatment-related skills.

The use of “ecologically valid” measures 

has been raised previously (e.g., Sussman 

et al. 1986). However, most available measure of cognitive function are lacking in this 

ecological validity. The use of measures of 

treatment-relevant information (e.g., Becker 

and Jaffe 1984; Godding et al. 1992; Roehrich 

and Goldman 1993) and the acquisition, retention, and application of specific skills such as 

cognitive-behavioral problem-solving or drink 

refusal (e.g., Nixon et al. 1992; Sanchez-Craig 

et al. 1987; Smith and McCrady 1991) seem 

like important directions to pursue (Knight and 

Longmore 1994).

A related issue involves determining the 

best measures of treatment outcome to investigate in relation to cognitive function. Donovan 

and associates (1984) found that measures of 

neuropsychological function were better in predicting posttreatment employment status than 

drink-related outcomes. Outcomes such as 

employment may be more directly related to the 

skills and abilities assessed by most measures 

of cognitive function than is drinking behavior. 

Although there is the hope of finding a causal 

link between cognitive impairment and relapse, 

no such link has yet been demonstrated convincingly (Knight and Longmore 1994).

Conclusions

The present results fail to support level of 

cognitive impairment, as assessed, as either a 

prognostic indicator of treatment outcomes or 

as a variable useful in matching clients to one 

of the three treatments presented in Project 

MATCH. A number of factors may have contributed to these findings, which failed to support 

the a priori hypotheses. It may be best to view 

cognitive function as one variable class that contributes to, but does not independently predict 

treatment outcomes. As Knight and Longmore 

(1994) suggested, the significance of cognitive 

impairment must be placed in a wider context 

that integrates findings concerning cognitive 

function with other psychosocial and treatment variables. Specifying these interactions 
79

Cognitive Impairment as a Client-Treatment Matching Hypothesis

with greater precision may provide useful 

clinical information to alcohol counselors and 

therapists.

Acknowledgments

This work was supported by grant number 

U10-AA08436 from the National Institute on 

Alcohol Abuse and Alcoholism.

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82

Matching Clients to Alcoholism 

Treatment Based on Psychopathology

Ned Cooney, Ph.D., Ray Anton, M.D., Joe Carbonari, Ed.D., Kathleen 

Carroll, Ph.D., Carrie Randall, Ph.D., and James Roberts, Ph.D.

ABSTRACT

The psychopathology-related findings from Project MATCH included examination 

on a priori matching hypothesis based on the Addiction Severity Index Psychiatric 

Composite score and a DSM–III–R diagnostic assessment. Hypothetical causal chain 

processes were also examined to gain a better understanding of why matching effects 

were or were not observed. Significant psychopathology matching interactions were 

found, but they were not consistent across time or outcome measures. The causal 

chain analyses did not reveal a mechanism of action for the matching interactions, further reducing confidence in the validity of the matching effects. With these limitations 

in mind, the following results provide a tentative basis for matching clients to treatment. Individuals without psychopathology had better outcomes after treatment when 

assigned to Twelve Step Facilitation (TSF) rather than Cognitive-Behavioral Coping 

Skills Therapy (CBT). Individuals with psychopathology did equally well after treatment when assigned to TSF or CBT, but had worse outcomes during treatment when 

assigned to Motivational Enhancement Therapy. None of the Project MATCH therapies provided treatment with an extensive focus on reducing psychiatric symptoms, so 

results cannot be generalized to such forms of therapy.

An extensive epidemiological study indicated that more than half of those seeking treatment for an alcohol use 

disorder in mental health or substance abuse 

settings have a comorbid nonsubstance use 

psychiatric disorder (Regier et al. 1990). 

These comorbid disorders have been shown to 

adversely affect substance abuse treatment outcomes. Some prognostic studies have employed 

a categorical, diagnostic approach to measuring 

psychopathology. For example, Rounsaville and 

associates (1987), using the National Institute 

of Mental Health (NIMH) Diagnostic Interview 

Schedule, found that comorbid DMS–III diagnoses generally predicted poorer outcomes among 

alcoholics.

Other studies have measured psychopathology using a dimensional, continuous approach 

such as the Addiction Severity Index Psychiatric 

(ASI Psych) subscale (McLellan et al. 1992). 

This subscale has been found to be a significant predictor of substance abuse treatment 

outcome using the interviewer severity rating 

(McLellan et al. 1983, 1984) and the composite score (Kadden et al 1989). Such findings are 

important because they suggest that substance 

abusers with psychopathology may require specialized treatment services.

Substance abuse outcome studies have 

also found significant interactions between 

ASI Psych scores and type of treatment. 

Ned L. Cooney, Ph.D.

VA Connecticut Healthcare System (116B)

950 Campbell Avenue

West Haven, CT 06516

E-mail: ned.cooney@yale.edu
83

Matching Clients to Alcoholism Treatment Based on Psychopathology

Woody and colleagues (1984) compared methadone-maintained opiate addicts assigned to 

paraprofessional counseling or to professional 

psychotherapy (cognitive-behavioral or supportive expressive psychotherapy). Clients with low 

ASI Psych ratings did equally well with either 

treatment approach. However, clients with mid 

to high psychiatric severity had better outcomes 

with professional psychotherapy.

Kadden and Cooney (Kadden et al. 1989; 

Cooney et al. 1991) compared two approaches 

to group therapy for alcoholics. Alcoholics without psychopathology had better outcomes with 

interactional group therapy, while those with 

psychopathology had better outcomes with 

cognitive-behavioral group therapy. In addition to these 2-way interaction results, studies 

by McLellan and his colleagues found higher 

order interactions involving ASI Psych interviewer ratings and type of substance abuse 

treatment (McLellan et al. 1983). Clients with 

low ASI Psych severity did well in all programs 

studies, and those with high severity did poorly 

in all programs. The responses of those with 

intermediate severity scores to different types 

of programs depended on the degree of associated problems in social functioning.

This chapter describes in detail the psychopathology-related findings from Project MATCH 

(Project MATCH Research Group 1992). This 

included examination of an a priori matching 

hypothesis based on the ASI Psych composite 

score and a diagnostic assessment. Hypothetical 

causal chain processes were examined to gain 

a better understanding of why matching effects 

were or were not observed.

The Psychopathology 

Matching Hypothesis

Drinking outcome will be a function of an interaction between psychopathology and treatment 

type, such that the slope of the regression line 

of psychopathology on drinking outcome will be 

more positive for the Motivational Enhancement 

Therapy (MET; Miller et al. 1992) and Twelve 

Step Facilitation (TSF; Nowinski et al. 1992) condition. This interaction is illustrated in figure 

1. This hypothesis was tested as two contrasts, 

one comparing CBT versus MET (excluding 

TSF) and the other comparing CBT versus TSF 

(excluding MET).

Figure 1. Hypothesized relationships between 

severity of dependence and drinking outcome 

for clients treated with TSF, CBT, and MET.

These a priori contrasts specified anticipated 

directional interaction effects, but they did not 

specify where on the psychopathology dimension to expect significant differences between 

treatments. Our theory, however, led us to 

expect that individuals with high psychopathology would have better outcomes with CBT 

than with MET or TSF. There was no theoretical reason to expect differences in outcome 

among CBT, MET, ad TSF for individuals with 

low psychopathology.

Rationale for the Matching 

Hypothesis

Empirical Evidence

Prior to Project MATCH, there had been no 

direct test of the specific hypothesis proposed 

here, although two substance abuse outcome 

studies found significant 2-way interactions 

between ASI Psych scores and type of treatment 

(Cooney et al. 1991; Kadden et al. 1989; Woody 

et al. 1984). Both studies found that CBT was 

more effective than alternative treatments for 

high psychopathology clients, while CBT and 

alternative treatments were not significantly 

different for low psychopathology clients.

Severity of psychopathology has also been 

found in several studies to be a main effect predictor of substance abuse treatment outcome 

(McLellan 1986). However, data from a study 

by Rounsaville and associates (1987) suggested 
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Part III: Psychological Dysfunction

that the relationship between psychopathology 

and alcohol treatment outcome might be moderated by sex. For men, having an additional 

diagnosis of major depression, antisocial personality, or drug abuse was associated with 

poorer outcome. For women, having depression was associated with a better outcome on 

some measures, while antisocial personality 

and drug abuse were associated with poorer 

outcome.

The finding of a differential treatment 

response for depressed men and women was 

not consistent. Rounsaville and colleagues 

reported significant gender by diagnosis interactions for only 4 correlated outcomes, all 

having to do with impairment due to drinking, out of 13 outcomes tested. Future reports 

based on Project MATCH data may shed some 

light on the question gender by psychopathology interaction effects on treatment outcome.

Theoretical Justification

Negative moods have been shown to elicit 

alcohol craving (Cooney et al. 1997), and 

most alcohol relapse situations involve negative moods (Marlatt 1996). It is reasonable to 

assume that treatments that reduce the frequency or intensity of negative moods will result 

in reduced drinking among individuals prone 

to experiencing these moods, that is, individuals with anxiety or affective disorders. TSF and 

MET were developed uniquely for the treatment 

of alcoholism, whereas CBT is a more general 

approach that had been developed for the treatment of anxiety and affective disorders and was 

subsequently adapted for use with alcoholics.

General strategies used to address psychopathology were incorporated into the Project 

MATCH version of CBT. The elective sessions 

include Starting Conversations, Nonverbal 

Communication, and Assertiveness to help 

clients cope with social anxiety; Receiving 

Criticism, Awareness of Anger, and Anger 

Management to help clients cope with anger; 

and Awareness of Negative Thinking, Increasing 

Pleasant Activities, and Managing Negative 

Moods and Depression to help clients cope 

with depression. On the other hand, TSF and 

MET offer no direct interventions for clients 

with psychopathology. MET’s brevity precludes anything but a focus on drinking and is 

client-structured, which may be too demanding for those with significant psychopathology. 

The TSF therapist makes the assumption that 

most psychopathology is the result of the disease process. Mild psychopathology is thought 

to improve with abstinence, and severe psychopathology is thought to require referral 

to a mental health professional for adequate 

treatment.

More intensive treatment is often 

recommended for alcoholics with high psychopathology, so we predicted a difference in 

outcome between CBT and MET in high psychopathology clients. Based on this prediction, 

we tested one a priori contrast dropping the 

TSF client group and examining the interaction between psychopathology and CBT/MET 

treatments. The interaction between psychopathology and CBT/TSF treatment was examined 

as the second a priori contrast. This second 

contrast tested treatments equated on intensity of treatment, allowing an interpretation 

of matching effects related to the content of 

treatment.

Operationalization of the 

Matching Variable

Psychopathology can be operationalized 

using either a global, continuous measure or 

a categorical variable based on a diagnostic 

assessment. Project MATCH utilized both measurement approaches, and a priori matching 

hypotheses were developed using the ASI Psych 

and the Computerized Diagnostic Interview 

Schedule (C–DIS). The ASI Psych measure consists of 11 items covering the occurrence of 

psychological problems (e.g., depression, anxiety, and anger) in the past 30 days. The C-DIS 

is a computer-administered interview based on 

the Diagnostic Interview Schedule (Blouin et al. 

1988; C–DIS Management Group 1981) which 

can yield psychiatric disorder identification 

based on the DSM–III–R.

Only the anxiety disorders, affective disorders, and antisocial personality disorders 

sections on the C–DIS were administered. 

Participants were considered to meet criteria 

for a current comorbid Axis I disorder if they 

met DSM–III–R criteria for any lifetime anxiety 

or affective disorder and reported one or more 
85

Matching Clients to Alcoholism Treatment Based on Psychopathology

relevant symptoms in the past 6 months. This 

definition of current diagnosis does not exactly 

correspond with the DSM–III–R definition of 

current diagnosis because the C–DIS does not 

provide sufficient probes to determine whether 

an individual has enough current symptoms to 

satisfy criteria for current diagnosis. Thus, the 

C–DIS may at times classify individuals as having a current disorder when they are actually in 

partial remission.

The ASI Psych score and the C–DIS diagnostic variable were only moderately correlated 

(aftercare sample point-biserial r=0.43, outpatient sample point-biserial r=0.27), so they 

were examined in separate matching analyses. 

The Steering committee selected ASI Psych as 

the primary matching variable because it had 

yielded significant attribute by treatment interactions in three previous abuse psychotherapy 

outcome studies (Kadden et al. 1989; McLellan 

et al. 1983; Woody et al. 1984).

To our knowledge, no substance abuse outcome study has reported a significant attribute 

by treatment interaction using an Axis I psychiatric diagnosis-based client variable. There 

was no a priori basis for determining the point 

on the psychopathology symptom continuum 

where CBT would be more effective than TSF 

of MET. It was hypothesized that the greater 

overall psychopathology present, the more 

incrementally effective CBT will be, relative to 

either TSF or MET.

Sample Characteristics

The Project MATCH sample had a mean 

pretreatment ASI Psych composite score in 

the outpatient arm of 0.19 (SD=0.19) with 38 

percent of participants having a zero score. 

The aftercare arm had a mean score of 0.23 

(SD=0.21) with 35 percent having a zero score. 

These scores are slightly lower than normative 

data from alcohol abusers (mean=0.24) reported 

by McLellan and associates (1992). Table 1 

shows the percentage of participants meeting 

our operational definition of current comorbid 

affective and/or anxiety disorders based on the 

pretreatment C–DIS assessment. Among participants with anxiety disorders, there was an 

approximately even distribution of participants 

meeting criteria for social phobia, simple phobia, agoraphobia, generalized anxiety disorder, 

Table 1. Percentage of participants meeting 

criteria for current comorbid DSM-III 0R 

diagnoses

Aftercare arm

(N = 748)

Outpatient arm

(N = 870)

No current 

comorbid diagnosis 56.0 67.9

Current comorbid 

anxiety diagnosis 35.7 25.7

Current comorbid 

affective diagnosis 20.5 13.8

Current anxiety or 

affective diagnosis 44.0 32.1

NOTE: Current DSM-III-R diagnoses are defined in the text 

and may include some cases in partial remission.

and panic disorder. Among those with affective 

disorders, most participants met criteria for 

major depressive disorder, with a few meeting 

criteria for bipolar disorder or manic episode.

Results

Prognostic Effects

The prognostic effects of retreatment ASI 

Psych and pretreatment current comorbid Axis 

I diagnosis on posttreatment alcohol consumption were examined in outpatient and aftercare 

samples aggregated across all three treatments. 

Prognostic analyses were conducted using a 

“latent growth” approach that was utilized in 

the previously reported analysis of matching 

effects (Project MATCH Research Group 1997; 

Longabaugh and Wirtz, this volume, pp. 4–17).

The prognostic model included a backward 

elimination adjustment for the other significant 

a priori matching attributes and their matching 

interaction effects.

There were no prognostic main effects of 

ASI Psych or comorbid Axis I diagnosis on percentage of days abstinent (PDA) or drinks per 

drinking day (DDD) across the 1-year posttreatment period in either the aftercare or 

outpatient arm of the study. ASI Psych did 

interact with time to predict PDA and DDD outcomes (p’s>.05) in the aftercare arm. Toward 

the end of the followup period, clients higher in 

psychiatric severity had fewer abstinent days 

compared to those lower in psychiatric severity. 
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Part III: Psychological Dysfunction

However, these time-limited prognostic effects 

accounted for less than 2 percent in outcome 

variance.

Interaction Effects

Psychopathology by treatment interaction 

effects were also modeled as a latent growth 

process. Tables 2 and 3 show F and nondirectional p values for all possible psychopathology 

interaction effects using the ASI Psych and 

C–DIS matching variables. The p values in 

these tables should be halved to determine the 

directional p values.

Each matching hypothesis in Project MATCH 

was tested separately at a family-wise type-1 

error rate of 5 percent. A Bonferroni correction 

was applied to take into account the two outcome variables and the two contrasts proposed 

for the psychopathology hypothesis, resulting 

in a corrected alpha level of 0.0125. This strategy does not take into account the number of 

different hypotheses that were tested, since the 

matching hypotheses were conceived of a conceptually independent of one another.

The only interaction effect that reached a 

Bonferroni-corrected level of significance was 

the psychiatric severity by treatment (CBT 

versus TSF) effect in the outpatient arm in 

the posttreatment period with PDA outcome 

(directional p=.01). The psychiatric severity 

by treatment by quadratic time effect was also 

significant for this contrast (p=.03). Figure 2 

illustrates this interaction effect in each of the 

12 posttreatment months.

Post hoc examination of this contrast by 

month indicated that there were significant (p< 

.05) psychiatric severity by treatment interaction effects beginning 1 month after treatment 

termination and continuing through the eighth 

month after the end of treatment. Interaction 

plots revealed that individuals without psychopathology reported approximately 87 percent 

of days abstinent in TSF treatment compared 

with 73 percent of days abstinent reported by 

those in CBT. The CBT and TSF regression 

lines crossed at a value of approximately 0.4 on 

the ASI Psych composite score, more than one 

standard deviation above the outpatient sample mean. With few clients having ASI Psych 

scores above this intersection point, it was not 

possible to determine whether there was a significant advantage for high psychopathology 

clients treated with CBT rather than TSF.

Table 2. Interaction effects based on Addiction Severity Index Psychiatric Composite score

Treatment

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

Outpatient arm

CBT vs. MET F 3.20 1.04 1.10 1.06 0.03 1.54 0.96 0.06 0.18 0.52 0.00 0.72

p .07 .31 .29 .30 .86 .21 .33 .81 .67 .47 .95 .39

CBT vs. TSF F 2.43 0.04 2.86 3.92 1.46 0.27 4.88 3.03 2.28 0.42 4.75 2.96

p .12 .84 .09 .05 .23 .60 .03 .08 .12 .51 .03 .08

MET vs. TSF F 0.08 0.66 0.34 0.76 1.77 2.96 1.28 1.96 3.39 1.72 4.49 0.62

p .78 .42 .56 .38 .18 .09 .26 .16 .07 .19 .03 .43

Overall F 1.94 0.56 1.47 1.97 1.09 1.55 2.45 1.70 1.92 0.86 3.09 1.49

p .14 .57 .23 .14 .34 .21 .09 .18 .15 .42 .05 .23

Aftercare Arm

CBT vs. MET F 0.05 0.52 0.50 1.12 1.66 0.01 0.62 0.09 2.25 0.74 0.17 0.59

p .82 .47 .48 .29 .20 .92 .43 .76 .13 .39 .68 .44

CBT vs. TSF F 0.01 0.03 1.74 1.39 1.02 0.26 0.21 0.46 0.25 2.37 0.14 1.77

p .90 .86 .19 .24 .31 .61 .64 .50 .62 .12 .70 18

MET vs. TSF F 0.12 0.31 0.13 0.01 0.08 0.17 1.59 0.98 1.04 0.44 0.62 0.30

p .72 .58 .55 .92 .77 .68 .21 .32 .31 .51 .43 .58

Overall F 0.06 0.29 0.87 0.84 0.92 0.15 0.81 0.51 1.18 1.19 0.32 0.90

p .94 .75 .42 .43 .40 .86 .45 .60 .31 .30 .73 .41

NOTE: MV=matching variable, Addiction Severity Index Psychiatric composite score; Tx=therapy condition; T=time; T2=quadratic 

time. All p values are nondirectional and should be halved to determine p values for a directional hypothesis test.
87

Matching Clients to Alcoholism Treatment Based on Psychopathology

Table 3. Interaction effects based on current DSM-III-R diagnosis from the Computerized 

Diagnostic Interview Schedule

Treatment

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

Outpatient arm

CBT vs. MET F 3.88 3.50 2.59 1.49 0.48 2.34 0.23 1.25 0.00 0.16 0.66 0.07

p .05 .06 .11 .19 .49 .13 .63 .26 .97 .69 .42 .80

CBT vs. TSF F 0.28 2.02 0.42 0.03 0.81 1.37 1.49 2.72 0.00 0.12 3.61 2.37

p .60 .16 .52 .86 .37 .24 .22 .10 .99 .73 .06 .12

MET vs. TSF F 2.23 0.26 5.43 2.40 0.04 0.49 0.50 0.23 0.00 0.56 2.89 1.56

p .13 .61 .02 .12 .84 .69 .48 .63 .98 .45 .01 .21

Overal F 4.45 3.57 8.00 1.93 0.19 1.61 0.57 2.00 0.00 0.07 4.57 1.88

p .12 .15 .06 .25 .65 .28 .47 .24 1.00 .75 .03 .25

Aftercare arm

CBT vs. MET F 3.76 2.02 0.02 1.12 0.52 0.05 0.86 0.40 1.12 0.21 0.27 0.62

p .05 .15 .90 .29 .47 .83 .35 .53 .29 .59 .60 .43

CBT vs. TSF F 1.02 0.88 0.00 0.01 0.37 1.18 0.00 0.00 0.35 0.61 0.01 0.01

p .31 .35 .97 .91 .54 .27 .99 .95 .56 .44 .90 .92

MET vs. TSF F 0.86 0.23 0.03 1.37 1.77 0.31 0.94 0.02 0.94 0.02 0.48 0.46

p .35 .63 .88 .24 .18 .19 .36 .58 .64 .19 .52 .49

Overall F 3.53 1.10 0.00 0.70 0.77 0.96 0.31 0.06 0.32 0.76 0.05 0.14

p .15 .35 .99 .43 .41 .38 .57 .79 .57 .42 .79 .69

NOTE: MV=matching variable, Computerized Diagnostic Interview Schedule Axis I, Tx=treatment, T=time, T2=quadratic time. All 

p values are nondirectional and should be halved to determine p values for a directional hypothesis test.

Several interaction effects in the outpatient arm approached the Bonferroni-corrected 

level of significance. The psychiatric severity by treatment (CBT versus TSF) contrast 

approached significance for the DDD outcome 

in the posttreatment period (directional p=.04) 

and for the PDA outcome in the within-treatment time period (directional p=.06). Also in the 

outpatient arm, the C–DIS comorbid diagnosis by treatment (CBT versus TSF) interaction 

approached the Bonferroni-corrected level of 

significance for the DDD outcome in the posttreatment period (directional p<.05).

Among the CBT versus MET contrasts and 

the TSF versus MET contrasts, none met 

Bonferroni-corrected significance levels, and 

only one (CBT versus MET, within-treatment 

PDA outcome) approached significance (directional p=.04). Note that no a priori TSF versus 

MET matching hypotheses were specified in 

advance. Outcomes for clients without psychopathology assigned to MET were intermediate 

between the outcomes for clients assigned to 

CBT and TSF.

Time to Event Outcomes

The primary outcome analyses in Project 

MATCH were conducted using the latent growth 

approach described above, with PDA and DDD 

outcome variables. It was of secondary interest, 

however, to determine the impact of treatment 

matching on time to relapse. Survival analysis 

techniques were used to investigate the time to 

first incidence of heavy drinking, where heavy 

drinking was defined differentially according to 

gender. Heavy drinking was indexed for males 

as six or more standard drinks in a single day 

and for females as four or more standard drinks 

in a day.

Time to event data were analyzed separately 

for each study arm (aftercare or outpatient), 

time window (within treatment or posttreatment), and psychopathology indicator (ASI 

or C–DIS). A nonproportional Cox regression 

model was used to test for differences in survival rates. The model was nonproportional 

because it included time itself as a covariate and 

thus allowed the hazard ratio to vary as a function of time (Cox and Oakes 1984). The model 

included main effects for site, treatment group, 

and psychopathology indicator. It also included 

treatment group by psychopathology indicator, 

site by treatment group, site by psychopathology indicator, site by treatment group by 
88

Part III: Psychological Dysfunction

Psychiatric Severity

Figure 2. Posttreatment plot of percentage of days abstinent in the outpatient arm showing the 

interaction between CBT and TSF treatments and baseline ASI Psych severity. The vertical axes are 

predicted outcome scores and the horizontal axes represent baseline ASI Psych composite scores, 

with higher scores indicating higher psychopathology. (Reprinted with permission from Journal of 

Studies on Alcohol, Vol. 58, pp. 7–29, 1997. Copyright by Alcohol Research Documentation Inc., 

Rutgers Center of Alcohol studies, Piscataway, NJ 08854.)
89

Matching Clients to Alcoholism Treatment Based on Psychopathology

psychopathology indicator, time by treatment 

group, time by psychopathology indicator, and 

time by treatment group by psychopathology 

indicator interactions. All categorical variables 

were coded using an effect encoding technique 

(Kirk 1982), and in the case of the ASI (i.e., the 

single quantitative measure), original scores 

were re-expressed as deviations from the grand 

mean to reduce multicolinearity. Additionally, 

the time variable was transformed to increase 

simultaneously the numerical accuracy and 

interpretability of the solution. The specific 

transformation used was T=ln(t)–ln(m), where t

was the original time point and in was the midpoint of the time window under consideration 

(i.e., m=45 days or m=180 days for within treatment and posttreatment analyses, respectively).

Within the context of survival analysis, the 

hypothesis corresponding to Contrast 1 predicts longer durations until first day of heavy 

drinking (i.e., longer survival) for clients with 

higher psychopathology levels when treated 

with CBT as opposed to MET. The hypothesis 

associated with Contrast 2 predicts that clients 

with higher levels of psychopathology will experience longer survival when given CBT rather 

than TSF. These two contrasts were tested 

using a Bonferroni-corrected alpha level (i.e., 

=0.05/2=0.025).

Table 4 portrays the sample size and the 

percentage of individuals who did not engage 

in any heavy drinking for each of the analyses 

described above (i.e., the percentage of clients 

who survived). Differing numbers of clients 

were administered the ASI and C–DIS instruments, which accounts for the varying sample 

sizes within each study arm. The percentage of clients surviving within a given arm by 

time window combination was almost identical 

across analyses of the ASI and C–DIS samples. 

Clients in the outpatient arm were significantly 

less likely to survive than clients in the aftercare 

arm. This finding held in both within-treatment 

(χ2(1)=16.5, p≤.001) and posttreatment (χ2(1)= 

7.05, p≤.008) time windows for the ASI sample.

Analogous differences were found in the 

C–DIS sample. Clients were also less likely to 

survive during the longer posttreatment window 

relative to the shorter within-treatment window. 

This difference was tested using McNemar’s 

corrected chi square (Edwards 1948) and was 

Table 4. Sample sizes and percentage of 

individuals who did not engage in heavy 

drinking within the specified time period

Within 

treatment

Posttreatment

ASI C-DIS ASI C-DIS

Outpatient arm

Sample size 947 870 947 870

Percent surviving 35% 36% 17% 16%

Aftercare arm

Sample size 767 748 767 748

Percent surviving 58% 59% 27% 28%

NOTE: The percentage of individuals who survived does not 

include cases censored due to missing data on drinking 

outcomes.

significant in both the outpatient (χ2(1)=112.47, 

p≤.001) and aftercare (χ2(1)=205.52, p≤.001) 

arms, corresponding to the ASI sample. Again, 

the same pattern of significant results was 

found for the C-DIS sample.

Table 5 describes the interaction effects from 

the Cox regression models that were analyzed. 

Reliable matching effects were limited to those 

examined in the C–DIS sample. Specifically, a 

matching effect for the CBT versus MET contrast was statistically significant in the analysis 

of outpatients within treatment. In this effect, 

CBT led to much better survival than did MET 

when clients exhibited a psychiatric diagnosis; 

however, the two treatments produced statistically similar outcomes when no diagnosis was 

present. An analogous finding was observed 

for the CBT versus TSF contrast, although 

this contrast only reached trend levels. In this 

instance, CBT led to better survival than TSF 

when considering clients with a C–DIS diagnosis. Figure 3 illustrates these two findings with 

survival curves developed from the KaplanMeier (1958) estimates.

Matched Versus Mismatched Clients

Another way to determine the magnitude 

of the observed psychopathology matching 

effects is to compare outcomes for outpatient 

clients after dividing all of them into matched 

or mismatched groups. Descriptive analyses 

were conducted based on the ASI latent growth 

results and the C–DIS survival analyses. In 
90

Part III: Psychological Dysfunction

Table 5. Survival analysis interaction effects based on models containing current DSM–III–R 

diagnosis from the Computerized Diagnostic Interview Schedule and the 

Addiction Severity Index

Treatment

Within treatment Posttreatment

MV×Tx MV×Tx×T MV×Tx MV×Tx×T

ASI C–DIS ASI C–DIS ASI C–DIS ASI C–DIS

Outpatient arm

CBT vs. MET χ2 1.19 6.27 0.09 3.63 1.02 2.13 0.80 1.59

p .28 .01 .77 .06 .31 .14 .37 .21

CBT vs. TSF χ2 0.11 4.03 2.10 2.27 0.90 0.03 0.01 0.01

p .75 .04 .15 .13 .34 .86 .92 .93

Overall χ2 1.24 6.80 2.42 3.90 3.60 3.12 0.95 2.33

p .54 .03 .30 .14 .17 .21 .62 .31

Aftercare arm

CBT vs. MET χ2 0.70 0.97 0.68 2.34 0.77 0.19 0.23 1.98

p .40 .33 .41 .13 .38 .66 .63 .16

CBT vs. TSF χ2 0.27 0.00 1.74 4.24 2.43 0.00 2.22 0.27

p .60 .99 .19 .04 .12 .98 .14 .60

Overall χ2 0.70 1.25 1.75 4.35 5.60 0.23 4.19 2.03

p .70 .54 .42 .11 .06 .89 .12 .36

NOTE: MV=matching variable, C–DIS and ASI; Tx=treatment, T=time. All p values are nondirectional and should be halved 

to determine p values for a directional hypothesis test. All chi-square values are Wald chi squares. Contrasts were 1 degree 

of freedom tests whereas tests involving the overall attribute by treatment interaction effect possessed 2 degrees of freedom.

the first analysis, high psychopathology clients were considered matched when they were 

assigned by randomization to CBT and mismatched when assigned by randomization to 

TSF, while low psychopathology clients were 

considered matched when assigned to TSF and 

mismatched when assigned to CBT.

All clients in the outpatient sample were 

divided into low and high psychopathology 

groups, with the cut point based on the ASI 

Psych score at the intersection point in figure 2. Clients with scores less than 0.4 were 

classified low psychopathology and those with 

scores greater than or equal to 0.4 were classified high psychopathology. Outcome was 

examined using a composite outcome variable based on drinking data from the Form 

90 (Miller 1996) and alcohol-related negative 

consequences from the DrInC questionnaire 

(Miller et al. 1995; see Zweben and Cisler 1996 

for a description of the composite outcome 

variable). Outcome was classified as “success” 

when a client reported no heavy drinking or Figure 3. Survival curves for the CBT versus 

MET matching contrast. Relapse defined as 

time until first day of heavy drinking.
91

Matching Clients to Alcoholism Treatment Based on Psychopathology

Figure 4. Matching to CBT versus TSF based on intake 

ASI Psychiatric composite scores in the outpatient arm 

across treatment and followup. Clients with ASI scores 

<0.4 were matched to TSF; those with scores 0.4 were 

matched to CBT.

Figure 5. Matching to CBT versus MET based on intake 

C–DIS Axis I diagnosis in the outpatient arm across 

treat¬ment and followup. C–DIS positive clients were 

matched to CBT; C–DIS negative clients were matched to 

MET.

alcohol-related negative consequences in the 

preceding 3 months. Outcome was classified 

as “failure” when a client reported any heavy 

drinking and/or consequences in the prior 3 

months. Figure 4 suggests that the ASI psychopathology matching effect may actually be 

a pseudo matching effect because the outcome 

of the matched group is no better than the outcome of the group of clients all assigned to TSF.

The second descriptive analysis was based 

on the significant matching effects found in the 

survival analysis of the C–DIS matching variable. C–DIS positive clients were considered 

matched when they were assigned by 

randomization to CBT and mismatched 

when assigned by randomization to 

MET, while C–DIS negative clients were 

considered matched when assigned to 

MET and mismatched when assigned 

to CBT. Figure 5 reveals a large 

matching effect on composite outcome 

during the treatment phase. Matched 

clients had more than a 20-percent 

higher success rate than unmatched 

or randomly assigned clients.

Testing the Causal 

Chain

The a priori psychopathology matching hypothesis predicted that greater 

pretreatment psychopathology scores 

would be associated with greater differential effectiveness of CBT relative 

to TSF. A causal chain was hypothesized that CBT treatment contained 

specific elements that addressed the 

needs of individuals with higher psychopathology, resulting in a reduction 

in symptoms in these individuals, 

which in turn led to reduced drinking 

after treatment. Results are presented 

that examine the following links in the 

causal chain:

■ Is there a greater emphasis on 

addressing psychological symptoms in the CBT treatment sessions 

than in the TSF sessions?

■ Is there a lower level of posttreatment psychological symptoms in 

the CBT condition than in the TSF 

condition, controlling for pretreatment psychological symptoms?

■ Is the level of posttreatment psychological symptoms predictive of drinking 

outcomes in the 12 months following 

treatment?

■ Are clients who relapse in the CBT condition less likely to attribute their first 

drink to a negative mood state than are 

clients who relapse in the TSF condition?

Is there a greater emphasis on addressing 

psychological symptoms in the CBT treatment 
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Part III: Psychological Dysfunction

sessions than in the TSF sessions? The assessment of treatment process in Project MATCH 

used methodology adapted from the NIMH 

Collaborative Study on Treatment of Depression 

(Elkins et al. 1985). Every Project MATCH session was videotaped, and Likert-type items for 

rating these tapes were generated from treatment manuals. After the MATCH Tape Rating 

Scale (MTRS) was developed, all Week 2 session 

tapes and a randomly selected sub-sample of 

150 Week 6 session tapes were 10-12 Mo rated 

(see Carroll et al. 1998 for a detailed description of the MTRS).

The MTRS included the following item, “To 

what extent did the therapist explicitly focus 

on the client’s psychopathology or problems 

in emotional or behavioral functioning?” The 

mean rating on this item was examined by 

treatment for the complete sample as well as 

on a subsample of clients with high pretreatment psychopathology. Ratings on this item 

were generally low, with 73 percent of aftercare 

sessions and 76 percent of outpatient sessions 

rated “not at all” for Week 2.

Analysis of variance showed no significant 

difference among CBT, MET, and TSF clients 

for this item at either Session 2 or Session 6 in 

the aftercare or outpatient arm. An analysis of 

the subsample of cases with high pretreatment 

psychopathology also found no significant differences among treatments for either arm at 

Session 2. However, it is possible that therapists 

did not have time to focus on psychopathology 

early in treatment with clients who were still 

drinking. Greater focus on psychopathology 

may have occurred later in treatment, when 

abstinence permitted clearer assessment of 

psychopathology.

Another analysis examined the frequency 

of occurrence of the optional psychopathology-focused CBT sessions in the low and high 

psychopathology samples. These optional 

sessions were Awareness of Anger, Anger 

Management, Awareness of Negative Thinking, 

Managing Negative Thinking, and Managing 

Negative Moods and Depression. Although psychopathology sessions were more frequently 

delivered to high psychopathology clients, 

these elective sessions made up only 11.2 percent of all sessions in the outpatient arm and 

14.8 percent of all sessions in the aftercare 

arm. This occurred because the CBT treatment 

manual specified that eight core sessions dealing with alcohol-related coping skills should 

be delivered before any elective sessions were 

delivered.

Is there a lower level of posttreatment psychological symptoms in the CBT condition than 

in the TSF condition, controlling for pretreatment 

psychological symptoms? A series of analyses of 

variance, controlling for site, site by treatment, 

and pretreatment ASI Psych scores revealed 

that the mean ASI Psych scores for the CBT, 

MET, and TSF conditions were not significantly 

different for the outpatient or aftercare arm at 

the end of treatment or at 6 and 12 months 

after treatment (all p’s >.1). A parallel analysis 

was done using pretreatment and posttreatment scores on the Beck Depression inventory, 

again with no significant differences among 

treatments. Thus, there was no evidence of 

greater reduction in psychopathology among 

participants in the CBT condition.

Is the level of posttreatment psychological 

symptoms predictive of drinking outcomes in the 

12 months following treatment? Four different 

repeated-measures analyses of covariance were 

conducted (aftercare and outpatient arms, PDA 

and DDD outcomes), adjusting for baseline 

drinking, site, treatment assignment, and site 

by treatment effects. Posttreatment ASI Psych 

was a significant predictor of drinking outcome 

only for DDD outcome in the outpatient arm (p= 

.013). Although they are significant, the prognostic effects are small, with posttreatment ASI 

Psych accounting for only 1 percent of variance 

in DDD outcome. The ASI Psych by time effect 

was significant for PDA and DDD in the aftercare arm, but post hoc analyses revealed that 

ASI Psych predicted drinking in only 2 or 3 of 

the 12 posttreatment months.

Somewhat stronger prognostic effects were 

found using the posttreatment scores on the 

Beck Depression Inventory (BDI). Posttreatment 

BDI predicted PDA and DDD in both the outpatient and aftercare arms (p’s <.0001), 

accounting for approximately 3 percent of variance in outcome.

Are clients who relapse in the CBT condition 

less likely to attribute their first drink to a negative mood state than are clients who relapse in 
93

Matching Clients to Alcoholism Treatment Based on Psychopathology

the TSF condition? An analysis was conducted 

to test the hypothesis that psychopathology 

symptoms are antecedent to relapse less often 

in the CBT condition than in the MET or TSF 

conditions. This analysis used only clients who 

reported a relapse in the followup interviews. 

The Reasons for Relapse Questionnaire was 

given at the 3-, 9-, and 15-month followups. 

This questionnaire contained three items pertaining to negative mood states as reasons for 

relapse: “feeling angry,” “feeling down or blue,” 

and “feeling uptight or anxious.”

The means for each of these three items were 

not significantly different for clients in the CBT 

condition compared with clients in the TSF 

condition at any of the three followup interviews, in either the outpatient or aftercare arm 

of the study. This analysis was also conducted 

on a subsample of clients with high ASI Psych 

scores at pretreatment (≥0.4). The results were 

also nonsignificant, but the sample size for the 

t-tests ranged from 14 to 28 per cell. This is 

because only clients with high pretreatment 

ASI scores who reported relapse in the followup 

period were entered into the analysis. Based 

on these analyses, there is no evidence that 

the CBT condition was associated with fewer 

relapses triggered by negative mood states 

compared with the MET or TSF conditions.

Summary

Based on causal chain results from the outpatient arm, there is some evidence to support 

the link in the causal chain that psychopathology at the end of treatment causes drinking in 

the following year. However, there is no evidence 

in either the outpatient or aftercare arm to support the link in the chain that CBT causes more 

reduction in psychopathology than does MET 

or TSF. It appears that both CBT and TSF were 

associated with decreased psychopathology, 

perhaps secondary to reductions in drinking.

Alcoholics Anonymous Attendance as a 

Mediator

A new causal chain hypothesis was developed after reviewing results showing that clients 

without psychopathology had better outcomes 

when assigned to TSF rather than CBT. This 

chain focused on AA attendance as a mediator 

of psychopathology matching.

We predicted that individuals without psychopathology would have a higher rate of AA 

attendance than those with psychopathology. 

If frequency of AA attendance were related to 

drinking outcome, then the group that went to 

the most AA meetings (i.e., low psychopathology, TSF clients) would have the best outcome. 

This would explain our psychopathology 

matching findings.

Does pretreatment ASI Psych predict AA meeting attendance during and/or after treatment? 

Pretreatment ASI Psych was not significantly 

related to AA meeting attendance during or 

after treatment in either the aftercare or outpatient arm. The pretreatment ASI Psych by 

treatment interaction effect on AA attendance 

was also nonsignificant, indicating that ASI 

Psych did not predict AA attendance differentially across the three treatments.

Pretreatment ASI Psych was also not a significant predictor of scores on the AA Involvement 

scale (Tonigan 1996) obtained at posttreatment. 

We found no evidence to support the idea that 

psychopathology interfered with AA affiliation.

Does AA attendance during outpatient treatment predict followup drinking? Across the 

three treatments, AA attendance during treatment predicted PDA at every month in the year 

after outpatient treatment, accounting for a 

small but statistically significant 1 to 4 percent 

of the variance.

Conclusions. We found that AA attendance 

predicted PDA, especially in the third month of 

treatment. However, we did not find ASI Psych 

to be predictive of AA attendance at any point 

in any treatment. Therefore, AA attendance 

could not function as a mediator of the psychopathology matching effect.

We also failed to find any moderator effects. 

There were no significant 3-way interaction effects of ASI Psych by AA attendance by 

Treatment. None was found with either all three 

treatments or with the CBT/TSF contrast. We 

also examined ASI Psych by AA attendance 

within-treatment (month 3) interactions, and 

none was found to be significant.
94

Part III: Psychological Dysfunction

Discussion

In the primary outcome analyses, a single 

psychopathology by treatment interaction effect 

was found that met criteria for a Bonferronicorrected level of significance. This was in 

the outpatient arm, contrasting CBT and TSF 

treatments using the PDA outcome. Clients 

without psychopathology on the ASI reported 

87 percent abstinent days in the posttreatment 

period when assigned to TSF compared with 

73 percent abstinent days reported by those 

assigned to CBT. The TSF advantage over CBT 

disappeared in clients with psychopathology.

The interpretation of this matching finding 

is clouded by the fact that Bonferroni-corrected 

significance levels were not achieved with the 

CBT–TSF contrast for interaction tests involving either posttreatment DDD outcomes, 

within-treatment PDA or DDD outcomes, or 

any contrasts with psychopathology defined as 

presence of a comorbid Axis I disorder on the 

C–DIS. Also, no psychopathology-related contrast was significant in the aftercare arm, and 

none of the a priori contrasts involving CBT 

versus MET was significant in either the outpatient or aftercare arm. When one statistical test 

out of many is found to be significant, there is 

the possibility that the significant finding is a 

type-1 error.

A descriptive analysis comparing matched 

versus mismatched groups of outpatient clients suggested that the ASI Psych matching 

effect may be a pseudo effect because the 

matched clients fared no better than the clients 

assigned to TSF. There is no reason to bother 

with assessing psychopathology with the ASI 

and providing access to alternative treatment 

approaches if equivalent outcomes can be 

achieved by assigning all clients to TSF.

Secondary outcome analyses using survival 

analysis of time to relapse also yielded only one 

significant result out of eight tests. This matching effect was also in the outpatient arm, but it 

involved the period during the active treatment 

phase rather than the year following treatment. 

Among clients with comorbid Axis I disorders, 

MET clients had a significantly higher relapse 

rate during treatment than CBT. The descriptive 

analysis of matched and mismatched clients 

suggests that this was a strong matching effect, 

with the matching strategy based on the C–DIS 

resulting in an improvement of more than 20 

percent in the rate of successful outcomes. 

Unfortunately, this matching approach did 

not improve outcomes after the termination of 

treatment. Nevertheless, these findings suggest 

that MET may not be the treatment of choice 

for individuals with psychopathology. The CBT 

advantage over MET with dual disordered clients may be more enduring if CBT treatment 

were extended beyond 12 weeks.

We were surprised by the lack of evidence 

for any enduring CBT advantage over the other 

treatments with high psychopathology individuals. Causal chain analyses shed some light 

on these results. Although the CBT manual 

contained many sessions addressing comorbid 

psychiatric symptoms, few of these sessions 

were delivered in Project MATCH due to the 

requirement that CBT therapists deliver the 

eight core CBT sessions to each participant 

before delivering any of the elective psychopathology-oriented ones. The mean number of 

sessions attended by CBT clients was approximately eight, so many clients were not in 

treatment long enough to receive an adequate 

dose of psychopathology-oriented treatment.

This interpretation is supported by the 

finding that videotape ratings revealed no differences among the three treatments in the 

degree of focus on psychopathology in Week 

2. However, there may have been little time for 

therapists to focus on psychopathology during 

the highly structured second session. Moreover, 

most therapists may have waited to focus on 

psychopathology until later in treatment, when 

abstinence would have allowed clearer assessment of psychopathology.

Causal chain analyses did not help explain 

the finding of a TSF advantage over CBT for 

outpatient clients without psychopathology. 

Although we hypothesized that psychopathology might interfere with AA affiliation, there 

was no relationship between pretreatment 

ASI Psych scores and AA attendance or AA 

involvement. Clinical lore has it that clients 

with psychopathology do not affiliate successfully with AA. It is possible that the facilitation 

offered by the TSF therapists was sufficient to 

overcome barriers to AA affiliation for clients 

with psychopathology.
95

Matching Clients to Alcoholism Treatment Based on Psychopathology

In the aftercare arm of the study, we did not 

find better outcomes among individuals without psychopathology in TSF treatment. The TSF 

advantage seen in the outpatient arm may have 

been diluted in the aftercare arm because most 

aftercare clients received recommendations to 

attend AA as a routine part of their inpatient or 

intensive outpatient treatment prior to entering 

Project MATCH aftercare treatment.

Another possible reason for the failure to 

find robust matching effects across study arms 

is that the psychopathology measures utilized 

in Project MATCH lacked sufficient reliability 

and validity. A study of the reliability of the ASI 

Psych composite score found that test-retest 

interrater reliability of the scale was lower than 

expected (Cooney, Carboneri, et al. 1997). The 

C–DIS has been found to overdiagnose individuals when compared with the Structured 

Clinical Interview for DSM–III–R and a consensus clinical diagnosis (Ross et al. 1994).

In conclusion, significant psychopathology 

matching interactions were found, but they 

were not consistent across outcome measures. 

The causal chain analyses did not reveal a 

mechanism of action for the matching interactions, further reducing confidence in the validity 

of the matching effects. With these limitations 

in mind, the following matching strategies are 

only tentatively recommended.

If one has the option of assigning outpatient clients to CBT or TSF, drinking outcomes 

after treatment may be improved by assessing clients with the ASI Psychiatric scale and 

assigning those with low scores to treatment 

utilizing the TSF approach rather than the CBT 

approach. Clients with higher ASI psychiatric 

severity may be assigned to either therapy. An 

equally effective alternative strategy would be 

simply to assign all clients to TSF rather than 

CBT.

Individuals with psychopathology treated 

with MET had significantly worse outcomes 

during the active treatment phase than those 

treated with CBT. This effect faded soon after 

termination of treatment. None of the Project 

MATCH therapies provided treatment with an 

extensive focus on reducing psychiatric symptoms, so results cannot be generalized to such 

forms of therapy.

Acknowledgments

This work was supported in part by grant 

number U10–AA10170–08 from the National 

Institute on Alcohol Abuse and Alcoholism.

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98

Sociopathy as a Client-Treatment 

Matching Variable

Ronald Kadden, Ph.D., Mark Litt, Ph.D., Ned Cooney, Ph.D., 

Dennis Donovan, Ph.D., Robert Stout, Ph.D., and 

Richard Longabaugh, Ed.D.

ABSTRACT

Sociopathic personality and the diagnosis of antisocial personality disorder (APD) 

were evaluated as potential attributes that could differentially influence response to 

treatment. It was predicted that clients with sociopathy or an APD diagnosis would 

have better outcomes with Cognitive-Behavioral Coping Skills Therapy (CBT) than with 

Motivational Enhancement Therapy (MET) because of CBT’s presumed lower reliance 

on the development of a working alliance between client and therapist, the inclusion in 

CBT of specific skills to manage anger, and the more structured nature of CBT. It was 

further hypothesized that those with APD or sociopathy would have better outcomes 

with Twelve Step Facilitation (TSF) than with MET because of the greater structure of 

TSF. Finally, CBT was hypothesized to be superior to TSF for these clients because 

it would teach them skills to manage their anger and because it does not require 

Alcoholics Anonymous (AA) attendance, which would be difficult for these clients to 

sustain. Only one of these predicted treatment contrasts was supported, and for only a 

very brief period of time, for just one of the outcome variables, and in only one arm of 

the trial. Furthermore, the hypothesized causal chains, relating client characteristics 

to outcome, for the most part did not operate as predicted: working alliance was not 

differentially effective for MET as opposed to CBT, treatment with CBT was not associated with a decrease in client anger, and MET was not found to be less structured 

than the other two treatments. However, attendance at AA was related to outcome, as 

expected, but contrary to expectation, those high in sociopathy or with APD attended 

AA at rates similar to other clients. The degree of anger reduction during treatment was 

also related to outcome, as predicted, but this effect was not limited to high sociopathy 

clients as had been anticipated. The failure to find matching of sociopathy or APD to 

any of the three treatments is at variance with two prior matching studies that did find 

matching with these client variables to CBT. Possible explanations for the failure to 

find matching effects are considered.

Sociopathy is often associated with alcoholism (Lewis et al. 1983; Mandell 1981). 

Alcoholics who exhibit antisocial personality traits are characterized by an earlier onset 

of excessive drinking and a more rapid progression to alcoholism than alcoholics who do not 

exhibit these traits (Hesselbrock et al. 1983). In 

addition, research suggests that sociopathy is 

a predictor of poor treatment outcomes among 

alcoholics. For example, Mandell (1981) documented a disproportionately high rate of drop 

out from alcoholism treatment by sociopathic 

Ronald Kadden, Ph.D.

Department of Psychiatry 

UConn School of Medicine

Farmington, CT 06030-3944

Email: Kadden@psychiatry.uchc.edu
99

Sociopathy as a Client-Treatment Matching Variable

clients, and Rounsaville et al. (1987) found that 

antisocial personality is one of several psychiatric disorders associated with poor treatment 

outcome among alcoholics.

Sociopathy as a Client-Treatment Matching 

Variable

Other studies, however, suggest that sociopathy may not always lead to poor treatment 

outcome. Vaillant’s (1983) long-term followup 

study found abstinence among 48 percent of 

those classified as both sociopaths and alcoholics, in contrast to only 28 percent of those 

without antisocial symptoms. Similarly, 

Longabaugh et al. (1994) found that alcoholics with antisocial personality disorder (APD) 

had better outcomes than non-APDs as a main 

effect of treatment, when measured by percentage of days abstinent.

Rationale for Matching 

Hypothesis

A number of researchers have noted that 

sociopaths lack internal motivation and social 

skills and may be unable to develop good 

therapeutic relationships (e.g., Cleckley 1941; 

Gerstley et al. 1989). From these observations, 

it follows that therapies which rely upon the 

development of interpersonal relationships are 

likely to be less effective for sociopaths.

One treatment approach that does not rely 

heavily upon the quality of the relationship 

be-tween therapists and clients, CognitiveBehavioral Coping Skills Therapy (CBT; Kadden 

et al. 1992), has been found effective with 

sociopathic alcoholics. Kadden and associates 

(1989) demonstrated significantly better outcomes among sociopathic clients treated with 

group CBT than nonsociopathic clients treated 

with this approach.

In contrast, sociopathic clients did less well 

than nonsociopathic clients when treated in an 

interactional group modality that relied heavily 

on the development of interpersonal relationships within the group. This pattern of results 

was maintained throughout a 2-year followup 

(Cooney et al. 1991). The Longabaugh et al. 

(1994) study is supportive of these findings: 

alcoholics with APD averaged fewer drinks per 

drinking day when treated with CBT than APD 

clients who were given relationally focused 

treatment. These two studies suggest that 

matching effects are likely when sociopathic 

clients are treated with CBT, as compared with 

alternative treatments that rely on interpersonal relationships as an active ingredient of 

treatment.

Structure is another attribute of treatment 

that is thought to be desirable for sociopaths 

(e.g., Frosch 1983). Among the treatments 

employed in the current study, CBT is considered to be highly structured. The Twelve Step 

Facilitation (TSF) approach is also structured, 

“with each session having a specific agenda 

and following a prescribed pattern” (Nowinski 

et al. 1992, p. 4). In this respect, these two 

treatments appear similar and stand in contrast to Motivational Enhancement Therapy 

(MET; Miller et al. 1992), which is viewed as 

being considerably less structured.

An additional similarity between CBT and 

TSF is that the therapeutic process of Alcoholics 

Anonymous (AA) has been identified as having, in part, a behavioral and cognitive focus 

(Brown 1993) that involves clear behavioral 

prescriptions and simple rules to help ensure 

abstinence. Miller (1978) has described specific 

areas of overlap between the approaches of AA 

and behavior modification.

Thus, there appear to be similarities between 

the structure and methods of the CBT and 

TSF approaches. As a result, similar effects of 

treatment were predicted for clients with sociopathy: they would have fairly good outcomes 

with either CBT or TSF, compared with MET.

There nevertheless are important differences 

between CBT and TSF despite the similarity in 

their degree of structure and in some of their 

methods. The specific content of CBT addresses 

skill deficits common among sociopathic alcoholics (e.g., skills for coping with criticism 

and anger), and improvements in these were 

expected to lead to reduced drinking. Therefore, 

sociopathic alcoholics, who were expected to 

enter treatment with fewer interpersonal skills, 

were predicted to show greater improvement 

with CBT than nonsociopathic clients. TSF 

treatment, on the other hand, was designed 

to facilitate attendance at AA meetings, which 

requires adequate interpersonal skills to 

develop relationships with peers at those meetings. However, since many sociopaths lack the 

social skills needed to develop positive interpersonal relationships, they were expected not 
100

Part III: Psychological Dysfunction

to develop good relationships within AA groups, 

whereas nonsociopaths should have less difficulty doing so and would therefore benefit more 

from AA.

Sociopathy and Antisocial 

Personality

An additional issue in this study relates 

to identifying the best measure of sociopathy 

for matching purposes: whether the categorical diagnosis of Antisocial Personality Disorder 

provided by DSM–III–R would be as effective 

a matching variable as a continuous measure of sociopathy, such as that provided 

by the Socialization scale of the California 

Psychological Inventory (CPI–So; Gough 1987). 

Matching to the categorical diagnosis would be 

advantageous because the DSM diagnostic system is in widespread clinical use and is well 

understood by clinicians.

Although the discrete and continuous methods of assessing sociopathy are conceptually 

related, the content of the two constructs differs: the DSM–III–R APD diagnosis focuses on 

overt antisocial behaviors, whereas sociopathy 

measured with the CPI–So reflects a number of 

underlying characteristics (Kadden et al. 1996).

As a result, it was recognized from the outset 

that comparisons between them would reflect 

differences along two dimensions: (1) continuous measurement versus categorical diagnosis 

and (2) the differing foci of the two constructs. 

A comparison between them could provide 

important information regarding the type of 

assessment that would be the most useful clinically for matching clients to treatments but 

would not be able to ascertain whether any differences found were due to differences in scale 

type or scale content.

The two matching studies cited previously 

examined the role of the categorical APD diagnosis in client-treatment matching. Longabaugh 

et al. (1994) found matching based on this diagnosis. On the other hand, Kadden et al. (1989) 

did not find significant APD by treatment-type 

interactions but did find matching when they 

used the CPI–So scale to assess sociopathy.

It should be noted that while the Longabaugh 

et al. and Kadden et al. studies both included 

cognitive-behavioral treatments, those 

treatments nevertheless differed from one 

another in a number of aspects, and the studies employed different outcome measures.

The Hypothesized 

Matching Contrasts

The predictions for the two related constructs, sociopathy and antisocial personality 

diagnosis, were identical and are described 

below. Differences in treatment outcomes 

were predicted for three contrasts among the 

treatments.

CBT Versus MET Contrast

The MET treatment relies heavily on persuasive communications, which require a high 

degree of rapport between therapist and client, 

an alliance that is likely to be more difficult for 

sociopaths to form. In contrast, CBT requires 

less of clients in terms of rapport and communication skills, relying more on coping skills 

training and behavioral exercises. Mandell 

(1981) recommended that behavioral treatment 

programs should address specific skill deficits 

common among sociopathic alcoholics (see 

also Woody et al. 1985; Barley 1986). These 

elements (e.g., skills for dealing with criticism 

and anger) are included among the CBT coping 

skills modules. By comparison, MET does not 

emphasize the learning of new coping skills, 

and therefore the sociopath receiving MET is 

less likely to acquire them.

Statement of the Matching Hypothesis: 

Drinking outcomes will be a function of an interaction between sociopathy/APD and treatment 

type, such that the slope of the regression line 

relating drinking outcome to sociopathy/APD 

will be greater for the CBT treatment condition 

than for MET. This interaction is illustrated in 

Panel A of figure 1. Although this figure shows 

an intersection of the regression lines, this contrast makes no prediction regarding whether or 

where the lines might intersect.

TSF Versus MET Contrast

Given the similarities in the degree of structure of the TSF and CBT interventions, as 

compared with MET, similar predictions were 

made for the TSF versus MET contrast as were 

made for the CBT versus MET contrast.
101

Sociopathy as a Client-Treatment Matching Variable

Figure 1. Predicted contrasts for Antisocial 

Personality Disorder for each of the three treatment pairings. The predicted contrasts for the 

sociopathy primary hypothesis are the same as 

those depicted for the APD hypothesis: simply 

substitute high or low levels of sociopathy for 

categorical diagnostic status (yes or no) on the 

abscissa of each panel.

Statement of the Matching Hypothesis:

Drinking outcomes will be a function of an interaction between sociopathy/APD and treatment 

type, such that the slope of the regression line 

relating drinking outcome to sociopathy/APD 

will be more positive for the TSF treatment condition than for MET. This interaction is illustrated 

in Panel B of figure 1, but here again no prediction was made regarding whether or where the 

regression lines might intersect.

CBT Versus TSF Contrast

Although it was expected that CBT and TSF 

would both be structured interventions, TSF differed from CBT in its reliance upon attendance 

at AA meetings. To benefit from AA, a client 

must possess adequate interpersonal skills to 

develop relationships with peers at those meetings, but many sociopaths lack those skills 

and would thus be unable to develop a positive 

relationship with an AA group. Assuming that 

attending and participating in AA meetings 

fosters sobriety, sociopaths will be less able to 

avail themselves of this resource and thus will 

have relatively poorer outcomes with TSF than 

with CBT.

Statement of the Matching Hypothesis:

Drinking outcomes will be a function of an interaction between sociopathy/APD and treatment 

type, such that the slope of the regression line 

relating drinking outcome to sociopathy/APD 

will be more positive for the CBT treatment condition than for TSF. This interaction is illustrated 

in Panel C of figure 1. Again, no prediction was 

made regarding whether or where the regression lines might intersect.

Operationalization of the 

Matching Variable

The continuous measure for the sociopathy 

primary matching hypothesis was the CPI–So, 

a 46-item true/false inventory (Gough 1987). It 

was selected based on validity data and practical considerations (Cooney et al. 1990). Scores 

were inverted for analysis: higher values indicate greater sociopathy.

The categorical measure for the APD secondary hypothesis was the DSM–III–R Antisocial 

Personality Disorder diagnosis, as determined 

by the Computerized Diagnostic Interview 

Schedule (C–DIS; Robins et al. 1989). For 

analysis, a “1” was coded if the diagnosis was 

present and “0” if absent. These client variables 

were assessed at intake into the study.

Secondary Outcome 

Analyses

In addition to the two standard trialwide 

outcome variables (percentage of days abstinent and drinks per drinking day), it was also 

proposed to test two additional dependent variables: time to first drink and time to first heavy 

drinking day, based on prior matching findings with these variables (Kadden et al. 1989; 

Cooney et al. 1991).

Causal Chains

CBT Versus MET Contrast

Three causal chain analyses were proposed 

to account for this contrast.

Quality of the Therapeutic Relationship

The first examined the role of therapeutic 

alliance as a mediator of the matching effect. 

It was anticipated that sociopathic/APD clients 

would form relatively poor working alliances in 

all the therapies in this study, but the impact 

of this effect would be greatest in MET, which 

relies heavily on the establishment of good rapport between client and therapist. Although the 

poor quality of the working relationship would 

be present in CBT as well, that modality relies 

more on skills training than on the relationship between client and therapist. Stated in the 
102

Part III: Psychological Dysfunction

form of a logical syllogism, the causal chain is 

as follows:

■ The more sociopathic the clients, the 

less their capacity to form a good therapeutic relationship.

■ The less the clients’ capacity to form a 

good therapeutic relationship, the less 

they will benefit from a therapeutic 

modality that relies on that relationship.

■ MET treatment makes greater use of the 

therapeutic relationship than does CBT.

■ The more sociopathic the clients, the less 

their success in MET, compared to CBT.

Quality of the therapeutic relationship was 

assessed by the Working Alliance Inventory 

(Horvath and Greenberg 1986) at the end of the 

second treatment session.

Anger

The second causal chain was based on 

improvement in the ability to manage anger 

as a mediator of the matching effect. It was 

anticipated that sociopathy/APD would be 

associated with higher anger ratings at baseline 

and that CBT, which addresses interpersonal 

skills in general and anger management specifically, would be associated with more decline in 

anger scores than MET. Among angry clients, 

greater reduction of anger from pretreatment 

to post-treatment would be associated with 

better treatment outcome. On the other hand, 

for those low in sociopathy (or without an APD 

diagnosis), it was expected that anger scores 

would be lower and therefore neither treatment 

would result in much change in anger. For 

these clients, then, there would be no relationship between change in anger and outcome.

The predictions were as follows:

■ Sociopaths will have higher levels of 

anger at intake than nonsociopaths.

■ Sociopaths will show more reduction 

in anger with CBT treatment than with 

MET.

■ Among sociopaths, greater reduction 

of anger will be associated with better 

drinking outcome.

■ Sociopathic/APD clients treated in CBT 

will have better outcomes than those 

treated in MET.

Project MATCH assessed anger at intake 

using the Spielberger Trait Anger Scale (TAS; 

Spielberger et al. 1983). Subjects scoring high 

on the TAS were assumed to have poor anger 

management skills.

Structure of Treatment

The final hypothesized causal chain for 

the CBT versus MET contrast examined the 

role of treatment structure as a mediator of 

the matching effect. It was expected that for 

sociopathic/APD clients, the greater the structure of treatment, the better the outcome. For 

non-sociopathic clients, it was predicted that 

treatment outcome would be independent of 

treatment structure. It was anticipated that 

CBT sessions would receive higher ratings on 

a treatment structure scale than MET sessions.

In syllogistic form, the causal chain is as 

follows:

■ Sociopathic/APD clients will have a 

greater likelihood of benefiting from a 

more structured treatment approach 

than from a less structured one.

■ Ratings of treatment sessions will show 

CBT to be more structured than MET.

■ Sociopathic/APD clients will be more 

likely to benefit from CBT than from 

MET.

Treatment structure was measured as one of 

the subscales of the Project MATCH Tape Rating 

Scale (Carroll et al. 1998) by independent raters watching videotapes of each participant’s 

second session of treatment.

TSF Versus MET Contrast

It was predicted that treatment structure 

would mediate the differences between the TSF 

and MET treatments for sociopathic/APD clients, who would have a greater likelihood of 

benefiting from a structured treatment. It was 

anticipated that ratings by independent evaluators would show TSF to be a more structured 

intervention than MET. Sociopathic/APD clients would therefore be more likely to benefit 

from TSF and less likely to benefit from MET. 

The sequence of logical steps for this proposition is the same as that specified for the 

preceding structure-of-treatment causal chain 

for the CBT versus MET contrast.
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Sociopathy as a Client-Treatment Matching Variable

CBT Versus TSF Contrast

Two causal chain analyses were proposed to 

account for this contrast.

Anger

The first causal chain analysis tested 

whether change in anger would mediate the 

matching effect. In similar fashion to the anger 

causal chain for the CBT versus MET contrast, 

it was anticipated that sociopathy/APD would 

be associated with higher anger at intake, that 

CBT would be associated with greater decline 

in anger than TSF, and that greater reduction 

of anger among sociopaths would be associated 

with better treatment outcome. It was therefore 

predicted that sociopathic/APD clients would 

have better drinking outcomes with CBT than 

with TSF. The logical steps for this proposition 

are the same as those specified for the anger 

causal chain of the CBT versus MET contrast.

AA Attendance

The second causal chain for the CBT versus 

TSF contrast postulated that AA attendance 

would mediate the matching effect. It was 

expected that those with high AA attendance 

would have better treatment outcomes. It was 

predicted that sociopathic/APD clients would 

be less likely to engage with AA than nonsociopaths because of their difficulty forming 

meaningful interpersonal relationships. It was 

expected that AA attendance would generally 

be associated with good outcome in the TSF 

treatment but that sociopaths would be less 

likely to attend AA. It was therefore anticipated 

that among sociopathic/APD clients, treatment 

outcomes would be worse for those treated with 

TSF, as opposed to CBT, because of the heavy 

TSF reliance upon AA attendance, whereas the 

effectiveness of CBT does not depend upon AA.

The predictions were as follows:

■ Clients with high AA attendance will 

have better outcomes.

■ AA attendance depends in part upon the 

formation of interpersonal relationships.

■ Because of their difficulty forming relationships, sociopaths will tend not to get 

involved in AA.

■ TSF relies for its effectiveness upon AA 

attendance.

■ Sociopaths will have poorer outcomes 

with TSF than with CBT because of their 

poor AA attendance.

Data on AA attendance were obtained at 

each followup assessment from the Form 90–F 

(Miller 1996).

Data Analysis

The primary tests of the matching hypotheses were conducted using hierarchical linear 

modeling (HLM) analyses. Separate analyses 

were performed for the period during which 

treatment took place (from intake to end of 

treatment, i.e., months 1–3), and for the period 

from end of treatment to the end of the 1-year 

followup (months 4–15). The primary dependent variables (DVs) in these analyses were 

(a) percentage of days abstinent (PDA, transformed to correct for nonnormal distributions) 

and (b) drinks per drinking day (DDD, also 

transformed), both of which were derived from 

Form 90 drinking assessments. Weekly values 

of these DVs were used in analyses for the 1–3 

month period, and monthly values were used 

for the 4–15 month period.

The covariate set used for these analyses consisted of the pretreatment value of the drinking 

DV, a dummy variable representing the treatment site, the interaction of site by treatment 

type, and the interaction of site by treatment by 

sociopathy. Of the effects estimated, only the 

following were examined for the present report: 

main effect for sociopathy, time by sociopathy, 

quadratic effect of time (Time2) by sociopathy, 

treatment site by sociopathy, time by treatment 

site by sociopathy, Time2 by treatment site by 

sociopathy, sociopathy by treatment, time by 

sociopathy by treatment, and Time2 by sociopathy by treatment.

A family-wise type-1 error rate of 0.05 was 

specified beforehand for each matching attribute. This was apportioned to each of the 

treatment contrasts specified for the hypothesis and was further divided by 2 to account for 

the two dependent variables. Significance levels 

for this hypothesis were partitioned among the 

three contrasts such that analyses involving the 

best justified contrast (CBT versus MET) were 

required to meet an overall significance level 

of 0.015 (i.e., 0.03/2) and analyses involving 
104

Part III: Psychological Dysfunction

the other two contrasts (CBT versus TSF and 

MET versus TSF) were each required to meet 

significance levels of 0.005 (i.e., 0.01/2). Main 

effects for treatment have been reported elsewhere (Project MATCH Research Group 1997a, 

b), and are not repeated here.

Results and Discussion

Results and discussion for the outpatient 

arm are presented first, followed by a similar 

presentation for the aftercare arm. Within each 

arm, matching results are presented in the following order: primary outcomes (PDA and DDD) 

during treatment, in the 1-year followup, and in 

the 3-year followup; secondary (time to event) 

outcomes; and causal chain findings. These 

are followed by discussion of the findings.

The presentation focuses primarily on the 

CPI–So client attribute, with additional comments referring to the APD client attribute 

(assessed by C–DIS), which was evaluated in 

the same manner as the sociopathy matching 

variable but in general had fewer findings.

Outpatient Arm—Results 

1–3 Month Period

HLM analyses of PDA during the period in 

which the treatments were provided found no 

effects attributable to sociopathy nor to any 

interaction of sociopathy with treatment assignment or with time. However, with DDD as the 

DV, a main effect for sociopathy emerged, such 

that clients scoring high in sociopathy reported 

more drinks per drinking day than did those 

low in sociopathy (F(1, 8656)=10.19, p<.01).

There were no effects attributable to antisocial personality diagnosis nor any significant 

interaction of APD with treatment assignment or with time, for either of the dependent 

variables.

 4–15 Month Followup Period

A significant interaction of sociopathy with 

linear time was found (F(1, 7857)=10.03; p< .01) 

with the PDA dependent variable. Examination 

of monthly means indicated that clients high 

in sociopathy maintained a fairly constant level 

of PDA throughout the followup period while 

those low in sociopathy fared progressively 

worse over the course of the followup year. This 

effect can be seen across the three panels of figure 2. No significant effects were found for any 

predicted interaction of sociopathy with treatment assignment. However, there was a finding 

in the unexpected direction for the CBT versus 

MET contrast (F(1, 6298)=3.07, p=.04): clients 

treated in CBT who were low on CPI–So sociopathy had higher PDA than clients with high 

sociopathy or clients treated in MET. With DDD 

as the dependent variable, no significant effects 

of any kind were seen.

Furthermore, no effects involving APD, either 

alone or in interaction with treatment type or 

with time, reached significance for either the 

PDA or DDD primary dependent variables.

3-Year Followup

Followup data were collected for outpatient 

subjects at 3 years posttreatment. Drinking 

data were averaged across 

months 37–39 (timed from the 

date of intake into the study) to 

provide one value for each of the 

two primary dependent variables, 

PDA and DDD (Project MATCH 

Research Group 1998). An analysis of covariance was performed 

for each dependent variable, 

using the same covariates as in 

earlier analyses. There was no 

significant effect of sociopathy 

nor an interaction of sociopathy 

with treatment assignment for 

either of the primary dependent 

variables at 3 years. Furthermore, 

neither APD nor the interaction of 

Figure 2. Percentage of days abstinent during three intervals 

of the followup period as a function of trichotomized sociopathy levels. Data are from the outpatient arm of the trial.
105

Sociopathy as a Client-Treatment Matching Variable

APD with treatment assignment was significant 

for either of the dependent variables.

Secondary Outcome Variables

In addition to examining data on the quantity (DDD) and frequency (PDA) of drinking, 

analyses were conducted to determine whether 

client-treatment matching might also be 

reflected in time until relapse. Two definitions 

of “relapse” were used: the most conservative 

one established the first drink after the beginning of treatment as a relapse, whereas the less 

conservative definition established the first 

heavy drinking day after the beginning of treatment as a relapse. Cox model regression 

analyses were used to determine the effect of 

CPI sociopathy on time to relapse measured in 

days since the beginning of treatment. The 

same was done for the interaction of treatment 

with sociopathy. In these analyses, the covariates were pretreatment levels of both primary 

drinking variables as well as terms representing site, treatment, and the interaction of site 

by treatment.

In the analysis of time to first drink, no significant effects were found for CPI sociopathy, 

for treatment, or for the interaction of treatment by sociopathy. However, in the analysis 

of time to first heavy drinking day, a main effect 

was found for CPI sociopathy (risk ratio=1.015, 

p<.05), such that those higher in sociopathy 

reached a heavy drinking day sooner than 

those lower in sociopathy. No effects on time to 

first heavy drinking day were found for treatment or for the interaction of treatment with 

sociopathy.

No significant effects were found for APD, 

treatment, or the interaction of APD by treatment in analyses of time to first drink or time 

to first heavy drinking day.

The A Priori Causal Chains

The matching hypotheses were formulated 

based on certain assumptions regarding treatment processes and the action of mediating 

variables; these were specified in terms of 

causal chains. An example of how these causal 

chains were operationalized is shown in figure 

3 for the differential effects of MET and CBT 

with respect to the working alliance mediating 

variable. In this model, sociopathy, measured 

by the CPI–So, was expected to be inversely 

Figure 3. Generic model of a causal chain. This example depicts expected client-treatment matching effects when contrasting the CBT and MET treatments, with working alliance as the mediating 

variable.
106

Part III: Psychological Dysfunction

related to working alliance; hence the minus 

sign above and below the arrows (for the outpatient and aftercare arms, respectively) in the 

left portion of the figure, for both the MET and 

CBT treatments. Moving to the right in the figure, it was anticipated that working alliance 

would be conditionally predictive of outcome, 

depending on the treatment delivered. Because 

the outcome of MET was expected to be positively related to the development of a working 

alliance, the proposed relationship between 

working alliance and outcome was depicted as 

positive in the upper half of the model by means 

of four separate terms, one for each of the two 

dependent variables in both arms of the study.

The terms that appear above the arrow represent the path coefficients for the outpatient 

arm. Those for the aftercare arm are below the 

arrow. The first term of each pair indicates the 

relationship to the PDA outcome; the second 

term, in parentheses, indicates the relationship 

to the DDD outcome. The plus sign above the 

second arrow suggests the anticipated positive 

relationship between working affiance and the 

PDA outcome, and the minus sign indicates 

the anticipated negative relationship between 

working alliance and the DDD outcome. The 

same relationship between working alliance 

and outcomes was predicted for MET clients 

in the aftercare arm, as indicated by the signs 

below the arrow. For CBT, however, outcome 

was expected to be independent of working affiance, and therefore the relationship between 

working alliance and outcome was expected to 

be nonsignificant (ns).

The differential impact of sociopathy on 

treatment outcome during the followup period 

was analyzed using maximum likelihood structural equations modeling procedures. The two 

primary outcome variables, PDA and DDD 

over months 4 through 15, were modeled as 

latent outcome variables: for each dependent 

variable, a single latent variable was created, 

based on the measures from each of the 12 

followup months (indicated by the arrows on 

the far right in figure 3). The multiple dependent variables that make up the latent outcome 

variable were each weighted according to their 

relative contributions to the overall outcome 

measure. This process was repeated for each of 

the dependent variables, across all six a priori 

causal chains that were proposed to account 

for the hypothesized impact of client sociopathy 

or APD on the outcomes of the three different 

treatments.

It should be noted at the outset that these 

models did not provide a good fit to the data 

obtained: the goodness-of-fit indexes (GFI), 

which should be close to 1.00, were at best 

about 0.85, with no more than 20 percent of 

the variance in outcome accounted for by any 

of them.

The results for the APD client attribute paralleled those for CPI-sociopathy as the matching 

variable, but tended to be weaker. Therefore, 

we have elected to present causal chain results 

only for the sociopathy client attribute.

Causal Chain Analyses

CBT Versus MET Contrast. The first causal 

chain for this contrast involved working alliance, as illustrated in figure 3. Results of the 

analyses for this chain are shown in figure 4. 

Path coefficients are interpreted as betas (standardized regression weights) in these models.

A key assumption underlying the working alliance causal chain is that clients who 

are high in sociopathy will have poorer social 

skills than those low in sociopathy. Although 

Project MATCH included no direct measures of 

social skills, CPI sociopathy was found to be 

negatively correlated with the Social Behavior 

and Overall Social Role Performance subscales 

of the Psychosocial Functioning Inventory 

(Feragne et al. 1983) and with length of residence, an indicator of social stability (r’s=-0.40, 

-0.30, and -0.15, respectively). These correlations suggest that clients who are higher in 

sociopathy do seem to have less social competence and less social stability than clients with 

low levels of sociopathy.

Nevertheless, despite that relationship, CPI–

So scores were only moderately inversely related 

to working alliance (path coefficients=-0.18 

and -0.15). Working alliance, in turn, was not 

predictive of outcome for MET clients but was 

significantly predictive of outcome for CBT clients. This result, contrary to our expectations, 

is consistent with the CPI–So by treatment 

interaction in the unexpected direction noted 

above in which CBT clients scoring low on 

sociopathy had better outcomes (higher PDA). 

CBT clients who scored low on sociopathy were 
107

Sociopathy as a Client-Treatment Matching Variable

Figure 4. Results of causal chain analysis of CBT versus MET contrast with working alliance as 

the mediating variable. *p<.05

more likely to have a better working alliance 

(path coefficient=-0.15), and higher alliance 

scores were related to better PDA outcome 

(path coefficient=0.19).

A second causal chain that was proposed 

for the CBT versus MET contrast involved the 

differential effect of treatment on client anger. 

The underlying assumption was that highsociopathy clients would be more angry than 

those low in sociopathy and would therefore be 

more likely to benefit from a reduction in anger. 

The initial part of that assumption was borne 

out: CPI sociopathy was significantly correlated 

with pretreatment anger (r=0.38). Those high 

in sociopathy had a mean trait anger score of 

33.3, whereas those low in sociopathy had a 

mean score of 26.7. The causal chain analyses 

regarding anger change are shown in figure 5. 

The model tests the proposition that treatment 

assignment (CBT versus MET) predicts change 

in anger, which in turn predicts outcome. In 

this model, low sociopathy clients were those 

scoring in the lowest third of the distribution 

of CPI–So scores, and high sociopathy clients 

were those in the highest third. Anger change 

in these analyses was calculated as the posttreatment anger score with pretreatment anger 

partialed out. A positive value for this term represents a decrease in anger from pretreatment 

to posttreatment.

Contrary to our expectations, treatment differences had no effect on change in anger for 

those high in sociopathy but had a modest paradoxical effect for outpatients low in sociopathy 

(path coefficient=-0.21): CBT was associated 

with an increase in anger (CBT was coded +1 

and MET coded -1 in these analyses). In the 

second link of the causal chain, as expected, 

a decrease in anger was associated with better 

PDA and DDD outcomes among high sociopathy clients and with better DDD among low 

sociopathy clients.

A third hypothesized causal chain involved 

therapy structure, positing that structured 

therapy would improve outcome for clients high 

in sociopathy. However, as shown in figure 6, 

treatment assignment made no difference in 

raters’ judgments of therapy structure: MET 
108

Part III: Psychological Dysfunction

Figure 5. Results of causal chain analysis of CBT versus MET contrast with anger change as the 

mediating variable, at low levels of sociopathy (bottom third of clients) and high levels of sociopathy 

(top third of clients). *p<.05

sessions were rated as being about as structured as the CBT sessions (the path coefficients 

for low and high sociopathy clients, -0.22 and 

0.04, respectively, were nonsignificant). At 

both levels of sociopathy, therapy structure 

was not related to treatment outcome, with one 

exception: greater structure was associated 

with fewer DDD, but only for low sociopathy 

clients, not for high sociopathy clients as had 

been forecast.

TSF Versus MET Contrast. For the TSF 

versus MET contrast, treatment assignment 

did have some impact on ratings of therapy 

structure (figure 7), with TSF rated as more 

structured than MET, but only among clients 

low in sociopathy (path coefficient=0.27). The 

only relationship between therapy structure 

and outcome, as in figure 6, was a fairly strong 

one (path coefficient=-0.73) for the DDD outcome among low sociopathy clients.

TSF Versus CBT Contrast. Sociopathy 

had been expected to be inversely related to 

attendance at AA, but as seen in figure 8, it 

had no association with AA attendance (path 

coefficients=0.09 and -0.14). Nevertheless, AA 

attendance was strongly predictive of outcome, 

even for clients in the CBT treatment condition 

(absolute value of all path coefficients 0.33). 

This was the only model that accounted for 

significant amounts of outcome variance (average multiple R2=0.45), although as with all the 

other models, its overall fit to the data was poor 

for all four analyses (average GFI=0.75).

Finally, change in anger was also hypothesized to be a mediator of treatment effect for the 

CBT versus TSF contrast, with those higher in 

sociopathy expected to benefit more from anger 

reductions, which would be more likely to occur 

with CBT. In these analyses, however (data not 

shown), CBT was not superior to TSF in reducing anger (average path coefficient=0.06), and 

anger change was not related to outcome (average path coefficient=0.15).
109

Sociopathy as a Client-Treatment Matching Variable

Figure 6. Results of causal chain analysis of CBT versus MET contrast with therapy structure as 

the mediating variable, at low levels of sociopathy (bottom third of clients) and high levels of sociopathy (top third of clients). *p<.05

Outpatient Arm—Discussion

Prognostic Effects of Sociopathy/APD

A main effect of client sociopathy on the DDD 

outcome was observed during the 3-month 

treatment period: as expected, sociopathic 

clients drank more on each drinking occasion. However, the only effect observed in the 

year following treatment was a sociopathy by 

time effect in which PDA became progressively 

worse among the less sociopathic outpatients, 

a finding inconsistent with the effect during 

treatment and with what had been anticipated. 

Although some weakening of treatment effectiveness over time might be expected, these 

effects in the primary outcome variables were 

inconsistent: they were not observed in both 

outcome variables, and not with APD as the client variable.

However, one of the secondary time-to-event 

outcome measures (time to first heavy drinking day) was significant in both arms in the 

expected direction: clients with high levels 

of sociopathy reached a heavy drinking day 

more quickly. Combining this with the DDD 

within-treatment finding suggests that clients 

who were more sociopathic were more likely to 

relapse sooner and to drink more when they 

did.

Inconsistencies in the present findings are 

reminiscent of those in the literature, where 

sociopathy is found to be a prognostic variable 

in some studies but not in others. This variability of findings may indicate that sociopathy 

is not as reliable an indicator of poor prognosis 

as has often been suggested, or at least that 

not all the variables relevant to understanding 

the impact of sociopathy on outcome have been 

identified (Longabaugh et al. 1994).
110

Part III: Psychological Dysfunction

Figure 7. Results of causal chain analysis of TSF versus MET contrast with therapy structure as 

the mediating variable, at low levels of sociopathy (bottom third of clients) and high levels of sociopathy (top third of clients). *p<.05, **p<.01, ***p<.001

Treatment Matching Effects

In the outpatient arm, no matching effects 

were observed with any of the treatments and 

either the sociopathy or the APD client variables. 

This contrasts with the findings of Longabaugh 

et al. (1994) in an outpatient sample in which 

CBT was particularly effective for clients with 

an APD diagnosis.

Causal Chain Analyses

The causal chains had been proposed as a 

means of empirically testing the reasoning behind the matching hypotheses. In the event 

that a hypothesis was not confirmed, it was 

hoped that the causal chains might provide 

useful information as to why the hypothesis failed. In the case of the sociopathy/APD 

hypotheses, several rationales were offered to 

account for each of the proposed matching contrasts, which became the basis for a number of 

different causal chains.

Working Alliance. Among outpatients in both 

MET and CBT, high sociopathy was associated 

with poorer working alliance, as anticipated. 

However, poorer working alliance was predictive of poorer drinking outcomes (both PDA and 

DDD) in CBT but not in MET, where this effect 

on outcomes had been anticipated. Thus, the 

a priori causal chain broke down because the 

findings in the second link were opposite to our 

prediction: CBT outcomes were associated with 

the strength of the working alliance whereas 

MET outcomes were not. Although it might be 

tempting, based on this, to recommend that clients high in sociopathy not be assigned to CBT, 

such a recommendation would contradict two 

prior findings in which sociopathic or APD clients had better treatment outcomes with CBT 

than did nonsociopaths/non-APDs (Kadden et 

al. 1989; Longabaugh et al. 1994).

Anger. As predicted, greater anger reduction 

was associated with a greater improvement in 

both PDA and DDD among high sociopathy 
111

Sociopathy as a Client-Treatment Matching Variable

Figure 8. Results of causal chain analysis of TSF versus CBT contrast with AA attendance as the 

mediating variable. *p<.05, **p<.01, ***p<.001

clients. However, contrary to our predictions, 

this was also true among low sociopathy clients 

(for the DDD outcome), and the improvements 

in outcome occurred regardless of whether 

clients had been treated in CBT or MET. The 

causal chain failed because the anticipated 

differential benefit of CBT did not materialize. 

This may be due in part to anger management 

training having been an elective session, to 

be delivered in one of the last four sessions of 

CBT. However, by that time, many of the CBT 

clients had already dropped out of treatment: 

average attendance for outpatients in CBT was 

8.27 sessions, and even fewer, 5.73, for those 

high in sociopathy. As a result, most clients 

missed the opportunity to receive the anger 

management session, perhaps explaining, in 

part, the lack of differential benefit of CBT for 

angry clients.

Structure. The causal chain based on treatment structure was not supported at any 

point. The independent raters did not view any 

of the three treatments as being more structured than the others, for the most part, and 

the relationship between the structure variable and outcome was inconsistent. Among 

outpatient clients with low sociopathy ratings, 

there was a significant relationship with DDD 

(in the expected direction—greater structure 

was associated with lower DDD), but none with 

PDA. This was true for all three treatments. 

However, it had been expected that treatment 

structure would have greater impact on the 

more sociopathic clients rather than on those 

who were less sociopathic. The reasons for the 

inconsistency between the outcome measures, 

and the deviations from expectation, are not at 

all clear.

AA Attendance. The predicted relationship 

between sociopathy and AA attendance also did 

not materialize. Nevertheless, AA attendance 

was related to both outcome variables across 

all three treatments, and most strongly for TSF 

clients. This is consistent with Morgenstern et 
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Part III: Psychological Dysfunction

al.’s (1997) finding that client involvement in 

AA after formal treatment was associated with 

better outcomes. These findings speak to the 

benefit of recommending AA involvement for 

alcoholics in treatment, regardless of the therapeutic approach. However, it should also be 

noted that these studies provide no evidence 

as to whether AA would be sufficient by itself, 

without any other treatment.

Summary for Outpatient Arm. The data 

indicate that working alliance was more effective for CBT clients than for MET clients, CBT 

was not associated with a decrease in anger 

(but rather with increased anger for clients 

low in sociopathy), and all three treatments 

were found to be about equally structured. 

These findings are at variance with our a priori 

predictions.

The proposed causal chains failed in their 

first link, with one exception. Sociopathy was 

not associated with either the degree of anger 

reduction or the amount of involvement in AA, 

and the treatments were not rated as being 

more or less structured than one another, but 

sociopathy was associated with poorer working 

alliance, as anticipated. The causal chains performed a little better in their second link, where 

at least one of the outcomes was related to each 

of the mediating variables, although the only 

consistent relationship was between AA attendance and outcome across all three treatments 

and both outcome variables.

Aftercare Arm—Results 

1–3 Month Period

No significant outcome effects emerged for 

sociopathy or for the interaction of sociopathy with treatment assignment or with time for 

either of the DVs during the period in which the 

treatments were provided. The same was true 

when the APD client attribute was substituted 

for sociopathy.

4–15 Month Followup Period

No significant effects of sociopathy or interactions of sociopathy with treatment type or 

time were found during the posttreatment 

year when either PDA or DDD was used as the 

dependent variable.

With respect to the APD analyses, no 

significant effects were found for any of the 

terms involving APD when PDA was the dependent variable. However, when DDD was the 

outcome, a significant interaction of APD by 

treatment by time was found (F(2, 8119)=4.91, 

p<.01). Examination of the means for DDD in 

each month of followup showed that early in 

followup, APD positive clients who had been 

treated in CBT had fewer DDD than did APDpositive clients treated in TSF (as predicted), 

with no treatment differences for APD-negative 

individuals. This relative advantage for CBT 

disappeared after 2 months and reversed over 

time, so that by the end of month 15, APDpositive individuals tended to fare slightly (but 

not significantly) better if treated in TSF as 

opposed to CBT.

Secondary Outcome Variables

In the analyses of time to first drink, a main 

effect was found for CPI–So (risk ratio=1.025, 

p<.05) such that those higher in sociopathy 

tended to take their first drink earlier than 

those lower in sociopathy. However, no effects 

on time to first drink were found for treatment, 

or for the interaction of treatment by sociopathy. A similar result was found in the analysis 

of time to first heavy drinking day, with a main 

effect for sociopathy (risk ratio=1.029, p<.05), 

as also noted above in the outpatient arm. 

Again, no effects on time to first heavy drinking 

day were found for treatment or for the interaction of treatment with sociopathy.

 With respect to the APD client attribute, no 

significant effects were found in analyses of 

time to first drink or time to first heavy drinking day.

Causal Chain Analyses

The reader is reminded that the aftercare 

coefficients associated with the CPI–So client 

attribute are the ones below the path arrows 

in the causal chain figures introduced in the 

outpatient section.

CBT Versus MET Contrast. In the aftercare 

arm, working alliance was not related to CPI–

So (path coefficients=-0.11 and -0.14, figure 4). 

Working alliance was related to the DDD outcome for MET clients (path coefficient=-0.14) 

as expected (greater working alliance associated with fewer DDD). As in the outpatient 
113

Sociopathy as a Client-Treatment Matching Variable

arm, working alliance was also related to both 

outcomes for CBT clients, contrary to our 

expectation that the relationship would only 

occur for MET clients.

With respect to the anger causal chain (figure 5), treatment differences had no impact 

on change in anger at either level of sociopathy. However, change in anger did have the 

expected impact on outcome among high sociopathy clients: decreased anger was associated 

with improved PDA and DDD outcomes (path 

coefficients=0.21 and -0.34, respectively). For 

aftercare clients, this effect occurred only among 

those with high sociopathy, as anticipated.

There were no differences in ratings of 

therapy structure (figure 6) between CBT and 

MET (path coefficients=0.18 and -0.01). The 

relationships between therapy structure and 

outcome were opposite to what had been predicted: greater structure was related to lower 

PDA (path coefficients=-0.35 and -0.20) and to 

more DDD among high sociopathy clients (path 

coefficient=0.20).

TSF Versus MET Contrast. The pattern of 

results for this contrast (figure 7) was largely 

similar to that for the CBT versus MET contrast, with therapy structure as the mediating 

variable. There were no differences in ratings 

of therapy structure between aftercare TSF 

and MET (path coefficients=0.12 and -0.01). 

A similar deviation from the predicted relationship between therapy structure and PDA 

outcome was observed here as in CBT versus 

MET: greater structure was related to fewer 

PDA at both levels of sociopathy (path coefficients=-0.57 and -0.23). However, in this case, 

greater structure was also related to a decline 

in DDD among high sociopathy clients (path 

coefficient=-0.61), a strong finding in the predicted direction despite the opposite-direction 

finding for the PDA outcome.

TSF Versus CBT Contrast. Client sociopathy 

was not related to AA attendance (path coefficients=-0.04 and 0.00, figure 8). Nevertheless, 

AA attendance was strongly related to outcome, 

more so for TSF clients than for CBT clients, as 

in the outpatient arm.

Change in anger was not related to type of 

treatment (TSF or CBT) nor to either of the outcome variables (data not shown).

Aftercare Arm—Discussion

Prognostic Effects of Sociopathy/APD

In the aftercare arm, there were no main 

effects of sociopathy or APD on either of the 

primary outcome variables (PDA or DDD). However, the anticipated effect of sociopathy/APD 

was found among the time-to-event measures: 

clients with higher ratings of CPI sociopathy relapsed more quickly to both a first drink and 

to a first day of heavy drinking than did clients 

with low sociopathy ratings.

Treatment Matching Effects

The lone significant matching effect for 

either the sociopathy or APD client variables 

(across both arms of the trial) was an APD by 

treatment by time effect in which APD-positive 

aftercare clients who were exposed to the CBT 

intervention had fewer DDD than clients who 

had been treated in TSF. This effect was in the 

predicted direction, but it dissipated over time. 

The effect was found only for DDD early in the 

posttreatment period but was not found for the 

PDA outcome nor with the sociopathy matching variable. Little weight can be given to an 

isolated finding that was statistically significant for only the first 2 months posttreatment. 

This stands in contrast to the finding of Cooney 

et al. (1991), also in an aftercare sample, that 

sociopathic clients treated in CBT had superior 

outcomes for 18 months following treatment.

Causal Chain Analyses

Working Alliance. Sociopathy was unrelated to working alliance in either CBT or MET. 

Nevertheless, working alliance was positively 

related to both drinking outcomes among CBT 

clients (contrary to initial expectations) but 

only to DDD among MET clients. The working 

alliance causal chain broke down because of 

the lack of a differential relationship between 

sociopathy and working alliance (it had been 

hypothesized that there would be a negative 

relationship between sociopathy and working 

alliance) and because the relationship of working alliance to outcome for CBT clients was 

more pronounced than for MET clients.

Anger. Change in anger was unrelated to 

the CBT or MET treatments for either high or 
114

Part III: Psychological Dysfunction

low sociopathy clients. Nevertheless, reduction 

in anger was associated with improvements 

in both PDA and DDD among high sociopathy 

clients, as anticipated, and was unrelated to 

drinking outcomes of low sociopathy clients, 

also as anticipated. The anger causal chain 

broke down because CBT was no more effective 

in reducing anger than MET.

Structure. The causal chain broke down in 

both links: in the inability of either CBT or TSF 

to demonstrate greater structure than MET 

and in the prediction that greater structure 

would be related to better drinking outcomes, 

especially for high sociopathy clients. In fact, 

greater structure of therapy was associated 

with worse PDA outcomes for both high and 

low sociopathy clients in all three treatments. 

The lone relationship in the predicted direction 

was that greater structure was strongly associated with fewer DDD in high sociopathy clients, 

although the corresponding PDA value was significant in the direction opposite to what had 

been predicted. The reasons for this inconsistency, or for the opposite-direction effects, are 

not understood.

AA Attendance. The anticipated relationship between sociopathy and AA attendance 

did not materialize: sociopaths were no less 

likely to get involved in AA than nonsociopaths. 

Thus the causal chain expectation that sociopathy would differentially affect the probability 

of AA attendance was not supported. However, 

degree of client involvement in AA was related 

to positive drinking outcomes across all three 

treatments.

Summary for Aftercare Arm. Sociopathy 

was not associated with hypothesized mediators such as working alliance, change in anger, 

or AA attendance. Furthermore, none of the 

treatments was rated as more structured than 

the others. Thus, the first link was not confirmed for any of the proposed causal chains.

As for the second link, working alliance, 

anger reduction, and AA involvement were all 

generally related to better drinking outcomes 

as hypothesized, but the predicted differential 

effects for high versus low sociopathy clients 

did not materialize. Therapy structure, on the 

other hand, was negatively rather than positively related to drinking outcome (greater 

structure was associated with worse PDA outcome, regardless of sociopathy level).

Overall Summary

Neither sociopathy nor APD had clear, consistent effects on outcomes, not directly nor in 

interaction with any of the three treatments. 

Although a few effects were found, they were 

isolated findings that were not consistent across 

the two outcome variables, the two arms of the 

trial, or characterizations of the client attribute 

(sociopathy versus APD). Of six hypothesized 

matching effects (three contrasts each for the 

sociopathy and APD client attributes), only 

one attribute by treatment by time effect was 

found. The only light the causal chains shed on 

this situation is that the anticipated differential 

effects of high versus low sociopathy upon the 

proposed mediating variables did not materialize. Thus, the basic reasoning underlying the 

a priori matching hypotheses did not receive 

empirical support. This was the case despite 

the fact that the hypothesized mediating variables did generally have at least some of the 

anticipated relationships with outcome: better working alliance, anger reduction, and AA 

involvement were for the most part associated 

with better drinking outcomes.

The failure to find support for the first link of 

the causal chains may to some extent explain 

the failure to obtain the hypothesized matching effects. However, it is also possible that the 

operationalizations of the mediating variables 

were inadequate or that we failed to identify the 

proper mediating variables.

One prior study did find treatment matching 

based on client sociopathy in an aftercare setting (Kadden et al. 1989; Cooney et al. 1991), 

and another found matching based on diagnosis of APD among outpatients (Longabaugh 

et al. 1994). The causal chains in the present 

study provide few clues as to why we failed to 

replicate the matching effects that were previously reported with the same client variables. 

Since neither of the earlier studies included 

tests of causal chains, there is little basis for 

understanding the substantial differences 

between the outcomes of those studies and the 

current one.
115

Sociopathy as a Client-Treatment Matching Variable

Various speculations can be offered as to 

why the present results do not confirm those 

prior independent studies. The Kadden et al./

Cooney et al. study employed group therapy, 

with heterogeneous groups of clients. In the 

CBT groups of that study, it is possible that the 

therapists may have adjusted the intervention 

to accommodate the needs of the lowest functioning members of each group, while largely 

ignoring the higher functioning clients, who 

as a result may have found the groups boring 

and unhelpful. A similar process may have also 

occurred in the “relationship enhanced” therapy employed by Longabaugh et al., in which 

the focus of treatment may have shifted somewhat from the alcoholic member of the dyad 

due to the presence of the significant other and 

therefore may have been less helpful in meeting 

the needs of APD alcoholics. In the individual 

therapy of the present study, the therapists 

could give their full attention to the particular 

needs of each client, so that the higher functioning clients may have had their needs met 

better and therefore benefited as much from 

the CBT intervention as the lower functioning 

clients and more than the higher functioning 

clients in the two earlier studies. For similar reasons, the sociopathic and APD clients 

who were assigned to TSF and MET may have 

benefited from the individual attention they 

received in them, thus minimizing the differences between those therapies and CBT.

Neither Project MATCH nor the other studies 

cited included measures of coping skills acquisition. Possibly some of the anomalous findings 

with respect to client-treatment matching with 

CBT might have been explained if the target of 

the CBT intervention, enhancement of coping 

skills, had been directly assessed. Similarly for 

the other treatments, relevant target behaviors 

or mediating variables may not have been identified or adequately measured.

A final issue to consider is the comparison 

between sociopathy and APD as alternative 

ways of characterizing clients for matching 

purposes. Kadden et al. (1989) found treatment matching based on sociopathy (CPI–So 

scores) but not based on the presence/absence 

of an APD diagnosis. Longabaugh et al. (1994), 

however, did find matching to APD diagnostic 

status. No conclusion can be drawn from the 

present data regarding the relative effectiveness 

of these two means of characterizing clients 

due to the lack of significant matching findings 

with either one of them.

As a result of this study, the status of sociopathy and APD as client matching variables 

is uncertain. They received support in prior 

studies but not in the present one. A number 

of differences between the earlier studies and 

the present one have been discussed, but it will 

remain for future research to settle the matter.

Acknowledgments

The research and publication efforts for this 

manuscript were supported by the National 

Institute on Alcohol Abuse and Alcoholism 

as the collaborative agreement award U10–

AA08438, by grant R01–AA09648 to Ronald 

Kadden, Principal Investigator, and by NIH 

General Clinical Research Center grant M01– 

R06192 to the University of Connecticut Health 

Center, Farmington, CT.

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118

Alcoholic Typology as an Attribute for 

Matching Clients to Treatment

Mark D. Litt, Ph.D., and Thomas F. Babor, Ph.D.

ABSTRACT

The typology of alcoholic clients developed by Babor and associates (1992) that defines the Type A/Type B distinction was evaluated as a basis for matching clients to 

treatment in Project MATCH. It was hypothesized that the more severe Type B alcoholics 

would have better outcomes if treated in Cognitive-Behavioral Coping Skills Therapy or 

Twelve Step Facilitation as opposed to Motivational Enhancement Therapy (MET), and 

that the less severe Type A alcoholics would fare better with MET. Hierarchical linear 

model analyses using monthly percentage of days abstinent and drinks per drinking day for the 12 months following treatment as the dependent variables revealed 

that Type B clients had fewer abstinent days over the followup period, as predicted. 

However, the analyses failed to show any other effects on outcome attributable to client type, or to any interaction of client type with treatment, in either arm of the study. 

Furthermore, client type was not predictive of outcome in the outpatient arm at the 

3-year posttreatment followup point, nor was type predictive of time to first slip or time 

to resumption of heavy drinking. Results are discussed in the context of general outcomes found in Project MATCH, and the utility of the alcoholic typology is evaluated.

Alcoholism appears to be a multiply 

determined entity, with biological, 

psychological, and social factors all 

interacting to produce a drinking problem (e.g., 

Tarter 1983). Recognition of the diverse nature 

of the alcoholic population has led to a search 

for homogeneous subtypes or groups of alcoholics that share similar characteristics. If such 

groups could be identified, it might be possible 

to devise treatments that would accommodate 

their specific needs and thereby maximize treatment effectiveness. This is the logic behind the 

creation of alcoholic typologies.

A number of typologies have been proposed 

to discriminate different subgroups of alcoholics (e.g., Cloninger 1987; Jellinek 1960; 

Morey and Skinner 1986). Few of these, however, have been replicated in new samples, and 

fewer still have demonstrated external validity 

by showing that different subtypes have better outcomes with different types of treatments 

(Brown et al. 1994).

In view of the multidimensional nature of 

alcoholic drinking, it has been suggested that 

a clinically meaningful and predictive typology 

would encompass multiple domains, including 

clinical course, genetic predisposition, drinking 

behavior, psychosocial functioning, and comorbid psychopathology. One such typology was 

developed by Babor and colleagues (1992), who 

used k-means cluster analysis with a heterogeneous sample of 321 alcoholics. The clustering 

solution identified two “types” of alcoholics who 

differed consistently across 17 defining characteristics in both the male and female samples. 

The first, termed “Type B alcoholics,” is characterized by a family history of alcoholism, 

Mark D. Litt, Ph.D.

Department of Behavioral Sciences and 

Community Health

University of Connecticut Health Center 

Farmington, CT 06030

Email: litt@nso.uchc.edu
119

Alcoholic Typology as an Attribute for Matching Clients to Treatment

premorbid childhood conduct problems, early 

onset of problem drinking, rapid progression 

of drinking problems, more psychiatric disturbance, greater alcoholism symptom severity, 

and poor prognosis. The second group, called 

“Type A alcoholics”, is characterized by later 

onset of drinking, fewer indicators of vulnerability, less psychiatric disturbance, a more 

benign alcohol-related problem profile, and 

better prognosis.

The results of the clustering analyses were 

consistent with historical and contemporary 

typological theories that have postulated similar subgroups of alcoholics, such as those 

described in Cloninger’s neurobiological learning model (1987). Other findings from the Babor 

et al. (1992) study and elsewhere suggested 

that an empirically derived multidimensional 

typology of alcoholism could have theoretical 

implications for explaining the heterogeneity 

among alcoholics and might provide a useful 

basis for treatment matching.

Prognostic Significance

There is evidence that Type A alcoholics differ from Type B alcoholics both in their overall 

prognosis and in their responses to different 

treatments. In their initial study of 321 male 

and female treated alcoholics, Babor et al. 

(1992) found that Type B alcoholics had significantly worse treatment outcomes than those 

classified as Type A at 12- and 36-month followups, as measured by total number of drinking 

days during followup and drinks per drinking 

day. Additionally, in a 3-year outcome study by 

Yoshino and Kato (1996), 259 Japanese alcoholics were classified as Type A or Type B in 

a similar manner. Type A alcoholics had lower 

mortality and higher abstinence rates than did 

the Type B alcoholics by the end of the followup 

period.

Prior Matching Effects

A study by Litt and associates (1992) indicated that client type was not only prognostic 

of outcome but might also be useful in matching clients to treatment. In this study, the 

two-group typology was replicated on a sample 

in which alcoholic clients had been randomly 

assigned to one of two different kinds of group 

aftercare treatment. The data used in this 

study were originally collected by Kadden and 

colleagues (1989) to evaluate the treatmentmatching implications of three theoretically 

based client dimensions: sociopathy, psychopathology, and neuropsychological status. 

Because the Kadden et al. (1989) study was 

designed to measure client characteristics in a 

way similar to the Babor et al. (1992) study of 

alcoholic subtypes, it provided an ideal opportunity to replicate the Type A–Type B distinction 

and to test the treatment-matching hypothesis 

with the cluster-derived typology.

Analyses of proportion of heavy drinking 

days immediately following aftercare treatment 

and at several subsequent followup points 

indicated that, consistent with the Babor et al. 

(1992) findings, Type A alcoholics fared better overall than Type B clients at the followup 

points (main effect for type: F(1, 43)=4.96, 

p<.05). Additionally, however, a significant client type by treatment interaction was found 

(F(1, 41)=4.10, p<.05). Type A clients fared best 

in interactional treatment and more poorly with 

coping skills training, whereas those clients 

classified as Type B alcoholics by the clustering 

procedure had better outcomes with the coping skills treatment and worse outcomes with 

interactional therapy. Differences in treatment 

response were maintained for 2 years from the 

beginning of aftercare treatment.

The results suggested treatment-matching effects wherein Type A clients were best 

matched to interactional group therapy and 

Type B clients were best matched to coping 

skills therapy. Effects sizes for matches versus mismatches were substantial: at the end of 

the 2-year followup, for example, 45 percent of 

matched clients were still abstinent compared 

with only 15 percent of mismatched clients. 

The authors speculated that the structured 

cognitive-behavioral treatment was well-suited 

to Type B clients, who may have benefited from 

the clear goals and procedures. The relationship-focused interactional therapy, on the other 

hand, was thought better suited to the Type A 

clients, who were less in need of basic skills 

than in receiving the motivation from others 

to use the skills they already had. The coping 

skills treatment was thus thought to be less 

relevant for the Type A clients in this study.
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Part III: Psychological Dysfunction

The Litt et al. (1992) study provided a convincing demonstration of the potential for using 

client type as a matching variable. Although 

the MATCH treatments were all delivered on an 

individual basis, there were significant similarities with the group treatments described 

in Litt et al. (1992) and Kadden et al. (1989). 

Motivational Enhancement Therapy (MET; 

Miller et al. 1992) in Project MATCH, for example, like the interactional group therapy in 

Kadden et al. (1989), was not considered to be 

a highly structured, intense treatment, but was 

expected to be more interpersonally centered, 

relying for its effectiveness on the ability of the 

therapist to quickly establish trust and rapport.

The MATCH Cognitive-Behavioral Coping 

Skills Therapy (CBT; Kadden et al. 1992) condition, on the other hand, was very much like the 

coping skills treatment in Kadden et al. (1989), 

with its emphasis on skills and problem-solving and its highly structured approach. Given 

the outward similarities in treatments, it was 

expected that Project MATCH clients would 

respond like the clients described in the Litt et 

al. (1992) study.

Based on our previous typological research 

and the results of the Litt et al. (1992) matching 

study, we proposed the following hypotheses 

(figure 1):

■ There would be an overall main effect for 

client types, with high severity (Type B) 

clients relapsing sooner, drinking more 

frequently, and experiencing more drinking-related problems than low severity 

(Type A) alcoholics in all treatment 

conditions.

■ Type B alcoholics would fare best in the 

CBT and Twelve Step Facilitation (TSF; 

Nowinski et al. 1992) treatments, and 

poorly in MET. Type A alcoholics would 

respond well to MET and relatively less 

well in TSF and CBT.

Rationale for the Matching 

Hypothesis

The matching findings of the Litt et al. (1992) 

study suggested that several important “active 

ingredients” of the treatments have differential 

importance for Type A and Type B alcoholics. 

Figure 1. Hypothesized outcomes in Project 

MATCH as a function of treatment assignment 

and alcoholic type.

We speculated that these active ingredients 

included structure of treatment and change 

in cognitions, change in psychopathology, and 

various therapist relationship factors.

Structure

We believed that Type B clients would be 

more likely to benefit from the highly structured, programmatic CBT and TSF treatments, 

in which performance and role expectations are 

clearly defined, than from the less structured, 

more open-ended form that MET takes. This 

prediction was based on evidence indicating 

that clients who are more severely impaired in 

terms of general psychopathology do poorly in 

traditional psychotherapy (Sloane et al. 1975), 

possibly because of a reduced ability to learn 

in unstructured treatment sessions (Truax and 

Carkhuff 1967).

Additionally, a similar differential success 

prediction would result from considering the 

client’s sociopathy. The Type A/Type B typology is influenced by the level of sociopathy as 

well as other vulnerability factors that affect 

both alcohol dependence and ability to control 

impulsiveness or regulate behavior. Type B clients show higher levels of sociopathy and other 

indicators of poor impulse control (e.g., conduct 

disorders) that may inhibit learning and skill 

acquisition in therapy situations that are not 

highly structured. Walker (1992), for instance, 

writes that substance abusers with antisocial 

personality have little awareness of their own 

thoughts and feelings and how they influence 
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Alcoholic Typology as an Attribute for Matching Clients to Treatment

their behavior. Treatment for these individuals 

should be kept simple, with a firm structure 

and the clinician clearly directing the process of 

therapy. The CBT approach in Project MATCH 

should have been ideally suited to these clients.

Nace (1989), in his discussion of substance 

abuse treatment for those with antisocial personality, also stresses the importance of firm 

structure in therapy, with the setting of clear 

limits and a constant focus on abstinence. In 

their lack of impulse control and need for abstinence the Type B clients resemble the classic 

gamma alcoholic (Jellinek 1960). These attributes make Type B clients good candidates for 

a 12-step approach (an approach designed originally for just such a population; Nowinski et al. 

1992) as well as the CBT treatment. Conversely, 

it was felt that Type A clients would feel unduly 

constrained by the highly structured CBT and 

TSF treatments and would prefer the more 

open-ended, less predictable MET approach.

Cognitive Change

It was expected that the structure and 

demands for behavior change inherent in the 

CBT and TSF treatments would be differentially useful for the Type B individuals and that 

treatment gains would be mediated by cognitive changes resulting from treatment. The 

treatment demands were expected to lead to 

behavior change that would result in increases 

in self-efficacy and motivation. Bandura (1977) 

has stated that the most influential source of 

self-efficacy is actual performance. By attending regular and frequent treatment sessions 

and stopping drinking, self-efficacy should be 

increased. It was believed further that the CBT 

and TSF treatments, by virtue of their clear 

demands for change, would increase motivation 

for change, leading the person to movement 

from a precontemplation or contemplation stage 

of change to an action stage.

Type A individuals, on the other hand, were 

not expected to benefit from the directiveness 

of CBT and TSF. It was believed that these persons would already be sufficiently motivated 

and that their self-efficacy would be enhanced 

by following through with their own plans for 

change.

Change in Psychopathology

In addition to other signs of impairment, Type 

B clients are more likely to score higher on indices of psychopathology such as depression and 

anxiety. Work by Rounsaville and others (e.g., 

Rounsaville et al. 1987) has indicated that those 

higher in psychopathology are likely to have 

poorer outcomes in treatment. Because of the 

explicit focus on psychopathology in CBT, it was 

expected that Type B clients would experi-ence 

relief of psychiatric symptoms and thus have 

better outcomes in this therapy. Additionally, 

because the MET treatment was so brief and 

had little content related to psychopathology, it 

was believed that Type B clients would derive 

less benefit in this treatment. Although TSF 

had no specific content focus on psychopathology, the frequent visits and support from AA 

were expected to help relieve psychopathology 

symptoms and thus predict better treatment 

outcomes for Type B clients.

Relationship Factors

It was thought that Type B clients, who are 

more likely to show sociopathic characteristics, 

would have more difficulty relating to the therapist and making use of the therapy process 

(Garfield 1978) than would Type A clients. Type 

A clients, on the other hand, being relatively 

unimpaired, would be able to make appropriate use of the therapist relationship in MET and 

might be put off by the lack of such a relationship in CBT, which was considered to be a less 

relationship-oriented therapy.

A variety of relationship issues having to do 

with the nature of the CBT and MET therapists 

could play a role in the outcomes of the various 

treatments. The MET therapists were trained 

to be persuasive as well as accepting and 

empathic in order to quickly establish the rapport required to help motivate clients. CBT and 

TSF therapists, on the other hand, were trained 

to be active, challenging, and confrontational. 

Type B clients, it was thought, would benefit 

from the clear directions for lifestyle change 

and the unwillingness of the CBT and TSF 

therapists to allow the Type B client to avoid 

making behavioral changes Minimally impaired 

Type A clients, however, might feel cornered 

or attacked by a confrontational therapist and 
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would prefer an approach that allowed them 

greater autonomy.

Causal Chain

To summarize, the factors presented above 

were expected to give rise to changes in certain measurable constructs that would have a 

bearing on outcome. The causal chain was as 

follows:

For Type B clients, CBT and TSF treatments, 

with their greater intensity and structure and 

their emphases on straightforward behavior 

change, would be well-suited to Type B clients’ 

learning style.

The skills focus in both treatments, presented in a highly structured and easily 

coherent manner by a therapist who makes 

performance expectations clear, would lead to 

increased cognitive shifts for change, reflected 

in greater motivation (stage of change), and 

greater self-efficacy.

Furthermore, practice of these skills in homework assignments (in CBT) should help increase 

clients’ self-efficacy, making it more likely they 

would practice the skills they learned and thus 

lead to better outcomes.

Finally, as Type B clients learned and practiced more skills and showed greater motivation 

to change, support relationships would improve 

and psychopathology would decrease (e.g., clients would become less depressed and anxious 

about relationships, work, etc.).

MET, by contrast, with its relatively unstructured focus on motivation would not deliver 

specific instruction in skills to stop drinking 

and would be less useful to Type B clients.

For Type A clients in MET, adaptive changes 

in cognitions were expected to occur, but in a 

different way from Type B individuals. Because 

Type A clients were relatively high functioning, 

it was expected that they would benefit from the 

low intensity of the therapy by receiving confirmation of their own plans to change (reflected 

in slight movement in stage of change, as from 

contemplation to action). Self-efficacy for change 

would also increase as a result. Social supports should also improve as treatment efforts 

at change were made. No changes would be 

expected in measures of psychopathology.

Hypothesized Matching 

Contrasts

Consistent with the theoretical rationale, it 

was concluded that the most obvious treatment 

contrasts would be those between CBT and 

MET, and TSF and MET. Given the similarities between CBT and TSF in terms of intensity, 

structure of therapy, and relationship factors, 

it was determined that, for Type A and Type B 

clients, the CBT and 12-step treatments would 

be almost equivalent. Because of this, these two 

treatments were combined in analyses such 

that the contrast of interest was that between 

MET clients and those in either CBT or TSF.

Operationalization of the 

Matching Variable

Assigning Clients

The original typology formulation was based 

on 17 defining characteristics that tapped 4 

conceptual domains: vulnerability factors, alcohol involvement, chronicity of alcohol problems, 

and comorbid psychopathology. In the MATCH 

data set, we identified 14 variables that measured each of the different domains of the 

typology, and first sought to replicate the twogroup typology using k-means cluster analysis. 

Results of the cluster analyses replicated those 

of the Babor et al. (1992) and Litt et al. (1992) 

studies. The most coherent solution was a twocluster solution that classed individuals as 

Type A or Type B.

Although the two-group Type A/Type B 

classification was clearly replicated with the 

Project MATCH variables using cluster analysis, it was thought that a more practical test of 

the typology-matching hypothesis would entail 

classifying people by means of simple a priori 

classification rules rather than by means of 

algorithms derived from a cluster analysis of 

the entire study sample. We therefore developed a quick classification decision-rule using a 

smaller number of variables with data obtained 

from a segregated sample of the first 40 clients 

enrolled at each Clinical Research Unit.

Based on previous work with three different 

alcoholic samples (Babor et al. 1992; Brown 

et al. 1994; and Litt et al. 1992), evidence 
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Alcoholic Typology as an Attribute for Matching Clients to Treatment

suggested that a reasonably good classification 

could be made on the basis of five variables, 

each measuring one of the following domains: 

familial risk, personality vulnerability, dependence severity, consequences of drinking, and 

psychopathology. These variables were considered to be the best representatives of the 

domains in the typology, and they could be 

measured reliably:

■ Family history of alcoholism (tapping 

risk; median number of first-degree relatives positive for alcohol abuse = 0.33)

■ MacAndrew Alcoholism scale score 

(MacAndrew 1965), as a measure of vulnerability (median score = 27.0)

■ Ethanol Dependence Syndrome Scale 

score (Babor 1996; median score = 35.0)

■ Physical consequences of drinking index 

(Babor et al. 1992; median score = 17)

■ Antisocial personality symptom count 

from the Computerized Diagnostic 

Interview Schedule for DSM–III–R 

(Robins et al. 1981; median count = 3.0 

symptoms).

The typology assignment (the type variable) 

was made according to a criterion rule, namely, 

that a person should be high on some minimum 

number of variables to be considered Type B 

(severe). Anyone who scored above the median 

on three of the five variables was classed as 

Type B.

Characteristics of the Variable

Once chosen, the 5 variables were tested 

against the complete 14-variable typology using 

data from the segregated sample. The 5-variable typology assignment corresponded very 

well with the cluster analysis-derived 14-variable typology assignment, with a sensitivity of 

0.93, a specificity of 0.79, and overall efficiency 

of 0.84.

The distribution of the type variable was 

examined in the complete data set (N=1726). 

The ratio of 54 percent Type A to 46 percent 

Type B was exactly what we expected for the 

complete data set. When the sample was 

divided by arm of study, Type As outnumbered 

Type Bs in the outpatient arm by 586 to 360 

(62 to 38 percent), but in the aftercare arm, the 

Type Bs outnumbered Type As 435 to 332 (57 

to 43 percent). As for distribution by sex, men 

were nearly evenly distributed between Types A 

and B, but Type A women outnumbered Type 

B women by almost 3 to 2 (63 to 37 percent). 

These distributions were consistent with previous research (Brown et al. 1994).

Finally, the correlations of the type variable 

with the other primary matching variables was 

assessed in the complete data set. The strongest association was with Alcohol Involvement 

(r=0.51), followed by sociopathy (r=0.41). 

Correlations with the other variables were relatively modest. These analyses indicated that 

the two-group typology is a robust classification 

scheme, replicated in several samples, and that 

the five-variable classification algorithm provides a good approximation of the more complex 

clustering of Type A and Type B individuals.

Results

The primary tests of the matching hypotheses for each arm of the study were conducted 

using hierarchical linear modeling (HLM) analyses. Separate analyses were performed for the 

period during which treatment was taking place 

(from intake to end of treatment, months 1–3), 

and for the period from end of treatment to the 

end of the 1-year followup (months 4–15). The 

primary dependent variables (DVs) in these 

analyses were (a) percentage of days abstinent 

(PDA; arcsin transformed to correct for nonnormal distributions) and (b) drinks per drinking 

day (DDD; square-root transformed). These DVs 

were measured weekly during the 1–3 month 

period, and monthly during the 4–15 month 

period. Details regarding these analyses can be 

found in Longabaugh and Wirtz’s chapter (pp. 

4–17) of this monograph.

The covariate set used for these analyses 

included the pretreatment value of the drinking DV. Although controlling for pretreatment 

drinking could partially nullify differences 

between client types, this covarying was done 

to make sure that any differences in outcome 

would be attributable to the longstanding, 

dispositional aspects of client type and not to 

recent drinking history.

A family-wise type-1 error rate of 0.05, specified beforehand for each matching attribute, 
124

Part III: Psychological Dysfunction

was further divided by 2 to account for the two 

dependent variables. Because only one contrast of interest (CBT and TSF versus MET) was 

specified for the client type attribute, effects had 

to reach a Bonferroni-corrected significance 

level of p<.025 (i.e., 0.05/2) to be considered 

meaningful.

Outpatient Arm

1–3 Month Period

HLM analyses performed for the period during treatment revealed no significant effects 

attributable to client type, either alone or in 

interaction with treatment assignment or time. 

These results were the same for both dependent variables.

4–15 Month Period

With PDA as the dependent variable, a significant main effect for typology was found (F(1, 

952)=5.93; p<.025), with Type B clients having 

a greater percentage of drinking days during this posttreatment period than did Type A 

clients. No effects significant at the Bonferronicorrected level were seen for any interaction 

of type with treatment site, with time, or with 

treatment assignment. With DDD as the dependent variable, no significant effects were seen.

3-Year Followup

For outpatient clients only, followup data 

were collected at the 3-year posttreatment 

point and covered study months 37, 38, and 

39. Drinking data from these 3 months were 

averaged to provide one value for each of the 

dependent variables, PDA and DDD, at 3 years. 

For neither of the dependent variables was 

client type, or the interaction of client type 

with treatment assignment, significant at the 

Bonferroni-corrected level of 0.025.

Aftercare Arm

1–3 Month Period

When PDA was used as the DV, no significant 

effects were seen for any of the terms involving 

client type. The same results were seen when 

DDD was the DV.

4–15 Month Period

In the aftercare arm, with PDA as the dependent variable, no significant effects were seen 

for client type or for any interaction of client 

type with treatment or with time. The same 

absence of effects was seen when the dependent variable was DDD. In brief, client type was 

found to be neither a prognostic variable nor 

an attribute that could be used for matching 

purposes when outcome was measured out to 

15 months.

Secondary Outcome Variables: Time 

to Event Measures

In addition to the primary outcome measures 

(PDA and DDD), three time to event measures 

were used in the evaluation of typology as a 

matching variable. The three measures were 

time to first drink, time to first heavy drinking 

day, and time to first period of three consecutive heavy drinking days. All times to events 

were measured from the beginning of treatment. Time was measured in days, and the data 

were obtained from the Form 90 (Miller 1996) 

timeline followback measure. A heavy drinking 

day was a day in which six or more drinks were 

consumed for men, or four or more drinks were 

consumed for women. Analyses were conducted 

using Cox proportional hazards modeling, with 

baseline drinking (both baseline PDA and baseline DDD), treatment site, treatment type, and 

site by treatment, as covariates and the interaction of treatment (CBT clients plus TSF clients 

combined versus MET clients) with client type 

entered as the last term in the model.

Results of the analyses indicated that client type was not predictive of any time to event 

outcome, either alone or in interaction with 

treatment type. This was true in both study 

arms.

Testing the Causal Chains

The matching hypotheses discussed above 

postulated more or less complex mediating 

steps, or causal chains, that would account 

for differential treatment outcomes for the 

two types of client. By examining these causal 

chains, we attempted to discover where our 

hypotheses failed and thus find out why client 
125

Alcoholic Typology as an Attribute for Matching Clients to Treatment

type appeared to have so little influence on the 

outcome of clients in project MATCH.

The A Priori Causal Chain

Causal chains were analyzed using structural equation modeling. The two primary 

out-come variables, PDA and DDD, which were 

measured at 12 time points (i.e., study months 

4 through 15), were modeled as latent growth 

curves as suggested by McArdle (McArdle 1986; 

McArdle and Epstein 1987). That is, for each 

of the two dependent variables, a single latent 

variable was created, made up of the measures 

taken at each of the 12 followup points. Since 

the model uses repeated-measures data, the 

latent factors are interpreted as chronometric 

(time-based) factors representing individual 

differences over time (McArdle 1986). In these 

factors, the dependent variable at each time 

point makes an independent weighted contribution to the latent outcome variable. Thus, the 

latent variable takes into account the changes 

for each individual at each time point.

Results

Three primary causal chain hypotheses were 

tested. The first hypothesis was that both CBT 

and TSF treatments would be more structured 

than MET but that only the Type B clients 

would benefit from this additional structure in 

terms of cognitive changes from pretreatment to 

posttreatment that would lead to improved outcomes. Thus, only Type B clients were expected 

to show a positive relationship between therapy 

structure and cognitive change. For Type A, we 

predicted a nonsignificant or even negative relationship between perceived therapy structure 

and cognitive change. This is the hypothesis 

represented in simplified form in figure 2.

The results of this first analysis are shown in 

figure 3. Therapy structure was determined by 

rating videotapes of therapy sessions, using a 

rating scheme developed by Carroll et al. (1998). 

Cognitive change, in the figure, is a latent variable made up of the pre-to-post change scores 

on the Alcohol Abstinence Self-Efficacy Scale 

(DiClemente et al. 1994) and the University of 

Figure 2. Hypothesized causal chain explaining expected client-treatment matching effect. In figures to follow, results for the outpatient arm are represented above the arrows and results for the 

aftercare arm are represented below the arrows. Outcome in these analyses is modeled as latent 

growth curves comprising the repeated DVs (month 4 DV through month 15 DV).
126

Part III: Psychological Dysfunction

Rhode Island Change Assessment (DiClemente 

and Hughes 1990), a measure of motivation. 

In the figure, both dependent variables and 

both arms of the study are represented. The 

path coefficients that appear over the arrows 

in the diagram show the results for the outpatient arm. Coefficients for the aftercare arm 

are shown below the arrows. The coefficients 

to the left indicate results when PDA was the 

outcome; coefficients in parentheses indicate 

results when the outcome was DDD. The coefficients are interpreted as beta weights. Asterisks 

indicate that the coefficients are significant at 

the p<.05 level. Coefficients above 0.25 are considered to indicate strong prediction.

Positive cognitive change (i.e., increase in 

self-efficacy and motivation for change) was 

strongly related to outcome (positively related 

to PDA and negatively related to DDD), except 

for Type B clients in the outpatient arm. 

Ratings of therapy structure, however, were 

only weakly related to treatment received, and 

rated structure was virtually unrelated to cognitive change. The weak relationships explain 

the poor fit of the models to the data; model chi 

squares were highly significant, and the goodness-of-fit indices averaged about 0.75.

The second hypothesis was that Type B 

clients would score higher on measures of 

psychopathology and that they would experience significant decreases in psychopathology 

(decreases in Addiction Severity Index (ASI) 

Psychiatric subscale scores) in the CBT and 

12-step treatments versus MET, but that Type 

A clients would not. Analysis of variance confirmed that Type B clients did in fact have 

higher baseline ASI Psychiatric scores than 

did Type A clients (F(1, 1699)=25.84, p<.001). 

The decrease in psychopathology was expected 

to be related to improved outcome. Results of 

these analyses are depicted in figure 4. (Change 

in psychopathology was calculated such that 

positive change indicated a decrease in severity 

and thus should be positively related to PDA 

and negatively related to DDD).

In this case, none of the hypothesized 

relationships held. Change (decrease) in psychopathology was virtually unrelated to outcome, 

Figure 3. Results of structural equations analyses of expected causal chains. Coefficients above 

the arrows are those for outpatient clients; the coefficients below the arrows refer to aftercare clients. Coefficients to the left refer to outcome expressed in PDA; coefficients in parentheses to the 

right refer to outcome expressed as DDD. Under Treatment Assignment, MET was coded as 0 and 

CBT+TSF was coded as 1. Asterisks indicate that the magnitude of the coefficient was significantly 

different from 0 (by t-test) at the p<.05 level.
127

Alcoholic Typology as an Attribute for Matching Clients to Treatment

regardless of dependent variable used or arm of 

study, except for a small effect for Type A clients 

in the outpatient arm. Additionally, treatment 

assignment apparently made no difference with 

respect to change in psychopathology. Again, 

the models shown were poor fits to the data, 

with highly significant model chi-square values 

and goodness-of-fit values averaging 0.75.

The last hypothesis was that in Type A clients 

there would be a positive relationship between 

treatment and working alliance, as measured by 

the Working Alliance Inventory (WAI; Horvath 

and Greenberg 1986), in that Type A individuals in MET would show greater WAI scores than 

in CBT+TSF. Working alliance was at best only 

weakly related to outcome, regardless of client 

type, study arm, or dependent variable (figure 

5). Additionally, the hypothesized differential 

effects of client type and treatment assignment on clients’ evaluations of working alliance 

failed to appear. Model chi-square values were 

once again highly significant, and goodnessof-fit statistics averaged only 0.80, indicating 

relatively poor fit of the models to the data.

Discussion

Client type is intended to describe more than 

just a client attribute. It is a multidimensional 

construct that encompasses biological and psychological vulnerability, drinking history, and 

likely prognosis. As such, it should have been 

an ideal matching variable—one would be hard 

pressed to think of two kinds of people who are 

more different or who should respond more differentially to treatment. Yet not only did client 

type not prove to be a useful matching variable 

in the present study, with the exception of predicting PDA in the 4–15 month period in the 

outpatient arm, it did not even turn out to be 

prognostic. These results are contrary to our 

own previous results indicating that client type 

can be a matching variable (Litt et al. 1992) as 

well as a number of studies showing that client 

type can be highly prognostic (e.g., Gibbs and 

Hollister 1993; Shanks et al. 1995; Yates et al. 

1993). The purpose of the present study was 

to explore which of our assumptions may have 

been in error.

Figure 4. Results of structural equations analyses of causal chain examining psychopathology 

change as a mediating variable. Coefficients above the arrows are those for outpatient clients; 

the coefficients below the arrows refer to aftercare clients. Coefficients to the left refer to outcome 

expressed in PDA; coefficients in parentheses to the right refer to outcome expressed as DDD. 

Under Treatment Assignment, MET was coded as 0 and CBT+TSF was coded as 1. Asterisks indicate that the magnitude of the coefficient was significantly different from 0 (by t-test) at the p<.05 

level.
128

Part III: Psychological Dysfunction

 Our hypotheses in this study were based 

upon results found in previous work. Type B 

clients, whose drinking history, consequences, 

and psychopathology were so much worse than 

those of Type A clients, were expected to fare 

more poorly than Type A individuals regardless 

of treatment. This occurred in only one arm 

(outpatient) on one dependent variable (PDA), 

and the effect vanished when other matching 

variables were included in the analyses (see 

Project MATCH Research Group 1997a). The 

fact that Type B clients did not do more poorly 

may be a clue as to what occurred in Project 

MATCH.

One possible reason that client type 

accounted for so little variance in outcome is 

that there was relatively little variance in outcome to start with, at least in terms of the 

two primary dependent measures, PDA and 

DDD. As has been described elsewhere (Project 

MATCH Research Group 1997a), drinking in 

all treatment conditions dropped dramatically 

from pretreatment to posttreatment. It is possible that a floor effect in outcome may have 

made the discovery of main effects for most client attributes almost impossible. If this were 

true, then no attribute by treatment interactions would be likely to be found either. This 

was largely the case in Project MATCH; only 

three attributes, psychiatric severity, trait 

anger, and alcohol dependence, showed attribute by treatment interactions that were not 

time-dependent (Project MATCH Research 

Group 1997a,b).

If lack of variance were responsible for the 

lack of effect attributable to client type, then 

most other variables and constructs would also 

fail to predict outcome. This, too, was the case 

in Project MATCH and showed up in both primary outcome measures and in time to event 

measures. In the structural equation models 

in the present chapter, only cognitive change, 

defined as increases in self-efficacy and motivation, predicted drinking outcomes.

Another possible reason why client type 

failed to account for treatment outcome, or 

play a role as a matching variable, is that our 

assumptions were wrong about what occurs 

Figure 5. Results of structural equations analyses of causal chain examining working alliance as 

a mediating variable. Coefficients above the arrows are those for outpatient clients; the coefficients 

below the arrows refer to aftercare clients. Coefficients to the left refer to outcome expressed in 

PDA; coefficients in parentheses to the right refer to outcome expressed as DDD. Under Treatment 

Assignment, MET was coded as 0 and CBT+TSF was coded as 1. Asterisks indicate that the magnitude of the coefficient was significantly different from 0 (by t-test) at the p<.05 level.
129

Alcoholic Typology as an Attribute for Matching Clients to Treatment

in therapy, or even about the importance of 

specific aspects of therapy. The structural 

equations models suggest that clients did not 

respond to treatment in the ways we expected. 

Despite distinct differences in how the treatments were designed and implemented (Carroll 

et al. 1998), these differences apparently were 

not reflected in client perceptions. MET, for 

example, was not perceived by clients as entailing increased working alliance relative to CBT 

and TSF, and CBT and TSF did not result in 

improvements in psychopathology relative to 

MET. Independent raters did, for the most part, 

perceive TSF and CBT as being more structured 

than MET, but this effect was neither strong 

nor ultimately very important to outcome. In 

summary, the findings suggest that whatever 

occurred in Project MATCH served generally 

to increase clients’ motivation and self-efficacy 

and that these changes were not associated 

with specific characteristics of the treatments.

An additional explanation for the failure of 

client type to predict outcome or to interact 

with treatment may be because the two types, 

although different on numerous dimensions, 

were nevertheless equivalent on some other 

attributes that may have been more important. 

An examination of the means on a variety of 

variables indicated that, as intended, the two 

types were substantially differentiated from one 

another on indicators of vulnerability, severity 

of dependence, and sociopathy and psychopathology. Differences on means between the two 

client types on these variables ranged from one 

to two standard deviations. But on two other 

variables, the results were quite different. On 

baseline readiness for change, an important 

prognostic variable in Project MATCH, Type 

B clients actually scored higher than Type A 

clients. And the two types were equivalent on 

another predictive variable, social support for 

drinking. Thus, the two types of clients may 

have been indistinguishable on some of the 

most significant attributes of the trial. Because 

neither readiness nor social support for drinking were measured in the earlier studies of 

client typology, it is not clear whether the Type 

B clients in those studies were fundamentally 

different from those in Project MATCH.

A final explanation for the differences between results found in Project MATCH and 

those found in earlier studies may have to do 

with an interaction between the Project MATCH 

clients and the way that treatment was delivered. In the Litt et al. (1992) report, for example, 

clients were treated in group therapies, and 

no concessions could be made for the special 

needs of individual group members. In Project 

MATCH, the clients were treated individually. Motivated clients, even if they were Type 

B clients, may have been able to extract from 

their therapists whatever it was they needed 

to change, regardless of therapy type, possibly 

including straightforward recommendations for 

change from MET therapists. (This might help 

explain why there were no apparent differences 

in rated structure between treatments for Type 

B clients.)

Given the results of Project MATCH, one 

question that now presents itself is whether 

alcoholic typology is worthwhile as an explanatory or clinical construct. The answer to that 

question should be considered in the context of Project MATCH itself. To the extent 

that Project MATCH represented an artificial 

treatment situation, with closely supervised 

individual treatment, frequent paid followups, 

and the participation and cooperation of significant others, then the validity of all potential 

client attributes was compromised. It might be 

argued that studies that have found prognostic, 

and even matching, effects for client type were 

somewhat better representatives of the actual 

state of substance abuse treatment than was 

Project MATCH. Bearing in mind the results 

from other studies, and the possible lack of 

generalizability of results from Project MATCH, 

the alcoholic typology remains an appealing 

construct. What Project MATCH does indicate, 

however, is that regardless of drinking severity 

and vulnerability, significant improvements in 

outcome are possible under the right circumstances. Even the most severe alcoholics can go 

far toward recovery.

Acknowledgments

This work was supported in part by grant 

number U10–AA08438 from the National 

Institute on Alcohol Abuse and Alcoholism.
130

Part III: Psychological Dysfunction

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133

Part IV: Personality Variables

Client Anger as a Predictor of Differential 

Response to Treatment

Prospects for Matching Clients to Alcoholism 

Treatments Based on Conceptual Level

The Search for Meaning in Life as a Predictor of 

Alcoholism Treatment Outcome

Religiosity and Responsiveness to Alcoholism 

Treatments

The Interpersonal Dependency Matching 

Hypothesis

The Gender Matching Hypothesis
134

Client Anger as a Predictor of 

Differential Response to Treatment

Holly Barrett Waldron, Ph.D., William R. Miller, Ph.D., and 

J. Scott Tonigan, Ph.D.

ABSTRACT

This client-treatment matching study examined the a priori matching hypothesis for a differential effect of client anger on treatment outcome for three treatments 

for clients with alcohol abuse or dependence: Motivational Enhancement Therapy 

(MET), Cognitive-Behavioral Coping Skills Therapy (CBT), and Twelve Step Facilitation 

(TSF). The findings provide strong support for the matching hypothesis. For outpatient clients higher in pretreatment anger, MET was associated with significantly more 

abstinence and less intense drinking, compared to CBT and TSF. These results indicate a distinct advantage in assigning angry outpatients to MET. Also, clients low in 

anger fared better in CBT and TSF than in MET, reflected in the disordinal rather than 

ordinal matching interaction. Results of analyses examining why the matching effect 

occurred were less clear. Process variables expected to underlie the differential effectiveness (attendance, treatment satisfaction, and therapeutic alliance) did not mediate 

the observed interaction. However, there was support for resistance as an important 

variable within the matching effect. Implications for the matching findings, the different pattern of findings for outpatients and aftercare clients, and future directions for 

pursuing the causal chain associated with the matching effects are discussed.

Anger is a basic negative human emotion defined as an internal, subjective 

feeling state with associated negative 

cognitions and physiological arousal patterns, 

the experience of which can vary in intensity 

and fluctuate over time in response to the environment (Spielberger et al. 1983; DiGiuseppe 

et al. 1994). Anger is differentiated from other 

emotions through a labeling process based on 

perceived stimulus conditions that evoke the 

emotion and/or on the inclinations to act which 

are associated with the emotion (Berkowitz 

1990). Labeling an emotion as anger may occur 

when interference with goals, a physical or psychological threat, or an intentional misdeed are 

perceived, or when expectations or attitudes 

are incongruent with the environment. An emotion that gives rise to motivation to respond to 

these perceptions (e.g., defend the self, change 

the environment, prevent mistreatment, or seek 

revenge on others) may also be labeled as anger 

(Edmondson and Conger 1996).

Anger can be conceptualized as having two 

components: (1) the general tendency or disposition (trait) of the individual toward anger 

that remains relatively stable across situations 

and (2) the feeling state of anger that fluctuates 

as a function of situational factors in the environment and the individual’s tendency toward 

anger (Spielberger 1988).

The concept of hostility, often used interchangeably with anger, is quite similar to 

Holly B. Waldron, Ph.D.

UNM Center for Family and Adolescent 

Research 

2700 Yale SE

Albuquerque, NM 87106

E-mail: hwaldron@unm.edu
135

Client Anger as a Predictor of Differential Response to Treatment

Spielberger’s notion of trait anger. Hostility has 

been defined as a personality trait characterized by the chronic display of angry affect and 

aggressive behaviors (DiGiuseppe et al. 1994). 

Thus, hostility reflects a combination of angry 

emotion and action. The rationale for the present matching hypothesis integrates theoretical 

and empirical literatures investigating anger 

and/or hostility, combining trait and state 

conceptions.

Rationale for Matching 

Hypothesis

Anger is common in ongoing interpersonal 

relationships (Averill 1982). Research has 

revealed a broad spectrum of negative concomitants of anger and of hostility, including 

impaired cognitive functioning (Laird et al. 

1982; Jaderlund and Waldron 1994), ill health 

(Barefoot et al. 1983; Diamond 1982; Smith et 

al. 1990), verbal or physical aggression (Maiuro 

et al. 1988; Margolin et al. 1988), impaired 

peer relationships among children (Dodge et al. 

1986), and disturbed marital and family relationships (Gottman and Krokoff 1989; Patterson 

1985). Moreover, hostility has been shown to 

preclude productive problem-solving and communication skills training and lead to poorer 

therapy outcome (Alexander et al. 1989; Barton 

et al. 1985; O’Donnell and Worrell 1973).

Clients entering treatment frequently present with anger problems (Deffenbacher et al. 

1994). According to DiGiuseppe and his colleagues (1994), angry clients generally do not 

seek treatment for help in changing their angry 

response into a healthier one; instead, they are 

often motivated to try to change the target of 

their anger rather than themselves. They tend 

to view their anger as justified, not deviant. 

Moreover, angry clients often deny that their 

anger is a problem and may be more resistant 

to treatment, rendering them more difficult to 

treat.

The therapeutic alliance can also be compromised by client anger. The success of the 

therapeutic alliance depends on the formulation of a bond based on warmth, acceptance, 

and trust between therapist and client and on 

their agreement on tasks and goals of therapy 

(Bordin 1976). A therapist’s attempts to change 

a client’s anger may be perceived by the client 

as the therapist’s disbelief that the client was 

wronged or that some transgressor was responsible for the problem (Walen et al. 1992). As a 

result, angry clients may have more difficulty 

forming a therapeutic bond and are less likely 

to reach agreement on goals of therapy than 

clients with other emotional problems. Angry 

clients may also be especially prone to responding defensively during therapy when exposed to 

direction or confrontation, therapist behaviors 

that have been shown to increase client resistance (Miller et al. 1993; Patterson and Forgatch 

1985).

Resistance in Therapy

Client resistance has traditionally been 

considered a theoretically important process 

variable in the psychotherapy literature. The 

concept of resistance is one of the cornerstones 

of psychoanalytic theory, which holds that 

“working through” clients’ reluctance to give 

up their symptoms is central to the process of 

change (Wolberg 1967). In the behavior therapy 

literature, resistance is often framed as client 

noncompliance, with therapists responsible for 

ensuring compliance in their prescription of 

therapeutic tasks (Hersen 1971).

Family theorists have viewed resistance as 

inherent in the change process because families 

attempt to maintain homeostasis in their relationship functioning (Aponte and VanDeusen 

1981) or as the therapist’s failure to attend adequately to cognitive or interpersonal relationship 

factors before implementing a behavior change 

plan (Alexander and Parsons 1982; Waldron 

and Slesnick 1998). In general, resistance has 

been attributed to a variety of sources, including factors that reside within individual clients 

(including anger), the product of an interpersonal interaction between therapist and client, 

the contribution of external environmental factors such as court-mandated treatment, limited 

efficacy of therapeutic practices, or a lack of 

therapist skill (Anderson and Stewart 1983; 

Lazarus and Fay 1982; Robin and Foster 1989).

Overt anger, argumentativeness, and oppositionality have been suggested as ways in 

which resistance can be manifested in therapy 
136

Part IV: Personality Variables

(Anderson and Stewart 1983; Newman 1994), 

although actual research making this connection has been rare. However, Chamberlain and 

her colleagues (1984) provide some support for 

the notion that pretherapy levels of anger may 

influence client resistance in therapy. These 

investigators found higher levels of resistance 

in agency-referred clients versus self-referred 

clients, and this resistance was associated with 

poorer treatment outcome. Possibly, clients who 

are referred for treatment by an agency (e.g., 

through the court system) are more defensive 

about their problems and respond to treatment 

with more resistant behaviors, such as denial 

of responsibility and projection of blame onto 

others.

Clients vary considerably in their presenting levels of resistance to therapy (Brehm 

1972; Hersen 1971; Anderson and Stewart 

1983). Client resistance, in turn, has been 

shown to be predictive of premature termination (Chamberlain et al. 1984; Kolb et al. 1985) 

and poorer treatment outcomes (Burns and 

NolenHoeksema 1991; Kolb et al. 1985; Miller 

et al. 1993; Orlinsky et al. 1994) across treatment models and client populations. In the 

areas of addictive behaviors, lack of compliance 

and failure to persist in treatment are particularly problematic (Clancy 1961; Dicicco et al. 

1978). For example, Miller et al. (1993) showed 

that problem drinkers who were more resistant 

during treatment, as evidenced by inattention, 

silence, changing the subject, or other negative responses such as denying responsibility 

for drinking, had higher levels of alcohol consumption a year after therapy had ended.

Therapist Behaviors and Treatment 

Outcome

A number of therapist behaviors have also 

been shown to influence therapy outcome. 

For example, therapist supportiveness and 

empathic style have been related to client 

improvement in therapy (Orlinsky and Howard 

1986), whereas therapist confrontation and 

defensive style have been related to less favorable out-comes (Alexander et al. 1976; Beutler 

et al. 1984; Waldron et al. 1997). In general, 

these same therapist behaviors have been 

associated with client resistance during treatment. For example, positive therapist attributes 

and behaviors such as warmth, support, and 

reframing appear to be related to more cooperative responding in therapy (Alexander et al. 

1976; Barbera and Waldron 1994; Patterson 

and Forgatch 1985; Robbins et al. 1996). 

Patterson and Forgatch (1985) also found that 

teaching and confronting (i.e., directive) behaviors were related to increased client resistance 

in the second session of family therapy.

The same pattern of findings has been shown 

in research on alcohol treatment. Among problem drinkers, empathic therapist style has 

been associated with favorable long-term treatment outcomes (Miller and Baca 1983; Miller et 

al. 1980; Valle 1981). Conversely, Milmoe and 

associates (1967) found that therapists’ hostile 

vocal tone predicted reduced treatment effectiveness for alcoholics, and Miller et al. (1993) 

found that a directive-confrontational style 

yielded significantly more resistance from clients, which in turn predicted poorer long-term 

outcome.

Motivational Enhancement Therapy 

(MET)

As a treatment approach, MET specifically 

targets client resistance and has as its primary 

goal the mobilization of clients’ commitment to 

change (Miller and Rollnick 1991; Miller et al. 

1992). A variety of strategies are prescribed to 

defuse and decrease resistant client behavior, 

including avoiding argumentation, expressing empathy, and providing the client with 

choices in therapy. Other strategies are applied 

to evoke clients’ own self-motivational statements of problem recognition, concern, need 

for change, and self-efficacy. Self-efficacy is 

viewed as essential to the process of change. 

According to Miller and Rollnick (1991), a client who perceives a threat or problem but does 

not believe that change is possible, will likely 

become defensive and turn toward denial or 

resistance instead of behavior change.

This emphasis on creating and eliciting motivation for change distinguishes MET from the 

other approaches examined by Project MATCH: 

Cognitive-Behavioral Coping Skills Therapy 

(CBT; Kadden et al. 1992) and Twelve Step 

Facilitation (TSF; Nowinski et al. 1992). The 

proposed anger hypothesis provides a unique 

challenge for MET. If MET is a successful 
137

Client Anger as a Predictor of Differential Response to Treatment

approach for increasing motivation, then clients who are more resistant to treatment, by 

virtue of their higher levels of anger, should fare 

better in this treatment specifically formulated 

to de-escalate resistance than in treatments 

which do not have this specific emphasis.

The Hypothesized 

Matching Contrast

A single between-group hypothesis was proposed with regard to the interaction of client 

anger with treatment modalities in influencing 

outcomes. Expressed in relation to the dependent variable of total alcohol consumption, the 

hypothesis was that clients high in anger will 

show significantly greater reduction in alcohol 

consumption when treated by MET than by 

either CBT or TSF. No difference between treatments was expected for clients low in anger.

This predicted interaction was expected 

because MET is designed specifically to reduce 

client resistance and enhance motivation, 

whereas both CBT and TSF are more directive 

and action-oriented methods. The matching 

effect was expected to occur via a causal chain 

including the following steps:

■ Angry clients have poorer treatment outcomes because they tend to be resistant 

to change.

■ MET is designed to diffuse resistance, 

while CBT and TSF are more action 

focused.

■ High anger clients should therefore fare 

better when assigned to MET relative to 

assignment to CBT and TSF.

Noteworthy, the causal model does not propose any changes in client anger associated 

with treatment assignment. High anger clients 

may remain so even when assigned to the MET 

therapy. The important point is that MET is 

intended to focus upon the resistance of angry 

clients not on their anger per se.

Operationalization of the 

Matching Variable

The State-Trait Anger Expression Scale (STAX) 

was used to measure client anger. This 44-item 

instrument was designed to measure angry 

emotion as a situational response (state) and as 

a disposition (trait) of an individual (Spielberger 

1988). The STAX is composed of eight subscales 

(State-Anger, Trait-Anger, Trait-Temperament, 

Trait-Reaction, Anger-In, Anger-Out, AngerControl, and Anger-Expression) as well a total 

anger score which was used to define the 

matching variable in this study. Spielberger 

(1988) reported subscale coefficient alphas 

ranging from 0.84 to 0.93, indicating adequate 

internal consistency of the measure. Studies 

examining the validity of the STAX scales have 

also supported the psychometric strength of 

the instrument (cf. Spielberger 1988). The total 

anger score was used as the continuous matching variable in this study.

Alcohol Consumption

Drinking outcomes were assessed by the two 

primary dependent variables selected for Project 

MATCH (1993, 1997a): percentage of days 

abstinent (PDA) during each followup interval 

and the average number of drinks per drinking day (DDD, with abstainers coded as zero). 

Research interviews were completed at 3, 6, 9, 

12, and 15 months after intake and (at seven 

outpatient sites) at 39 months as well. Over 90 

percent of cases were interviewed for each followup interval.

Causal Chain Variables

The logic of the causal model was straightforward: angry clients tend to have poorer 

outcomes because they are resistant to change. 

MET diffuses this negative aspect of client resistance while CBT and TSF exacerbate resistance 

because of their action focus. Central to testing the causal model was the development of a 

measure reflecting client resistance. No a priori 

measure was included in the Project MATCH 

baseline assessment battery to measure resistance to change. Several measures, however, 

were included to assess client motivation for 

change (e.g., SOCRATES; Miller and Tonigan 

1996). Logically, two SOCRATES subscales—

Recognition of problem and Taking Steps toward 

change—represent the antithesis of client resistance. In the absence of a more direct measure 

of resistance, we selected these two subscales 

as reverse proxies for client resistance (e.g., 

anger and problem recognition should be negatively correlated at baseline).
138

Part IV: Personality Variables

Results

Complete intake data needed for computation of the anger score were provided by 

94 percent (n=896) of the outpatient and 95 

percent (n=734) of the aftercare samples. On 

average, aftercare clients reported significantly higher anger (M=30.81, SD=7.63) than 

did outpatient clients (M=29.46, SD=7.25), 

t(1,628)=3.68, p<.0002, but the absolute difference in mean values (about one item) was 

not considered clinically meaningful. No mean 

differences in baseline anger were found 

among the three randomly assigned outpatient 

treatment conditions, F(2, 893)=1.71, p<.18. 

Female outpatient clients reported somewhat 

higher anger than did their male counterparts, 

t(894)=2.03, p<.04. In the aftercare sample, no 

mean differences in baseline anger were found 

among treatment conditions, F(2, 731)=0.42, 

p<.66, or between genders, t(732)=1.04, p<.30.

Finally, and in both outpatient and aftercare samples, baseline anger was significantly 

(p<.001) and positively related with other client 

matching variables discussed in this monograph, including overall psychiatric severity 

(outpatient r=0.31, aftercare r=0.24), meaningseeking (outpatient r=0.43, aftercare r=0.40), 

alcohol dependence (outpatient r=0.26, aftercare r=-0.23), antisocial personality disorder 

(outpatient r=0.26, aftercare r=0.23), sociopathy (outpatient r=0.37, aftercare r=0.40), and 

motivation for change (SOCRATES; outpatient 

r=0.15, aftercare r=0.16).

Outpatient Sample

Prognostic Effects of Anger

The main effect of client anger on posttreatment drinking was examined using several 

analytical techniques. Zero-order and secondorder partial correlations were computed to 

show the extent of association between anger 

and proximal posttreatment drinking (months 

4–9), not taking into account variation in posttreatment drinking attributable to site and 

treatment conditions. The prognostic effects of 

client anger were also examined in the context 

of hierarchical linear modeling (HLM) in which 

site, treatment, and linear and quadratic time 

main effects as well as their product terms with 

each other and with anger were included in the 

omnibus model.

Contrary to prediction, clients’ anger level 

measured at intake was not related to frequency (r=-0.01, p<.84) or intensity r=0.01, 

p<.71) of drinking during the first 6 months 

after treatment. Controlling for intensity and 

frequency of drinking at intake, second-order 

partial correlations also reflected no significant 

relationship of anger to drinking during followup. In the HLM context, no prognostic effect 

of anger was found using monthly values of frequency (p<.75) and intensity (p<.89) of drinking 

during posttreatment months 4–12. Post hoc 

analysis indicated that anger and PDA were 

weakly and negatively related in two sites (r=-

0.10 and -0.11) and virtually unrelated in the 

three remaining sites (r=0.08, 0.04, and 0.02).

Tests of the Anger Matching Hypothesis

The a priori hypothesis stated that clients 

higher in anger at intake would fare better 

(higher PDA and fewer DDD) during posttreatment when assigned to MET relative to similar 

clients assigned to the combined CBT and TSF 

conditions. The analytic strategy for testing of 

the a priori hypotheses has been described in 

detail elsewhere and will only be briefly outlined here (Project MATCH 1993, 1997a, b; 

Longabaugh and Wirtz, this volume, pp. 4–17).

HLM was used to test the secondary matching hypothesis, and three statistical tests were 

conducted to evaluate the parallelism of the two 

slopes representing the relationship between 

anger and a primary outcome measure by the 

specified treatment contrast, here MET versus 

CBT and TSF combined.

One of these tests examined whether the two 

slopes were parallel and, if not, if nonparallelism was in the predicted direction. Importantly, 

this test collapsed the 12 months of followup 

and can be interpreted as an omnibus test of 

whether the predicted match was present for 

the entire 12 months of followup. The second 

and third statistical tests evaluated whether 

a finding of nonparallelism varied across the 

12 months of followup in a linear or quadratic 

fashion, and rejection of the null hypothesis for 

these tests led to post hoc monthly contrasts 

to identify the months in which the predicted 

match was manifest.
139

Client Anger as a Predictor of Differential Response to Treatment

For exploratory purposes, this same analytical strategy was used to examine potential 

client anger matching during the 12 weeks 

of treatment. The only difference between the 

posttreatment and within-treatment analyses 

was that the former used monthly drinking values and the latter used weekly PDA and DDD 

values. Finally, a 3-year followup was conducted for the outpatient sample, and matching 

findings have been reported elsewhere (Project 

MATCH Research Group 1998). A classical 

MANCOVA approach was used to test predicted matching hypotheses with the 3-month 

interval before the distal interview providing 

outcome data for the matching analyses in the 

outpatient sample. Time was not included in 

the model as a within-client factor.

Results of all prospective tests of the clientanger matching hypothesis are reported in table 

1 under the column labeled, “Planned matching contrast.” Columns to the right, labeled 

“Unplanned matching contrasts,” show the 

pairwise treatment matching contrasts. These 

contrasts were not prospectively specified and 

are provided as exploratory analyses. The top 

portion of the table shows the probability values associated with the matching hypothesis 

tests during the within-treatment phase of the 

study. As shown, no support was found for 

client-anger matching during the 12 weeks of 

treatment in the outpatient sample.

Strong support was found for the clientanger matching hypothesis, however, for the 12 

months posttreatment. Here, the omnibus HLM 

matching hypothesis was supported on both 

primary dependent measures (PDA p<.014 and 

DDD p<.011). Examination of figure 1 shows a 

disordinal interaction of anger with both PDA 

and DDD by the specified contrast. As predicted, and throughout the entire 12-month 

followup period, clients higher in initial anger 

had higher PDA and lower DDD when assigned 

to MET, relative to high anger clients assigned 

to CBT and TSF. In absolute magnitude, for 

Table 1. State-trait secondary matching findings in the Project MATCH outpatient sample: 

Planned and unplanned matching contrasts (probability values)

Planned matching 

contrast Unplanned matching contrasts

[MET vs. CBT and TSF] [MET vs. CBT] [MET vs. TSF] [CBT vs. TSF]

During treatment

PDA .11 .06 .39 .26

PDA (linear) .23 .42 .20 .65

PDA (quadratic) .12 .29 .10 .58

DDD .31 .39 .36 .97

DDD (linear) .25 .59 .14 .37

DDD (quadratic) .51 .63 .50 .87

Twelve months posttreatment

PDA .01 .02 .12 .44

PDA (linear) .69 .65 .82 .81

PDA (quadratic) .30 .83 .11 .16

DDD .01 .06 .03 .85

DDD (linear) .95 .72 .60 .88

DDD (quadratic) .95 .81 .89 .70

Extended 39-month followup

PDA .001

DDD .002

PDA = percentage of days abstinent 

DDD = drinks per drinking day
140

Part IV: Personality Variables

clients at the highest decile of anger, a 9-percent 

difference in PDA was obtained between MET 

and the combined CBT/TSF groups, and 1.2 

fewer drinks were consumed on drinking days 

when assigned to MET. Not predicted, clients 

initially lower on anger had significantly fewer 

abstinent days relative to low anger clients assigned to CBT and TSF and also reported more 

intense drinking relative to low anger clients 

assigned to TSF.

Prospective statistical tests provided further 

strong support for the client-anger matching 

hypothesis at 3-year followup in the outpatient 

sample. Again, clients with higher baseline 

anger assigned to MET reported significantly 

more abstinent days (p<.0005) and drank significantly less intensely (p<.0015) than high 

anger clients assigned to CBT and TSF 3 years 

after the end of treatment. Not anticipated, but 

similar to findings in the 12 months immediately after treatment, the interaction was 

disordinal. Here, at 3 years after treatment 

ended, low-anger clients assigned to TSF and 

CBT reported higher PDA and fewer DDD than 

low-anger clients assigned to MET. Using raw 

data, the average PDA for the highest third of 

Figure 1. Posttreatment plot of percentage of 

days abstinent and drinks per drinking day 

showing the interaction between the three 

Project MATCH treatments and anger, in the 

outpatient arm. The vertical axes represent predicted outcome scores and the horizontal axes 

represent baseline anger, with higher scores 

indicating higher anger. The triangles on the 

horizontal axes indicate the 10th and 90th percentiles for anger in this study arm. Reprinted 

with permission from Addiction, Vol. 92, Issue 

12, pp. 1671-1698,1997. Copyright by Taylor 

& Francis Ltd., Oxfordshire, UK, http:/www.

tandf.co.uldjournals. (—CBT; - -MET; ...TSF)

angry clients assigned to MET was 8.4 percentage points higher than for those assigned to 

CBT or TSF. Using this same procedure for the 

lowest third on the anger variable, low-anger 

clients assigned to CBT and TSF reported, on 

average, 14.4 percent more abstinent days 

than low-anger clients assigned to MET.

Causal Chain Analyses

We approached the analysis of our proposed 

causal mechanisms with enthusiasm. Only 

two a priori omnibus contrasts were supported 

by both primary dependent measures across 

months 4–15 in Project MATCH, and the anger 

hypothesis was the only such hypothesis supported for the entire 12 months after treatment 

and the extended 3-year followup.

The first step in our analysis was to assess 

the relationship between baseline client anger 

and resistance. Our model proposed a positive 

correlation between these constructs. Because 

our indicators of resistance were, in fact, antitheses of resistance (SOCRATES: Recognition of 

the problem and Taking Steps toward change), 

support for the first link of the causal chain 

would be evidenced with negative correlations 

between the two SOCRATES scales and client 

anger. One of the obtained correlations was 

statistically significant but not in the predicted 

direction. Specifically, client baseline anger 

was positively and significantly related with 

Recognition (r=0.08, p<.02), and Taking Steps 

was unrelated with client anger (r=0.03, p<.46).

Inconsistent findings using the two 

SOCRATES scales led to the search for other 

variables reflecting client resistance to change. 

We turned to measures of the client-therapist 

working alliance (WAI; Horvath and Greenberg 

1989) collected at the second week of treatment. 

This assessment was completed by clients after 

the second therapy session and inquired about 

the extent to which clients perceived (1) a bond 

with the assigned therapist, (2) agreement with 

treatment goals, and (3) agreement with therapeutic tasks.

Bivariate correlations with these measures 

partially supported our prediction that angrier 

clients would be more resistant to treatment. 

For example, client anger and agreement with 

therapeutic goals were significantly and negatively related (r=-0.10, p<.008), anger and 
141

Client Anger as a Predictor of Differential Response to Treatment

bonding were unrelated r=-0.04, p<.26), and 

anger and task agreement tended to covary 

negatively (r=-0.05, p<.18). We concluded that 

the first assumption of our model was, at best, 

partially supported. Clearly, the relationship 

between client anger and resistance was more 

complex than we originally conceived, perhaps 

compounded by our relatively crude proxies of 

client resistance.

Reduction of resistance in MET was the critical proposed mediating process in our causal 

model explaining the obtained matching effect. 

Hierarchical multiple regression analyses 

(MRA) were therefore conducted to partial out 

both the main effect of resistance and the resistance by treatment interaction from the single 

degree of freedom (df) client anger matching 

test. Substantial support for the proposed 

causal model would be obtained if inclusion of 

these two resistance terms in the model eliminated the observed matching effect.

Also included in the model were baseline 

values of the dependent measure, site, treatment, and anger main effects as well as all 

first-, second-, and third-order product terms. 

These analyses used the drinking data for the 

12 months posttreatment and, because the 

obtained matching effect was not time dependent, we collapsed the 12 monthly values of 

drinking (PDA and DDD) into single time point 

outcome measures. Further, because of the 

complexities in operationalizing resistance, 

we repeated these analyses five times for each 

dependent measure (PDA and 

DDD), using separately each of 

the five proxy measures of client resistance.

Results of these 10 MRA 

runs are reported in table 2. 

As shown, when the dependent measure was PDA, the 

incremental effect of the single 

df anger matching contrast 

was no longer significant after 

controlling for any one of the 

five resistance terms. These 

findings suggest that treatment outcome was mediated 

in the a priori client anger 

hypothesis by client resistance—measured five different 

ways. Less support for the mediational role of 

client resistance was found using the drinking 

intensity measure as the dependent variable. 

Here, only Taking Steps appeared to mediate 

the treatment outcome, and the remaining four 

measures of resistance did not appear to mediate treatment outcome in the context of the 

specified treatment contrast.

Aftercare Sample

Prognostic Effects of Anger

No significant relationship was obtained between baseline anger and frequency (r=0.06, 

p<.13) or intensity (r=-0.05, p<.19) of drinking during the 6 months after treatment. Even 

smaller relationships were found using secondorder correlations controlling for frequency and 

intensity of drinking for the 90 days before 

treatment (largest obtained r=-0.04, p<.27). 

Controlling for site, time, and treatment main 

effects and their interactions, HLM analyses 

also indicated that anger did not predict frequency (p<.34) or intensity (p<.23) of drinking 

for aftercare clients during the 12 months 

after treatment. Unlike the outpatient sample, 

examination of the replicability of relationships 

across aftercare sites indicated relative stability 

of findings and, hence, substantial confidence 

can be placed in the lack of prognostic importance of anger in predicting posttreatment 

drinking.

Table 2. Significance of client-anger matching hypothesis 

tests (PDA and DDD) after separately partialing out five 

measures of client resistance: Outpatient sample 12 months 

posttreatment (probability values)

Resistance measure PDA1 Mediators DDD2 Mediators

Recognition of the problem 0.06 0.001 0.04 0.014

Taking Steps to make 

changes 0.24 0.001 0.18 0.001

WAI:

Therapeutic Bond 0.09 0.118 0.04 0.297

Goal Agreement 0.06 0.006 0.02 0.062

Task Agreement 0.08 0.002 0.03 0.058

1 PDA—a summary measure of percentage of days abstinent calculated for the 

entire 12-month period

2 DDD—a summary measure of drinks per drinking day calculated for the entire 

12-month period

NOTE: Values in columns 1 and 3 after controlling for effects of the index of 

resistance; columns 2 and 4 computed before controlling for effects of the index 

of resistance.
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Part IV: Personality Variables

Tests of the Anger Matching Hypothesis

The rationale of the client-anger hypothesis and the analytical strategy for testing the 

prediction were the same for the aftercare and 

outpatient samples. The aftercare sample did 

not have an extended 3-year followup, so prospective tests of the hypothesis could only be 

conducted separately for the 12 weeks of treatment and the 12 months after treatment.

Table 3 presents the results of the prospective HLM tests of the client-anger hypothesis 

for the aftercare sample. None of the planned 

matching contrasts supported the client-anger 

hypothesis (during or after treatment), whether 

as an omnibus test collapsing time or when time 

was modeled as a linear or quadratic function.

One concern about combining treatments 

was that predicted effects may occur in one of 

the two being combined, but the absence of the 

predicted effect in the second group may wash 

out an overall (combined) predicted effect. 

Examination of the unplanned matching contrasts in table 2 indicates that this concern was 

unwarranted for the client-anger hypothesis. 

None of the unplanned pair-wise contrasts was 

significant either during the 12 weeks of treatment or during the 12 months posttreatment.

Causal Chain Analyses

Complete lack of support for the anger 

matching hypothesis in the aftercare sample 

led to an examination of where and how our 

prediction failed. The first step, of course, was 

to assess whether baseline client anger related 

with our measures of resistance to change. In 

this regard, we again used the two scales of the 

SOCRATES as reverse proxies of client resistance augmented with secondary measures of 

client resistance—the three scales of the WAI 

collected at the second week of therapy.

Bivariate correlations indicated that client anger was unrelated to the Taking Steps 

scale of the SOCRATES (r=0.03, p<.94) and significantly and positively related to Recognition 

(r=0.13, p<.001). Contrary to our predictions, 

therefore, angrier clients also tended to report 

higher Recognition (proxy for lower resistance). 

This finding is consistent with findings in the 

outpatient sample.

Unlike the outpatient sample, however, secondary measures of client resistance in therapy 

did not support our causal model. Specifically, 

baseline anger was unrelated with client report 

of therapeutic bond (r=-0.02, p<.72) and agreement of treatment goals (r=-0.05, p<.27) and 

tasks (r=-0.03, p<.52). Absence of a matching 

effect in the aftercare sample therefore seems 

most likely because of a breakdown in the initial rationale underlying the anger hypothesis.

Post Hoc Analyses

Although not specified in the anger matching hypothesis, a question of some import is 

how, if at all, client anger changed during the 

treatment phase of the trial. Equally important 

is the question of whether potential changes 

in client anger predicted posttreatment functioning. Post hoc analyses were conducted to 

examine these questions in order to gain a 

better understanding of the anger matching 

variable used in Project MATCH.

Significant overall reductions in client anger 

were found between intake and end of treatment in both outpatient and aftercare samples 

(outpatient: t(815)=-11.95, p<.001; aftercare 

t(632)=-8.56, p<.001). Extent of pre-post change 

in client anger, however, was not differentially 

related to treatment group assignment in either 

the outpatient (F(2, 813)=0.21, p<.81) or the 

aftercare sample (F(2, 632)=0.56, p<.57).

Analyses reported earlier indicated that 

client baseline anger was not predictive of 

posttreatment drinking in either the outpatient 

or the aftercare sample. A related question 

is, were changes in client anger predictive of 

posttreatment drinking? To this end, bivariate 

correlations were computed between pre-post 

treatment change in client anger and 12-month 

summary measures of PDA and DDD. 

Treatment groups were collapsed because of 

the finding that change in anger was unrelated 

to treatment assignment. In the aftercare sample, anger reduction was unrelated to either 

drinking intensity (r=0.00, p<.98) or frequency 

of abstinent days (r=-0.02, p<.62) during the 12 

months of followup. In contrast, in the outpatient sample, anger reduction was associated 

with significantly less drinking when drinking occurred (r=-0.08, p<.02), but not with 
143

Client Anger as a Predictor of Differential Response to Treatment

Table 3. State-trait matching findings in the Project MATCH aftercare sample: Planned and 

unplanned matching contrasts (probability values)

Planned matching 

contrast Unplanned matching contrasts

[MET vs. CBT and TSF] [MET vs. CBT] [MET vs. TSF] [CBT vs. TSF]

During treatment

PDA .26 .46 .23 .70

PDA (linear) .91 .78 .93 .72

PDA (quadratic) .59 .62 .66 .94

DDD .40 .62 .34 .68

DDD (linear) .93 .61 .71 .40

DDD (quadratic) .56 .70 .54 .85

Twelve months posttreatment

PDA .18 .25 .67 .46

PDA (linear) .46 .90 .77 .68

PDA (quadratic) .23 .61 .43 .81

DDD .38 .44 .77 .29

DDD (linear) .13 .26 .40 .75

DDD (quadratic) .09 .62 .06 .19

PDA = percentage of days abstinent 

DDD = drinks per drinking day

frequency of abstinent days (r=0.05, p<.19). 

Changes in client anger during treatment were 

unrelated to 3-year drinking on both drinking 

measures.

Discussion

Strong support was found for the a priori 

matching hypothesis that drinking outcome 

would be differentially influenced by the interaction of client anger and treatment modality. 

For outpatient clients higher in pretreatment 

anger, MET was associated with significantly 

more abstinence and less intense drinking, 

compared to CBT and TSF. These results 

indicate a clear advantage in assigning angry 

outpatients to MET.

Exactly why this matching effect occurred 

remains unclear. Process variables expected 

to underlie the differential effectiveness (attendance, treatment satisfaction, and therapeutic 

alliance) did not mediate the observed interaction. Nevertheless, there was support for the 

causal chain in that the observed matching 

effect disappeared when any one of five resistance proxies was entered as a covariate. This 

suggests that resistance was an important variable within the matching effect.

A key may lie in more complex analyses of 

therapy process to be obtained from detailed 

coding of session videotapes, where in-session 

resistance could be measured more precisely. 

Clients who behave in a hostile manner may, 

for example, typically elicit more negative 

responses from their therapists. Snyder and 

Swann (1978) have shown that hostile expectations generally elicit hostile behavior from 

interaction partners. In a psychotherapy setting, Pope and Tabachnick (1993) found that 

therapists experience feelings such as anger 

with their clients and these feelings may be 

translated into angry behaviors during therapy. 

These would be expected to result in an escalation of client resistance, which is associated 

with poorer client outcomes. Indeed, Miller et 

al. (1993) found that it was not the presence of 

positive “motivated” client speech, but the relative absence of client resistance that predicted 

more positive outcomes in MET, and showed 

that client resistance was under the experimental control of therapist style (cf. Patterson 

and Forgatch 1985).
144

Part IV: Personality Variables

It may be that such angry reciprocation by 

therapists to hostile clients is suppressed by 

training in MET. Peterson and colleagues (1997) 

conducted a sequential analysis of therapist 

and client behaviors during the various phases 

of MET sessions in Project MATCH. Only MET 

processes and outcomes were examined, and 

no comparisons were made with other therapy approaches. We were interested, among 

other things, in studying client behaviors that 

followed therapist confrontation responses, 

expecting to see an increase in resistance. It 

is noteworthy, in reference to current findings, 

that not a single “angry” or confrontational 

response was found in videotapes from 22 different MET therapists participating in the trial. 

This suggests that MET therapists were indeed 

not reciprocating clients’ negative behaviors, 

which could account for better outcomes for 

angry clients in the MET condition. A full testing 

of this relationship will require more extensive 

examination of videotapes from all three treatment conditions.

Alternatively, angry clients may be more reactive to therapists’ attempts to structure their 

behavior. In accord with the theory of reactance, Brehm (1972) maintained that clients 

will behave in ways that preserve their personal 

freedom, even if doing so is countertherapeutic. 

Traditional behavioral treatment models characterize the therapist role as a teacher or guide. 

Cognitive-behavioral coping skills interventions 

targeting client anger typically specify the content and sequencing of therapist techniques to 

be presented to clients. Providing choices to clients and leading clients to draw therapeutically 

sound conclusions on their own (e.g., through 

Socratic methods) may be more effective 

(Newman 1994). In one study, Deffenbacher 

et al. (1994) suggested that a more inductive, 

client-focused approach to skills training in 

which strategies for anger management are 

elicited from the clients and not imposed on 

them would be more effective in lowering resistance and improving outcome. They found that 

the inductive application of skills training was 

significantly more effective than standard skills 

training approaches in reducing daily anger. 

MET specifically emphasizes personal freedom 

of choice and prescribes strategies that elicit 

self-directing responses from client.

In the MET model, motivation and resistance are viewed as products of the interaction 

between client and therapist (Miller 1983). 

Similarly, Kiesler (1982) argued that the therapeutic relationship derives from reciprocal 

exchanges between therapists and clients. 

According to Kiesler, the evoking style (e.g., 

anger) that some clients bring to therapy and 

the manner in which therapists respond to 

them likely reflects an important aspect of the 

therapeutic relationship. Examining therapy 

interactions in micro-analytic detail, then, may 

provide a more fruitful strategy for understanding the dynamic relationship that develops 

between angry clients seeking treatment for 

alcohol problems and MET therapists, offering 

clues to how that relationship influences treatment outcome. Well-developed microanalytic 

therapy coding systems (e.g., Chamberlain et 

al. 1986; Pinsof, 1980) could be used to compare the patterns of therapist behaviors and 

cooperative and resistant responses of clients 

across the three treatment conditions. Such an 

examination may illuminate the actual causal 

chain underlying the matching results found in 

this study.

Also worthy of further attention is the unanticipated finding that clients low in anger fared 

better in CBT and TSF than in MET, reflected 

in the disordinal rather than ordinal matching 

interaction. One possibility is a mismatch of 

MET (originally designed for clients less ready 

for change) with clients who are less angry and 

resistant and are already more motivated for 

change. Such clients may be ready for and prefer the more structured change strategies of 

CBT or TSF.

Functional family therapy (FFT), which is 

one therapeutic model used to treat families 

with drug and alcohol problems, integrates 

both motivational and behavior change strategies in dealing with substance use problems 

in families (Alexander and Parsons 1982). The 

impact of FFT depends in part on the specific 

techniques prescribed, but perhaps even more 

so on the particular sequencing or timing of 

the techniques across sessions, with readiness for change as the focus of earlier sessions 

and behavior change strategies implemented in 

later sessions, after motivational and assessment tasks have been completed. According 
145

Client Anger as a Predictor of Differential Response to Treatment

to the model, attempting to institute behavioral changes with unmotivated families will 

evoke resistance, but with enhanced motivation, families can move more quickly into the 

behavior change phase (Waldron and Slesnick 

1998). Motivational interviewing has been used 

effectively as an add-on component to other 

treatment models (Bien et al. 1993; Brown and 

Miller 1993). Combining MET with other, more 

structured approaches for clients with alcohol problems, taking into account client anger 

and readiness for structured interventions, 

might allow therapists to tailor the treatment 

to clients’ stage of readiness and provide for the 

most optimal treatment match.

Also unexplained is the strong matching 

effect among outpatients, but no matching 

effect among aftercare clients. Both received 

outpatient treatment in Project MATCH, the 

difference being that aftercare clients had 

recently completed a week or more of inpatient 

or day hospital treatment. The causal chain 

analyses reported above did not reveal significant process differences that would account for 

a matching effect in only one arm of the study. 

Microanalyses of session videotapes again may 

prove more revealing.

Finally, it should be noted that client anger 

was measured in a somewhat limited way for 

present analyses. Using the STAX, we were 

able to find support for differential treatment 

outcomes, but a more comprehensive measure including other aspects of anger such as 

indices separating frequency, intensity, and 

appropriateness of angry feelings may provide 

additional information in understanding and 

addressing anger in treatment and in helping to 

elucidate the specific aspects of an anger-prone 

person’s anger experiences. A more refined 

behavioral measure might produce clearer 

findings. Certainly, client resistance behaviors coded from therapy sessions have proved 

useful in predicting treatment outcomes (e.g., 

Miller et al. 1993).

Acknowledgments

The research and publication efforts for 

this manuscript have been supported by 

the National Institute on Alcohol Abuse and 

Alcoholism as the collaborative agreement 

award U10–AA08435.

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and irritable behavior. In: Chesney, M.A., 

and Rosenman, R.H., eds. Anger and 

Hostility in Cardiovascular and Behavioral 

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1985. pp. 83–100.

Patterson, G.R., and Forgatch, M.S. Therapist 

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Psychology 53:846–851, 1985.

Peterson, T.; Waldron, H.B.; and Miller, W.R. 

“A Sequential Analysis of Client and 

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Enhancement Therapy for Problem 

Drinking.” Unpublished manuscript, 

University of New Mexico, Albuquerque, 

1997.

Pinsof, W.M. The Family Therapist Coding 

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for Family Studies/The Family Institute 

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Northwestern Memorial Hospital, 1980.

Pope, K.S., and Tabachnick, B.G. Therapists’ 

anger, hate, fear, and sexual feelings: 

National survey of therapist responses, 

client characteristics, critical events, formal complaints, and training. Professional 

Psychology: Research and Practice 24:142–

152, 1993.

Project MATCH Research Group. Rationale 

and methods for a multisite clinical trial 

matching patients to alcoholism treatment. 

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alcohol-ism treatments to client heterogeneity: Project MATCH posttreatment 

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Research 22(6): 1300–1311, 1998.

Robbins, M.S.; Alexander, J.F.; and Newell, 

R.M. Journal of Family Psychology 10:28–

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Robin, A.L., and Foster, S. Negotiating Parent 

Adolescent Conflict: A Behavioral-Family 

Systems Approach. New York: Guilford, 

1989.

Smith, T.W.; Sanders, J.D.; and Alexander, J.F. 

What does the Cook and Medley Hostility 

Scale measure? Affect, behavior, and attributions in the marital context. Journal of 

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Snyder, M., and Swann, W.B. Behavioral confirmation in social interaction: From social 

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Spielberger, C.D. Manual for the State-Trait 

Anger Expression Scale (STAX). Odessa, 

FL: Psychological Assessment Resources, 

1988.

Spielberger, C.D.; Jacobs, G.; Russell, S.; and 

Crane, R.S. Assessment of anger: The 

State-Trait Anger Scale. In: Butcher, J.N., 

and Spielberger, C.D., eds. Advances in 

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Valle, S.K. Interpersonal functioning of alcoholism counselors and treatment outcome. 

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family. In: Miller, W.R., and Heather, N., 

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Waldron, H.B.; Turner, C.W.; Barton, C.; 

Alexander, J.F.; and Cline, V.B. The contribution of therapist defensiveness to 

marital therapy process and outcome: A 

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1967.
149

Prospects for Matching Clients to 

Alcoholism Treatments Based on 

Conceptual Level

John P. Allen, Ph.D.

ABSTRACT

Conceptual level refers to a broad experiential style of relating to one’s environment. Individuals low in the dimension tend to think in rather concrete ways and often 

conceptualize issues as dichotomous rather than as complex and multidimensional. 

Previous research has suggested that alcoholics low in conceptual level tend to do better with more structured interventions. In Project MATCH, it was hypothesized that 

lower conceptual level patients would derive greater reduction in frequency and intensity of drinking if assigned to Twelve Step Facilitation and that higher conceptual level 

subjects would fare better with Motivational Enhancement Therapy. This matching 

hypothesis was not confirmed, but, to at least some extent, this may have been due to 

very poor reliability in the measure of conceptual level chosen.

Conceptual level (CL) refers to a consistent, 

pervasive cognitive and motivational 

style of relating to one’s environment. 

CL is measured as a continuous variable ranging from cognitive simplicity (i.e., a proclivity to 

view situations and issues as concrete “blackwhite” dichotomies) to cognitive complexity (i.e., 

a sensitivity to subtle nuances and a tendency 

to abstract about situations and see issues 

from multiple perspectives).

Individuals low in CL are believed to learn 

and function most effectively in an environment 

which structures their experiences for them. 

High CL individuals, however, are assumed to 

do best in a nondirective, unstructured environment which allows them maximal freedom 

to make decisions for themselves. Conversely, 

low CL individuals would be predicted to be 

confused and threatened in environments 

stressing self-direction and judgment.

Two lines of research suggest that matching 

based on congruence of CL of clients with certain aspects of treatment may improve outcome. 

Most relevant is McLachlan’s (1972) work 

dealing specifically with alcoholics. General 

research on psychotherapy and educational 

interventions also suggests that CL relates to 

choice of interventions differing in degree of 

structure.

Previous Research

McLachlan (1972) studied 92 alcoholic inpatients at the Donwood Institute in Toronto, 

Canada. The sample appears to have been reasonably high in social stability as reflected by 

the fact that most were currently married, the 

mean educational level was 12.7 years, and 

they were being treated in a private facility. 

John P. Allen, Ph.D., MPA

Associate Director of Treatment Studies (DCPR)

National Institute on Alcohol Abuse and 

Alcoholism

Willco Building, Suite 505

6000 Executive Blvd., MSC 7003 

Bethesda, Maryland 20892-7003 

E-mail: jallen@willco.niaaa.nih.gov
150

Part IV: Personality Variables

While information on severity of dependence 

was not reported, only about one-third had 

received prior treatment for an addiction problem. Clients with “known” brain damage and 

those with severe physical or emotional problems such as to render them inappropriate for 

group therapy were excluded from the study.

Prior to initiation of therapy, clients were administered the Paragraph Completion Method 

(PCM; Hunt et al. 1978) and a verbal intelligence measure. Five group therapists also 

completed the PCM. Interrater reliability of 

the clients’ CL thus measured was 0.93 with 

an internal consistency coefficient of 0.82. 

(Reliabilities for staff members’ CL were not 

reported.) Fifty-three clients were classified as 

low CL (i.e., PCM score <1.5). Using higher cut 

points, two therapists were classified as low CL 

and three as high CL. Nurses in consort also 

ranked therapists on nondirectiveness in treatment. A Spearman’s rho of 0.90 was reported 

between degree of non-directiveness and CL for 

therapists.

The treatment program is briefly summarized in the report as 26 hours of group 

psychotherapy conducted over 3 weeks. The 

overall intervention strategy was identified as 

reconstructive, client-centered, and involving frequent use of psychodrama. The stated 

goals of therapy were to help clients cope with 

problems contributing to their dependency on 

alcohol, to enhance self-confidence, and to 

improve interpersonal skills.

Outcome measures on eight therapy rating 

scales were obtained from clients. The group 

therapist and group nurse also rated each client on overall improvement by using a 6-point 

ordinal scale from “worsened” to “complete 

alleviation of presenting problem.” Nurses and 

therapists also evaluated clients on improvement in functioning in group therapy and 

outside the hospital. Further, at least one family member was interviewed to assess client 

functioning outside of the hospital.

Outcome was gauged by the client’s score 

on the first varimax-rotated factor derived from 

the correlational matrix of ratings by clients, 

nurses, and therapists. This factor was labeled 

“Client-Rated Improvement” and was loaded 

primarily by items dealing with the client’s 

estimate of global benefit from group therapy, 

including the item dealing with changes in 

“complaint or symptoms that brought you for 

treatment.” Note that the study did not directly 

ask about changes in drinking status.

The primary statistical analysis was a 2×2 

analysis of covariance. CL of therapist and client served as independent variables, verbal IQ 

as a covariate, and standardized score on the 

client improvement factor as the dependent 

variable. The main effects were not significant. 

However, the client-therapist CL interaction 

effect was highly significant (p<.005), indicating 

that matched clients rated their improvement 

as higher than did those mismatched to 

therapists.

McLachlan (1974) provided 12- to 16-month 

followup data on these clients and, further, 

examined the impact of two different venues for 

providing aftercare as they interact with client 

CL.

The two types of aftercare were:

■ Weekly meetings and weekly phone 

calls for the first year following inpatient 

treatment.

■ Letters to clients encouraging them to 

write to another client who was a member of the inpatient therapy group.

Drinking data were available for 87 of 

the original 92 clients. Seven additional clients missing posttreatment data in the 1972 

study were added. Outcomes were classified 

according to a 4-point ordinal scale from “no 

improvement” to “fully abstinent since treatment.” Informants for determining followup 

status were clinic secretaries (who also played 

a role in treatment), a counselor, a physician, 

and fellow clients who had maintained contact 

with the client since discharge. When ratings 

were inconsistent across sources, the lower 

rating was selected for purposes of the outcome 

evaluation.

Urban clients received the first type of aftercare described and out-of-town clients received 

the second, “low structure,” type of aftercare. Clients were distinguished as matched 

or mismatched based on their CL being high 

or low and their treatment being high or low 

in structure. Outcomes were dichotomized by 

combining no improvement and some improvement categories (39 percent of clients) and 

much improvement and fully abstinent categories (61 percent of clients).
151

Prospects for Matching Clients to Alcoholism Treatments Based on Conceptual Level

Analyses focused on how matching to inpatient group therapist related to outcome and 

how matching to the two types of aftercare 

related to outcome. Both types of matching were 

associated with about the same rate of recovery (70 percent) and both kinds of mismatching 

with about the same rate of recovery (50 percent). Seventy-seven percent of the “doubly 

matched” clients had favorable outcomes but 

only 38 percent of the “doubly mismatched” clients were judged as recovered.

Beyond McLachlan’s work specifically on 

alcoholics, the topic of matching clients to 

psychological treatments and students to 

educational interventions has proven quite 

popular, and a comprehensive review of these 

studies has been prepared by Stoppard and 

Miller (1985). Due to the number of such studies, only some of the general conclusions are 

offered:

■ Most research has dealt with “contemporaneous” matching (i.e., matching of 

clients with treatments based on the 

client’s current CL) rather than “developmental” matching (i.e., trying to enhance 

the clients’ CL by placing them in a setting slightly above that appropriate to 

their current CL).

■ Client satisfaction or reported comfort is a common outcome measure for 

such studies since CL theory posits that 

therapeutic rapport and empathy are 

strengthened when clients are assigned 

to interventions based on CL. A small 

number of studies (e.g., McLachlan’s), 

however, have looked at the effect 

of matching on resolution of client 

problems.

■ With the exception of McLachlan’s (1972) 

study, degree of formal intervention 

structure has served as the treatment 

variable.

■ Only 20 percent of the reviewed studies 

failed to discover a matching effect. Also, 

only one study discovered a matching 

effect inconsistent with CL theory, and 

this study was rather poorly controlled.

■ Research on CL far more typically reveals 

interaction effects than main effects.

■ Most matching studies have revealed 

ordinal effects, with low CL clients 

responding better when matched to 

condition. High CL individuals seem 

less affected by differences in treatment 

structure.

The Matching Hypotheses

Clients higher in CL will have a higher percentage of days abstinent when treated by 

Motivational Enhancement Therapy (MET; Miller 

et al. 1992) than by Twelve Step Facilitation 

(TSF; Nowinski et al. 1992). Conversely, clients 

lower in CL will do better in this regard when 

treated by TSF.

Clients higher in CL will consume more alcohol 

on the days in which they do drink when treated 

by TSF than when treated in MET. Conversely, 

clients lower in CL will do better in TSF.

The hypothesized interaction effect is portrayed in figure 1.

Figure 1. Hypothesized matching effect for conceptual level.

Measurement

CL in Project MATCH was measured using 

the PCM. The test consists of seven brief stems 

alluding to basic life themes such as parent 

roles. Clients are asked to complete each stem 

by writing at least three sentences describing their reactions. Guidelines in the manual 

(Hunt et al. 1978) provide general principles 

for scoring responses according to degree of CL 

demonstrated. The manual also offers numerous examples of various level CL responses for 
152

Part IV: Personality Variables

each stem. The client’s score is the average of 

the highest three responses.

Psychometric properties of the PCM were 

computed using as a subject pool the first 40 

clients from each of 8 research sites and 38 from 

the ninth. The observed frequency distribution was generally normal, with skewness and 

kurtosis of only -0.34 and 0.29, respectively. 

Although only nine clients failed to attempt the 

PCM, nearly 16 percent of the item responses 

were unscorable due to paucity of content.

Interrater reliability, split-half reliability, 

and coefficient alpha based on 103 response 

protocols were 0.73, 0.41, and 0.49, respectively. Factor analysis revealed that the first 

unrotated principal component accounted for 

35 percent of the total variance and, with the 

exception of a single item, all items loaded at 

least 0.55. The parents item correlated at only 

0.13 with the first principal component score.

Results

The hypothesized model predicted that the 

interaction of type of treatment (TSF versus 

MET) and conceptual level of the client would 

be significantly related to drinking outcome. 

Figure 2 schematizes the relationship actually 

observed on the outcome variable percentage 

of days abstinent. Above and below the line 

respectively are the p-values for the outpatient 

arm and the aftercare arm of MATCH. Neither 

is statistically significant.

At least three reasons seem possible for the 

failure to demonstrate the predicted effect. 

Obviously, there may, in fact, be no relationship 

Figure 2. P-values of relationships of CL and 

treatment to drinking outcome. 

in nature between the variables, despite the 

earlier findings reported by McLachlan (1972, 

1974) and the generally positive findings 

noted by Stoppard and Miller (1985) in the 

educational literature. (A recently reported 

study (Nielsen et al. 1998) has also found that 

matching alcoholic outpatients to degree of 

treatment structure was associated with treatment retention. Unlike in MATCH, however, 

this investigation did not deal with drinking 

outcomes per se.)

The second possibility lies with difficulties 

in scoring the PCM. In our hands, the interrater reliability of the measure was only 0.73, 

despite a 1-week training course given to the 

raters. Factor analysis of the PCM as well as 

coefficient alpha also suggested that the content of the PCM is heterogeneous.

Thirdly, it was assumed that TSF would 

be more structured than MET. This was only 

demonstrated in the outpatient arm of the 

trial. Nevertheless, the relationship of degree 

of structure of treatment and conceptual level 

to drinking was not significant in either arm of 

the study.

Future research on the relationship of 

conceptual level of the client and degree of 

alcoholism treatment structure might more 

profitably employ an alternative measure to 

the PCM as well as contrast treatments differing in structure more extremely than the 

interventions in Project MATCH. Interventions 

might also be assessed against other relevant, 

but nondrinking, outcomes such as treatment 

retention and client satisfaction.

References

Hunt, D.E.; Butler, L.F.; Noy, M.E.; and Rosser, 

M.E. Assessing Conceptual Level by the 

Paragraph Completion Method. Toronto: 

Ontario Institute of Studies in Education, 

1978.

McLachlan, J.F.C. Benefit from group therapy 

as a function of patient-therapist match on 

conceptual level. Psychotherapy: Theory, 

Research and Practice 9(4):317–323, 1972.

McLachlan, J.F.C. Therapy strategies, personality orientation and recovery from 

alcoholism. Canadian Psychiatric 

Association Journal 19:25–30, 1974.
153

Prospects for Matching Clients to Alcoholism Treatments Based on Conceptual Level

Miller, W.R.; Zweben, A.; DiClemente, C.C.; and 

Rychtarik, R.G. Motivational Enhancement 

Therapy Manual: A Clinical Research Guide 

for Therapists Treating Individuals With 

Alcohol Abuse and Dependence. Project 

MATCH Monograph Series. Vol. 2. DHHS 

Pub. No. (ADM) 92–1894. Rockville, MD: 

National Institute on Alcohol Abuse and 

Alcoholism, 1992.

Nielsen, B.; Neilsen, A.S.; and Wraae, 0. Patienttreatment matching improves compliance 

of alcoholics in outpatient treatment. The 

Journal of Nervous and Mental Disease 

186(12):752–760,1998.

Nowinski, J.; Baker, S.; and Carroll, K. Twelve 

Step Facilitation Therapy Manual: A Clinical 

Research Guide for Therapists Treating 

Individuals With Alcohol Abuse and 

Dependence. Project MATCH Monograph 

Series. Vol. 1. DHHS Pub. No. (ADM) 

92–1893. Rockville, MD: National Institute 

on Alcohol Abuse and Alcoholism, 1992.

Stoppard, J.M., and Miller, A. Conceptual level 

matching in therapy: A review. Current 

Psychological Research and Reviews 

46–68, 1985.
154

 The Search for Meaning in Life as 

Predictor of Alcoholism 

Treatment Outcome

J. Scott Tonigan, Ph.D., William R. Miller, Ph.D., and 

Gerard J. Connors, Ph.D.

ABSTRACT

The core literature of Alcoholics Anonymous (AA) identifies two prerequisites for successful affiliation and positive outcome within AA. These are a sense of “hitting bottom” 

and a willingness to accept and practice a spiritual program for recovery. This chapter 

presents the rationale and operational definition for client meaning-seeking, a composite measure intended to represent these characteristics. A prospective matching 

hypothesis was developed in Project MATCH which stated that meaning-seeking and 

posttreatment percentage of days abstinent would be positively and strongly related 

within the Twelve Step Facilitation (TSF) condition, whereas a weaker positive relationship would be found in the combined Cognitive-Behavioral Coping Skills (CBT) 

and Motivational Enhancement (MET) therapies. Similarly, client meaning-seeking and 

drinking intensity were predicted to be weakly related within the combined CBT and 

MET conditions, but strongly and negatively related for clients assigned to TSF. Partial 

support for the prospective matching hypothesis was found in the aftercare sample, 

but not in the outpatient sample. Proposed causal mechanisms underlying the hypothesis were partially supported in the outpatient sample, and adjustments to the causal 

model led to partial support of the prospective model in the aftercare sample.

I

nvolvement in Alcoholics Anonymous (AA) is 

widely recommended as an aid in recovery. 

Meta-analytic reviews indicate that such 

engagement is associated with more favorable outcomes (Emrick et al. 1993) and that 

the magnitude of benefit may vary according 

to whether clients received prior outpatient 

or inpatient treatment (Tonigan, Toscova et 

al. 1996). Substantial effort has been directed 

toward the identification of predictors of AA 

affiliation, with at least 62 client characteristics examined in one or more studies. Client 

attributes most associated with AA affiliation 

include heavier drinking, more severe alcohol 

dependence, and experienced loss of control 

when drinking (Emrick et al. 1993).

Nevertheless, even the strongest predictors 

of AA affiliation account separately for less than 

10 percent of the variance in AA attendance and 

involvement, indicating that currently identified predictors are of limited clinical utility in 

identifying whom to refer or not to refer to AA. 

Predictor variables appear to have been selected 

most often on the basis of convenience (what 

J. Scott Tonigan, Ph.D.

Center on Alcoholism, Substance Abuse and 

Addictions (CASAA) 

2350 Alamo SE

The University of New Mexico

Albuquerque, NM 87131 

jtonigan@unm.edu
155

 The Search for Meaning in Life as Predictor of Alcoholism Treatment Outcome

can be measured easily) rather than what ought 

to be measured from a conceptual analysis of 

AA. We believe that the core literature of AA 

offers some clear predictions of the likely characteristics of AA affiliates, many of which have 

been ignored in alcohol research.

This chapter describes the rationale and 

development of one composite variable to predict AA affiliation and involvement for clients 

assigned to a formal Twelve Step Facilitation 

(TSF; Nowinski et al. 1992) therapy. The composite variable was derived from a theoretical 

perspective, combining recommendations made 

in the core AA literature with findings from AA 

research. The predictive utility of this composite 

variable was examined prospectively with the 

Project MATCH (1993, 1997) clinical samples, 

the results of which are presented here along 

with an evaluation of the conceptual causal 

chain underlying predicted relationships.

Rationale of the Client 

Matching Variable

The central question underlying this investigation was, “Who will be most likely to affiliate 

with and derive benefit from AA?” According 

to the basic “big book” of AA (1976), “hitting 

bottom” is essential. This bottoming out, as 

understood in AA, is characterized not so much 

by catastrophic consequences as by a spiritual/

existential bankruptcy and a sense of hopelessness about one’s ability to control drinking 

despite awareness of its consequences. The 

big book describes hitting bottom as a period 

of “incomprehensible demoralization” that is 

necessary but not sufficient for recovery. Also 

needed, by AA’s account, is a willingness to 

accept help and to pursue a spiritual path to 

recovery. This distinction is reflected both in the 

common statement in meetings that “AA is not 

for everybody who needs it, but for those who 

need it and want it,” and in advice for working 

with an alcoholic (Alcoholics Anonymous 1976):

If he is not interested in your solution, if 

he expects you to act only as a banker for 

his financial difficulties or a nurse for his 

sprees, you may have to drop him until he 

changes his mind. (p. 95) . . . We find it a 

waste of time to keep chasing a man who 

cannot or will not work with you. (p. 96)

Typical research measures of motivation for 

change do not really capture this concept of hitting bottom. While change readiness is tacitly 

present in AA’s third tradition and in the AA 

program’s emphasis upon “one day at a time” 

(McCrady 1994), an AA understanding of this 

critical willingness refers more to a critical 

desire for meaning in life that is found through 

spiritual means.

Meaning-Seeking as a 

Composite Matching 

Variable

There are two potentially distinct aspects of 

this spiritual/meaning crisis. One aspect is an 

experienced lack of meaning in life. Crumbaugh 

and Maholick’s (1976) Purpose in Life (PIL) 

scale was designed specifically to assess current sense of meaning, and it has been used in 

prior studies of substance abuse (Black 1991). 

On the PIL, lower scores reflect a relative lack of 

perceived current life meaning. Such meaning 

deficit is, we believe, a better operationalization 

of hitting bottom, as understood in the core AA 

literature, than are measures of drinking patterns, consequences, or dependence levels. Yet 

a meaning void alone does not capture the AA 

element of willingness. A second Crumbaugh 

(1977) instrument, the Seeking of Noetic Goals 

(SONG) scale, was developed as a companion 

measure to the PIL and was designed to assess 

the extent of desire for and seeking of greater 

meaning in life. Higher scores on the SONG 

reflect a quest for greater meaning.

Because both elements seem essential to 

capture AA’s understanding of hitting bottom, 

we reasoned that a difference score (SONG 

minus PIL) would be a reasonable operational 

definition. High positive values on this meaning-seeking difference score reflect a low sense 

of current meaning combined with a strong 

desire for greater meaning. Negative meaningseeking values, on the other hand, suggest that 

current sense of purpose exceeds the desire for 

greater meaning in life. This difference score 

can range from +120 (maximum SONG score of 

140, minimum PIL score of 20) to -120 (minimum SONG score of 20, maximum PIL score of 

140). In this way, meaning-seeking can be conceived as a point along a continuum between 

these extremes.
156

Part IV: Personality Variables

Predicted Relationships

A primary matching hypothesis in Project 

MATCH (1993, 1997) was that higher scores on 

meaning-seeking (hitting bottom) would predict 

better outcomes in the TSF treatment, which 

explicitly focuses on issues of spirituality and 

meaning (Nowinski et al. 1992). Consistent 

with core AA writings, we also expected that 

lower meaning-seeking scores, reflecting distance from hitting bottom, would be associated 

with poorer outcomes in TSF. This, then, illustrates a testable hypothesis derived directly 

from AA theory:

In terms of the two primary outcome measures 

chosen for Project MATCH, we predicted that 

meaning-seeking and posttreatment percentage 

of days abstinent (PDA) would be strongly and 

positively related for clients assigned to TSF, and 

that meaning-seeking and posttreatment drinks 

per drinking day (DDD) would be strongly and 

negatively related for clients assigned to TSF.

We had no reason to predict a relationship between meaning-seeking and outcomes 

for clients assigned to the other two treatments studied in Project MATCH: Motivational 

Enhancement Therapy (MET; Miller et al. 1992) 

and Cognitive-Behavior Coping Skills Therapy 

(CBT; Kadden et al. 1992). Consequently, we 

pooled clients in these conditions into a single 

group and made the prediction that meaningseeking would be modestly related to outcomes 

(PDA and DDD) for these non-TSF clients.

An important contribution of the Project 

MATCH analytic strategy is the requirement 

that prospective hypotheses delineate and 

statistically test the causal mechanisms supporting the hypothesis. Our meaning-seeking 

predictions were based upon the assumptions 

that the content of the TSF modality would 

be more relevant to higher meaning-seeking 

clients and, consequently, meaning-seeking 

would be more positively related with satisfaction with treatment within the TSF condition 

than in the combined MET and CBT conditions. 

In turn, meaning-seeking and two measures of 

treatment compliance within TSF (AA meeting 

attendance and percentage of therapy sessions 

attended) would be more positively related 

in TSF than in the combined CBT and MET 

conditions. Finally, we expected that these 

mechanisms would become manifest in a more 

positive relationship between meaning-seeking 

and attendance and involvement in AA both 

during and after treatment for clients assigned 

to TSF and that higher AA involvement would 

predict (and maintain) better outcome (higher 

PDA and lower DDD).

Results

Of the 1,726 clients studied in Project 

MATCH, 96 percent provided sufficiently complete data at intake to compute the composite 

meaning-seeking matching variable. No mean 

differences were found among the three treatment groups in either the outpatient (F(2, 914)= 

0.14, p<.87) or the aftercare (F(2, 737)=0.09, 

p<.92) arms of the study, although clients’ mean 

levels of meaning-seeking did vary significantly 

within both outpatient sites (F(4, 910)=3.13, 

p<.014) and aftercare sites (F(5, 734)=4.7, 

p<.0003). A reasonably normal distribution 

was obtained within each arm for the composite variable (outpatient: M=-16.50, SD=29.09, 

skewness=0.17; aftercare: M=-10.90, SD= 

30.21, skewness=0.27).

Outpatient Sample

Prognostic Effect of Meaning-Seeking

The prognostic effect of baseline meaningseeking was examined separately for outpatient 

and aftercare clients. These analyses were run 

separately for the treatment period (months 

13) and for the followup period (months 4–15). 

Prognostic effects for the primary dependent 

measures (PDA and DDD) were examined in 

a hierarchical linear modeling (HLM) context 

which controlled for site, treatment, and time 

(linear and quadratic terms) and their interactions with each other and the meaning-seeking 

attribute (Project MATCH Research Group 1997; 

Longabaugh and Wirtz, this volume, pp. 4–17).

Baseline meaning-seeking was not predictive of abstinence (PDA) during the 12 weeks 

of treatment in the outpatient (p<.17) sample.

Likewise, no prognostic effect was observed for 

PDA during the 12 months of followup in the 

outpatient (p<.27) sample. This finding was 

consistent across sites, evidenced most clearly 

in the nonsignificant site by meaning-seeking 
157

 The Search for Meaning in Life as Predictor of Alcoholism Treatment Outcome

product terms in the HLM tests (all p’s >.30). In 

contrast, at intake, meaning-seeking did predict drinking intensity (DDD) during outpatient 

treatment (p<.04), a finding that was relatively 

consistent across sites (site by meaning-seeking interaction, p<.39). Examination of scatter 

plots indicated that outpatient clients higher in 

meaning-seeking reported more DDD during 

treatment, relative to outpatients with lower 

baseline meaning-seeking scores. The prognostic effect between meaning-seeking and DDD 

faded during followup (months 4–15) in the 

outpatient sample (p<.78), and little variation 

in this finding was identified when the matching attribute was crossed by site or treatment 

condition.

Testing of the Primary Matching 

Hypothesis

The rationale for testing of the prospective 

Project MATCH primary hypotheses and protection of type 1 error rate has been described 

elsewhere (Project MATCH Research Group 

1993, 1997; Longabaugh and Wirtz, this volume, pp. 4–17) and will not be repeated in 

detail. Essentially, 24 HLM tests specifically 

examined the meaning-seeking hypothesis, 12 

conducted from data during treatment and 12 

with followup data. The 12 parallel HLM tests 

included: (1) four tests of the matching hypothesis collapsing time, separately for PDA and for 

DDD, with aftercare and outpatient samples, (2) 

the same four tests of the matching hypothesis 

as it potentially changed in magnitude across 

time in a linear function, and (3) the same 

series of four tests of the matching hypothesis as it potentially changed in magnitude 

across time in a quadratic function.

Figure 1 shows the mean monthly 

trans-formed PDA values by the dichotomized meaning-seeking attribute (low: 0 or 

negative score, high: ≥+1) for outpatients 

in the TSF condition and in the combined 

CBT and MET conditions. Consistent with 

our predictions, high meaning-seeking 

clients assigned to TSF reported a higher 

frequency of abstinent days than did low 

meaning-seeking TSF clients. Twelve 

months after treatment, for example, high 

meaning-seeking clients assigned to the 

TSF condition reported, on average, 7 percent more abstinent days relative to low 

meaning-seeking clients assigned to the TSF 

group. Contrary to expectations, however, 

this same trend—albeit a smaller, 2-percent difference in abstinent days 12 months 

after treatment—was found in the non-TSF 

groups, although as reported earlier, the overall prognostic relationship was not statistically 

significant.

Table 1 provides the probability values 

associated with the results of the overall and 

time-bound matching tests of the meaningseeking attribute for the within-treatment 

and posttreatment periods for both primary 

dependent measures. None of the planned 

tests (n=12) was significant, and only modest 

variation in matching results was found across 

outpatient sites.

Also reported in table 1 are the unplanned 

pairwise matching contrasts for the meaningseeking attribute. None of these 36 post hoc 

contrasts supported the matching hypothesis. 

Thus, for outpatients, we found no support for 

the prospective and post hoc hypothesis predicting a differential treatment response in 

frequency and intensity of drinking between 

TSF and the combined CBT and MET groups 

on the basis of the meaning-seeking attribute.

Causal Chain Analyses

The hypothesis predicted that high meaningseeking clients would feel more comfortable, 

and be more receptive to, the content of the 

TSF modality relative to low meaning-seeking clients. This positive response of higher 

Figure 1. Comparison of high versus low meaning-seeking outpatient clients assigned to TSF and 

combined CBT and MET. (Percentage of days abstinent for the 12 months after treatment completion.)
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Part IV: Personality Variables

Table 1. Summary of a priori and post hoc meaning-seeking matching tests: Outpatient sample 

(N= 952) (probability values)

Planned contrast Unplanned contrasts

TSF vs. CBT+MET TSF vs. CBT TSF vs. MET CBT vs. MET

During treatment

Percentage of days abstinent .85 (.87)1 .59 .83 .47

Percentage of days abstinentLinear .60 (.54) .81 .51 .69

Percentage of days abstinentQuadratic .78 (.94) .82 .49 .38

Drinks per drinking day .90 (.86) .62 .78 .46

Drinks per drinking dayLinear .75 (.82) .62 .96 .67

Drinks per drinking dayQuadratic .99 (.89) .87 .87 .75

Posttreatment: Months 4–15

Percentage of days abstinent .32 (.43)1 .51 .30 .70

Percentage of days abstinentLinear .90 (.79) .72 .57 .38

Percentage of days abstinentQuadratic .13 (.11) .12 .30 .63

Drinks per drinking day .27 (.46) .55 .51 .95

Drinks per drinking dayLinear .54 (.70) .77 .71 .93

Drinks per drinking dayQuadratic .22 (.21) .12 .56 .35

1Probability values not controlling for site × treatment × matching attribute interaction are in parentheses.

meaning-seeking clients in TSF was predicted to 

be manifest in greater satisfaction with therapy, 

higher AA attendance and therapy compliance, 

and more involvement in AA. Higher involvement in AA was, in turn, expected to lead to 

more positive outcomes. Client meaning-seeking was expected to be unrelated to the content 

and processes of the CBT and MET conditions.

Several analytic strategies were used to test 

the adequacy of the meaning-seeking causal 

chain. Structural equation modeling (Byrne 

1994) was initially chosen to test underlying assumptions of the model, but deletion of 

cases because of missing process data reduced 

the outpatient sample by 29 percent and the 

aftercare sample by 33 percent. The approach 

eventually adopted was hierarchical multiple 

regression analyses which, after controlling 

for site, matching attribute, and treatment 

main effects, tested the matching attribute by 

treatment product term (i.e., meaning-seeking by TSF versus CBT+MET combined). In 

this analysis, rejection of the null hypothesis 

for the product term would indicate a differential relationship between meaning-seeking 

and the criterion measure (a variable in our 

causal model) by the specified treatment contrast. Unstandardized and standardized beta 

weights were examined to assess the nature of 

any observed differential relationship between 

meaning-seeking and a causal variable nested 

within treatment conditions.

Table 2 summarizes the causal chain analyses for the outpatient sample. Complete 

support for the prospective model would result 

in p values <.05 under the column heading 

Interaction, indicating a different relationship 

(by treatment condition) between meaningseeking and listed causal variables. Complete 

support for the model would then have large 

and positive beta weights for TSF and nonsignificant beta weights for CBT+MET.

As shown, findings were mixed in their 

support of the proposed causal chain. Client 

meaning-seeking was unrelated to client report 

of goal compatibility and task compatibility in 

the TSF condition and was negatively related 

with report of goal compatibility and task 

compatibility in the combined CBT and MET 

conditions. Trends for client satisfaction with 
159

 The Search for Meaning in Life as Predictor of Alcoholism Treatment Outcome

Table 2. Summary of causal chain analyses for 

outpatient sample (N=952)

Causal (criterion) 

variable Interaction TSF

CBT+ 

MET

Therapy process

Client Satisfaction With 

Treatment p<.067 .03 .15

WAI: Relationship Bond p<.051 .04 -.10

WAI: Goal Compatibility p<.021 -.01 -.18

WAI: Task Compatibility p<.003 -.02 -.14

Percent Therapy 

Sessions Attended p<.231 -.11 -.10

AA attendance

Months 1–3 p<.015 .13 .04

Months 4–6 p<.041 .14 .09

Months 6–9 p<.160 .09 .03

AA involvement

Months 1–3 p<.961 .12 .16

Months 4–9 p<.870 .09 .10

NOTE: WAI refers to client ratings of the therapeutic relationship (at Week 2 of treatment) using the Working Alliance 

Inventory (Horvath and Greenberg 1986). Unstandardized β 

weights.

treatment and bond with the therapist similarly 

reflected a negative relationship with meaningseeking in the CBT+MET condition but not for 

TSF clients. It appears, therefore, that high 

meaning-seeking clients found the goals and 

tasks of TSF less unacceptable than those of 

the CBT and MET clients combined.

A core assumption was supported by the 

finding that meaning-seeking predicted AA affiliation in the TSF group but not in the CBT and 

MET groups. Regression analyses for AA attendance excluded outliers (AA values exceeding 

3 SD from treatment group mean), although 

findings did not substantially differ when outlier cases were retained in the analyses. Both 

during treatment (months 1–3) and for the 

3 months immediately following treatment 

(months 4–6), meaning-seeking was positively 

and significantly predictive of AA meeting 

attendance for TSF clients, and meaning-seeking was marginally related to AA attendance in 

the CBT and MET groups. The predicted relationship was not observed for our measure of 

AA involvement (Tonigan, Connors et al. 1996).

Figure 2 demonstrates a clear and consistent ranking across 39 months of followup of 

a trichotomized meaning-seeking variable and 

percentage of days AA attendance as predicted 

for TSF clients. Mean values of the meaningseeking variable were highest for the high 

meaning-seekers, followed by the medium 

and low meaning-seekers (at all followups), 

and post hoc scheffe tests indicated that these 

differences were significant at 3 (p<.006), 12 

(p<.041), and 15 (p<.016) months followup.

Figure 2. Trichotomized meaning-seeking 

attribute for TSF outpatient clients.

Did the positive association between meaning-seeking and AA attendance for outpatient 

TSF clients become manifest in positive outcome? Two hierarchical multiple regression 

analyses were conducted to examine this question, with summary measures of frequency 

(PDA) and intensity (DDD) of drinking (months 

4–9) separately serving as criterion measures. 

Site, baseline drinking (PDA and DDD), treatment, and meaning-seeking main effects were 

entered first, followed by main effect of percentage of days AA attendance and, in the 

third block, the product term representing percentage of days AA by the a priori treatment 

contrast (TSF versus CBT+MET).

The product term with PDA at proximal followup as the dependent measure (months 4–9) 

was not significant (p<.13), indicating no differential relationship between AA attendance and 

frequency of abstinent days by treatment condition (TSF vs. CBT+MET). The main effect of AA, 

after controlling for site, treatment, baseline 
160

Part IV: Personality Variables

drinking, and meaning-seeking main effects 

was significant (p<.0001) and accounted for 4 

percent of variance in frequency of drinking for 

the first 6 months after the end of treatment. 

The slope associated with the AA predictor (b= 

0.44, t(844)=6.28) was positive, indicating that 

higher rates of AA attendance during treatment 

were predictive of higher frequency of abstinent 

days during followup.

With drinking intensity as the dependent 

measure, a significant differential relationship was obtained between percentage of days 

AA attendance during treatment and drinking 

intensity (months 4–9) by treatment condition 

(TSF versus CBT+MET), t(843)=2.14, p<.03. 

Inspection of DDD regressed on percentage 

of days AA indicated that, within TSF, DDD 

was negatively and significantly related with 

AA attendance (b=-1.68, p<.001), and that AA 

attendance and drinking intensity were not 

related in the combined CBT and MET conditions (b=-0.80, p<.06). These findings are 

consistent with the prospective model described 

above.

Aftercare Sample

Prognostic Effect of Meaning-Seeking

Prognostic effects of the meaning-seeking 

variable on PDA and DDD were examined during 

treatment and for the 12 months after treatment. These effects were studied in the context 

of HLM and, like the analyses for the outpatient 

sample, these analyses controlled for variation 

accounted for by site, treatment, and matching 

attribute main effects and their interactions. 

Baseline meaning-seeking was not predictive 

of PDA either during treatment (p<.19) or for 

the 12 months after treatment (p<.11) in the 

aftercare sample. This finding was consistent 

across the aftercare sites as indicated by the 

nonsignificant site by meaning-seeking interaction terms. Unlike the outpatient sample, 

meaning-seeking and drinking intensity were 

statistically unrelated in the aftercare sample during treatment (p<.06) and remained at 

the trend level of a relationship during the 12 

months after treatment (p<.06). Little variation 

across aftercare sites was identified in regard 

to this trend finding.

Testing of the Primary Matching 

Hypothesis

Statistical tests of the meaning-seeking 

hypothesis in the aftercare sample were parallel to the tests conducted in the outpatient 

sample. Twenty-four tests were conducted; 

12 of these focused on the treatment phase of 

the study, and 12 tests examined the matching hypothesis during the 12 months after 

treatment.

Figure 3 shows the monthly transformed PDA 

outcomes for high versus low meaning-seeking 

aftercare clients in TSF and in the combined 

non-TSF conditions (low: 0 or negative score, 

high: ≥1). In the aftercare TSF condition, high 

meaning-seeking clients fared somewhat less 

well during early followup (10 percent fewer 

abstinent days in month 4, p<.03), but this 

pattern reversed into the predicted direction 

during later months. Contrary to our prediction 

that meaning-seeking would be less related to 

PDA in the non-TSF conditions, low meaningseeking clients consistently fared better than 

did high meaning-seeking clients throughout 

the followup period. Differences in PDA outcome between high and low meaning-seeking 

in the non-TSF conditions were significant for 

followup months 7–15, with the largest monthly 

difference representing 10-percent fewer abstinent days for high meaning-seeking clients.

Table 3 gives the probability values associated 

with the 12 matching tests of the meaningseeking hypothesis in the aftercare sample. No 

support was found for the hypothesis during 

Figure 3. Comparison of high-low meaningseeking aftercare clients assigned to TSF and 

combined CBT and MET. (Percentage of days 

abstinent 12 months after treatment.)
161

 The Search for Meaning in Life as Predictor of Alcoholism Treatment Outcome

Table 3. Summary of a priori and post hoc meaning-seeking matching tests: Aftercare sample 

(N=774)(probability values)

Planned contrast Unplanned contrasts

TSF vs. CBT+MET TSF vs. CBT TSF vs. MET CBT vs. MET

During treatment

Percentage of days abstinent .95 (.92)1 .65 .57 .26

Percentage of days abstinentLinear .63 (.79) .77 .57 .75

Percentage of days abstinentQuadratic .16 (.69) .49 .07 .21

Drinks per drinking day .37 (.58) .68 .24 .39

Drinks per drinking dayLinear .77 (.88) .59 .99 .54

Drinks per drinking dayQuadratic .50 (.94) .68 .43 .67

Posttreatment: Months 4–15

Percentage of days abstinent .27 (.05) .09 .78 .12

Percentage of days abstinentLinear .01 (.003) .04 .02 .69

Percentage of days abstinentQuadratic .07 (.02) .10 .10 .99

Drinks per drinking day .57 (.33) .29 .96 .22

Drinks per drinking dayLinear .49 (.27) .38 .72 .57

Drinks per drinking dayQuadratic .16 (.05) .13 .32 .55

1Probability values excluding the site × treatment × matching attribute interaction are in parentheses.

the treatment phase of the study on either PDA 

or DDD. A significant attribute by treatment 

(TSF versus CBT+MET) by timeLinear interaction was found, however, on the PDA measure 

for followup months 4–15. This nondirectional 

interaction test was significant after Bonferroni 

correction for type 1 error, indicating that the 

magnitude of the difference in slopes for the 

specified treatment contrast varied across time. 

Monthly followup tests were conducted to ascertain if the change in slopes across time was in 

the predicted direction, and this was partially 

confirmed. In this regard, monthly contrasts 

for months 5 through 14 were p<.05, with only 

baseline drinking covaried; monthly contrasts 

for months 11, 12, and 13 were p<.10 when 

adjusted for baseline drinking, site, and site by 

time effects. No support was found for the predicted meaning-seeking hypothesis using the 

DDD primary outcome measure during the 12 

months of followup. Inspection of directional 

monthly contrasts suggested that the absence 

of a differential treatment response between 

meaning-seeking and drinking intensity was 

consistent across time, and little variation 

was observed between meaning-seeking and 

drinking intensity when crossed by site and 

time.

Table 3 also gives the results of 36 

unplanned pairwise contrasts of the matching 

hypothesis. As shown, two contrasts, both for 

PDALinear months 4–15, were significant. These 

contrasts involved separately testing CBT and 

MET against TSF and indicate that the planned 

contrast that was significant (TSF versus 

CBT+MET, PDALinear months 4–15) was not the 

result of either CBT or MET “pulling” the combined outcome for these groups in a particular 

direction. In this regard, the contrast of CBT 

and MET (PDALinear months 4–15) should not be 

significant, which was the case.

Causal Chain Analyses

The prospective hypothesis was partially supported in the aftercare sample, but only on the 

abstinence measure and only midway through 

the 12 months of followup. Examination of the 

causal mechanisms underlying the hypothesis was undertaken to untangle the complex 

matching findings. As before, hierarchical 

regression analyses were conducted with attention directed to the interaction term as evidence 
162

Part IV: Personality Variables

of a differential relationship between meaningseeking and causal variables. Likewise, extreme 

values of percentage of days AA attendance led 

to the removal of a limited number of cases, 

and findings did not differ when these cases 

were included in the analyses.

Table 4 gives the results of the causal chain 

analyses for the aftercare sample. Surprisingly, 

despite the partial support for the matching 

hypothesis in the aftercare sample on the frequency of drinking measure, none of the tests 

of a differential relationship between meaningseeking and causal variables was significant. 

Specifically, no differential relationship was 

found between meaning-seeking and therapy 

process, AA attendance, or AA involvement measures. The only trend (p<.10) was in a direction 

opposite to that predicted: Meaning-seeking 

was negatively related with AA involvement 

during treatment for TSF clients.

Additional analyses were conducted to identify where the meaning-seeking assumptions 

went astray. A major concern was the discrepancy between outpatient and aftercare samples 

in the relationship between meaning-seeking 

and subsequent AA attendance for TSF clients. 

Many aftercare clients received exposure to AA 

principles and attended AA meetings during 

their formal inpatient treatment experience. In 

tandem with the fact that the TSF modality was 

highly effective in boosting AA attendance, it is 

plausible that the proposed relationship was 

overwhelmed in the aftercare sample because 

of uniformly high AA attendance rates in each 

of the three treatment conditions.

We reasoned that while meaning-seeking 

may not have predicted differential rates of 

AA exposure at specific followup points, the 

matching variable may have predicted different 

patterns of AA attendance across time, which, 

in turn, might explain the partial matching effect. Tonigan and colleagues (in press) 

reported, for example, that discrete patterns of 

AA attendance in Project MATCH included the 

following categories:

1. No AA attendance

2. AA attendance during the 12 weeks of 

treatment only

3. AA attendance during treatment with 

some attendance during followup up to 

but not past month 9

Table 4. Summary of causal chain analyses 

for aftercare sample (N=774)

Causal (criterion) 

variable Interaction TSF

CBT+ 

MET

Therapy process

Client Satisfaction With 

Treatment p<.939 .10 .06

WAI: Relationship Bond p<.450 -.12 -.04

WAI: Goal Compatibility p<.737 -.09 -.11

WAI: Task Compatibility p<.890 -.08 -.04

Percent Therapy 

Sessions Attended p<.239 .04 -.07

AA attendance

Months 1–3 p<.804 .06 .07

Months 4–6 p<.780 .04 .02

Months 6–9 p<.361 .11 .04

AA involvement

Months 1–3 p<.060 -.10 .08

Months 4–9 p<.153 -.07 .05

NOTE: WAI refers to client ratings of the therapeutic relationship (at Week 2 of treatment) using the Working Alliance 

Inventory (Horvath and Greenberg 1986). Unstandardized β 

weights.

4. Sustained AA attendance during treatment and all followup months

5. Erratic AA attendance during and after 

treatment, with months between AA 

meeting attendance

Discarding the last category because of the 

limited number of cases, we conducted a Chi 

square test (4 × 6) to determine whether pattern of AA attendance (four categories) varied 

across the trichotomized meaning-seeking 

attribute nested within the a priori matching 

contrast (six categories).

Figure 4 depicts the interaction of interest 

(χ2(15)=29.80, p<.013), and offers substantial support for the underlying rationale of the 

meaning-seeking causal chain. In particular, about 60 percent of the medium and high 

meaning-seeking clients assigned to aftercare 

TSF attended AA throughout the treatment and 

followup phases of the study. In comparison, 

about 50 percent of the low meaning-seeking 

clients assigned to aftercare TSF reported sustained AA attendance. In contrast, high and 

medium meaning-seeking clients assigned to 

CBT and MET attended AA throughout the 
163

 The Search for Meaning in Life as Predictor of Alcoholism Treatment Outcome

Figure 4. Patterns of AA attendance by trichotomized meaning-seeking attribute within 

matching treatment contrast (aftercare sample). 1 = No AA attendance; 2 = AA attendance 

during 12 weeks of treatment only; 3 = AA attendance during treatment with some attendance 

during followup up to but not past month 9; 4 = 

Sustained AA attendance during treatment and 

all followup months.

study about as much as low meaning-seeking 

clients assigned to TSF.

An important question was, did this finding replicate in the outpatient sample? 

Findings (χ2(15)=203.41, p<.0001) suggest 

that the general relationship between patterns of AA attendance and meaning-seeking 

is stable in both the aftercare and outpatient samples. Specifically, high and medium 

meaning-seekers assigned to the outpatient 

TSF treatment reported more sustained AA 

attendance throughout the study than did 

low meaning-seekers in TSF. In addition, low 

meaning-seeking clients in TSF reported more 

sustained AA attendance than high meaningseeking clients assigned to either CBT or MET. 

Not predicted, proportionally more high meaning-seekers assigned to TSF discontinued AA 

attendance after treatment than did low and 

medium meaning-seekers assigned to TSF.

Discussion

What can be learned from these complex 

findings? Some aspects of the predicted causal 

chain were supported, and others were not. 

The TSF treatment did significantly increase 

AA attendance, relative to the CBT and MET 

groups, in both arms of the trial. We did not find, 

however, that high meaning-seeking clients 

were differentially enthusiastic and compliant 

with the TSF treatment, as we had predicted. If 

anything, the higher meaning-seekers were differentially unenthusiastic about the goals and 

tasks presented to them in outpatient CBT and 

MET, and this relationship was absent in TSF. 

Thus, although the expected pattern (positive 

slope for TSF and no relationship for other 

groups) was not found, the direction of differential preference was as predicted for outpatients.

Planned causal chain analyses did not 

explain the partially supported meaning-seeking matching effect in the aftercare sample. 

Our hypothesis that meaning-seeking would be 

more strongly and positively related with treatment compliance and AA attendance within 

TSF during treatment was not supported.

Was the absence of a differential relationship 

between meaning-seeking and AA attendance 

the result of the unexpected and high overall 

rate of AA attendance in the aftercare sample? 

Post hoc analyses support this interpretation. 

In particular, high and medium meaning-seeking clients assigned to aftercare TSF had more 

sustained AA attendance throughout the study 

than low meaning-seeking clients in TSF. This 

relationship was tacitly assumed in the causal 

model but not explicitly specified in the a priori 

causal chain model.

We believe findings in the outpatient and 

aftercare samples can be meaningfully integrated. First, client meaning-seeking was 

predictive of different rates (outpatient) or 

patterns (aftercare) of AA attendance. In the 

outpatient sample, this relationship became 

manifest quickly because it was unconfounded 

by prior inpatient treatment emphasis on AA. 

In the aftercare sample, this relationship was 

confounded by initially high rates of AA attendance in all treatments, but became manifest 

in the latter part of the study. In both samples, 

the assumption that client meaning-seeking 

was positively predictive of comfortability with 

treatment tasks, goals, and therapist bonding 

was not supported. Whether these findings will 

be replicated with more detailed process-oriented variables warrants future research.
164

Part IV: Personality Variables

The key mechanism of increased AA attendance in the meaning-seeking causal model was 

supported in both samples, but the predicted 

matching effect was only partially obtained in 

the aftercare sample. Why? It is our belief that 

a focused and high threshold in formal 12-step 

emphasis is required to produce the matching 

effect, a threshold met in the aftercare sample 

but not for TSF outpatients. If true, 12-step 

focused therapies should consider methods to 

enhance treatment fidelity when implementing 

client treatment matching strategies.

Before drawing conclusions, some caveats 

are in order. First, Project MATCH was not 

designed as a study of AA per se, but only of 

a Twelve-Step Facilitation therapy as one of 

three approaches in treating alcohol problems. 

Measures of AA attendance and involvement 

such as those selected for use in this trial 

implicitly assume that AA is a homogeneous 

entity. In fact, there are substantial differences 

among AA groups, and stronger affiliation 

and prognostic profiles might emerge from 

analyses sensitive to AA group heterogeneity 

(Montgomery et al. 1993; Tonigan et al. 1995).

It is also possible that the measure of meaning-seeking used in this study, albeit normally 

distributed, was not optimal as an operational 

definition of hitting bottom. It showed only a 

weak prognostic relationship to treatment outcomes in general and largely failed to predict 

differential response to treatments. Future 

studies might seek a multivariate definition of 

hitting bottom that includes not only meaningseeking but other measures such as severity 

of negative consequences and dependence, 

depression and hopelessness, and perceived 

lack of personal control over drinking. Factor 

or cluster analytic strategies could be applied 

to identify the complex phenomenon of hitting 

bottom. Alternatively, a criterion-referenced 

definition could be derived by regression strategies. Although this contains some risk of logical 

circularity, hitting bottom is in fact popularly 

conceptualized as the final crisis that precedes 

recovery.

Acknowledgment

Preparation of this chapter was supported 

in part by grants U10–AA08435 and K05–

AA00133 from the National Institute on Alcohol 

Abuse and Alcoholism.

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Rychtarik, R.G. Motivational Enhancement 

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166

Religiosity and Responsiveness to 

Alcoholism Treatments

Gerard J. Connors, Ph.D., J. Scott Tonigan, Ph.D., and 

William R. Miller, Ph.D.

ABSTRACT

The proposition evaluated in this chapter is that clients who are more comfortable 

with religious beliefs and practices would derive greater benefit from a treatment that 

incorporated spiritual themes and practices. In terms of the Project MATCH treatments, it was expected that clients with higher levels of religiosity would benefit more 

from the Twelve Step Facilitation (TSF) treatment than would clients lower in religiosity. No relationship beyond a weak prognostic effect of religiosity on treatment 

outcome was predicted for clients in the Cognitive-Behavioral Coping Skills Therapy 

or Motivational Enhancement Therapy conditions. Tests of this matching hypothesis 

revealed no support for the predicted match among either the outpatient or aftercare 

populations sampled. The evaluation of the causal chain presumed to underlie the 

hypothesized matching effect showed limited support for the proposed chain among 

aftercare clients, where it was found that religiosity among the TSF clients was linearly 

related to the degree of therapeutic task compatibility. In terms of other analyses, it 

was found that aftercare clients reported greater religiosity at pretreatment than did 

outpatient clients and that pretreatment religiosity predicted positive posttreatment 

drinking outcomes. Taken together, religiosity did not emerge as a viable matching 

dimension with the treatments evaluated in Project MATCH, although it does appear 

that religiosity may play a role in the prediction of the therapeutic relationship among 

aftercare clients and of posttreatment drinking behavior. Future research on these 

relationships will help specify the role of religiosity in alcoholism treatment and identify circumstances under which religiosity might productively be focused on in the 

treatment endeavor.

Despite the consistent indication that 

religion plays a significant role in many 

people’s lives (e.g., Hoge 1996), there 

has been remarkably little research on the role 

of religiosity and religious beliefs in treatment 

specifically and the behavior change process 

more generally. This lack of systematic research 

attention has been evident in the general psycho-therapy literature as well as in addictions 

treatment research (Larson et al. 1998).

Project MATCH provided the opportunity to 

evaluate differential outcomes associated with 

response to three alcoholism treatments as a 

function of pretreatment levels of client religiosity. To our knowledge, no prospective study 

has examined whether client religiosity interacts with alcoholism treatment modalities to 

produce differential outcomes. In the arena of 

Gerard J. Connors, Ph.D.

Research Institute on Addictions

1021 Main Street

Buffalo, NY 14203

E-mail: connors@ria.buffalo.edu
167

Religiosity and Responsiveness to Alcoholism Treatments

treatment for depression, Propst (1980) found 

in a randomized trial that religiously oriented 

clients with depression fared better when spiritual themes were integrated into a cognitive 

restructuring intervention than when such 

material was omitted, regardless of the religious orientation of the therapist. Propst et 

al. (1992) subsequently replicated this finding in another clinical trial, demonstrating 

differential ineffectiveness of “nonspiritual” 

cognitive-behavioral therapy for religious-oriented depressed individuals when treated by 

nonreligious therapists.

As described in detail elsewhere (Project 

MATCH Research Group 1993), three treatments were employed in Project MATCH: 

Cognitive-Behavioral Coping Skills Therapy 

(CBT; Kadden et al. 1992), Motivational 

Enhancement Therapy (MET; Miller et al. 1992), 

and Twelve Step Facilitation (TSF; Nowinski et 

al. 1992). The use of these three treatments with 

two distinct clinical populations—outpatients 

and aftercare clients—permitted the evaluation 

of predictions regarding differential outcomes 

associated with extent of client religiosity and 

participation in these three treatments. The 

proposition developed for evaluation in this 

chapter is based on a “comfort model” between 

client characteristics and treatment philosophy 

and content. In this regard, it was expected that 

clients who are more comfortable with religious 

beliefs and practices would derive greater benefit from a treatment that incorporated spiritual 

themes and practices. In terms of the Project 

MATCH treatments, it was expected that clients 

with higher levels of religiosity would benefit 

more from the TSF treatment than would clients lower in religiosity. No relationship beyond 

a weak prognostic effect of religiosity was predicted for the CBT and MET conditions.

There are a variety of ways in which spiritual dimensions manifest themselves in a 

12-step-based intervention. A review of the 12 

steps reveals a heavy emphasis on faith in God 

(Step 2), surrender to God’s will (Step 3), prayer 

and continued meditation (Step 11), and other 

spiritual behaviors (Steps 8–10). A therapeutic focus on these issues was hypothesized to 

be more comfortable, acceptable, and effective 

specifically for clients more attuned to and/or 

in need of personal religiosity.

The TSF treatment used in Project MATCH 

was designed in part to encourage and facilitate 

attendance at and participation in Alcoholics 

Anonymous (AA) meetings. There have been 

modest positive correlations reported between 

client religious and spiritual practices and AA 

affiliation. For example, extent of religious and 

spiritual activities, such as prayer and meditation, has been found to be positively related 

to AA affiliation (Fichter 1982; Laundergan 

and Kammeier 1978). Relatedly, Harris et al. 

(1990) reported that individuals who felt that 

God actively worked in their lives were significantly more likely to affiliate and that direct 

experiences of God also were positively related 

to affiliation.

The present chapter describes the evaluation 

of the matching hypothesis that individuals 

higher in religiosity would benefit more from 

the TSF intervention than would clients lower 

on this dimension, and that there would be 

no such relationship (beyond a weak prognostic effect) among clients receiving the CBT 

and MET interventions. In addition, the causal 

chain developed to account for the proposed 

match is described and evaluated.

Methods

Operationalizing Religiosity

The total score from the Religious Background and Behaviors (RBB) questionnaire 

(Connors et al. 1996) was used to assess religious practices. The RBB is composed of 13 

items. On the first item, clients identified the 

term that best describes them: atheist, agnostic, unsure, spiritual, religious. On the following 

six items, the clients indicated (on an 8-point 

Likert scale) the frequency with which they had 

engaged in the following behaviors during the 

past year: thought about God, prayed, meditated, attended worship services, read/studied 

scriptures/holy writings, and had direct experiences of God. The last six items assessed 

these domains in terms of lifetime occurrence 

on a 3-point ordinal scale. The item content 

was intended to capture behaviors traditionally associated in the literature with religiosity. 

Past year and lifetime assessments were used 

so that recent religious behaviors could be 

identified.
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Part IV: Personality Variables

The first item of the RBB was assigned a 

score from 0 to 4, ranging from atheist to religious (atheist=0, agnostic=1, and so forth). The 

remaining items were recoded before summing 

to calculate summary scale scores. Specifically, 

each of the remaining responses was reset such 

that 1=0, 2=1, 3=2, 4=3, and so forth. This procedure was implemented to establish an RBB 

scale scoring floor of zero (rather than 13).

Psychometric evaluation of the RBB 

(Connors et al. 1996) has shown that the 

measure comprises two factors, labeled God 

Consciousness and Formal Practices, and that 

the RBB possesses excellent test-retest reliability and satisfactory internal consistency. In this 

regard, the test-retest correlation over a 3-day 

interval was found to be 0.97, and the internal 

item consistency for the combined study arms 

(outpatient and aftercare) at intake to be 0.86 

(N=1637).

Statement of the Matching Hypothesis

A single between-group slope contrast 

was proposed with regard to the interaction 

of pretreatment religiosity and treatment in 

influencing two drinking outcome variables: 

percentage of days abstinent (PDA) and drinks 

per drinking day (DDD). Specifically, it was 

predicted that (a) religiosity and PDA would 

be significantly and positively related within 

the TSF condition, with a weaker relationship 

manifest within the combined CBT and MET 

conditions and (b) religiosity and DDD would 

be significantly and negatively related within 

the TSF condition, with a weaker relationship 

manifest within the combined CBT and MET 

conditions.

Statement of the Causal Chain

It was predicted that the TSF therapeutic focus would be more acceptable to clients 

reporting such behaviors as regular practice of 

prayer, meditation, direct experiences of God, 

and reading of scriptures. This acceptability 

was predicted to become manifest in higher 

rates of TSF therapy attendance (relative to 

clients with lower levels of religiosity), higher 

rates of AA attendance and involvement during 

the 12 weeks of treatment, and more positive 

client reports of the client-therapist therapeutic affiance (as measured in the second week 

of treatment using the Horvath and Greenberg 

(1986) Working Alliance Inventory (WAI)). It was 

believed that these facets of the therapeutic 

process combined would lead to more positive 

posttreatment outcomes.

Results

Preliminary Analyses

General sample characteristics for the two 

arms of the study are described elsewhere (Project 

MATCH Research Group 1997). Examination 

of baseline RBB total scores indicated that 

aftercare clients reported significantly higher 

(p<.01) mean RBB scores (M=38.61, SD=11.31) 

than outpatients (M=35.36, SD=10.94), with 

no main effect of gender on RBB mean scores 

(p<.06). Intake RBB total scores were weakly 

and positively related with AA attendance in 

the 90 days prior to study recruitment (r=0.11 

for outpatients, r=0.13 for aftercare clients), 

and involvement in AA for the year prior to 

recruitment was moderately related with RBB 

total scores (r=0.22 for outpatients, r=0.27 for 

aftercare clients).

Virtually no relationship was found within 

study arm (outpatient, aftercare) between RBB 

scores and measures of psychiatric severity (Alcohol Severity Index (ASI; McLellan et 

al. 1980) psychiatric severity subscale and 

the Beck Depression Inventory (BDI; Beck et 

al. 1961)), with the largest absolute value of 

r equal to 0.05. In terms of the two primary 

dependent measures (PDA and DDD, assessed 

at baseline), RBB scores were more related to 

PDA, having a weak association in the aftercare arm (r=0.15) and a weaker yet association 

in the outpatient arm (r=0.08). Consistent with 

measures of alcohol consumption, RBB scores 

were unrelated or weakly related to adverse 

consequences reported by aftercare clients 

(r=0.01) and outpatients (r=0.10).

Aftercare Sample

Prognostic Effect of RBB

The prognostic effect of baseline RBB scores 

on treatment outcome was examined in two 

phases. First, the univariate correlations 

between scores on the RBB and the trial’s two 
169

Religiosity and Responsiveness to Alcoholism Treatments

primary outcome measures (PDA and DDD) 

were calculated for the 6-month period following the end of treatment. The correlation 

between RBB scores and PDA was 0.08 (p<.05) 

and between RBB scores and DDD was -0.02 

(p>.50). Looking at the RBB subscales, the 

correlations between the God Consciousness 

subscale scores and PDA and DDD, respectively, were 0.03 (p>.40) and 0.02 (p>.50). The 

correlations between the Formal Practices subscale scores and PDA and DDD, respectively, 

were 0.10 (p<.01) and -0.05 (p>.15).

Next, the prognostic effect of RBB scores on 

treatment outcome was examined after controlling for baseline drinking, site, treatment, and 

time (linear and quadratic) main effects and 

their interactions. Analyses of the prognostic 

effects of this secondary matching variable on 

the trial’s two primary outcome measures (PDA 

and DDD) were thus derived from the larger 

hierarchical linear modeling (HLM) analyses 

examining potential matching effects of the 

RBB variable.

A significant and positive main effect of the 

RBB on PDA for months 4–15 was found after 

controlling for baseline drinking, site, time, 

and treatment main effects and their interactions (p<.01). This finding indicated that RBB 

scores at intake were linearly related to PDA 

across the 12-month posttreatment period. 

Post hoc dismantling of the total RBB measure 

into its two subscales (God Consciousness 

and Formal Practices) suggested that extent 

of self-reported formal religious practices (e.g., 

regularity of church attendance) accounted 

for the overall prognostic effect. Although RBB 

scores positively predicted the frequency of 

abstinence (i.e., PDA), no relationship in the 

aftercare arm was found between baseline RBB 

scores and drinking intensity (i.e., DDD) during 

the 12 months of followup (p<.48). Little variation by site was found (p<.16), and examination 

of the relationship between drinking intensity 

and the two subscales of the RBB likewise suggested the absence of a statistically or clinically 

significant finding.

Matching Effects

The formal RBB matching hypothesis stated 

that RBB and posttreatment PDA would be 

positively related within the TSF condition and 

that a weaker relationship would be found 

between RBB and PDA within the combined 

CBT and MET conditions. It was also predicted 

that RBB and DDD would be negatively related 

in the TSF condition and, again, that a weaker 

relationship would be found between RBB 

and DDD within the combined CBT and MET 

conditions.

Testing of the RBB matching hypothesis 

was conducted using the HLM procedure. 

Discussed in detail elsewhere (Project MATCH 

Research Group 1997; Longabaugh and Wirtz, 

this volume, pp. 4–17), this procedure led to the 

testing of the a priori contrast in three ways: (1) 

an overall matching effect involving slope comparisons of TSF versus CBT and MET collapsed 

across time, (2) a contrast of TSF and CBT and 

MET slopes as they changed across time in a 

linear function, and (3) changes across time in 

the specified contrast in a quadratic context. 

Thus, conducted separately for aftercare and 

outpatient samples, 6 significance tests (3 tests 

× 2 dependent measures, PDA and DDD) were 

used to evaluate the RBB matching hypothesis.

Table 1 summarizes the probability values 

derived from the HLM analyses using PDA and 

DDD as the primary dependent measures in the 

a priori RBB secondary matching hypothesis. 

Findings are presented for the 12-week phase 

of treatment labeled “during treatment,” and 

for the 12-month posttreatment period labeled 

“after treatment.” Along with the planned 

contrast labeled TSF versus CBT+MET, three 

unplanned pairwise contrasts are reported 

for exploratory purposes. None of the a priori 

contrasts supported the prediction of a differential relationship by treatment between 

intake RBB scores and PDA or DDD during 

treatment or during the 12 months of followup. 

Likewise, none of the exploratory post hoc pairwise contrasts supported the hypothesis that 

the relationship between PDA and RBB scores 

would vary by treatment condition.

Casual Chain Analyses

Lack of support for the RBB secondary matching hypothesis led us to question 

whether the proposed therapeutic mechanisms 

failed to become operative during treatment as 

predicted or, instead, whether proposed mechanisms be-came operative during treatment 
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Part IV: Personality Variables

Table 1. Summary of aftercare HLM religiosity and responsiveness to treatment 

hypothesis tests: Probability values associated with tests of during and after 

treatment client-treatment matching

Predicted match Unplanned matching contrasts

TSF vs. MET+CBT TSF–CBT TSF–MET CBT–MET

During treatment

PDA .39 .43 .50 .91

PDA linear .91 .92 .92 .69

PDA quadratic .19 .09 .09 .24

DDD .21 .30 .25 .91

DDD linear .75 .83 .73 .90

DDD quadratic .19 .52 .11 .34

Twelve months posttreatment

PDA .18 .21 .30 .82

PDA linear .45 .36 .70 .60

PDA quadratic .56 .69 .54 .83

DDD .40 .64 .32 .60

DDD linear .39 .40 .52 .83

DDD quadratic .84 .78 .94 .83

but failed to influence treatment outcomes as 

predicted. Path analyses were used to examine 

these questions, dividing treatment outcomes 

as either proximal (months 4–9) PDA and DDD 

or as distal (months 10–15) PDA and DDD. 

Several strategies were evaluated in deriving 

estimates of posttreatment drinking outcome. 

Monthly PDA and DDD values were considered 

most consistent with the HLM analyses, but 

conducting 12 path analyses for each primary 

dependent measure would result in 48 analyses across the two study arms, which would 

be clearly undesirable from a type 1 error 

rate perspective. Alternatively, selection of a 

few months to examine the proposed mechanisms had merit, but how were months to be 

selected? In the end, we considered the proximal and distal measures to be both clinically 

relevant and more stable indices of posttreatment functioning.

Figure 1 shows the path analytic findings 

separately for the TSF and combined CBT 

and MET conditions in the aftercare sample. 

The first link in the causal chain predicted a 

more positive relationship between the three 

measures depicting therapeutic working alliance collected after the second week of therapy 

and pretreatment RBB scores in the TSF condition relative to the combined CBT and MET 

conditions. This prediction was partially supported. In the TSF condition, pretreatment RBB 

scores were significantly and positively related 

to client agreement on TSF therapeutic tasks, 

but not to agreement on therapeutic goals and 

client-therapist bonding. Also predicted, these 

measures describing therapeutic alliance were 

unrelated to pretreatment RBB scores in the 

combined CBT and MET groups.

The second link of our causal model predicted that increased comfortableness with 

therapy at Week 2 would result in higher rates 

of AA attendance during treatment, increased 

therapy compliance—measured by therapy 

attendance—and greater satisfaction with therapy when measured at the 3-month followup. 

Only one of nine possible relationships in the 

TSF path analysis supported this prediction, 

i.e., WAI goal predicting satisfaction with treatment (β=0.26, p<.05). Thus, we concluded that 

the second link in our casual model was not 

tenable. Finally, we proposed that increased AA 

and therapy attendance and satisfaction with 

treatment would produce more positive outcomes. This prediction received strong support 

on both primary dependent measures at proximal and distal followup periods for both TSF 

and the combined CBT and MET groups.
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Religiosity and Responsiveness to Alcoholism Treatments

Figure 1. Comparison of aftercare TSF and combined CBT and MET path analysis models assessing religiosity as a differential predictor of treatment response. For the PDA and DDD outcomes, 

the superscripts (1, 2, 3) indicate that in each case the first number provided (before the slash) is 

the result for the proximal (months 4–9 posttreatment period) outcome, and the second number 

after the slash is the result for the distal (months 10–15 posttreatment period) outcome.

Outpatient Sample

Prognostic Effects of RBB

The correlations between baseline RBB 

scores and PDA and DDD (for the 6-month 

period following treatment), respectively, were 

0.08 (p<.05) and -0.07 (p<.10). The correlations between the God Consciousness subscale 

scores and PDA and DDD, respectively, were 

0.09 (p<.01) and -0.04 (p>.25). The correlations 

be-tween the Formal Practices subscale scores 

and PDA and DDD, respectively, were 0.06 

(p<.10) and -0.07 (p<.05).

The prognostic effect of RBB scores on outcome next was examined after controlling for 

baseline drinking, site, treatment, and time 

(linear and quadratic) main effects and their 

interactions. As before, these analyses of the 

prognostic effects of this secondary matching variable on the PDA and DDD were thus 

derived from the larger HLM analyses examining potential matching effects of the RBB 

variable. Baseline RBB scores and frequency 

of posttreatment abstinence were not related 

in the outpatient arm (p<.29). Examination of 

the RBB interactions with site and time indicated minimal variation, and, hence, limited 

qualification to these main effect analyses. 

RBB scores, however, were significantly and 

negatively predictive of posttreatment drinking intensity such that higher baseline RBB 

scores were predictive of less DDD during the 

12 months of posttreatment followup (p<.044). 
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Part IV: Personality Variables

Table 2. Summary of outpatient HLM religiosity and responsiveness to treatment 

hypothesis tests: Probability values associated with tests of during and after 

treatment client-treatment matching

Predicted match Unplanned matching contrasts

TSF vs. MET+CBT TSF–CBT TSF–MET CBT–MET

During treatment

PDA .38 .43 .48 .90

PDA linear .79 .88 .75 .89

PDA quadratic .98 .92 .95 .88

DDD .55 .64 .57 .95

DDD linear .71 .67 .83 .82

DDD quadratic .99 .83 .84 .69

Twelve months posttreatment

PDA .80 .95 .80 .86

PDA linear .62 .78 .25 .17

PDA quadratic .34 .09 .91 .07

DDD .77 .86 .75 .90

DDD linear .88 .20 .28 .02

DDD quadratic .85 .96 .69 .67

Study of the two RBB subscales showed that 

much of this prognostic effect was accounted 

for by the Formal Practices scale.

Matching Effects

The a priori RBB matching hypothesis was 

identical for outpatient and aftercare clients, 

and the proposed mechanisms in our causal 

model paralleled those described for the aftercare sample. Table 2 provides the probability 

values associated with the HLM tests of the 

during and after treatment periods using the 

two primary dependent measures. Unplanned 

pairwise contrasts are also provided for exploratory purposes. As shown, none of the planned 

contrasts for the RBB matching hypothesis 

were supported on either dependent measure 

either during or after treatment. Likewise, 

accounting for the large number of significance 

tests, no support was found for a differential 

effect of pretreatment RBB scores in any of the 

unplanned pairwise contrasts reported in table 

2. Consistent with findings in the aftercare 

sample, no support was found for asserting 

that pretreatment religiosity is an important 

consideration in assigning clients to treatments 

similar to those offered in Project MATCH.

Causal Chain Analyses

Figure 2 shows the path analysis models 

depicting the proposed mechanisms underlying 

the RBB a priori hypothesis. Like the aftercare analyses, treatment outcome was divided 

according to proximal (months 4–9) and distal 

(months 10–15) periods. Standardized coefficients (β) indicated that the first link of our 

model was not supported in the TSF condition. 

Specifically, pretreatment RBB scores were 

not predictive of client perceptions of therapeutic working alliance at 2 weeks in the TSF 

condition. Further, with one exception (WAI 

task agreement and AA meeting attendance, 

β=0.25, p<.05), perceptions of therapeutic 

working alliance were not predictive of mechanisms proposed to effect differential outcomes, 

i.e., increased AA and therapy attendance and 

greater treatment satisfaction. As among the 

aftercare participants, the expectation that the 

increased AA and therapy attendance and satisfaction with treatment would produce more 

positive outcomes was generally supported 

for both TSF and the combined CBT and MET 

groups. Thus, the mechanisms proposed in 

the model to effect change (AA attendance, 

therapy attendance, treatment satisfaction) did 
173

Religiosity and Responsiveness to Alcoholism Treatments

Figure 2. Comparison of outpatient TSF and combined CBT and MET path analysis models assessing religiosity as a differential predictor of treatment response. For the PDA and DDD outcomes, 

the superscripts (1, 2, 3) indicate that in each case the first number provided (before the slash) is 

the result for the proximal (months 4–9 posttreatment period) outcome, and the second number 

after the slash is the result for the distal (months 10–15 posttreatment period) outcome.

so. Although these factors predicted change, 

the effect was not differential across treatment 

modalities as would be required for matching 

to occur.

Discussion

The formal testing of the hypothesis that pretreatment religiosity would be positively related 

to posttreatment drinking outcomes within the 

TSF condition and only weakly related within 

the combined CBT and MET conditions was 

not supported. As such, it does not appear that 

matching clients on the basis of religiosity to 

these alcoholism treatments will yield differential outcomes (at least as assessed in the context 

of percentage of days abstinent and drinks per 

drinking day).

The evaluation of the causal chain for the outpatients provided no support for the presumed 

underlying mechanisms of the hypothesis. In 

this regard, there was no differential relationship by treatment (TSF versus CBT and MET 

combined) between level of religiosity and therapeutic alliance. It would appear at the least that 

this matching hypothesis was not supported 

because of the erroneous assumption that a 

12-step approach would be more acceptable to 

clients higher in pretreatment religiosity.

A different picture emerged from the causal 

chain analysis for the aftercare clients. Here 
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Part IV: Personality Variables

it was found that religiosity within the TSF 

condition was positively related to one of the 

dimensions of the therapeutic alliance (task 

compatibility). There was no such relationship 

for aftercare clients in the other two treatment 

conditions. Thus, there was some support for 

the causal chain in the aftercare arm, although 

it did not manifest itself in increased engagement in the proposed active ingredients of TSF. 

However, the paths between this affiance component and AA attendance during treatment 

were not significant.

 Although the aftercare causal chain analyses provided support for certain links in 

the chain, most links were unconnected. For 

example, therapeutic alliance did not lead to 

greater levels of AA attendance during treatment. Nevertheless, the finding that religiosity 

was positively related to one aspect of therapeutic alliance among aftercare clients is 

noteworthy and might be pursued productively 

in subsequent research on engaging clients in 

the therapeutic process and on the working 

alliance between clients and therapists more 

generally.

The results discussed above concern the 

role of religiosity in differential relation to the 

TSF and combined CBT and MET treatments. 

However, there were other, more general, findings that are noteworthy and have implications 

for future clinical research. First, it was of 

interest that aftercare clients reported greater 

levels of religiosity than did outpatients. It is 

possible that the relationship between religiosity and therapeutic affiance in a 12-step 

facilitation treatment will be evident in clients 

with an overall higher level of religiosity (as in 

the case of these aftercare clients). However, 

this possibility is speculative, and there exists 

a host of other explanations that might account 

for that relationship emerging in this aftercare 

population.

A second finding of note was that pretreatment religiosity predicted outcomes in both the 

outpatient and aftercare arms of the trial, albeit 

on different outcome measures. Religiosity positively predicted posttreatment percentage of 

days abstinent (but did not predict drinks per 

drinking day) among aftercare clients, and negatively predicted drinks per drinking day (but 

did not predict percentage of days abstinent) 

among the outpatients. Thus, in each case religiosity was associated with better outcomes. 

Interestingly, these prognostic effects in both 

cases appeared to be carried by the formal 

religious practices scale of the RBB measure. 

This is consistent with the more general robust 

finding that religious involvement is a protective factor against the development of alcohol 

problems (Gorsuch 1995).

Taken together, these findings raise several 

interesting questions for subsequent clinical 

research on religiosity. While religiosity did not 

emerge as a viable matching dimension with 

the treatments evaluated in Project MATCH, it 

does appear that religiosity may play a role in 

the prediction of the therapeutic relationship 

(at least among aftercare clients) and of posttreatment drinking behavior. Future research 

on these relationships will help specify the 

role of religiosity in alcoholism treatment and 

identify circumstances under which religiosity 

might productively be focused on in the treatment endeavor.

Acknowledgments

This research was supported by grants U10–

AA08431 and U10–AA08435 from the National 

Institute on Alcohol Abuse and Alcoholism. The 

authors gratefully acknowledge the collaboration of the Project MATCH Research Group in 

this research.

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176

The Interpersonal Dependency 

Matching Hypothesis

Robert G. Rychtarik, Ph.D.

ABSTRACT

Individuals high in interpersonal dependency were predicted to have better outcomes 

when assigned to Twelve Step Facilitation (TSF) treatment relative to either CognitiveBehavioral Coping Skills Therapy (CBT) or Motivational Enhancement Therapy (MET). 

TSF was thought to satisfy their dependency needs by stressing reliance on a “higher 

power” and the support and nurturance of the Alcoholics Anonymous (AA) group. 

Their higher level of attendance and satisfaction with TSF treatment and their high 

level of AA involvement were predicted to mediate their improved outcomes over interpersonally dependent individuals in CBT and MET. Individuals low in interpersonal 

dependency, on the other hand, were predicted to have better outcomes in CBT and 

MET relative to TSF. These clients were predicted to benefit most from CBT and MET, 

which emphasize individual self-management and self-change, respectively. These 

hypotheses were not supported. Some limited support was found for the notion that 

within the MET outpatient sample, high interpersonally dependent individuals had 

slightly poorer posttreatment outcome in percentage of days abstinent than did those 

low in interpersonal dependency. At long-term followup, results also suggested that, 

among the more intense treatments (i.e., CBT and TSF), high interpersonally dependent individuals fared better on drinking intensity outcomes relative to those low 

in interpersonal dependence. These effects, however, were small. Limitations of the 

interpersonal dependency measure and the need to study interpersonal dependency 

in the context of other variables are discussed.

Contemporary conceptualizations define 

interpersonal dependency as the extent 

to which an individual strives for and 

works to maintain nurturant, supportive relationships (Bornstein 1992). The interpersonal 

dependency construct has been associated with 

the development and progression of alcoholism 

for some time. As summarized by Bornstein 

(1992), several correlational studies have shown 

a positive association between dependency 

and alcoholism. Overall, alcoholic individuals have been found to have higher scores 

on dependency measures than abstainers or 

social drinkers, even when controlling for other 

psychopathology.

Prospective, longitudinal research, however, 

suggests that premorbid dependency level does 

not predict subsequent risk for the onset of 

alcoholism (Jones 1968, 1971; Kammeier et 

al. 1973; Valiant 1980). Rather, dependencyrelated behaviors (e.g., dependent thoughts, 

feelings, and behaviors) show a significant 

increase upon the development of alcohol problems. Thus, interpersonal dependency appears 

to be a result of the alcohol problem rather than 

the cause of it. Nevertheless, level of interpersonal dependency at the time of treatment may 

in turn influence subsequent relapse and treatment outcome and may interact with treatment 

approach.

Robert G. Rychtarik, Ph.D.

Research Institute on Addictions

1021 Main Street

Buffalo, NY 14203

E-mail: rychtari@ria.buffalo.edu
177

The Interpersonal Dependency Matching Hypothesis

Rationale for the Matching 

Hypothesis

A basic assumption of the Twelve Step 

Facilitation (TSF; Nowinski et al. 1992) approach 

is that recovery from alcohol dependence can 

be achieved through redirecting an individual’s dependency needs onto a “higher power” 

and creating dependence on the support and 

nurturance of the Alcoholics Anonymous (AA) 

group. This approach contrasts markedly with 

the Cognitive-Behavioral Coping Skills Therapy 

(CBT; Kadden et al. 1992) and Motivational 

Enhancement Therapy (MET; Miller et al. 1992) 

approaches in which emphasis is placed, in general, on an individual’s own self-management 

and decision-making. The current hypothesis 

is, therefore, based on the assumption that 

the level at which an individual strives for and 

works to maintain supportive relationships (i.e., 

level of interpersonal dependency) will interact 

with treatment type to influence outcome.

Individuals high in interpersonal dependency 

are hypothesized to be more responsive to the 

TSF model given that it complements their own 

cognitive schema and addresses their own psychological needs by providing and stressing the 

importance of reliance on supportive relationships with a higher power and the AA group. It 

is hypothesized that individuals high in interpersonal dependency will benefit most from the 

TSF condition because they will work harder to 

win the approval of their therapist and the support group and will attend AA more frequently.

While highly dependent individuals also may 

be compliant and learn new skills in the CBT 

condition over the course of treatment, they 

may have difficulty implementing and/or maintaining them in the posttreatment environment 

in the absence of additional structured support and nurturance for such change—which 

is particularly important for them. Thus, these 

individuals are hypothesized to have poorer 

outcomes under the CBT condition.

Similarly, highly dependent individuals also 

are hypothesized to benefit less in the MET 

condition due to the fewer treatment sessions 

available upon which to develop the therapeutic 

relationship, level of support, and nurturance 

required by these individuals. In addition, as with 

the CBT condition, the absence of structured 

support and nurturance posttreatment may 

make it more difficult for these individuals to 

implement and/or maintain treatment gains.

It should be noted that while differences may 

exist between the CBT and MET conditions during treatment (due primarily to the intensity 

of treatment provided and the opportunity for 

individuals high in interpersonal dependency to 

develop a strong therapeutic relationship), the 

current hypothesis focuses on posttreatment 

functioning and predicts no difference between 

CBT and MET in the long term.

Individuals low in interpersonal dependency 

are hypothesized to respond less favorably to 

the TSF group relative to the CBT and MET 

conditions due to their already higher levels of 

self-reliance and less need for the high level of 

support and nurturance that may be present in 

self-help groups. These individuals may respond 

most favorably to CBT and MET given the 

emphasis on self-management and self-change 

in these conditions. In fact, individuals low in 

interpersonal dependency are hypothesized to 

view the TSF treatment as less acceptable given 

its heavy emphasis on reliance on others and 

therefore may fail to follow through with treatment and AA group attendance.

Individuals low in interpersonal dependency 

are not hypothesized to differ with respect to 

outcome between the CBT and MET conditions. 

These individuals are predicted to do well in 

the planned absence of any additional posttreatment support that characterizes these two 

treatment conditions.

Recent research on the interaction between 

family functioning and interpersonal autonomy (a measure of interpersonal dependency) 

provides some support for the current hypothesis. McKay and colleagues (1992) found that 

interpersonal autonomy alone did not predict 

drinking outcome at posttreatment. Autonomy, 

however, was found to interact with pretreatment family functioning to predict outcome. 

Clients who were low in autonomy and who 

reported high levels of family dysfunction at 

pretreatment had poorer drinking outcomes 

at followup. Among high-autonomy subjects, 

however, pretreatment family functioning 

was unrelated to drinking at followup. Thus, 

individuals with high levels of interpersonal 

dependency (i.e., low autonomy) appear to be 
178

Part IV: Personality Variables

vulnerable to more and heavier drinking in 

the absence of supportive, nurturing relationships in the family. Individuals with low levels 

of dependency (i.e., high autonomy) appear to 

be immune from the effects of family dysfunction. Importantly, this effect was found only for 

male clients.

In a subsequent report, McKay and associates (1993) found that for low-autonomy 

individuals, perceived improvement in family 

functioning during treatment was associated 

with better outcome at followup. For highautonomy individuals, however, no significant 

association between improvement in family 

functioning and outcome was found. Taken 

together, while the studies by McKay and colleagues do not specifically address the current 

hypothesis, they do provide some of the first 

empirical support for the possible mediating 

role of interpersonal dependency in alcoholism 

treatment outcome.

Finally, some additional indirect support 

for the current hypothesis can be derived 

from research on predictors of affiliation with 

AA. Among other variables, high affiliative 

and group dependency needs have been suggested in some studies to be associated with 

affiliation with AA (see Ogborne and Glaser 

1981). Importantly, AA affiliation also has been 

positively associated with improved outcome 

among individuals during and following treatment (Emrick et al. 1993). Thus, the improved 

outcome predicted for high interpersonal 

dependency individuals in the TSF condition 

may result from their greater affiliation with AA 

during and after treatment.

To summarize, empirical research on the 

interaction between interpersonal dependency 

and treatment type is extremely limited. The 

theoretical support for the above hypothesis far 

outweighs the empirical evidence, hence the 

current hypothesis is necessarily exploratory 

in nature.

The Matching Hypothesis

The dependency by treatment interaction 

hypothesis can be summarized as follows. The 

higher the level of interpersonal dependency prior 

to treatment, the higher the percent of posttreatment abstinent days (and the lower the number 

of drinks per drinking day) in the Twelve Step 

Facilitation relative to the Cognitive-Behavioral 

Coping Skills Therapy and Motivational 

Enhancement Therapy conditions. The lower the 

interpersonal dependency prior to treatment, 

the higher the percentage of abstinent days 

(and the lower the number of drinks per drinking day) under the MET and CBT conditions and 

the lower the percentage of abstinent days (and 

higher drinks per drinking day) under TSF. In 

sum, interpersonal dependency was expected 

to be positively associated with outcome in TSF 

but to have a negative or nonsignificant relationship with outcome in both CBT and MET.

Figure 1 gives a diagrammatic presentation of the variables and pathways within each 

treatment condition hypothesized to mediate 

the interaction between interpersonal dependency and treatment type. The figure depicts 

the pathways from pretreatment interpersonal 

dependency to attendance, treatment satisfaction, posttreatment AA involvement, and 

subsequent proportion of abstinent days. It 

was anticipated that the pathway from interpersonal dependency to proportion of treatment 

sessions attended would be significant and 

positive in the CBT and TSF conditions but not 

in the MET condition.

In addition, it was hypothesized that the positive dependency-attendance relationship would 

be higher in TSF relative to CBT. Interpersonal 

dependency also was anticipated to be strongly 

related to higher levels of treatment satisfaction at the end of treatment among participants 

in TSF relative to participants in either CBT or 

MET. In fact, higher levels of dependency were 

predicted to be negatively related to satisfaction with treatment among MET participants.

Interpersonal dependency also was predicted 

to have positive direct and positive indirect 

effects (i.e., through attendance and treatment 

satisfaction) on AA involvement during the 

posttreatment period among TSF participants 

but not among participants in either CBT or 

MET. This effect, in turn, was anticipated to 

indirectly and positively influence subsequent 

percentage of days abstinent and negatively 

influence drinks per drinking day in the TSF 

group.

It should be noted that no significant 

direct effect of interpersonal dependency on 
179

The Interpersonal Dependency Matching Hypothesis

Figure 1. Causal model of the interpersonal dependency by treatment interaction 

hypothesis

outcome was predicted. Rather, interpersonal 

dependency was predicted to indirectly affect 

outcome through its direct effects on treatment attendance, treatment satisfaction, and 

AA involvement. As shown in figure 1, these 

indirect pathways were all predicted to be positive in the TSF condition but either negative or 

nonsignificant (designated “0” in the figure) in 

the CBT and MET conditions. A similar model 

(not shown) also was applied to the drinks per 

drinking day variable. For this outcome measure, however, better functioning was exhibited 

by lower levels of drinking.

Operationalization of the 

Matching Variable

Interpersonal dependency was measured 

using the 14-item Assertion of Autonomy 

Scale (AAS) of the Interpersonal Dependency 

Inventory (IDI; Hirschfeld et al. 1977). The IDI 

is one of a number of widely used measures of 

dependency (Bornstein 1992) and overall shows 

good reliability and construct validity. The AAS 

is one of three factor-analytically derived scales 

of the IDI. The scale is proposed to measure 

the ex-tent to which individuals profess to be 

indifferent to or independent of the evaluation 

of others and the extent to which they express 

the belief that their self-esteem depends on the 

approval of others. Hirschfeld and associates 

(1977) reported a cross-sample factor-analytic 

congruency coefficient of greater than 0.80 

for this scale. Split-half reliability coefficients 

in three different samples ranged from 0.72 

to 0.91. As administered, high scores on this 

scale reflect higher levels of independence and 

thus lower levels of interpersonal dependency.

Responses on the AAS have not been found 

to be influenced by age, social desirability, 

depression, or anxiety. Educational level has 

shown a small but significant positive relationship with autonomy. Also, men have been 

found to report significantly higher levels of 

autonomy than women (Hirschfeld et al. 1977). 

The AAS was also successfully used in McKay 

and colleague’s (1992, 1993) research examining influences on drinking outcome (see above). 

For the purpose of the present hypothesis, scoring was reversed on the AAS so that high scores 

on the scale reflected higher levels of interpersonal dependency.

Results

Findings regarding the hypothesized prognostic association between alcohol involvement 

and each of the two primary outcome measures, percentage of days abstinent (PDA) and 

drinks per drinking day (DDD), are presented 

first.

 Next, the results of tests of the predicted 

interaction for each arm and each outcome 

measure are given. Finally, the variables 

hypothesized to account for the interaction 
180

Part IV: Personality Variables

(or its failure) are shown using path analyses. 

Results are presented separately for outpatient 

and aftercare arms.

Outpatient Arm

Prognostic Effect of Interpersonal 

Dependency

No significant interpersonal dependency 

effect or dependency by time interaction was 

observed for either PDA or DDD during either 

the period of treatment or 12-month followup 

among outpatients. The effect of interpersonal 

dependency did approach significance at the 

39-month followup (p<.0649).

Interaction of Interpersonal Dependency 

With Treatment

Table 1 presents results of the tests of the 

overall interpersonal dependency by treatment 

interaction terms for PDA and DDD for both 

treatment and followup periods. Also included 

are tests of the effects of the interaction over 

linear and quadratic time as well as tests of 

the individual treatment contrasts. No overall 

significant treatment by interpersonal dependency interaction was found in the outpatient 

arm within the treatment period. During the 

posttreatment period, however, the dependency 

by treatment interaction approached noncorrected significance (p=.054) for PDA. Individual 

contrasts indicated this potential interaction 

reflected differences in slopes between CBT 

and MET and not the predicted differences 

between TSF and CBT or MET. The nature of 

this potential interaction on PDA collapsed 

across the followup period, using multiple 

regression procedures, is further depicted in 

figure 2. Consistent with our hypothesis, there 

was a significant, though small, negative association between interpersonal dependency and 

PDA in the MET condition. However, no significant dependency PDA association was found in 

either the CBT or TSF groups.

At the 39-month followup, the interaction between interpersonal dependency and 

treatment condition approached significance 

for PDA (p=.09) and was significant at the 

uncorrected level of p=.02 for DDD. The latter 

interaction, however, was not in the predicted 

direction. As shown in figure 3, there was no 

significant relationship between interpersonal 

dependency and DDD in the MET condition. 

In both the CBT and TSF conditions, however, 

higher levels of interpersonal dependency were 

associated with fewer drinks per drinking day. 

This effect occurred in the presence of a significant main treatment effect (p=.0251) which 

favored TSF.

These results must be viewed with caution 

given the long-term nature of the followup. 

Nevertheless, the findings suggest that level 

of interpersonal dependency is not related to 

drinking intensity outcomes in low-intensity 

treatments such as MET. In higher intensity 

treatments, however, the higher the interpersonal dependency, the better the outcome.

It is interesting to note that at high levels of 

interpersonal dependency, the predicted effect 

was supported (i.e., individuals were doing better in TSF than in either CBT or MET). At low 

levels of interpersonal dependency, however, 

Table 1. Interpersonal Dependency—Outpatient

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT–MET F 1.41 -.71 .93 .51 -.05 -1.36 2.41 -1.81 -.11 -1.02 -2.02 .67

p .15 .48 .35 .61 .96 .18 .02 .07 .91 .31 .04 .50

CBT–TSF F .16 .76 1.01 -.11 .01 -1.58 .94 -.24 -1.19 .88 -1.29 .90

p .87 .44 .31 .91 .99 .11 .35 .81 .23 .38 .20 .37

MET–TSF F -1.22 1.42 .06 -.60 .06 -.18 -1.47 1.54 -1.02 1.82 .76 .19

p .22 .16 .96 .55 .95 .85 .14 .12 .31 .07 .44 .85

MV × Tx F 1.15 1.01 .64 .21 .00 1.49 2.92 1.85 .83 1.66 2.12 .45

p .31 .36 .53 .81 1.00 .22 .054 .16 .43 .19 .12 .64

NOTE: MV=matching variable, Interpersonal Dependency; Tx=treatment; T=linear time; T2=quadratic time; PDA=percentage of 

days abstinent; DDD=drinks per drinking day
181

The Interpersonal Dependency Matching Hypothesis

Figure 2. Nature of interpersonal dependency 

by treatment interaction approaching significance for Percentage of days abstinent across 

followup months 4–15 in the outpatient arm.

Figure 3. Noncorrected significant interpersonal dependency by treatment interaction on 

drinks per drinking day (DDD) for long-term 

followup months 37–39 in the outpatient arm.

TSF and MET both appear to result in better 

outcomes than CBT. The essentially parallel 

slopes of TSF and CBT, however, indicate that 

the relationship between interpersonal dependency and DDD is not significantly different 

between these treatments. Yet, because of the 

main effect for TSF, the level of outcome is different between the two groups.

The A Priori Causal Model

As noted above, there was no support for 

the hypothesized interaction between interpersonal dependency and treatment assignment. 

To identify the reasons for this failure, the 

hypothesized causal model presented in figure 

1 was examined within a structural equations 

framework with posttreatment followup drinking data.

The treatment satisfaction measure was 

the sum of 4 items on a posttreatment questionnaire which asked the clients to rate, on a 

5-point scale, their satisfaction with the therapist (1 = Very Satisfied, 5 = Very Dissatisfied), 

whether they would return to the program in 

the future (1 = Definitely Yes, 5 = Definitely 

Not), the extent to which the program met their 

needs (1 = Almost all of my needs have been met, 

5 = None of my needs have been met), and their 

overall satisfaction with the treatment received 

(1 = Very Satisfied, 5 = Very Dissatisfied). 

Preliminary analyses indicated that these 4 

items formed a single factor which accounted 

for 68.5 percent of the variance. The internal 

consistency of this satisfaction measure was 

0.84. For the purposes of this analysis, scoring of the satisfaction measure was reversed 

so high scores represented greater treatment 

satisfaction. AA involvement was measured 

using the AA Involvement Scale (Tonigan et al. 

1996) administered at the 9-month (6 months 

posttreatment) followup. Treatment attendance 

represented the proportion of treatment sessions attended. The effect of these measures 

on predicting the PDA during followup months 

10–12 was examined. The arcsine transformation was used for the attendance and PDA 

variables.

The path model was analyzed separately for 

each of the treatment groups and in a multigroup analysis using EQS (Bentler 1995). 

Analyses included only those clients for whom 

complete data on all variables in the model 

were available (N’s of 238, 222, and 268 for 

CBT, MET, and TSF, respectively). The overall 

chi square was used to assess the comparability of the model across the different treatments. 

The results of the multiple-group analysis for 

PDA and DDD are presented in figures 4 and 

5, respectively. A partially constrained model 

fit the data best for both PDA, χ2=6.92, df=12, 

n.s., and DDD, χ2=10.97, df=12, n.s. Consistent 

with the hypothesis, AA involvement in months 
182

Part IV: Personality Variables

7–9 was positively and significantly related to 

PDA in months 10–12, regardless of treatment 

condition. Moreover, as expected, proportion of 

sessions attended in TSF had a positive and significant effect on AA involvement that was not 

found in either CBT or MET. Similarly, higher 

satisfaction at posttreatment was significantly 

associated with more AA involvement in TSF 

but not in the other two treatments. The failure of the hypothesized interaction is evident, 

however, in the fact that interpersonal dependency showed no significant relationship with 

any other treatment or posttreatment variable 

in the model.

Figure 4. Multiple-group comparison of the 

causal model applied to percentage of days 

abstinent in the outpatient arm.

Aftercare Arm

Prognostic Effects of Interpersonal 

Dependency and the Interpersonal 

Dependency by Treatment Interaction

No significant effect of dependency on either 

outcome measure and no dependency by time 

interaction occurred in the aftercare arm (table 

2). No significant or near significant interactions 

or individual contrast effects were observed.

Figure 5. Multiple-group comparison of the 

causal model applied to drinks per drinking 

day in the outpatient arm.
183

The Interpersonal Dependency Matching Hypothesis

The A Priori Causal Model

The multiple-group path analysis applied 

to the aftercare data also found the best fit in 

a partly constrained model for both PDA, χ2= 

14.66, df=12, n.s., and DDD, χ2=18.7, df=12, 

n.s., respectively (figures 6 and 7). Sample N’s 

were 200, 191, and 170 for CBT, MET, and TSF, 

respectively. A pattern similar to that obtained 

in the outpatient arm was found. AA involvement during months 7–9 was significantly 

associated with a higher proportion of abstinent 

days in months 10–12. Attendance was positively associated with greater AA involvement 

in TSF but not in CBT or MET. Higher levels 

of satisfaction also were associated with more 

AA involvement in TSF but not CBT or MET. 

Finally, unlike the outpatient arm, and contrary to predictions, interpersonal dependency 

was positively associated with AA involvement 

in the CBT condition but not in TSF or MET.

Discussion

We found no support for the hypothesis that 

interpersonal dependency interacts with treatment type to influence outcome. Some partial, 

yet very weak, support was obtained in the outpatient arm for the prediction that individuals 

high in interpersonal dependency would benefit less in the MET condition. However, this 

finding only applied to differences between 

CBT and MET and not TSF. In the long-term 

followup, we also found support for the notion 

that individuals high in interpersonal dependency benefited from either of the more intense 

treatments (i.e., CBT and TSF) relative to lowintensity treatment (i.e., MET). Given that the 

full interaction term either only approached 

significance or met uncorrected criteria in each 

case, we conclude that interpersonal dependency alone holds little clinical value as a 

variable for triaging clients to either CBT, MET, 

or TSF.

Results of the causal models were clear in 

demonstrating that interpersonal dependency 

had no direct or indirect effect on outcome 

among outpatients regardless of treatment type 

received. Only in the aftercare arm did dependency show a significant positive indirect effect 

on outcome through its positive direct association with AA involvement, and then only among 

CBT participants. Why this effect was found 

only in CBT is not clear. Perhaps the combination of a period of intense treatment (usually 

of a 12-step nature) followed by cognitivebehavioral treatment is an important factor in 

facilitating continued involvement in AA. The 

exact mechanisms of such a model, however, 

remain to be elucidated.

Some additional cautions regarding the current results must be made. First, we used only 

the Assertion of Autonomy Scale of the IDI (with 

reverse scoring) for our interpersonal dependency measure. It may be that use of other 

measures of dependency would yield different 

results. Also, it is perhaps not surprising that a 

measure of interpersonal dependency by itself 

would provide little value in client-treatment 

matching. Human behavior is so complex that 

only when interpersonal dependency is evaluated within higher order interactions with other 

variables (e.g., motivation, family functioning, 

alcohol involvement) could its role on outcome 

be detected. In fact, the work of McKay and 

Table 2. Interpersonal Dependency—Aftercare

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT–MET F .23 -.37 .25 -.90 -.51 .07 .87 -.62 -.72 .75 -.21 -.45

p .81 .71 .80 .37 .61 .94 .38 .53 .47 .45 .84 .65

CBT–TSF F 1.53 -1.19 -.05 -.58 -1.39 .78 1.65 -1.51 .03 1.37 -.84 .27

p .13 .23 .96 .56 .16 .44 .10 .13 .98 .17 .40 .79

MET–TSF F 1.31 -.85 -.28 .26 -.92 .72 .80 - .91 .72 .64 -.63 .69

p .19 .39 .78 .80 .36 .47 .42 .37 .47 .53 .53 .49

MV × Tx F 1.32 .74 .05 .42 .98 .36 1.37 1.15 .35 .94 .38 .25

p .27 .48 .95 .66 .38 .70 .25 .32 .71 .39 .69 .78

NOTE: MV=matching variable, Interpersonal Dependency; Tx=treatment; T=linear time; T2=quadratic time; PDA=percentage of 

days abstinent; DDD=drinks per drinking day
184

Part IV: Personality Variables

Figure 6. Multiple-group comparison of the 

causal model applied to percentage of days 

abstinent in the aftercare arm.

Figure 7. Multiple-group comparison of the 

causal model applied to drinks per drinking 

day in the aftercare arm.

associates (1992, 1993) would appear to support this notion. Further exploratory analyses 

of the MATCH data set may help to explain 

such pathways.

In sum, interpersonal dependency was not 

found to interact with treatment type to influence outcome. While AA involvement was 

found to influence outcome, the hypothesized 

role of interpersonal dependency in mediating 

this relationship was not found. Research, with 

alternate dependency measures, that examines 

de-pendency in combination with other prognostic variables may further understanding in 

this area.

Acknowledgment

This research was supported by grant U10–

AA08431 from the National Institute on Alcohol 

Abuse and Alcoholism. I gratefully acknowledge the collaboration of the Project MATCH 

Research Group and, in particular, the helpful comments of Richard Longabaugh and 

Philip W. Wirtz on the conceptualization of this 

hypothesis and on its writeup.

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186

The Gender Matching Hypothesis

Frances K. Del Boca, Ph.D., and Margaret E. Mattson, Ph.D.

ABSTRACT

This chapter evaluates the gender matching hypothesis in Project MATCH, which 

states that women will benefit more from Cognitive-Behavioral Coping Skills Therapy 

(CBT) than from Twelve Step Facilitation (TSF) and that the differentia] outcomes for 

the two treatments will be greater for female clients than for males. CBT was expected 

to address the ancillary problems (e.g., external stressors, negative mood) that are 

more prevalent among female alcoholics; at the same time, TSF, which would encourage women to attend Alcoholics Anonymous (AA) meetings, was expected to increase 

guilt and undermine self-esteem and assertion. Tests of the matching contrasts failed 

to provide support for the hypothesis in either arm of the trial. Gender did produce 

significant prognostic effects in analyses of the aftercare arm, with women reporting 

a higher proportion of abstinent days and fewer drinks per occasion than men did. 

Causal chain analyses produced mixed results. Male and female clients were shown 

to differ in terms of their initial treatment needs, and followup status with respect to 

these needs was related to drinking outcomes. Contrary to prediction, however, CBT 

sessions for women, as compared with those for men, were not appreciably more likely 

to teach general problem-solving or mood-management skills. Further, women did not 

avoid AA meetings. Attendance at self-help meetings was comparable for the sexes in 

the outpatient arm; in the aftercare study, women attended significantly more meetings and reported a higher degree of AA involvement.

Until recently, relatively few empirical 

studies have directly compared outcomes for alcoholic men and women 

in differing treatments (McCrady and Raytek 

1993). Nevertheless, clinicians strongly recommend gender-specific approaches to the 

treatment of alcohol and other substance 

abuse problems. The literature is replete with 

reports contrasting male and female alcoholics 

along dimensions that are purported to have 

relevance for the delivery of treatment (cf. Del 

Boca 1994). Further, gender has often been 

implicated in alcoholism typologies, with the 

implicit or explicit suggestion that the etiology 

and course of alcoholism differ for the two sexes 

(cf. Del Boca and Hesselbrock 1996). The factors that contribute to gender differences are 

sometimes presumed to be fundamentally biological or genetic in origin; more often, however, 

the sources of gender-related variations are not 

fully articulated.

 Rather, gender is regarded as a surrogate 

for a variety of genetic factors (e.g., biological 

sex) and sociocultural influences (e.g., genderrole prescriptions) that differentiate members of 

our two most important and recognizable social 

categories. This perspective is adopted in the 

present formulation.

The Hypothesized 

Matching Contrast

It is hypothesized that females will experience 

more favorable outcomes in Cognitive-Behavioral 

Frances K. Del Boca, Ph.D.

Department of Psychology

4202 East Fowler Avenue, BEH 339

University of South Florida

Tampa, FL 33620-8200

E-mail: fdelboca@chumal.cas.usf.edu
187

The Gender Matching Hypothesis

Coping Skills Therapy (CBT; Kadden et al. 1992) 

than in Twelve Step Facilitation (TSF; Nowinski 

et al. 1992) in Project MATCH and that this differential will be significantly larger for females 

than for males. No prediction is made about 

the direction of the difference between the two 

treatment modalities for males. A significant 

main effect for client sex, with women showing 

better outcomes than men, would be consistent with the literature. However, the gender 

hypothesis formulation for Project MATCH 

includes no prediction regarding gender main 

effects. The gender matching hypothesis may 

be represented as follows:

Females Males

[(CBT – TSF) > 0] >> (CBT – TSF)

Rationale for the Matching 

Hypothesis

Although there is little directly relevant research evidence for matching treatment to 

gender, there is considerable indirect empirical 

support in addition to theoretical justification.

Empirical Evidence

Major reviews of the treatment outcome literature on alcoholic women agree that there 

is limited research evidence regarding the 

differential effectiveness of various therapeutic modalities or treatment components (e.g., 

Vannicelli 1984; Institute of Medicine 1990; 

Lex 1990). Without differentiating among 

treatments, reviewers conclude, however, 

that female alcoholics benefit from treatment 

despite their greater concomitant psychological, social, and medical problems, and some 

studies report that they fare better at followup 

than their male counterparts (cf. Jarvis 1992; 

Vannicelli 1984; Vannicelli and Nash 1984; 

Annis and Liben 1980).

The limited research that has examined 

treatment outcomes for alcoholic women tends 

to be consistent with the matching hypothesis framed above. It should be emphasized, 

however, that this research has relied on uncontrolled studies that usually involve treatments 

that differ somewhat from those used in Project 

MATCH. For example, Lyons and associates 

(1982) examined treatment outcomes for 1,340 

clients from 17 alcoholism treatment programs 

in New York State. Programs were classified as 

having one of three different treatment orientations: peer group, rehabilitation, or medical. 

Alcoholic men achieved the most favorable 

outcomes when treated in programs with a 

peer-group orientation (similar to Alcoholics 

Anonymous); programs with rehabilitation 

and medical orientations reported comparable 

success rates, not far below those with a peergroup approach. In contrast, female clients 

fared best in medically oriented programs; the 

least improvement was observed for treatments 

with a peer-group orientation.

Similarly, female clients who received a gender-specific treatment in a random assignment 

study in Sweden achieved superior outcomes 

compared with women in conventional therapy 

(Dahlgren and Willander 1989). The authors 

concluded that women in the latter group were 

unwilling to be confronted by male clients, 

a likely circumstance in traditional 12-step 

programs.

In an attempt to examine factors that may 

contribute to differential outcomes for women 

and for men in alcoholism treatment, Jarvis 

(1992) analyzed 20 treatment outcome studies using meta-analytic techniques. Although 

the studies varied along many dimensions, 

Jarvis concluded that those showing a male 

advantage in terms of outcome more often 

involved “inpatient programmes incorporating 

psychotherapy, milieu therapy and Alcoholics 

Anonymous” (p. 1253). In contrast, according 

to Jarvis, alcoholic women tended to fare better 

in behavioral treatments.

Alcoholic women tend to have specific 

material needs and psychological problems 

that might be best addressed with particular 

treatment programs. Documented differences 

between female and male alcoholics that are 

pertinent to the treatment matching hypothesis 

can be summarized in terms of four interrelated domains:

■ External stressors and negative mood

■ Role demands

■ Psychopathology and emotional problems

■ Self-esteem and instrumentality
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Part IV: Personality Variables

External Stressors and Negative Mood

Although some studies do not support the 

notion that women consume alcohol in response 

to stressful or traumatic events (Morrissey 

and Schuckit 1978; Cooke and Allan 1984), 

research has consistently found that women 

attribute their drinking to external stressors 

and negative mood states more often than do 

men (e.g., Morrissey and Schuckit 1978; cf. 

Lex 1990; Del Boca 1994). Compared with 

men, female alcoholics who seek treatment 

are more often unemployed and beneficiaries 

of public assistance. Data from panel surveys indicate that women drink to cope with 

difficult life events, especially reproductive 

problems (Wilsnak et al. 1986) and that they 

are more likely to do so than men (Institute of 

Medicine 1990). Such findings have led reviewers to argue that treatments that teach coping 

skills and relapse prevention techniques, such 

as cognitive-behavioral approaches, should be 

particularly effective for women (e.g., Lex 1990; 

Institute of Medicine 1990).

Role Demands

Alcoholic women often report having more 

childcare responsibilities and family problems 

than alcoholic men, and they are more likely 

than men to seek treatment specifically for 

marital problems, family disruption, physical 

illness, or emotional difficulties (e.g., Marsh 

and Miller 1985; Duckert 1987; Ginther and 

Kadden 1990). Such problems are frequently 

related to alcohol consumption but often are 

not acknowledged as such (Beckman 1984; 

McCrady and Raytek 1993), possibly because 

of the greater social stigma associated with 

alcoholism in females (Gomberg 1988). In addition to teaching women methods for resisting 

alcohol use, the cognitive-behavioral approach 

is likely to provide coping skills that will generalize to other difficult situations. Further, CBT, 

if successful, should challenge and alter maladaptive cognitions regarding alcohol use.

Psychopathology and Emotional 

Problems

Several studies have found that female alcoholics experience higher rates of psychiatric 

disorders, particularly affective disorders, than 

women in general. Diagnoses of depression and 

anxiety disorders are significantly more prevalent among female than among male alcoholics, 

who are more likely to evidence antisocial 

personality (ASP) disorder (Hesselbrock and 

Hesselbrock 1993). CBT, with its emphasis 

on the cognitive restructuring of perception, 

is likely to be most effective in helping women 

to overcome depression and anxiety. Further, 

because CBT teaches methods for managing stressful situations, women may be less 

likely to become involved in circumstances 

that contribute to depression and anxiety and 

that ultimately lead to drinking. CBT is also 

expected to be beneficial to alcoholic men with 

psychiatric problems. Male alcoholics high in 

sociopathy have been found to respond better to CBT than to other approaches (Kadden 

et al. 1989). Thus, the higher prevalence of 

ASP among alcoholic men suggests a possible 

advantage for CBT in Project MATCH.

Self-Esteem and Instrumentality

Many reviewers have noted that alcoholic 

women lack self-esteem and assertiveness. 

Female alcoholics are frequently characterized 

as passive, dependent, and low in self-efficacy 

(e.g., Beckman 1978). These attributes are often 

used to explain the tendency for women to be 

influenced by alcoholic or substance abusing 

significant others (Hser et al. 1987). As a consequence, several authors have recommended 

that the treatment of female clients include 

assertiveness training (e.g., Beschner et al. 

1981; Institute of Medicine 1990; Lex 1990). As 

implemented in Project MATCH, an important 

goal of CBT treatment is to enhance self-efficacy 

and promote independent decision-making. 

The successful management of affect, cognition, and behavior over time, coupled with the 

effective application of coping strategies, particularly refusal skills, should serve to enhance 

self-esteem, increase feelings of self-efficacy, 

and promote instrumentality.

Theoretical Justification

In addition to alcohol and substance abuse 

research, several other lines of investigation 

converge in support of the gender matching 

hypothesis. These include research and theory 

in the more general areas of sex differences and 
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The Gender Matching Hypothesis

gender roles, and studies of the actual behavior 

of men and women in groups.

Factors which differentiate male and female 

alcoholics also tend to differentiate the sexes 

more generally. That is, nonalcoholic women differ from nonalcoholic men in terms of material 

resources, the prevalence of affective disorders 

and sociopathy, and other characteristics that 

distinguish their alcoholic counterparts (Hilton 

1987, 1988). These and other sex differences 

can be summarized in terms of two salient features of traditional gender roles (Gilligan 1977):

■ Females typically have less power than 

males, resulting in less control over 

resources and outcomes, with concomitant low self-efficacy and negative affect.

■ Women evidence a greater concern for, 

and assume more responsibility for, the 

quality of interpersonal relationships.

In sum, by virtue of their socialization, 

social position, and sex stereotypic expectations, women are more likely than men to 

lack self-worth and instrumentality, to experience depression and anxiety, and to drink in 

response to stress and negative mood states. 

Because of their heightened sense of personal 

responsibility, and because of the greater 

social disapproval of female drinking, alcoholic 

women are also more likely to exhibit guilt and 

worry regarding the impact of their alcohol use.

Expressive and communal are terms used 

to characterize the psychological orientation 

associated with the emphasis on connection, 

feelings, and interpersonal relationships in 

the traditional female role; instrumental and 

agenic are used to summarize the self-assertive, autonomous, and controlling attributes 

that are central to the conventional male role 

(e.g., Block 1973; Spence 1985). Although the 

traditional feminine emphasis on interpersonal 

responsibility may contribute to the array of 

problems that alcoholic women present in 

treatment, lack of instrumentality is likely to be 

more problematic. In more general research on 

sex differences, high instrumentality has been 

found to be a strong predictor of self-esteem 

and psychological adjustment (e.g., Taylor and 

Hall 1982).

CBT is expected to be the most effective 

of the Project MATCH treatments for women 

because it directly addresses the problem areas 

characteristic of alcoholic women. In terms of 

gender-role orientation, CBT enhances the 

development of instrumentality and self-efficacy. Furthermore, it is the only Project MATCH 

therapy with active ingredients that effectively 

address problems other than drinking per se 

(negative affect, psychopathology, poor coping 

skills) Thus, CBT is the only treatment designed 

to deal specifically with those factors that differentiate alcoholic women and men, factors 

that are likely to contribute to female alcohol 

use prior to treatment and precipitate relapse 

following periods of sobriety.

Compared with CBT, TSF is hypothesized 

to be less effective for female clients for three 

reasons. First, although delivered individually in Project MATCH, a major aim of TSF is 

to involve clients in Alcoholics Anonymous (AA) 

and to encourage them to attend AA meetings. 

Because women are more socially stigmatized for heavy drinking, they more often drink 

alone. As a consequence, it has been argued 

that women prefer, and derive more benefit 

from, individual treatment, because the experience of social dis-approval can be minimized 

(Jarvis 1992; Cronkite and Moos 1984). The 

confrontational style of many AA groups may 

exacerbate guilt feelings related to drinking 

without providing the coping skills necessary 

to deal effectively with negative mood states.

Second, the confrontational behavior characteristic of many AA meetings is incongruent 

with the traditional female role, which prescribes noncombative, conciliatory behavior for 

women. There is considerable research indicating that men are more dominant in mixed-sex 

groups than women, that they simply talk and 

interrupt more often, and that they are more 

likely to assume leadership roles (Bartol and 

Martin 1986). These tendencies are especially 

marked in groups with a preponderance of 

males. Since alcoholism is more prevalent 

among males than females, men in AA groups 

are likely to outnumber women by a significant 

margin, which may discourage women from 

active participation.

Third, TSF is expected to be less effective 

than CBT because it fails to deal with problems 

relating to self-esteem and instrumentality. 

Because AA participants are urged to accept 
190

Part IV: Personality Variables

their powerlessness over alcohol and to put 

their faith in a higher power, this approach 

may tend to undermine, rather than promote, 

the development of instrumentality and a sense 

of self-worth.

In contrast to female clients, there are reasons to argue that alcoholic men will respond 

favorably to both CBT and TSF. As with their 

female counterparts, there is considerable 

heterogeneity within this population. Some 

males will evidence the same types of problems as have been described for females, and 

these men are expected to fare well in CBT. In 

addition, more positive outcomes for CBT clients high in sociopathy have been observed in 

prior treatment-matching research (Kadden et 

al. 1989), although this study employed group 

rather than individual therapy.

Twelve-step approaches also appear to 

benefit alcoholic men, particularly when participation occurs in groups (e.g., Cronkite and 

Moos 1984). It has been suggested that male 

clients benefit from group therapy because 

men, unlike women, tend to drink in groups. 

Heavy alcohol consumption in group settings is 

not only socially approved for men but is also 

quite consistent with gender-role expectations. 

Alcohol may serve a social function for men, 

and the camaraderie often associated with male 

drinking may find a substitute in group settings 

such as AA (Jarvis 1992). Because TSF is delivered as individual therapy in Project MATCH, it 

is anticipated that the positive outcomes experienced for TSF men will depend on the extent 

to which participants attend AA meetings as an 

adjunct to Project MATCH treatment.

Operationalization of the 

Matching Variable

Because gender is correlated with a variety 

of attributes that are related to treatment need, 

it might be argued that each of these other 

attributes offers an alternative explanation 

for any Gender × Treatment condition interactions obtained in Project MATCH. Conversely, 

it is suggested here that gender effectively summarizes a variety of interconnected individual 

difference factors that are related to drinking 

and differentially related to treatment response. 

More importantly, most of these differences 

arise in large part from individual and social 

expectations that are fundamentally based on 

gender (cf. Del Boca and Ashmore 1986). Thus, 

gender is an appropriate variable for use in 

matching clients to alcoholism treatment.

Gender was operationalized in terms of the 

client’s self-identification as a male or female. 

The outpatient sample included 688 males 

and 264 females; participants in the aftercare 

arm were 619 men and 155 women. Thus, the 

majority of clients in both arms of the trial were 

male (72.3 percent in outpatient and 80 percent in aftercare). Specific sites varied in terms 

of their relative proportions of men and women, 

ranging from 10.8 percent females in one aftercare site to 35.4 percent in one outpatient site.

Only those clients assigned to the CBT and 

TSF treatment conditions were included in 

testing the hypothesis. This resulted in an outpatient sample of 453 men (217 in CBT, 236 

in TSF) and 183 women (84 in CBT, 99 in TSF) 

and an aftercare sample of 405 males (202 in 

CBT, 203 in TSF) and 108 females (64 in CBT 

and 44 in TSF). Clients assigned to Motivational 

Enhancement Therapy (Miller et al. 1992) are 

reported for completeness.

Results

Matching and Prognostic Effects

Outpatient Arm

Table 1 presents a summary of the results 

of the latent growth curve analyses used to test 

the gender matching hypothesis. As shown, 

the hypothesis was not supported for either the 

12-week treatment phase or the 1-year posttreatment followup period. This finding was 

consistent across the two primary outcome 

variables in Project MATCH, percentage of days 

abstinent (PDA) and drinks per drinking day 

(DDD). Further, gender was not a significant 

predictor of drinking outcomes during either 

the 12-week treatment phase or the posttreatment period (Project MATCH Research Group 

1997, 1998a).

The gender matching hypothesis was tested 

in the outpatient arm at the 3-year followup 

point. Again, there was no evidence to support the hypothesis nor was there a simple 
191

The Gender Matching Hypothesis

Table 1. Gender effects—Outpatient arm

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT–MET t -1.13 .91 -.64 -.23 .79 -.28 .00 .44 -.16 -.05 -1.23 .07

p .26 .36 .52 .82 .43 .78 .99 .66 .87 .96 .22 .95

CBT–TSF t -1.97 1.46 .00 -.72 .70 -.31 -.73 1.14 1.27 1.48 -.34 -.30

p .05 .14 .99 .47 .48 .76 .46 .25 .20 .14 .73 .76

MET–TSF t -.84 .55 .69 -.50 -.11 -.02 -.74 .69 1.45 1.44 .95 -.38

p .40 .59 .49 .61 .91 .99 .46 .49 .15 .15 .34 .71

MV × TX F 1.94 1.08 .29 .28 .36 .06 .37 .67 1.29 1.47 .83 .08

p .14 .34 .74 .75 .70 .94 .69 .51 .28 .23 .44 .92

NOTE: MV=matching variable, Gender; Tx=treatment; T=time; T2=quadratic time; PDA=percentage of days abstinent; DDD=drinks 

per drinking day; CBT=Cognitive-Behavioral Coping Skills Therapy; MET=Motivational Enhancement Therapy; TSF=Twelve Step 

Facilitation

NOTE: All p values are nondirectional and should be halved to determine p values for a directional test.

prognostic effect for either of the two primary 

outcome measures (Project MATCH Research 

Group 1998b).

Aftercare Arm

The results of tests of the gender matching 

hypothesis for the aftercare arm are presented 

in table 2. The hypothesis was not confirmed for 

either of the two timeframes examined (within 

treatment and posttreatment) in analyses of 

either dependent measure.

Gender did, however, have a significant but 

small prognostic effect on outcome, with aftercare women reporting higher proportions of 

abstinent days (p=.004) and fewer drinks per 

occasion (p=.038) than men, and the DDD effect 

became more pronounced over time (p=.017) 

(Project MATCH Research Group 1997).

The A Priori Causal Chain

Figure 1 is a schematic representation of 

the causal chain that underlies the gender 

matching hypothesis. This depiction follows 

the rationale presented above. Gender is associated with treatment needs that are believed 

to be more effectively addressed by CBT than 

by TSF. As suggested by prior research, female 

alcoholics are likely to experience more ancillary problems than males as well as guilt 

and anxiety in relation to drinking (external 

stressors/negative mood), to assume greater 

responsibility in spousal and parental roles 

(role demands), and to evidence higher levels of 

anxiety and depression (psychopathology/emotional problems). At the same time, they may 

be less able to cope with their problems as a 

result of low self-worth and self-assertion (selfesteem/instrumentality). Three specific aspects 

of CBT (that are missing in TSF) are thought 

to address these factors-skills training, mood 

management, and assertiveness training. The 

philosophy of AA and the confrontational style 

of AA meetings may impede recovery. Female 

clients are expected to improve differentially on 

intervening outcome measures (skill acquisition, improved role performance, reduction in 

psychopathology, and improved self-efficacy) 

in response to these therapy factors which, in 

turn, are hypothesized to affect both the frequency and quantity of alcohol consumption.

Although the model depicted in figure 1 

contains many components, it is a relative simplification of the processes that underlie the 

gender matching hypothesis. The cluster of 

characteristics that differentiate female from 

male alcoholics in the hypothesized model tend 

to be interrelated, and their relationships to 

drinking and alcohol-related problems comprise a complex network of direct and indirect 

pathways. For example, stressful life events may 

contribute directly to alcohol use in females and 

indirectly to consumption through their impact 

on negative affective states, which also influence alcohol use. Drinking, in turn, tends to 

produce problems that create stress and aggravate feelings of depression and anxiety. The 
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Part IV: Personality Variables

Table 2. Gender effects—Aftercare arm

Treatment 

Contrast

Within treatment Posttreatment

Mv × Tx Mv × Tx × T MV × Tx × T2 Mv × Tx Mv × Tx × T MV × Tx × T2

PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD PDA DDD

CBT-MET t -.32 .03 -.47 - .01 -1.49 1.39 .08 -.15 -.73 .59 .31 .21

p .75 .98 .64 .99 .14 .16 .94 .88 .46 .56 .75 .83

CBT-TSF t -1.79 1.52 -1.41 .53 1.00 -.92 .42 .42 .84 .98 -.20 - .51

p .07 .13 .16 .60 .32 .36 .68 .67 .40 .33 .84 .61

MET-TSF t -1.33 1.35 -.85 .49 2.30 2.13 .45 .53 1.45 -.34 - .47 - .66

p .19 .18 .40 .62 .02 .03 .65 .60 .14 .73 .64 .51

MV × Tx F 1.70 1.36 1.00 .17 2.66 2.29 .12 .15 1.06 .50 .11 .24

p .18 .26 .37 .84 .07 .10 .88 .86 .35 .61 .89 .79

NOTE: MV=matching variable, Gender; Tx=treatment; T=time; T2=quadratic time; PDA=percentage of days abstinent; DDD=drinks 

per drinking day; CBT=Cognitive-Behavioral Coping Skills Therapy; MET=Motivational Enhancement Therapy; TSF=Twelve Step 

Facilitation

NOTE: All p values are nondirectional and should be halved to determine p values for a directional test.

active ingredients in CBT collectively influence 

the relevant intervening variables, which, in 

turn, result in decreased alcohol consumption.

Testing the Causal Chain

The causal chain outlined above involves 

four testable predictions, each of which may be 

phrased as a question:

■ Are the hypothesized gender differences 

in treatment needs present at baseline?

■ Did CBT and TSF treatment delivery differ as anticipated?

■ Did the two treatments differentially 

affect the intervening variables as 

predicted?

■ Are the intervening variables related to 

drinking outcomes?

Each question is examined in turn for the 

two arms of the trial.

Outpatient Arm

Are hypothesized baseline gender differences 

present? That is, did male and female clients 

in Project MATCH differ significantly in terms 

of treatment needs? To test this component 

of the causal chain, the sexes were compared 

on a variety of specific indicators that reflect 

the four domains specified in figure 1. Because 

the assessment battery was not designed specifically for this purpose, it was necessary to 

Figure 1. Gender treatment matching hypothesis causal chain.
193

The Gender Matching Hypothesis

examine responses to individual items within 

questionnaires to operationalize these domains, 

in addition to examining scores on standardized measures. The results of t-tests comparing 

outpatient men and women on scale scores are 

summarized in table 3.

The Project MATCH assessment battery 

did not include a comprehensive measure of 

stressful life circumstances. However, two sets 

of items, one from the trial’s screening questionnaire, the other from the Addiction Severity 

Index (ASI; McLellan et al. 1992), provide evidence of differential economic hardship and 

stressful life experiences. Females were significantly less likely to be employed than males (38 

versus 58 percent), and they were also significantly more likely to report a history of physical 

or sexual abuse (60 versus 37 percent). Within 

the same domain, three scales from the Alcohol 

Use Inventory (AUI; Wanberg et al. 1977) 

assessed drinking in relation to negative mood 

states: Anxious Concern About Drinking, 

Drink to Manage Mood, and Guilt and Worry 

Associated With Drinking Outpatient women 

consistently reported more negative affect associated with drinking than did men; significant 

differences were obtained for all three relevant 

AUI scales.

The results were mixed for the Role Demands 

domain. In terms of item indicators, female clients reported more childcare responsibilities 

than did their male counterparts (37 versus 32 

percent), and they were less likely than men 

to report living with a spouse (26 versus 32 

percent). Neither difference, however, was statistically significant. There were also no gender 

differences on the Social Role Performance 

scale of the Psychosocial Functioning Inventory 

(Feragne et al. 1983). On the other hand, 

females in the outpatient study reported receiving significantly less Family Social Support, 

and they were significantly more likely than 

men to attribute their alcohol use to marital 

difficulties on the AUI scale, Drink to Deal With 

Marital Problems.

Results were more consistent with predictions in the Psychopathology/Emotional 

Problems domain. As indicated in prior 

research, affective disorders were more prevalent among females than males, whereas men 

were more likely to receive an ASP diagnosis. 

Women also scored significantly higher on the 

ASI Psychiatric Severity scale and on the Beck 

Depression Inventory (BDI; Beck et al. 1961).

Although no measure of self-esteem per se 

was included in the assessment battery, gender differences on instruments reflecting self 

esteem/instrumentality were all consistent 

with expectations. Women scored lower than 

men in instrumentality (“masculinity”) on 

the Personal Attributes Questionnaire (PAQ; 

Spence et al. 1974) and higher on expressiveness (“femininity”) as well as on the Assertion 

of Autonomy scale of the Interpersonal 

Dependency Inventory (Hirschfeld et al. 1977). 

In addition, they reported significantly lower 

levels of alcohol self-efficacy (DiClemente et al. 

1994).

In summary, significant gender differences 

were obtained for 17 of 20 intake assessment 

variables that span 4 relevant treatment need 

domains. These differences indicate that the 

treatment needs of outpatient women tended 

to differ from those of male clients, and they 

provide confirmation for the first prediction 

derived from the gender matching hypothesis 

causal chain.

Did CBT and TSF treatment delivery differ 

as anticipated? The Project MATCH therapies 

have been shown to be discriminable in terms 

of critical ingredients (Carroll et al. 1998). For 

purposes of investigating the causal chain, 

however, it is also important to show that males 

and females received sufficient exposure to the 

two treatments and that the specific elements 

hypothesized to affect outcomes were present in CBT and TSF. To begin to address this 

issue, session attendance rates within the two 

treatments were compared in a 2 (Gender) × 2 

(Treatment condition) ANOVA. No significant 

main effects were found, and there were no sex 

differences in session attendance rates as a 

function of treatment assignment. On average, 

outpatient CBT males attended 8.28 therapy 

meetings and females completed 8.25 sessions; 

comparable values for TSF were 7.49 and 7.46.

Next, exposure to particular treatment elements was examined. In terms of CBT, sex 

differences in the session content (as recorded 

by the therapist) of each CBT session were 

examined. Two types of measures were evaluated: (1) session topics and (2) therapist 
194

Part IV: Personality Variables

Table 3. Baseline gender differences in treatment need domains by study arm—Mean (SD)

Intake variable

Outpatient arm Aftercare arm

Males Females p Males Females p

External stressors/negative mood

Anxious Concern About Drinking (AUI) 13.1 (4.9) 15.0 (4.7) .000 14.9 (4.6) 15.9 (4.5) .026

Drink to Manage Mood (AUI) 4.9 (2.3) 5.7 (2.1) .000 5.5 (2.1) 6.0 (1.8) .015

Guilt and Worry Associated With Drinking 

(AUI)

5.6 (2.3) 6.1 (2.1) .001 6.3 (2.2) 6.5 (2.1) .454

Role demands

Social Role Performance (PFI) 3.5 (.77) 3.5 (.79) .278 3.3 (.93) 3.3 (.88) .452

Family Social Support 4.3 (2.3) 3.6 (2.6) .000 4.2 (2.4) 4.2 (2.5) .407

Drink to Deal with Marital Problems (AUI) 1.9 (2.0) 2.6 (2.2) .003 2.2 (2.0) 3.0 (2.2) .005

Psychopathology/emotional problems

Psychiatric Severity Scale (ASI) .19 (.19) .22 (.20) .052 .21 (.20) .32 (.24) .000

Beck Depression Inventory 9.3 (7.7) 11.6 (8.8) .001 10.2 (8.1) 12.4 (10.0) .013

Self-esteem/instrumentality

Instrumentality (MASC-PAQ) 28.4 (5.1) 26.1 (5.4) .000 27.9 (5.3) 24.6 (5.4) .000

Expressiveness (FEM-PAQ) 30.0 (4.5) 32.1 (4.3) .000 29.9 (5.0) 32.6 (4.6) .000

Masculinity-Feminity (PAQ) 24.7 (3.8) 27.7 (4.5) .000 25.2 (4.0) 29.4 (3.7) .000

Alcohol Self-Efficacy 3.0 (.81) 2.8 (.76) .036 3.2 (1.0) 3.2 (1.0) .366

Interpersonal Dependency 40.4 (7.0) 41.9 (6.9) .005 40.4 (7.0) 43.2 (7.6) .000

NOTE: AUI=Alcohol Use Inventory; ASI=Addiction Severity Index; PAQ=Personal Attributes Questionnaire; PFI= Psychosocial 

Functioning Inventory. One-tailed t-tests were used to test for between-group differences.

ratings of the degree to which the sessions 

emphasized problem-solving and coping skills. 

Session topics were considered separately for 

core and elective therapy sessions. The former focus more specifically on drinking (e.g., 

drink-refusal skills) and were intended for all 

CBT participants, whereas the elective sessions 

were designed to cover problems that were of 

particular relevance to individual clients (e.g., 

anger management).

The second type of measure, therapist session ratings, was designed to assess the type 

of activity that took place, independent of the 

specific topic (e.g., apply a problem-solving 

strategy). It should be noted that, although 

these treatment process data provide important 

clues as to what occurred in therapy, there is 

considerable missing data on these measures, 

with the analyses at week 12 examining only 25 

percent of the outpatient sample. (Consistent 

with the treatment attendance data, the relative 

proportions of males and females approximate 

those for the outpatient sample more generally 

across the various sessions.) In addition, the 

responses represent only the therapists’ (and 

not the clients’) perceptions.

Table 4 indicates, for treatment sessions #2 

through #12, the extent to which the elements 

of CBT specified in the causal chain were primary topics in therapy. The first indicator, core 

sessions, sums across two topics—problemsolving and drink-refusal skills; the second 

indicator, elective sessions, includes three 

topics—introduction to assertiveness, managing negative thinking, and managing negative 

moods.

Overall, core therapy sessions accounted for 

78 percent of the outpatient sessions included 

in table 4. Core therapy sessions pertinent to 

the causal chain occurred most often during 

relatively early sessions of treatment, specifically sessions #4 through #7. However, for a 

notable minority of men, these topics were covered in later sessions. Elective sessions dealing 

with assertiveness and negative thinking or 

moods tended to occur later in the treatment 
195

The Gender Matching Hypothesis

phase, and they were more prevalent for women 

clients.

In addition to a summary of session topics, table 4 presents mean therapist ratings of 

the extent to which problem-solving and coping skills were emphasized during the session. 

The index presented is based on four separate 

ratings:

■ Discussion of high-risk situations in 

terms of coping skills used

■ Therapist attempts to “teach, model, rehearse, review, or discuss specific skills”

■ Application of a problem-solving strategy

■ Formulation of coping strategies to 

anticipate high-risk situations

Ratings were made using a 5-point scale 

ranging from l=“not at all” to 5=“extensively.” 

Regardless of the specific session topic, therapists tended to report more emphasis on 

problem-solving/coping skills with female than 

with male clients, but the differences tended to 

be small and not statistically significant.

The patterns in table 4 suggest that CBT 

treatment delivery for both sexes involved 

the elements specified in figure 1. Moreover, 

these elements were more likely to be present 

for female than for male clients, presumably 

because of their greater relevance. However, 

the percentages in table 4 suggest that many 

women for whom data are available did not 

receive elective sessions dealing with the targeted issues. And, because the elective session 

topics were more often dealt with during the 

later weeks of treatment, many participants 

had ceased attending therapy (recall the mean 

number of sessions attended for both sexes was 

approximately eight). Therapist ratings present 

a similar picture. Problem-solving and coping skills were emphasized for clients of both 

sexes, and, in absolute terms, the ratings tend 

to average only somewhat above the midpoint 

of the 5-point rating scale. Thus, it is questionable whether female participants received 

adequate exposure to the critical ingredients of 

CBT specified by the hypothesis.

With respect to TSF, the gender matching 

hypothesis suggests that female clients may 

find AA meetings both uncomfortable and 

counter-productive in terms of their specific 

treatment needs. Table 5 presents a summary 

of male and female attendance rates at selfhelp meetings.

Table 4. CBT checklist therapist reports: Outpatient arm

Session (n)

CBT session topics Therapist session ratings

Core sessions: 

Problem-solving and 

drink-refusal skills (%)

Elective sessions: 

Assertiveness and 

mood management (%)

Problem-Solving and 

Coping-Skills Index 

Mean (SD)a

Males Females Males Females Males Females

2 (268) 2.6 .0 .5 .0 3.58 (.58) 3.73 (.54)

3 (256) 9.2 4.6 .0 .0 3.55 (.60) 3.71 (.61)

4 (234) 62.3 76.1 1.8 1.6 3.69 (.64) 3.82 (.65)

5 (223) 56.0 61.0 1.2 2.3 3.52 (.71) 3.78 (.62)

6 (219) 26.7 26.6 .7 6.7 3.52 (.72) 3.71 (.58)

7 (204) 15.7 21.4 1.4 1.8 3.50 (.68) 3.66 (.64)

8 (191) 5.3 15.1 12.1 14.2 3.42 (.75) 3.47 (.62)

9 (164) 8.4 2.0 16.0 22.0 3.37 (.75) 3.40 (.74)

10 (160) 6.3 .0 15.3 35.7 3.30 (.70) 3.57 (.67)

11 (133) 4.3 .0 16.2 24.3 3.33 (.77) 3.30 (.76)

12 (83) .0 .0 1.6 .0 3.25 (.80) 3.06 (.87)

a The Problem-Solving and Coping-Skills index represents the mean rating using a response scale ranging from 1 (“not at all”) 

to 5 (“extensively”) across four items: (1) discussion of high-risk situations in terms of coping skills used; (2) therapist attempts 

to “teach, model, rehearse, review, or discuss specific skills”; (3) application of a problem-solving strategy; and (4) formulation 

of coping strategies to anticipate high-risk situations.

NOTE: Bold entries for therapist session ratings indicate significant gender differences, two-tailed t-test, p<.05.
196

Part IV: Personality Variables

Table 5. Gender differences in AA meeting 

attendance during the followup period

Time

CognitiveBehavioral 

Therapy

Twelve Step 

Facilitation

Males Females Males Females

Outpatient arm

3 months .04 .04 .21 .23

6 months .05 .05 .13 .12

9 months .05 .05 .11 .13

12 months .06 .05 .10 .10

15 months .07 .05 .10 .08

Aftercare arm

3 months .29 .26 .40 .43

6 months* .21 .16 .25 .37

9 months* .20 .13 .19 .32

12 months* .17 .12 .18 .30

15 months* .16 .11 .19 .40

NOTE: Cell entries represent the mean proportion of days 

that clients reported attending meetings during each followup time window as assessed via the Form 90 interview. 

*t-tests indicate significant (p<0.05, two-tailed) gender differences for those in the TSF condition at these time points.

This measure, derived from Form 90 (Miller 

1996) interviews, represents the mean percentage of days within each 3-month followup 

period that meetings were attended. As shown, 

male and female clients reported comparable 

levels of meeting attendance. Both sexes were 

more likely to attend meetings in the TSF condition than in CBT. And, while attendance 

remained relatively stable in CBT, it tended to 

decline somewhat over time in TSF.

To assess differences in active participation in AA, female and male scores on the 

followup version of the Alcoholics Anonymous 

Involvement scale (Tonigan et al. 1996) were 

compared. No differences were found at any 

of the time points tested (3, 9, and 15 months 

following treatment initiation), suggesting that 

meeting-attendance rates do not mask underlying differences in involvement in AA.

Did the two treatments differentially affect 

the intervening variables? Is the gender matching hypothesis supported for those intervening 

outcomes that are differentially targeted by 

CBT and TSF? Four of the baseline measures 

used to establish sex differences at intake were 

repeated at 6-month intervals during the followup phase of the trial. Repeated-measures 

ANOVAs were performed using gender and 

treatment condition as between-clients factors 

and scores on the hypothesized intervening 

variables at baseline and 3-, 9-, and 15-month 

followup points as the dependent variables. 

The results of these analyses failed to support this set of linkages in the causal chain. In 

analyses of ASI Psychiatric Severity, the Beck 

Depression Inventory, Alcohol Self-Efficacy, 

and Social Role Performance, only one significant interaction involving gender and treatment 

assignment was obtained, and this result was 

inconsistent with the predicted effect. A significant Gender × Treatment condition × Time 

interaction was found for Psychiatric Severity 

(F=4.07, df=3,1218, p=.007). As shown in table 

6, during the followup period (and particularly toward the end of the period), TSF women 

reported fewer psychiatric symptoms on the 

ASI, and greater reductions from baseline 

levels, than those assigned to CBT, whereas 

changes for males were comparable across the 

two treatments.

Are the intervening variables related to drinking outcomes? Table 7 presents correlations 

between the intervening variable indicators 

and drinking measures at the end of treatment 

Table 6. ASI Psychiatric Severity Scale 

Gender x Treatment interaction effect: 

Outpatient sample—Mean (SD)

Time

CognitiveBehavioral 

Therapy

Twelve Step 

Facilitation

Males

(n=168)

Females

(n=74)

Males

(n=183)

Females

(n=79)

Baseline .20 .23 .19 .21

(.20) (.21) (.19) (.20)

3 months .14 .17 .12 .14

(.17) (.21) (.17) (.17)

9 months .13 .15 .11 .13

(.18) (.21) (.16) (.16)

15 months .09 .18 .09 .11

(.16) (.22) (.15) (.16)

Gender × Time: F=2.82; df=3,404; p=.039.

Gender × Treatment Assignment × Time: F=4.07; df=3, 1218; 

p=.007.
197

The Gender Matching Hypothesis

and 1 year later. Despite the considerable variation in the strength of the relationships (r’s 

ranging from 0.01 to 0.51), all of the correlations are in the expected direction. Correlations 

involving the ASI tend to be weak and nonsignificant; relationships between BDI and Social 

Role Performance scores and drinking are 

larger. Not surprisingly, associations are strongest for the Alcohol Self-Efficacy scale, which 

is most directly related to drinking. On average, the entries in table 7 are higher for female 

than for male clients. Thus, although not completely consistent across measures, intervening 

outcome variables tend to be correlated with 

alcohol consumption indicators during the followup period.

In summary, causal chain analyses for the 

outpatient sample produced mixed results. 

Gender differences were found in terms of the 

treatment needs specified in the model, and 

scores on the intervening variables were correlated with drinking outcomes. However, 

indicators of treatment process suggested that 

CBT and TSF may not have been delivered and 

experienced as anticipated. Although more 

emphasis was given to general coping skills 

and mood management for female clients in 

CBT than for males, the magnitude of the difference was relatively small, and the amount 

of attention paid to these areas was somewhat 

limited. In terms of TSF, men did not attend 

AA meetings more often than women, and they 

did not evidence higher levels of AA involvement. Further, only one significant Gender 

× Treatment condition interaction effect was 

observed for the intervening outcome variables 

that were the direct targets of the treatments 

in the causal chain, and the pattern of results 

was opposite that predicted.

Aftercare Arm

Are hypothesized baseline gender differences 

present? As described above, male and female 

clients were compared in terms of a variety of 

measures of treatment need. As in the outpatient study, aftercare women were significantly 

less likely to be employed than men (41 versus 48 percent), and they were more likely to 

report a history of physical or sexual abuse (68 

versus 30 percent). As shown in table 3, significant differences in the predicted direction were 

obtained for two of the three AUI scales in this 

domain, Anxious Concern About Drinking and 

Drink to Manage Mood.

As in the outpatient study, mixed results 

were obtained for measures in the role demands 

domain. Female clients were significantly more 

likely to report childcare responsibility than 

were males (36 versus 24 percent); they were 

also less likely to report living with a spouse 

(31 versus 38 percent), but this association did 

not achieve statistical significance. As shown 

in table 3, no sex differences were found for 

the Social Role Performance or Family Social 

Table 7. Correlations between hypothesized intervening variables and drinking outcomes

Variables

Males Females

PDA4 PDA15 DDD4 DDD15 PDA4 PDA15 DDD4 DDD15

Outpatient arm

Social Role Performance .20 .20 -.22 -.28 .26 .21 -.36 -.25

ASI Psychiatric Severity -.08 -.01 .13 .09 -.05 -.15 .08 .26

Beck Depression Inventory -.16 -.24 .14 .27 -.36 -.25 .32 .40

Alcohol Self-Efficacy .39 .42 -.33 -.35 .50 .51 -.50 -.47

Aftercare arm

Social Role Performance .17 .27 -.25 -.37 .20 .19 -.19 -.42

ASI Psychiatric Severity -.08 -.14 .10 .29 -.18 -.28 .12 .36

Beck Depression Inventory -.29 -.38 .28 .47 -.36 -.30 .26 .46

Alcohol Self-Efficacy .35 .45 -.34 -.47 .30 .48 -.30 -.49

Entries are Pearson product moment correlations between intervening variables and drinking outcomes that correspond in 

terms of followuf time point. PDA4 and PDA15 refer to percentage of days abstinent at the 4- and 15-month time points, respectively; DDD and DDD15 represent drinks per drinking day at these same points. All entries are statistically significant (p<.05) 

except for those underscored.
198

Part IV: Personality Variables

Support scales, although aftercare women 

scored significantly higher than men on the AUI 

scale, Drink to Deal With Marital Problems.

Predicted differences were also found for the 

psychopathology/emotional problems domain. 

As in the outpatient sample, aftercare females 

were more often diagnosed with affective disorders, and males with ASP. Women scored 

significantly higher on the ASI Psychiatric 

Severity scale and on the Beck Depression 

Inventory (see table 3).

In terms of self-esteem/instrumentality, 

aftercare women scored lower than men on 

the PAQ Instrumentality (“masculinity”) scale 

and higher on Expressiveness (“femininity”) 

and Interpersonal Dependency. No differences, 

however, were found for Alcohol Self-Efficacy.

In sum, the aftercare results roughly paralleled those for the outpatient sample. Predictions 

were confirmed for 15 of 20 variables across 

the four treatment need domains. The profiles 

for aftercare clients of both sexes were consistently higher in problem severity than those for 

the outpatient sample. The gender differences 

found in these analyses indicated that women 

and men have different needs in areas that 

should be differentially addressed by the study 

treatments, and that these needs were more 

marked in the aftercare sample.

Did CBT and TSF treatment delivery differ 

as anticipated? As with the outpatient sample, 

the examination of this question began with an 

analysis of treatment compliance rates. Again, 

a 2 (Gender) × 2 (Treatment condition) ANOVA 

was performed. No gender differences were 

found as a function of treatment assignment.

Aftercare CBT males attended, on average, 

8.08 therapy meetings compared with 7.81 sessions for women. TSF attendance rates were 

also similar for males and females (7.32 and 

7.23, respectively).

Next, therapists’ descriptions of CBT session content and ratings of the extent to which 

problem-solving and coping skills were emphasized were examined. Results for the aftercare 

study are presented in table 8. As noted above, 

these data should be viewed cautiously, especially during the late weeks of treatment (for 

Week 12, data are available for only 43 percent of participants). As in the outpatient arm, 

across sessions the relative proportions of each 

gender approximate those for the entire aftercare sample.

Table 8. CBT checklist therapist reports: Aftercare arm

Session (n)

CBT session topics Therapist session ratings

Core sessions: 

Problem-solving and 

drink-refusal skills (%)

Elective sessions: 

Assertiveness and 

mood management (%)

Problem-Solving and 

Coping-Skills Index 

Mean (SD)a

Males Females Males Females Males Females

2 (226) 1.7 0 0 0 3.50 (.62) 3.46 (.57)

3 (206) 4.4 2.1 0 0 3.51 (.57) 3.46 (.51)

4 (189) 76.6 81.8 .1 0 3.79 (.59) 3.65 (.46)

5 (180) 67.9 81.4 .7 0 3.63 (.63) 3.67 (.56)

6 (175) 16.9 29.3 .7 0 3.58 (.71) 3.62 (.61)

7 (170) 11.5 20.0 3.8 0 3.53 (.69) 3.54 (.62)

8 (159) 6.4 5.7 15.3 17.2 3.42 (.76) 3.50 (.62)

9 (153) 7.6 2.9 16.1 17.2 3.46 (.67) 3.47 (.67)

10 (145) 7.2 0 23.2 27.2 3.43 (.80) 3.55 (.69)

11 (135) 5.7 3.4 18.0 41.3 3.39 (.72) 3.43 (.68)

12 (116) 0 0 1.1 3.6 3.22 (.89) 3.19 (.84)

a The Problem-Solving and Coping-Skills index represents the mean rating using a response scale ranging from 1 (“not at all”) 

to 5 (“extensively”) across four items: (1) discussion of high-risk situations in terms of coping skills used; (2) therapist attempts 

to “teach, model, rehearse, review, or discuss specific skills”; (3) application of a problem-solving strategy; and (4) formulation 

of coping strategies to anticipate high-risk situations.
199

The Gender Matching Hypothesis

The pattern for the aftercare sample is 

similar to that for the outpatient arm. For 

both genders, there was more focus on problem-solving and coping skills during the core 

treatment sessions, which accounted for 76 

percent of those included in table 8. In general, the same conclusions and caveats apply. 

Elements of CBT specified in the causal chain 

were clearly evident in treatment; however, the 

incompleteness of the data, coupled with the 

occurrence of elective elements relatively late in 

the treatment phase, raise questions regarding 

adequacy of exposure.

Table 5 summarizes aftercare male and 

female attendance rates at self-help meetings. 

Compared with the outpatient study, higher 

rates were exhibited by both men and women 

in the aftercare arm for both CBT and TSF. In 

fact, attendance was higher for aftercare CBT 

clients than it was for TSF outpatients. Contrary 

to the hypothesis, women in the TSF condition 

re-ported significantly higher rates of meeting 

attendance than did men for all posttreatment 

followup periods. Aftercare women in TSF also 

had higher AA Involvement scores at the 3-, 

9-, and 15-month followup points, but the difference only reached statistical significance at 

the last evaluation (M=3.03, SD=1.94 for males, 

and M=3.88, SD=1.90 for females; t=-2.45, df= 

216; p=.015).

Did the two treatments differentially affect the 

intervening variables as predicted? Repeated 

measures ANOVAs were run using Gender 

and Treatment condition as between-clients 

factors and scores on the hypothesized intervening variables (Social Role Performance, 

ASI Psychiatric Severity, Beck Depression 

Inventory, and Alcohol Self-Efficacy) at baseline 

and 3-, 9-, and 15-month followup points as 

the dependent variables. Again, the results did 

not support this pathway in the causal chain.

Are the intervening variables related to drinking outcomes? As table 7 shows, the overall 

pattern of correlations between intervening 

variables and drinking outcome measures at 

the end of treatment and 1 year later at the 

15-month followup point was similar to that 

obtained for the outpatient study. In general, 

the correlations tended to be slightly higher for 

the aftercare sample.

Thus, although there was considerable variability in the magnitude of the associations (r’s 

ranging from 0.08 to 0.49), the results tended to 

support this link in the causal chain. For both 

sexes, scores on variables such as Social Role 

Performance, Psychiatric Severity, Depression, 

and Alcohol Self-Efficacy were associated with 

both the frequency (PDA) and quantity (DDD) of 

drinking during followup.

In summary, results of the causal chain 

analyses for the aftercare arm were similar 

to those obtained for the outpatient sample. 

Gender differences in treatment needs were 

documented, and scores on the intervening variables were associated with alcohol 

consumption measures. However, treatment 

process data analyses suggested that CBT and 

TSF were not experienced as predicted. As in 

the outpatient arm, the emphasis placed on 

general coping skills and mood management 

in CBT sessions for female clients was relatively limited. Contrary to predictions, aftercare 

women attended AA meetings more of-ten than 

did their male counterparts, and they reported 

stronger AA involvement during the followup 

period. Further, no Gender x Treatment condition interactions were found for the intervening 

outcome variables targeted by the treatments 

in the causal chain.

Discussion

The direct tests of the gender matching 

hypothesis failed to support the formulation 

presented above. Evidence in support of the 

hypothesis was not obtained in either arm of 

the trial or in either timeframe under study. 

Analyses aimed at determining where the 

hypothesized causal chain “broke down” indicated that some linkages were consistent with 

the formulation and that other pathways may 

require reconsideration.

The hypothesized differential treatment needs 

for males and females were evidenced by baseline gender differences in four treatment need 

domains: external stressors/negative mood, 

role demands, psychopathology/emotional 

problems, and self-esteem/instrumentality. At 

the other end of the causal sequence, analyses 

indicated that scores on the intervening variables specified in the formulation were related 
200

Part IV: Personality Variables

to drinking outcomes. Thus, the causal chain 

depicted in figure 1 appears most problematic 

in terms of differential therapeutic content and 

gender differences in response to treatment. 

CBT appeared to address the specific problems 

and treatment needs of female clients in only 

a limited fashion. Topics covered in therapy 

appear not to have been those expected to be of 

most concern to the women in the trial.

The failure of CBT to address fully the identified treatment needs of women in Project 

MATCH may reflect the manner in which the 

treatment was implemented in the trial rather 

than factors intrinsic to the therapy itself (cf. 

Donovan 1998). A hallmark of cognitive-behavioral approaches is a functional analysis of the 

client’s drinking behavior. To distinguish CBT 

from Motivational Enhancement Therapy, this 

analysis was not performed in Project MATCH, 

nor were results of the comprehensive baseline 

assessment made known to CBT therapists. 

Further, both the individual format and the 

specified content and pace of the treatment 

sessions (eight core sessions, a different topic 

each week) may have impeded skill acquisition. 

Group sessions permit more modeling, role 

playing, and behavioral rehearsal of new skills; 

the content and pace requirements for the 

treatment reduced the number of elective sessions pertinent to the specific treatment needs 

of women that took place. More flexibility in the 

manual-guided treatment may have produced 

different results (Donovan 1998).

Perhaps more importantly, the discomfort 

hypothesized to occur for female clients in 

the TSF condition was not evident. In terms 

of session attendance, females were equally 

compliant in CBT and TSF, and, in the TSF 

condition, they evidenced higher AA/12-step 

meeting attendance rates than did male clients 

in the aftercare study, the arm of the trial in 

which women had more favorable outcomes 

than men. This contradiction of the hypothesis 

formulation may, however, be more apparent 

than real. The extent to which the meetings 

attended by women in the trial conform to the 

traditional view of AA as confrontational and 

male dominated is not known. AA has become 

quite diverse in terms of its offerings (McCrady 

and Delaney 1995), and it may be that women 

in the trial participated in meetings that did not 

have, and possibly explicitly rejected, formats 

and norms that might be construed as hostile 

toward women or intolerant of female selfassertion. Further, such AA groups may have 

provided female clients, many of whom were 

socially isolated, with the means for building 

a social support network (McCrady and Raytek 

1993).

The improvements in response to treatment 

suggest that both CBT and TSF succeeded in 

addressing many of the needs of clients of both 

genders. CBT might have had more impact 

if more time and attention were specifically 

devoted to those problems that are more prevalent among women. On the other hand, some 

of the problems themselves may be as much 

a consequence of drinking as a cause. This 

argument has been applied to understanding 

the negative affect and psychopathology frequently reported by alcoholics (e.g., Schuckit 

and Hesselbrock 1994; Verheul et al. 2000). 

To the extent that this is the case, one might 

expect that sobriety achieved in either treatment would have at least some beneficial effect 

in the other problem domains.

Finally, the findings do not rule out the possibility that gender-treatment matching may 

improve outcomes when other treatment factors 

are considered. “Gender-specific” treatments 

are usually defined less in terms of general 

therapeutic philosophy or modality and more in 

terms of factors that relate to ease of communication (e.g., female therapist, group sessions 

with other female participants) and content 

(special attention to specific issues such as 

sexual and physical abuse, reproductive problems). Such factors were not examined in the 

present study, and there is some evidence that 

they can produce superior treatment outcomes 

(Dahlgren and Willander 1989).

Acknowledgments

Preparation of this publication was supported by grant number U10–AA08430 from 

the National Institute on Alcohol Abuse 

and Alcoholism as part of the Cooperative 

Agreement on Matching Patients to Alcoholism 

Treatments.
201

The Gender Matching Hypothesis

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205

Part V: Motivation and Self-Efficacy

Motivation Hypothesis Causal Chain Analysis

Alcohol Problem Recognition and Treatment 

Outcomes

Self-Efficacy as a Matching Hypothesis: Causal 

Chain Analysis
206

Motivation Hypothesis Causal Chain 

Analysis

Carlo C. DiClemente, Ph.D., Joseph Carbonari, Ph.D.,

Allen Zweben, D.S.W., Tanya Morrel, Ph.D., and 

Rebecca E. Lee, Ph.D.

ABSTRACT

The motivation hypothesis examined the interaction of initial motivational readiness 

to change. This was measured by a single score derived from a revised, alcohol-specific version of the University of Rhode Island Change Assessment, with a Motivational 

Enhancement Therapy (MET) versus Cognitive-Behavioral Coping Skills Therapy 

(CBT) contrast. Primary outcome analyses revealed a minimal matching effect, with 

low-motivation clients in MET having more days abstinent in contrast to CBT clients 

at the very end of the followup period in the outpatient arm of the Project MATCH 

trial. Examination of the hypothesized mediating and moderating variables that were 

assumed to account for any matching effect, however, demonstrated that the treatments did not differentially influence client-therapist working alliance, client coping 

activities, or attendance during treatment. Nor did the treatments differentially affect 

the posttreatment variables of motivational readiness to change, processes of change, 

or alcohol abstinence self-efficacy. There was support for some of the hypothesized 

generic causal mechanisms. Therapy attendance and client reports of the therapeutic 

alliance influenced drinking outcomes. However, no supporting evidence was found 

for any proposed treatment-specific causal mechanisms. Instead, there was strong 

support across all three treatments for the impact of initial motivational readiness 

to change on working alliance, client processes of change, and, most importantly, on 

drinking frequency and intensity outcomes over the 1-year followup period and at 

the 3-year followup. Findings related to motivation were always more robust among 

outpatient participants compared to aftercare clients. Implications of these findings 

for MET and for understanding the process of change in alcoholism treatment are 

discussed.

The motivation hypothesis in Project 

MATCH was developed based on the 

ongoing work on the Stages of Change 

construct from the Transtheoretical Model 

of intentional human behavior change 

(DiClemente and Prochaska 1998; Prochaska 

and DiClemente 1984, 1992; DiClemente 1993b; 

Prochaska et al. 1992). The Stages of Change 

identified by the model segment the process of 

change for addictive as well as other behaviors 

into five steps or stages. These stages begin with 

Precontemplation, where the individual is not 

seriously considering change. As individuals 

experience the process of change, they progress 

through Contemplation and Preparation stages 

before reaching the Action Stage where they 

actually begin to make the change. Finally, after 

Carlo C. DiClemente, Ph.D.

Professor and Chair

Department of Psychology

University of Maryland Baltimore County 

1000 Hilltop Circle

Baltimore, MD 21250

E-mail: diclemen@umbc.edu
207

Motivation Hypothesis Causal Chain Analysis

a significant period of action, they reach the 

Maintenance stage where the change is firmly 

established and incorporated into the individual’s current behavioral repertoire. Each stage is 

assumed to have its own tasks and issues that 

need to be resolved before successfully moving 

to the next stage. Individuals in earlier stages 

need motivation and commitment and use cognitive and experiential processes of change to 

move forward. Individuals in the Action and 

Maintenance stages need behavioral processes 

to make the change and sustain it (DiClemente 

and Prochaska 1998; DiClemente and Scott 

1997; Prochaska et al. 1992; Perz et al. 1996).

Stage status is assumed to relate to readiness to change a particular behavior and to 

determine behavior change activity and outcomes. Stages have been assessed using many 

different measures. This study used a multiitem, multisubscale measure based on the 

University of Rhode Island Change Assessment 

(URICA) measure (McConnaughy et al. 1989; 

DiClemente and Hughes 1990). The modified 

version is alcohol problem specific and has four 

7-item subscales measuring attitudes related to 

the Precontemplation, Contemplation, Action, 

and Maintenance stages. These subscales combine to form a second-order factor that we have 

labeled Readiness to Change (Carbonari et al. 

1994). Although the stages are conceptualized 

as discrete steps in the process of change, the 

motivational readiness score can indicate where 

each subject is on these stages measured as 

on a continuum of readiness. The continuous 

measure increases the power and sensitivity of 

analytic procedures.

The Matching Hypothesis

The hypothesis related to motivation posited that clients low in motivational readiness 

to change would have better outcomes in the 

Motivational Enhancement Therapy (MET; Miller 

et al. 1992) than in Cognitive-Behavioral Coping 

Skills Therapy (CBT; Kadden et al. 1992). The 

underlying mechanism for this matching effect 

was assumed to be the connection between 

readiness to change the alcohol behavior (in 

actuality the lack of readiness) with the specific 

motivational dimensions of MET and the lack 

of an adequate motivational emphasis in CBT. 

The motivational strategies delivered by the

MET therapist and received by the client 

assigned to MET were assumed to promote and 

interact with motivational readiness to change 

(DiClemente 1991; Miller and Rollnick 1991; 

Miller et al. 1992). For example, we would 

expect a differential response from participants 

in MET compared to those in CBT to the following questions:

■ Did the client feel accepted and 

understood?

■ Did the therapist create the tensions 

that shifted the pros and cons of the 

problem behavior and address the 

ambivalence of the client well enough to 

create movement?

■ Were the therapist and client aligned on 

the goals and tasks of the therapeutic 

process?

■ Did they develop a viable change plan for 

this client?

■ Did the therapist help the client create 

the level of alcohol abstinence self-efficacy that would support change?

If these tasks were accomplished in MET 

and less so or not at all in CBT for the lowmotivated clients, we should see differences 

in drinking outcomes that would support the 

matching hypothesis.

If the motivational dimensions of MET 

occurred and functioned as hypothesized, it is 

assumed that the low-motivated client would 

become more engaged in the therapy and in the 

process of change than in the comparison therapy. Once engaged, this client would develop 

increased motivation to change and move to 

action with greater probability than a similar 

client in CBT where the focus was on getting 

clients to take action right from the start by 

working on identifying triggers and on developing skills. Low-motivated clients would be 

mismatched most in the CBT condition, which 

is more action oriented, and would not engage 

as completely in treatment nor have as good 

outcomes in CBT without the specific focus 

on motivation offered in MET (DiClemente 

et al. 1992). Although no formal hypothesis 

was made that included clients in the Twelve 

Step Facilitation (TSF; Nowinski et al. 1992) 
208

Part V: Motivation and Self-Efficacy

treatment, we thought that TSF approaches 

and the support and modeling provided by 

AA meetings would contain more motivational 

components than CBT (DiClemente 1993a). 

Therefore, in terms of participants’ drinking 

outcomes, TSF would possibly fall somewhere 

between CBT and MET if clients with low motivation could be engaged in the 12-step and AA 

program.

The hypothesis focused only on the low end 

of the motivational readiness scale because of 

the obvious matching potential between MET 

and low motivation. CBT could be expected 

to do well with the highly motivated or ready 

client. However, a motivated client could do 

well in most treatments. If there were any 

mismatching at the high end of motivational 

readiness, it would most likely occur between 

high-motivated clients and TSF, since there 

may be a clash between personal motivation 

to change and the admonition of the 12-step 

tradition to admit powerlessness over drinking 

(DiClemente 1993a). No firm hypotheses, however, were developed for the highly motivated 

client. A more complex, higher order interaction 

with other variables, like interpersonal skills or 

environment, would be needed to adequately 

address the interaction of motivation with other 

aspects of the client-treatment match, particularly at the high end of motivation.

Assumed Causal Chain

The proposed causal chain analyses for the 

motivational hypothesis focused on the match 

between low motivation and readiness to 

change in the MET versus CBT contrast. If this 

matching and mismatching occur, the explanation should lie in the motivational enhancement 

dimensions of MET and the lack of readiness of 

the client (DiClemente et al. 1992). The hypothesized intermediate variables are displayed in 

figure 1. The causal chain analysis would proceed in the following sequence of events:

1. The interaction of the baseline readiness 

and the MET treatment would create a 

more positive and productive therapeutic 

alliance for the low-motivated clients in 

MET.

2. Compliance is both an intermediate matching variable and another possible link in 

the causal chain analysis (DiClemente 

and Scott 1997). Low-motivated clients 

in MET could be expected to attend more 

Figure 1. Causal links for the motivation hypothesis.
209

Motivation Hypothesis Causal Chain Analysis

treatment weeks than would those in 

CBT. As envisioned for this hypothesis, 

the therapeutic alliance should influence compliance measured either by the 

percentage of sessions attended or the 

number of weeks attended. There should 

also be a link between compliance and 

the client’s readiness to change at the 

3-month followup.

3. Change process activities, as reported 

by the client at the end of each therapy 

session and, most comprehensively, 

at the 3-month evaluation, represent 

the active coping mechanisms, according to the Transtheoretical Model, by 

which movement through the stages and 

behavior change are accomplished. If the 

MET active ingredients are working as 

expected, we should see some differences 

by treatment in process activity either 

during treatment or posttreatment or at 

both time points. Change process activity, in turn, should influence drinking 

outcomes.

4. A more positive therapeutic alliance created through the interaction of MET with 

low motivation should contribute to an 

increase in the readiness assessed at the 

end of treatment. Readiness to change 

measured at the 3-month followup is the 

next link in the chain and should ultimately influence posttreatment drinking.

5. Another variable that we believe can 

influence the matching to outcome connection is client self-efficacy to abstain 

from drinking measured at posttreatment 

(DiClemente et al. 1995). Client alcohol 

abstinence self-efficacy at the 3-month 

end of treatment evaluation could also 

serve as an intermediate outcome variable since MET is specifically directed at 

increasing self-efficacy of the clients in 

abstaining from their drinking.

6. Drinking outcomes (percentage of days 

abstinent, PDA; drinks per drinking day, 

DDD) during treatment and particularly 

drinking outcomes throughout the 4- to 

15-month followup period represent the 

final links and the criterion variable that 

all the preceding variables will ultimately 

impact.

In addition to these causal paths outlined above, motivational readiness could be 

expected to have some direct effects on intermediate and outcome variables as has occurred 

in prior research (DiClemente and Prochaska 

1998). In figure 2, the arrows suggest all the 

main effects of motivational readiness on compliance, processes of change, working alliance, 

and posttreatment drinking.

Figure 2. Baseline motivational readiness to 

change hypothesized main effects.

Measures

Drinking outcome measures used in our 

analyses were the Project MATCH primary outcome measures of PDA and DDD as reported 

in the primary matching hypotheses article (Project Match Research Group 1997a; 

Longabaugh and Wirtz, this volume, pp. 4-17). 

Analyses were done using transformed variables. Tables reflect retransformed percentage 

of days and number of drinks.

Motivational readiness to change was 

assessed using a revised URICA. The original version is generic and has four subscales 

with 8 items representing each subscale 

(McConnaughy et al. 1989). Items are rated on 

a 5-point Likert scale ranging from 1 (strongly 

disagree) to 5 (strongly agree). For our analyses, a 7-item per subscale version was used 

that had been supported in prior alcoholism 

treatment research (DiClemente and Hughes 

1990; Carney and Kivlahan 1995; Isenhart 

1994). This measure demonstrated solid psychometric properties with Alpha internal 
210

Part V: Motivation and Self-Efficacy

consistency coefficients for the four subscales 

ranging from 0.74 to 0.82 in the aftercare arm 

and 0.75 to 0.86 in the outpatient arm. The 

readiness score for each client was calculated 

by adding the means of the contemplation, 

action, and maintenance subscales together 

and then subtracting the precontemplation 

mean. This scoring reflects a second-order factor reported earlier (Carbonari et al. 1994). This 

measure was administered at baseline and at 

the 3-month posttreatment assessments.

Although sometimes used in analyses as 

a continuous score, readiness scores for clients in each arm of the trial were divided into 

thirds on this measure in order to create low, 

medium, and high motivation subgroups. These 

three subgroups parallel, for the most part, 

precontemplation, contemplation, and preparation/action stages of change (Carbonari et 

al. 1994) and may assist in understanding the 

interactions and effects for the low-motivation 

subgroup. Table 1 illustrates the overall means, 

ranges, and standard deviations of readiness 

scores for outpatient and aftercare clients in 

the different level of motivation subgroups.

Table 1. Descriptives for readiness groups 

(trichotomized)

Readiness

Motivational readiness group

Low Medium High

Outpatient arm n=322 n=314 n=310

Mean (SD) 8.7 (1.2) 10.5 (0.5) 12.4 (0.7)

Range 2.6–9.7 9.9–11.3 11.4–14.0

Aftercare arm n=249 n=262 n=257

Mean (SD) 9.4 (1.1) 11.2 (0.5) 12.7 (0.5)

Range 1.6–10.3 10.4–11.9 12.0–14.0

The Working Alliance Inventory (Horvath 

and Greenberg 1986) was used to assess the 

working alliance. The WAI is a 36-item measure that consists of three subscales that 

address the goals of therapy (Goal), agreement about the tasks of therapy (Task), and 

the bond between the client and therapist 

(Bond). Ratings are made on a 7-point Likerttype scale (ranging from 1=never to 7=always) 

on the extent to which the respondent agrees 

with the statement, and a global score is calculated by taking the sum of the 36 items (after 

accounting for reverse-scored items). In Project 

MATCH, the correlations between subscale 

scores and the total score ranged from 0.87 to 

0.96. Parallel forms were used for therapist and 

client ratings. Since the client and the therapist completed the WAI following the second 

session (Connors et al. 1997), those ratings 

were selected for use in these analyses.

Compliance with treatment was measured 

as the number of weeks of treatment attendance. Although MET consisted of only four 

sessions, the sessions extended over the 12 

weeks. The three treatments were equal in 

duration but not intensity. Comparable data for 

all three treatments were available for weeks 1, 

2, 6, and 12 (see Mattson et al. 1998 for greater 

detail).

Processes of change represent client coping 

activities and measures of processes of change 

that have been used with different addictive 

behaviors (DiClemente and Prochaska 1998; 

Prochaska et al. 1988; Snow et al. 1994). In 

Project MATCH, processes of change were 

assessed using a 40-item scale that contained 

subscales for each of 10 processes with 4 items 

for each process. Clients were asked to indicate on a 5-point Likert scale (ranging from 

1=never to 5= very frequently) how often they 

had experienced each of the activities or events 

listed. Twenty of the items comprise experiential processes, which tap into cognitive and 

affective processes such as consciousness raising and self-reevaluation, and twenty items 

comprise behavioral processes such as stimulus control and contingency management. 

Both the 20-item subscales and the 40-item 

total scale have good psychometric properties 

with Chronbach Alphas of 0.90 for the experiential and 0.91 for the behavioral subscales 

(DiClemente et al. 1996).

At posttreatment, clients completed the full 

40-item version. A brief 8-item version of the 

processes of change measure was administered 

at the end of each therapy session as part of a 

Client Session Report. This measure contained 

four behavioral process items, two experiential 

process items, and two helping relationship 

items.
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Motivation Hypothesis Causal Chain Analysis

Self-efficacy was assessed using the Alcohol 

Abstinence Self-Efficacy Scale (DiClemente 

et al. 1994). Clients rated their confidence to 

abstain from alcohol in 20 different situations 

on a 5-point Likert-type scale (ranging from 

1=not at all to 5=extremely). Clients also rated 

their degree of temptation to drink in these 

same 20 situations. Scores were computed 

separately for confidence and temptation. The 

mean level of efficacy was computed such that 

the scores ranged from 1 to 5, with 1 being 

not very confident and 5 being very confident 

across all 20 situations. Temptation to drink 

scores were computed in the same way. 

Results

Main Effects

Motivational readiness to 

change emerged as one of the 

best predictors of drinking behavior during the treatment period 

(Project MATCH Research Group 

1998a) and throughout the posttreatment period for the clients 

in the outpatient arm of the trial. 

Motivation predicted both PDA 

and DDD outcomes for each of the 

followup periods for these clients 

(table 2). This initial readiness to 

change continued to be the best 

predictor of both PDA and DDD 

drinking outcomes at the 3-year 

posttreatment followup (Project 

MATCH Research Group 1998b). 

However, initial motivational 

readiness to change did not predict either during treatment or 

posttreatment drinking outcomes 

in the aftercare arm. Mean values of motivational readiness to 

change were slightly higher for 

clients in the aftercare arm than 

for those in the outpatient arm. 

However, assessing readiness 

while clients were on the inpatient 

unit or engaged in intensive treatment did not yield scores on this 

measure of motivation that were 

related to posttreatment drinking.

Matching Effects

The test of the motivation matching hypothesis provided some support for the matching 

hypothesis only in the outpatient arm. For outpatient clients, significant interaction effects 

for the hypothesized CBT versus MET contrast 

as well as for the CBT versus TSF contrast 

occurred for posttreatment drinking outcomes 

but not for drinking during treatment (table 

3). For the aftercare clients, there were no significant motivational readiness by treatment 

interaction effects (Bonferroni corrected) for 

the within treatment or posttreatment drinking 

outcomes for the hypothesized contrast.

Table 2. Comparison of group means and standard 

deviations for quarterly posttreatment drinking

Posttreatment 

drinking

Motivational readiness group

Low Medium High Comparisons

Outpatient arm

PDA

Quarter 1*** .72 (.33) .76 (.31) .81 (.29) low<high

Quarter 2*** .69 (.33) .75 (.33) .80 (.30) low<medium, high

Quarter 3*** .68 (.34) .74 (.35) .78 (.31) low<medium, high

Quarter 4*** .65 (.36) .76 (.32) .78 (.32) low<medium, high

DDD

Quarter 1*** 5.5 (6.4) 4.3 (4.9) 4.3 (5.3) low>medium, high

Quarter 2*** 5.5 (5.5) 4.5 (5.1) 4.3 (5.5) low>medium, high

Quarter 3*** 5.5 (5.9) 4.5 (5.3) 4.1 (5.5) low>medium, high

Quarter 4*** 5.5 (5.7) 4.1 (5.0) 4.1 (5.4) low>medium, high 

Aftercare arm

PDA

Quarter 1 .84 (.28) .85 (.28) .85 (.28) ns

Quarter 2 .83 (.28) .81 (.32) .81 (.32) ns

Quarter 3 .81 (.30) .80 (.33) .81 (.32) ns

Quarter 4 .81 (.31) .78 (.34) .80 (.34) ns

DDD

Quarter 1 4.1 (6.9) 4.0 (7.0) 3.7 (5.9) ns

Quarter 2 3.9 (6.7) 4.6 (7.1) 4.9 (8.1) ns

Quarter 3 3.8 (5.5) 4.5 (7.3) 4.6 (8.2) ns

Quarter 4 4.0 (6.1) 4.7 (7.5) 4.2 (8.0) ns

NOTE: The four different quarters by group were analyzed individually using 

ANOVA procedures. Post hoc comparisons were made using the Tukey’s B procedure. Pairwise comparisons that were significant are designated by a < or > 

symbol. If differences were not significant, a comma was used. For outpatient 

arm, N’s ranged from 288 to 313. For aftercare arm, N’s ranged from 229 to 250; 

all comparisons were nonsignificant. PDA=percentage of days abstinent; DDD= 

drinks per drinking day. ***p<.005
212

Part V: Motivation and Self-Efficacy

Table 3. Hierarchical linear modeling results 

for the matching hypothesis

MV × Tx 

contrast

Overall 

effect 

(F)

CBTMET 

(t)

CBTTSF

(t)

METTSF 

(t)

Outpatient arm

Within Tx

MV × Tx

PDA 0.05 -0.02 -0.27 -0.24

DDD 0.68 -0.69 0.48 1.16

MV × Tx × T

PDA 0.44 -0.35 0.59 0.92

DDD 0.03 0.20 -0.02 -0.22

Posttreatment

MV × Tx

PDA 0.03 -0.16 -0.23 -0.05

DDD 0.17 -0.04 0.50 0.50

MV × Tx × T

PDA 4.50** -2.83** 2.25** -0.76

DDD 3.43* -1.63 -2.58** -0.77

Aftercare arm

Within Tx

MV × Tx

PDA 2.61 -2.23* -1.52 0.73

DDD 1.38 1.66 0.82 -0.84

MV × Tx × T

PDA 2.16 -1.79 -1.79 0.03

DDD 1.36 1.60 1.11 -0.51

Posttreatment

MV × Tx

PDA 2.15 -0.89 -2.07* -1.14

DDD 2.10 1.80 1.72 -0.10

MV × Tx × T

PDA 0.67 1.05 0.93 -0.13

DDD 0.37 -0.85 -0.28 0.56

NOTE: MV=matching variable, Motivational Readiness; Tx 

=treatment; T=linear time; PDA=percentage of days abstinent; DDD=drinks per drinking day. F tests were used for the 

overall effect, and t-tests were used for pairwise treatment 

contrasts. *p<.05 **p<.025 (refers to Bonferroni-corrected 

level of significance)

As mentioned, there was a significant attribute by treatment by time interaction for 

outpatient clients on posttreatment drinking 

that is illustrated in figure 3. These results are 

complicated. In the month immediately following the end of treatment, low-motivated clients 

in the CBT condition had significantly more days 

abstinent compared to those in MET, contrary 

to the hypothesis. However, with the passage 

of time, low-motivated CBT clients increased 

their drinking and decreased their percentage 

of days abstinent. In contrast, toward the end 

of the followup period, low-motivated MET clients appeared to gain more days abstinent, so 

that by the last month of the followup period, 

low-motivated clients in MET had significantly 

more abstinent days than low-motivated clients in CBT. This late-emerging effect could be 

due to the MET clients beginning to take more 

responsibility for their drinking, as was urged 

in the MET condition, combined with the failure of the CBT clients to sustain abstinence 

over time. However, a late-emerging effect that 

lasted for only 1 month provided minimal support for the motivation by treatment interaction 

hypothesized. Thus, it would be interesting to 

see if the proposed causal chains assumed to 

be operating were actually visible in the subsequent analyses.

At the 3-year followup, which assessed 

drinking outcomes for the 90 days prior to the 

end of the third year posttreatment for outpatient participants, there was no support for 

the motivation matching contrast. Percentage 

of days abstinent for the low-motivated CBT 

clients was approximately 58 percent (SD=39 

percent) and for the MET clients was 54 percent (SD=38 percent). Thus, this late-emerging 

effect at the end of the first year did not continue to produce a long-term effect. However, 

it is not known when that effect disappeared 

between the posttreatment month 12 assessment and the 36-month assessment.

A nonhypothesized significant interaction 

of motivation by treatment by time occurred 

in the CBT versus TSF contrast for both PDA 

and DDD, again only in the outpatient arm (not 

shown). What seems to be occurring in this 
213

Motivation Hypothesis Causal Chain Analysis

Figure 3. Monthly posttreatment plots of percentage of days abstinent for treatment by time by 

attribute interaction for motivation contrast between CBT and MET among outpatients. The interaction at month 15 was significant in the predicted direction (p<.05). The vertical axis represents 

percentage of days abstinent and the horizontal axis represents motivation scores. (Reprinted 

with permission from Journal of Studies on Alcohol, Vol. 58, pp. 7–29, 1997. Copyright by Alcohol 

Research Documentation Inc., Rutgers Center of Alcohol studies, Piscataway, NJ 08854.)
214

Part V: Motivation and Self-Efficacy

contrast is that at the high end of the readiness 

to change scale, TSF clients were reporting 

more abstinence and fewer drinks per drinking 

day than their CBT counterparts immediately 

posttreatment at the beginning of the followup 

period. However, these differences disappeared 

by the end of the followup period.

Causal Chain Analysis

Working Alliance

The first interaction between motivation and 

treatment was assumed to be an interaction 

with the working alliance as evaluated independently by both the client and the therapist. 

Analyses of the client and therapist reports 

completed immediately after the second session 

of treatment indicated that there was a significant main effect for motivational readiness. In 

both the outpatient and the aftercare arms of 

the trial, individuals with greater readiness to 

change at baseline reported significantly higher 

levels of alliance with the therapist on therapeutic task, goals, and bond, compared with their 

low-readiness counterparts (table 4). Therapist 

reports of task and goal on the WAI were also 

related to client readiness in the outpatient but 

Table 4. Comparison of group means and standard deviations for 

working alliance scores at Session 2

Working Alliance

Motivational readiness group

Low Medium High Tukey’s B comparison

Outpatient arm

Client ratings

Total score*** 205.0 (24.7) 209.9 (24.8) 219.1 (21.0) low<medium<high

Task subscale*** 68.9 (9.0) 70.8 (8.8) 74.1 (7.2) low<medium<high

Goal subscale*** 68.4 (8.8) 69.5 (9.0) 73.5 (7.5) low, medium<high

Bond subscale*** 67.6 (9.3) 69.7 (9.4) 71.4 (8.6) low<medium<high

Therapist ratings

Total score*** 188.0 (25.0) 191.1 (24.2) 196.2 (24.0) low, medium<high

Task subscale*** 62.2 (8.7) 63.5 (8.1) 65.2 (8.5) low, medium<high

Goal subscale*** 60.7 (9.9) 61.9 (9.7) 64.2 (9.3) low, medium<high

Bond subscale 65.1 (8.6) 65.7 (8.5) 66.8 (8.1) ns

Aftercare arm

Client ratings

Total score* 208.2 (25.6) 213.6 (20.8) 221.1 (22.6) low<medium<high

Task subscale* 70.1 (8.9) 71.7 (7.7) 74.6 (8.1) low, medium<high

Goal subscale* 69.2 (9.2) 71.4 (7.6) 73.6 (8.2) low<medium<high

Bond subscale* 68.9 (9.8) 70.4 (7.9) 72.9 (8.3) low, medium <high

Therapist ratings

Total score* 192.9 (26.5) 195.4 (24.5) 196.8 (26.6) ns

Task subscale* 64.6 (8.8) 65.4 (8.3) 66.3 (8.5) ns

Goal subscale* 62.9 (10.2) 64.2 (9.3) 64.6 (10.0) ns

Bond subscale 65.5 (9.1) 65.8 (8.8) 65.9 (9.4) ns

NOTE: The total score and three different subscale scores by group were analyzed individually using ANOVA procedures. Post 

hoc comparisons were made using the Tukey’s B procedure. Pairwise comparisons that were significant are designated by a < 

symbol. If differences were not significant, a comma was used. N’s range from 247 to 269 in the outpatient arm and from 181 to 

213 in the aftercare arm.

*p<.05 ***p<.005
215

Motivation Hypothesis Causal Chain Analysis

not the aftercare arm of the trial in exactly the 

same manner (higher client readiness=greater 

reported alliance).

Working alliance ratings at Session 2, 

whether provided by the client or the therapist, 

were significant predictors of treatment participation and drinking behavior during treatment 

and the 12-month posttreatment period in 

the outpatient arm of the trial (Connors et al. 

1997). Ratings of the therapeutic alliance by 

the clients at Session 2 in the aftercare arm 

were not predictive of participation or outcome. 

However, ratings of the alliance by the therapists in the aftercare sample did predict PDA 

during treatment and followup. Thus, there is 

evidence of a significant effect of working alliance on participation and drinking outcomes 

that is most strongly demonstrated in the outpatient arm. However, this effect is modest in 

the amount of variance (3.5 percent at most) 

explained. Moreover, there were no significant 

differences among the three therapies on the 

WAI scores in either arm.

Additional analyses of the alliance data 

revealed that the best predictor of the client 

WAI ratings was the client’s motivational readiness to change, with Beta weights of 0.25 in 

outpatient and 0.23 in aftercare (Connors et 

al. 2000). Motivation acts as a mediator of the 

effect of the alliance on drinking outcomes. 

When motivation is added into the WAI prediction equation, the ability of the WAI to predict 

drinking outcomes is weakened significantly, 

indicating a connection between motivation 

and ratings of the alliance.

In all these analyses, however, there were no 

significant interactions between type of treatment and motivational readiness related to the 

WAI scores. Nor were there any significant differences in WAI scores by treatment. Thus, WAI 

scores appeared related to client readiness to 

change independently of the type of treatment 

offered. There was no support for the hypothesized causal link connecting specific treatment 

effects on the working alliance depending on 

level of motivation. Low-motivated MET clients 

did not demonstrate significantly better working alliances with the therapist than did their 

CBT counterparts.

Compliance

Project MATCH clients had very high levels 

of compliance with treatment attendance. On 

average, clients attended 7 to 8 of the scheduled 

12 sessions and 3 to 4 of the 4 MET sessions 

(Mattson et al. 1998). Treatment attendance 

correlated moderately with drinking outcome 

(PDA) immediately posttreatment for both CBT 

(r=0.39) and TSF (r=0.44) clients in both arms of 

the trial. However, there was little demonstrated 

connection between treatment attendance and 

PDA for the MET clients (r=0.12 at posttest and 

0.06 at the 1-year followup), possibly due to 

a ceiling effect in their 4-session attendance. 

Thus, there was a link between compliance and 

outcomes as hypothesized.

However, there was no relationship between 

client motivational readiness and treatment 

compliance in either arm of the trial. Moreover, 

there was no interaction between treatment 

condition and motivational readiness in terms 

of compliance in either arm of the trial. As can 

be seen in table 5, weeks in treatment were very 

similar for all treatment groups. Motivational 

readiness to change did not predict compliance 

as was hypothesized. There was no support for 

the hypothesized causal link between levels of 

readiness, type of treatment, and compliance. 

This finding is at variance with several prior 

studies where Stage of Change was predictive 

of attendance and compliance (Smith et al. 

1995; DiClemente et al. 1991).

Table 5. Comparison of group means and 

standard deviations for treatment compliance 

by motivational readiness group

Treatment 

compliance

Motivational readiness group

Low Medium High

Outpatient n=322 n=314 n=310

Number of 

treatment weeks

8.6 (4.2) 8.7 (4.3) 8.6 (4.2)

Aftercare n=249 n=262 n=257

Number of 

treatment weeks

7.7 (4.9) 8.0 (4.7) 8.4 (4.4)

NOTE: The number of treatment weeks by group was analyzed using ANOVA procedures. Post hoc comparisons were 

made using the Tukey’s B procedure. All comparisons were 

nonsignificant.
216

Part V: Motivation and Self-Efficacy

During-Treatment Change 

Process Activity

In both arms of the trial, behavioral and 

experiential processes of change reported by 

clients at the 3-month posttreatment assessment were not significantly different across the 

three treatments (table 6). The brief assessments of process activity reported by clients 

during treatment in their client session reports 

indicated that TSF clients had slightly but significantly higher levels of experiential process 

activity than CBT clients in the outpatient arm, 

and than MET clients in the aftercare arm. With 

this minor exception, individuals in all three 

treatments reported similar levels of experiential and behavioral process activity despite the 

fact that the three treatments had very different 

philosophies, therapists, and active ingredients 

as assessed by objective and 

independent observers (Carroll 

et al. 1998).

There were, however, significant effects of baseline 

motivational readiness to 

change on client process activity both during and immediately 

after treatment in both the outpatient and aftercare arms of the 

trial (table 7). Clients with higher 

levels of motivational readiness 

to change at baseline reported 

significantly higher levels of 

process activity during treatment and at the posttreatment 

assessment. In the outpatient 

arm, the differences in process 

activity between groups high 

and low in motivation were more 

pronounced. However, there 

were no significant interactions 

between motivational readiness and the specific treatments 

on process activity at any time 

point. Low-motivated clients did 

not demonstrate more process 

activity in MET than in CBT. 

Thus, there was no support 

for the causal link assumed to 

involve the processes of change. 

However, for the outpatient and 

aftercare clients, their behavioral 

process activity reported at Session 2 and both 

behavioral and experiential process activity at 

the end of treatment did predict posttreatment 

drinking outcomes. Once again, there was support for the hypothesized relationship between 

processes of change and drinking outcomes in 

general but not for the treatment-specific contrast between MET and CBT.

Posttreatment Readiness to Change

Another element in the causal chain predicted significant differential shifts in readiness 

to change from baseline to posttreatment produced by the matching of treatments with 

motivational readiness group. Once again, there 

were significant differences between groups of 

individuals high and low in motivational readiness in terms of their changes in readiness 

Table 6. Comparison of group means and standard 

deviations for processes of change by treatment assignment

Processes CBT MET TSF Comparisons

Outpatient arm

Session 2

Total 3.1 (0.7) 3.1 (0.8) 3.1 (0.7) ns

Behavioral 3.5 (0.9) 3.3 (1.0) 3.4 (0.9) ns

Experiential* 2.6 (1.0) 2.8 (1.0) 2.9 (1.0) CBT<TSF

Helping relationship 2.8 (1.1) 2.9 (1.1) 2.7 (1.0) ns

Posttreatment

Total 3.1 (0.6) 3.1 (0.6) 3.1 (0.7) ns

Behavioral 3.2 (0.7) 3.1 (0.7) 3.1 (0.8) ns

Experiential 3.0 (0.6) 3.0 (0.6) 3.1 (0.7) ns

Aftercare arm

Session 2

Total 3.4 (0.8) 3.3 (0.8) 3.4 (0.7) ns

Behavioral 3.7 (0.9) 3.6 (0.9) 3.7 (0.9) ns

Experiential* 2.9 (1.0) 2.8 (1.0) 3.0 (0.8) MET<TSF

Helping relationship 3.3 (1.1) 3.3 (1.1) 3.2 (1.1) ns

Posttreatment

Total 3.3 (0.6) 3.3 (0.6) 3.3 (0.6) ns

Behavioral 3.4 (0.7) 3.3 (0.7) 3.4 (0.7) ns

Experiential 3.2 (0.7) 3.2 (0.7) 3.2 (0.6) ns

NOTE: The four different types of processes by group were analyzed individually 

using ANOVA procedures. Post hoc comparisons were made using the Tukey’s 

B procedure. Pairwise comparisons that were significant are designated by a < 

symbol. Mean values range from 1 to 5. N’s range from 229 to 318 (outpatients) 

and from 213 to 234 (aftercare) for posttreatment processes and are smaller for 

Session 2 processes.

*p<.05
217

Motivation Hypothesis Causal Chain Analysis

Table 7. Comparison of group means and standard deviations for processes of change by 

motivational readiness group

Processes

Motivational readiness group

Low Medium High Tukey’s B comparisons

Outpatient arm

Session 2

Total*** 2.8 (0.7) 3.1 (0.7) 3.3 (0.7) low<medium<high

Behavioral*** 3.2 (0.9) 3.3 (1.0) 3.7 (0.9) low, medium<high

Experiential*** 2.4 (1.0) 2.9 (1.0) 3.0 (1.0) low<medium<high

Helping relationship*** 2.5 (1.0) 2.8 (1.0) 3.0 (1.2) low<medium<high

Session 6

Total*** 2.9 (0.7) 3.2 (0.7) 3.3 (0.8) low<medium,high

Behavioral*** 3.4 (0.9) 3.5 (0.9) 3.8 (0.9) low, medium<high

Experiential*** 2.3 (0.9) 2.8 (1.0) 2.8 (1.1) low<medium, high

Helping relationship* 2.7 (1.0) 3.0 (1.0) 3.0 (1.1) low<medium, high

Posttreatment

Total*** 2.9 (0.6) 3.1 (0.5) 3.3 (0.6) low<medium<high

Behavioral*** 2.9 (0.7) 3.2 (0.6) 3.4 (0.7) low<medium<high

Experiential*** 2.8 (0.7) 3.1 (0.6) 3.2 (0.7) low<medium<high

Aftercare arm

Session 2

Total*** 3.2 (0.7) 3.4 (0.8) 3.6 (0.8) low, medium<high

Behavioral*** 3.5 (0.9) 3.7 (0.9) 3.9 (0.9) low<high

Experiential*** 2.7 (0.9) 2.8 (1.0) 3.1 (0.9) low, medium<high

Helping relationship*** 3.0 (1.1) 3.2 (1.1) 3.5 (1.1) low, medium<high

Session 6

Total*** 3.1 (0.9) 3.3 (0.8) 3.5 (0.8) low<high

Behavioral*** 3.4 (1.0) 3.6 (0.9) 3.8 (0.9) low<medium<high

Experiential 2.7 (1.0) 2.7 (1.0) 2.8 (1.0) ns

Helping relationship 3.2 (1.1) 3.2 (1.1) 3.4 (1.1) ns

Posttreatment

Total*** 3.1 (0.6) 3.3 (0.6) 3.5 (0.7) low<medium<high

Behavioral*** 3.2 (0.7) 3.4 (0.7) 3.6 (0.8) low, medium<high

Experiential*** 3.0 (0.7) 3.2 (0.6) 3.4 (0.7) low, medium<high

NOTE: The four different types of processes by group were analyzed individually using ANOVA procedures. Post hoc comparisons 

were made using the Tukey’s B procedure. Pairwise comparisons that were significant are designated by a < symbol. If differences 

were not significant, a comma was used. Mean values range from 1 to 5. N’s range from 290 to 310 (outpatient) and from 214 to 

233 (aftercare) for posttreatment processes and are smaller for Sessions 2 and 6 processes. ***p<.005 *p<.05

during the course of treatment but no significant effects by treatment or any treatment by 

readiness interactions. There were changes in 

motivational readiness from pretreatment to 

posttreatment that differed by initial level of 

motivation. However, the direction of the change 

was somewhat surprising. The high-readiness 

group showed a decrease in motivational readiness larger than the medium-readiness and 

low-readiness groups. This occurred in both 

arms of the trial (figure 4). Contrary to what 

we hypothesized, a decrease in readiness score 
218

Part V: Motivation and Self-Efficacy

Figure 4. Mean motivational readiness by 

base-line readiness groups.

from pretreatment to post-treatment was a 

positive indicator of drinking outcome. In addition, end of treatment (3-month assessment) 

readiness scores did not predict posttreatment 

drinking outcomes in either outpatient or aftercare arms.

Client motivation as assessed by the URICA 

at the end of treatment was problematic and 

confusing. What seemed to be occurring was 

that client ratings on the four URICA subscales 

shifted in understandable but unexpected 

directions once the clients had achieved some 

successful cessation of drinking (Carbonari et 

al. 1994; Carbonari and DiClemente 2000). 

Thus, the overall readiness scores at posttreatment were not comparable to pretreatment 

readiness scores. When decomposed back into 

the constituent subscales, however, some end 

of treatment subscales were predictive of posttreatment drinking, with the action subscale 

scores being a positive predictor of drinking outcomes and maintenance scores being 

a negative predictor. It was also clear that 

these shifts occurred differentially by readiness group. The high baseline readiness group 

changed drinking behaviors and responses to 

the URICA items differently from the low readiness group. Once again, however, there were 

no treatment-specific differences found in the 

shifts in motivational readiness from pretreatment to posttreatment as was found with the 

other hypothesized causal links.

Alcohol Abstinence Self-Efficacy

Efficacy to abstain from drinking across 

a number of situations was examined to see 

if the assumed motivational readiness by 

treatment interaction in the prediction of client abstinence self-efficacy at the 3-month 

assessment existed. However, no significant 

interactions were found in either arm of the 

trial. Moreover, efficacy did not differ by treatment condition. Posttreatment self-efficacy 

was related to drinking outcomes during the 

followup period for outpatients (DiClemente 

et al. this volume; Project MATCH Research 

Group 1997b). However, there was no support 

for a causal link or even a differential impact 

of baseline motivational readiness interacting 

with the treatments to affect client efficacy to 

abstain from drinking.

Discussion

There was some support among outpatient 

clients for a delayed matching effect for motivational readiness to change in Project MATCH. 

However, the hypothesized superiority of MET 

for the low-motivated clients occurred during the last month of the 12-month followup 

period. Moreover, immediately posttreatment, 

PDA and DDD outcomes were in the opposite 

direction from those hypothesized, with the 

low-motivated CBT clients doing better. This 

suggested that the hypothesized relationships 

forming the causal chain for this hypothesis 

would not emerge. Indeed, none of the causal 

linkages between treatments and motivation 

were demonstrated in our analyses. There were 

no significant differences or interactions for 

low-motivated clients in MET compared to CBT 

on reported working alliance, compliance, processes of change, posttreatment readiness, or 

abstinence self-efficacy.

What did emerge in these analyses was support for a generic, across treatment, modest 

positive relationship link among baseline motivation, the therapeutic alliance, and treatment 
219

Motivation Hypothesis Causal Chain Analysis

attendance/compliance with posttreatment 

drinking outcomes. The most striking findings, 

however, were the strong, independent, predictive effects of baseline motivational readiness 

to change on drinking outcomes and other 

variables. Motivation at intake predicted working alliance during treatment, processes of 

change during and after treatment, and shifts 

in posttreatment readiness to change across 

both arms of the trial. Motivation at intake also 

predicted drinking outcomes throughout the 1 

year and also at a 3-year followup after treatment in the outpatient arm of the trial.

MET did not differentially affect low-motivated clients as expected in the motivational 

hypothesis, even though MET was discriminable from the other two treatments and MET 

therapists did appear to be adhering to the 

manual (Carroll et al. 1998). With the exception 

of the one posttreatment matching outcome 

finding at month 12, MET did not interact with 

motivation as hypothesized. At the end of the 

3-year followup, the hypothesized motivation 

by treatment effect was not evident. It may 

be that these less motivated MET clients did 

learn that change of their drinking was their 

responsibility so that over time they began to 

achieve greater abstinence during the 1-year 

posttreatment period. However, there were 

few indications on any process measures during or after treatment to support this claim. 

Low-motivated MET clients did not have any 

higher abstinence self-efficacy or process activity immediately posttreatment. Motivation by 

treatment interactions were almost nonexistent. Therefore, it is not surprising that there 

was not much support for matching effects.

In marked contrast to the lack of interaction 

effects between treatments and motivation were 

the highly significant effects of motivational 

readiness to change on the working alliance as 

reported by both client and therapist as well 

as on client processes of change both during 

and immediately after treatment. These effects 

were always in the expected direction, with 

higher levels of motivation for change associated with better working alliance and greater 

process activity. Moreover, the baseline motivational readiness to change predicted drinking 

throughout the posttreatment period (Project 

MATCH Research Group 1997a) and all the 

way out to 39 months postbaseline for the 

outpatients (Project MATCH Research Group 

1998b). Motivation related to all positive predictors of drinking outcomes and demonstrated 

a moderator effect on the relationship between 

working alliance and drinking outcomes. Both 

process and outcome data supported the role of 

motivational readiness to change in achieving 

and sustaining abstinence and decreasing the 

intensity of drinking.

Although significant relationships in the 

expected direction were found for motivational 

readiness to change with working alliance and 

processes of change among the aftercare clients, results in this arm of the study were more 

variable and less predictive overall. Baseline 

values of readiness to change were not predictive of posttreatment drinking, contrary to 

outpatient results. There are several possible 

explanations for these differences between outpatient and aftercare clients in terms of their 

self-assessments at baseline and at posttreatment. One possible explanation is that the 

URICA scales are useful only for clients entering treatment and measure Precontemplation, 

Contemplation, and Action better when clients currently have the problem and are in a 

pre-action stage. Most prior studies using the 

URICA have examined outpatients who were 

at the initial entry point of therapy. Aftercare 

clients, on the other hand, were assessed for 

the first time while they were on an inpatient 

unit or at an intensive day treatment setting. 

Abstinence was supported by the restrictions 

and safety of being in a hospital or day treatment setting. Thus, their perceptions of their 

readiness to change could be overly optimistic. Inaccuracies in self-evaluation could lead 

to poor predictive ability of the measure. This 

interpretation is supported by the findings 

related to self-efficacy (DiClemente et al. this 

volume).

In addition, once individuals have achieved 

abstinence, responses to the questions in the 

Precontemplation, Contemplation, Action, 

and Maintenance subscales of the URICA 

become more problematic. Once abstinence 

is achieved, action and maintenance subscale 

scores become better predictors of continuing 

successful abstinence since these subscales 

contain items reflecting commitment to stop 

drinking and struggling with relapse (Carbonari 

et al. 1994). In fact, once clients become 
220

Part V: Motivation and Self-Efficacy

abstinent, they begin to increase their agreement with precontemplation items since they 

see themselves as resolving the problems and 

not resisting change. Evaluation of state-like 

constructs such as readiness to change and 

self-efficacy are sensitive to current status in 

making the behavior change and to an intensive/inpatient treatment setting. In retrospect, 

if we could have assessed readiness and efficacy during the first week after clients were 

released from the more intensive treatment, we 

might have obtained a measure of these constructs that would be a more relevant predictor 

of outcomes.

In the aftercare arm, there was a lawful, 

expected relationship between motivation and 

process activity, with more motivated clients 

using more processes during and after treatment. More motivated clients were doing more 

to change. However, differences between the 

motivational groups were not large. Even the 

less motivated clients were using processes of 

change at rather high levels, so there may not 

have been enough variability to make a difference in drinking outcomes. In aftercare, there 

were fewer significant differences by motivation 

for process activity at Session 2, Session 6, and 

posttreatment. Low-motivated aftercare clients 

had process scores that were equal to medium 

or high groups among outpatients. However, 

despite restriction in range, even for these 

aftercare clients, process activity in Session 2 

and posttreatment significantly predicted posttreatment drinking.

Although there are compelling data supporting a common path of change where readiness 

to change and processes of change are related 

to each other and to drinking outcomes, there is 

little evidence that the treatments differentially 

affected or interacted with any of these variables. Clients in all three treatments reported 

similar working alliance scores, process of 

change activities, abstinence self-efficacy, and 

levels of compliance. Across all three treatments, there was good evidence for client 

differences in readiness to change. We need to 

continue to examine this homogeneity of treatment effect among a heterogeneous population 

of participants. Clients appear to be experiencing a common process of change that is being 

influenced similarly across the three different 

treatments.

Through a variety of experiences and contextual variables (i.e., life events), individuals 

enter therapy with varying levels of motivation that lead to change activity occurring in 

the pretreatment period. Thus, many clients 

may have done significant work getting ready 

to make changes in their drinking prior to 

entry into treatment. The predictive ability of 

the Session 2 variables (working alliance, processes of change) indicates that clients were 

employing critical coping activities early in the 

treatment. MET may not be able to influence 

this process with only the two initial sessions 

in any significant way above and beyond what 

has already occurred.

These results indicate that we need to understand better the larger process of change for 

drinking behavior in order to be able to better promote movement through that change 

process. Very different treatments delivered 

in different doses of intensity did not affect 

this change process differentially. Over the 

long run, Motivational Enhancement Therapy 

did as well as more established and intensive 

treatments. However, in the variables assessed 

in Project MATCH, MET did not affect client 

motivation or movement through the process 

of change in any way that differed from CBT 

and TSF. Clearly, we need to understand how 

to influence motivational readiness to change.

It has been argued that this trial had only 

very motivated clients and that the level of 

motivation was too high for the entire sample 

to influence outcomes with our treatments. 

However, the motivational levels on the URICA 

reported among outpatients in this trial were 

comparable to those from a general outpatient 

treatment program with few exclusion criteria (DiClemente and Hughes 1990). Moreover, 

the fact that baseline levels of motivation continue to predict drinking outcomes well beyond 

the end of treatment indicates that there was 

enough variability to affect drinking outcomes. 

However, none of the treatments interacted with 

initial levels of motivation sufficiently to disrupt 

the relationship between motivation on entry 

to treatment and drinking outcomes. Clearly, 

we need to understand better how treatments 

interact with the process of change in order to 

improve our ability to influence motivation to 

change.
221

Motivation Hypothesis Causal Chain Analysis

Acknowledgment

The authors want to acknowledge all the 

research assistants, therapists, and investigators 

who made this project possible. We particularly 

want to thank those at the Houston and Milwaukee 

clinical research units for their help in every 

aspect of the work. The research and publications 

efforts for this manuscript have been supported 

by the National Institute of Alcohol Abuse and 

Alcoholism (NIAAA) as the collaborative research 

award U10–AA08432 called Project MATCH. We 

would like to thank Drs. Richard Fuller, John 

Allen, and Margaret Mattson from NIAAA for their 

untiring assistance throughout the project. We 

also want to thank Lori Bellino and the research 

team at the HABITS laboratory at the University 

of Maryland Baltimore County for their assistance 

in the final phases of this project.

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Kadden, R.; Carroll, K.M.; Donovan, D.; Cooney, 

N.; Monti, P.; Abrams, D.; Litt, M.; and 

Hester, R. Cognitive-Behavioral Coping Skills 

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Abuse and Dependence. Project MATCH 

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1992.

Mattson, M.E., and Del Boca, F.K., with Carroll, 

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Donovan, D.; Kadden, R.M.; McRee, B.; 

Rice, C.; Rychtarik, R.G.; and Zweben, 

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report. Psychotherapy: Theory, Research and 

Practice 4:494–503, 1989.

Miller, W.R., and Rollnick, S. Motivational 

Interviewing: Preparing People to Change 

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1991.

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Abuse and Dependence. Project MATCH 

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Institute on Alcohol Abuse and Alcoholism, 

1992.

Miller, W.R.; Zweben, A.; DiClemente, C.C.; and 

Rychtarik, R.G. Motivational Enhancement 

Therapy Manual: A Clinical Research Guide 

for Therapists and Individuals With Alcohol 

Abuse and Dependence. Rockville, MD: 

National Institute on Alcohol Abuse and 

Alcoholism, 1992.

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Abuse and Alcoholism, 1992.

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change in the modification of problem behavior. In: Hersen, M.; Eisler, R.; and Miller, 

P.M., eds. Progress in Behavior Modification.

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1992. pp. 184–214.

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223

Alcohol Problem Recognition and 

Treatment Outcomes

William R. Miller, Ph.D., and J. Scott Tonigan, Ph.D.

ABSTRACT

A matching hypothesis within Project MATCH predicted better outcomes for clients low in problem recognition (as measured by the SOCRATES) when treated in 

Motivational Enhancement Therapy (MET), as compared with two other treatment 

approaches. SOCRATES scores, both before and after treatment, did reliably predict 

drinking outcomes during followup in both outpatient and aftercare arms of the trial. 

These prognostic relationships were observed in all three treatment conditions, and no 

support was found for the predicted matching effect. As predicted, aftercare clients in 

the MET condition did show significantly greater increases in problem recognition during treatment. Also as expected, initial motivation (recognition) predicted the quality of 

therapist-client working alliance in the other two treatments but not in MET, a finding 

observed in both arms of the trial. That is, MET appears to have removed the prognostic effect of pretreatment motivation on working alliance. The causal chain broke 

down, however, because neither change in problem recognition nor working alliance 

was reliably related to treatment outcomes. Taking Steps proved to be the most reliable 

predictor (among the SOCRATES scales) of treatment outcome. This is consistent with 

a larger literature showing that client action toward change (sometimes called adherence or compliance) is a robust predictor of more successful outcomes.

Project MATCH afforded an opportunity 

to test the prognostic properties of pretreatment motivation for change. The 

matching hypothesis regarding motivation was 

based on the University of Rhode Island Change 

Assessment (URICA) scale, a report of which is 

found elsewhere in this volume (DiClemente et 

al.). Because the items of the URICA are phrased 

in general terms for applicability to a broad 

range of possible problems, it seemed useful to 

determine whether more alcohol-specific motivation is predictive of treatment outcomes.

The Stages of Change Readiness and 

Treatment Eagerness Scale (SOCRATES) was 

designed to query motivational aspects specific to problem drinking. Its initial item pool 

was constructed to sample stages of change as 

they may apply to drinking behavior, with items 

intended to correspond to the Precontemplation, 

Contemplation, Determination, Action, and 

Maintenance stages of the Transtheoretical 

Model (Prochaska and DiClemente 1986). In 

subsequent waves of factor analyses, however, 

we were unable to reproduce factors corresponding directly to these constructs. Instead, 

the SOCRATES consistently yields three factors 

with test-retest reliability and good crosscultural and internal consistency (Miller and 

Tonigan 1996). The first of these scales, termed 

Recognition, contains items originally assigned 

to the Determination (loading positively) and 

William R. Miller, Ph.D.

Department of Psychology 

University of New Mexico 

Albuquerque, NM 87131-1161 

E-mail: wrmiller@unm.edu
224

Part V: Motivation and Self-Efficacy

Precontemplation (loading negatively) scales 

and seems to center around the drinker’s awareness and acknowledgment of alcohol-related 

problems. A second factor, labeled Taking 

Steps, combines the Action and Maintenance 

items, all loading in the same direction, and 

reflects the drinker’s prior and current efforts to 

change. Items from the Contemplation domain, 

as contained in the SOCRATES, form a separate third factor. These were written to capture 

uncertainty about alcohol problems within their 

sentence structure (“There are times when I 

wonder if I drink too much”), and the scale is 

termed Ambivalence. Given the observed difference in factor structure, names of the URICA 

stages and combination scales were specifically 

avoided so as not to suggest that the SOCRATES 

taps the same general constructs.

The Matching Hypothesis

Following logic similar to that for the motivation hypothesis, we reasoned that less motivated 

clients would benefit differentially from the 

Motivational Enhancement Therapy (MET; Miller 

et al. 1992) as compared with MATCH’S two more 

action-oriented treatments, Cognitive-Behavioral 

Coping Skills Therapy (CBT; Kadden et al. 1992) 

or Twelve Step Facilitation (TSF; Nowinski et 

al. 1992). A similar finding was reported by 

Heather and associates (1996) such that at 

earlier stages of readiness (precontemplation, 

contemplation), motivational interviewing was 

associated with better outcomes in comparison 

to behavioral skill training, whereas clients in 

action and maintenance stages showed similar 

responses to the two treatment methods.

Our original intention was to subtract the 

SOCRATES Precontemplation scale score from 

its Determination scale score, as a measure 

focused on earlier motivational stages. The 

discovered factor structure of the SOCRATES, 

while not corresponding to stages of change, 

fortunately did lend itself to the testing of this 

hypothesis because the Recognition factor manifests this difference score in its item loadings 

(original Determination items loading positively 

and Precontemplation negatively). Thus, the 

SOCRATES Recognition scale score was chosen 

as the matching variable. A difference in dependent variable slopes was hypothesized for MET 

versus the other two treatments, predicting a 

higher percentage of abstinent days and fewer 

drinks per drinking day in the MET condition at 

lower levels of alcohol problem recognition.

The Causal Chain

The SOCRATES Recognition interaction was 

expected because MET was designed specifically 

to address client motivation (including problem recognition), whereas the other approaches 

were more action oriented. Two core assumptions drove the causal model specified below. 

First, the MET condition would disrupt the 

normally strong prognostic effect of motivation 

on favorable outcome by raising awareness of 

alcohol-related problems among clients with 

low pretreatment motivation. Second, actionoriented therapies are intended for highly 

motivated clients, but low-motivation clients 

are disadvantaged when assigned to actionbased therapy. This disadvantage was predicted 

to result in poorer working alliance and treatment outcomes in CBT and TSF. This effect was 

hypothesized to operate via a specified causal 

chain including the following assumptions:

■ Higher motivation levels both before 

and after treatment will predict better 

outcomes.

■ For clients initially low in problem recognition, MET will result in a greater 

increase in Recognition than will occur 

in CBT or TSF.

■ Therefore, baseline level of problem 

recognition will be positively related to 

therapist working alliance in the combined CBT and TSF conditions but not 

in the MET condition.

■ As a result, the prognostic effect of 

baseline problem Recognition on outcome will be stronger in the combined 

CBT and TSF groups relative to the MET 

condition.

■ Better therapeutic working alliance will 

be related to end-of-treatment problem 

Recognition levels in MET, which in turn 

will influence drinking outcomes.

■ The differential effect of MET on working 

alliance and/or on problem recognition 

will account for the predicted matching 

interaction of treatments with outcomes, 
225

Alcohol Problem Recognition and Treatment Outcomes

such that clients low in Recognition will 

fare better in MET than in the combined 

CBT and TSF conditions.

Working alliance was determined through 

the Working Alliance Inventory (WAI; Horvath 

and Greenberg 1986), a brief self-report measure intended to assess client impressions of 

client-therapist therapeutic alliance. In Project 

MATCH, the WAI was generally administered 

after the second week of therapy, which most 

often corresponded to the end of the second 

therapy session. The WAI yields three scales 

and a total score, with higher scores indicating higher endorsement or agreement. The 

three scales were client impressions of clienttherapist bonding, and client agreement with 

designated therapeutic tasks and goals.

Procedures for testing the final matching 

hypothesis, including use of covariates in the 

analyses and control of site, treatment, and 

matching variable interactions, have been 

described in detail elsewhere (Project MATCH 

Research Group 1997a; Longabaugh and Wirtz, 

this volume, pp. 4–17). Path analyses within 

treatment groups were used as the primary 

tool for testing links of the causal chain. As 

throughout Project MATCH, these analyses were 

conducted separately in the two arms (aftercare 

and outpatient) of the trial.

Results

Findings are presented separately for the 

aftercare and outpatient arms of the MATCH 

trial. Presented separately for each arm, findings first focus on the prognostic value of 

SOCRATES scores (including the matching 

variable, Recognition) on the two primary dependent measures during posttreatment months. 

Various strategies to aggregate daily drinking 

data were evaluated. Description of client drinking as during (months 1–3) and after (months 

4–15) treatment has heuristic advantages, but 

post hoc analyses of monthly drinking during the 12 months of followup (months 4–15) 

substantially increase type-1 error rate. This 

concern is especially acute in the context of 

conducting causal chain analyses, where alternative choices of monthly values to evaluate 

proposed causal models may substantially alter 

findings. For these reasons, monthly values 

for these two dependent measures across the 

12 months of followup were recalculated and 

divided into proximal followup (months 4–9) 

and distal followup (months 10–15).

This is followed by results of the formal tests 

of the matching hypothesis using hierarchical 

linear modeling (HLM). Finally, causal chain 

analyses are presented to clarify secondary 

HLM matching analyses, highlighting unanticipated aspects of the causal chain and showing 

how the purported mechanisms of the matching 

hypothesis varied by aftercare and outpatient 

study arms.

Outpatient Arm

Prognostic Effects of SOCRATES

Table 1 provides the most liberal interpretation of the prognostic value of the SOCRATES 

in predicting the two primary dependent variables in MATCH: percentage of days abstinent 

(PDA) and drinks per drinking day (DDD) at 

proximal and distal followups in the outpatient 

arm. Based upon transformed PDA and DDD 

measures, second-order partial correlations 

controlling for intake values of PDA and DDD, 

but not treatment condition, indicate that the 

Recognition and the Taking Steps scales significantly and positively predict PDA and DDD 

at both proximal and distal followup intervals. Clients higher in Recognition and Taking 

Steps at intake reported both higher rates of 

abstinence and less drinking intensity across 

followups. In contrast, Ambivalence scores were 

unrelated to frequency and intensity of drinking 

at both followup intervals.

Table 1. Prognostic main effect of SOCRATES 

scales on outpatient proximal and distal 

treatment outcome for both primary 

dependent measures1

SOCRATES 

scale

Proximal followup 

Months 4–9

Distal followup 

Months 10–15

PDA DDD PDA DDD

Ambivalence -.01 -.03 -.01 -.00

Recognition .11* -.10* .11* -.10*

Taking Steps .21* -.18* .18* -.17*

1 Second-order partial correlations between SOCRATES scales 

and PDA and DDD, controlling for baseline PDA and DDD.

*p<.05
226

Part V: Motivation and Self-Efficacy

Hierarchical multiple regression analysis 

(MRA) was used to examine the joint and unique 

contribution of the three SOCRATES scales in 

predicting PDA (proximal and distal), after controlling for baseline values of PDA and DDD, and 

for treatment main effects (effect coded). This 

approach was judged more conservative than 

the former because, unlike the partial analyses 

which did not control for treatment main effects, 

these analyses also discarded redundant variance 

among the three SOCRATES scales in predicting client drinking status. MRA results (N=866) 

indicated that, in combination, the three scales 

accounted for a significant increment in variance 

(4.5 percent) above that predicted by baseline 

values of PDA and DDD and treatment group 

assignment (11.5 percent), F(3, 858)=15.24, 

p<.001. Single degree of freedom (df) tests of the 

beta weights (β) showed, like the partial analyses, that Recognition (β= 0.07) and Taking Steps 

(β=0.19) were jointly and uniquely predictive of 

early followup PDA, whereas client ambivalence 

was unrelated to frequency of drinking. At distal followup, the three SOCRATES scales jointly 

added a significant increment in variance (3.5 

percent) in PDA, above baseline drinking measures and treatment group assignment, F(3, 

835)=11.72, p<.001. Single df tests indicated 

that pretreatment Taking Steps (β=0.16, p<.001) 

and Recognition (β=0.08, p<.03) again uniquely 

predicted distal PDA, while client pretreatment 

Ambivalence was unrelated to distal PDA.

A second wave of prognostic analyses 

included an HLM approach that controlled for 

the variables already described as well as site 

effects and matching variable by site and matching variable by treatment interactions. This 

analysis was conducted only for the Recognition 

scale, which was the matching variable. Here, 

and across the entire 12 months of followup, 

both PDA and DDD were significantly related to 

clients’ Recognition scores such that higher selfreported problem recognition at baseline was 

associated with higher PDA (p<.0001) and lower 

DDD (p<.014).

Testing the Matching Hypothesis

Table 2 provides the unadjusted probability 

values (=0.05) of the HLM tests of the recognition matching hypothesis reported in Project 

MATCH during (Project MATCH Research Group 

1998) and after treatment (Project MATCH 

Research Group 1997b). Also included are 

pairwise treatment contrasts not predicted nor 

previously reported but provided as an aid to 

interpret how the Recognition matching variable 

may have unanticipated mediating effects.

Bonferroni-corrected HLM analyses using 

PDA did not support the a priori hypothesis 

when time was collapsed for the during (p<.40)

or posttreatment (p<.52) phases of the trial. 

Tests of the predicted matching effect when 

the contrast was examined across time as a 

linear or as a quadratic function likewise did 

not support the hypothesis during (p<.58, linear; p<.14, quadratic) or after treatment (p<.04, 

linear; p<.15, quadratic). None of the post hoc 

monthly contrasts supported the prediction that 

clients with lower intake recognition fared better (higher PDA) in the MET condition relative 

to low-recognition clients assigned to the combined CBT and TSF conditions. Figure 1 depicts 

the general relationship tested in the HLM analyses, and one can see the modestly prognostic 

effect of client recognition but the absence of a 

statistically significant and/or clinically relevant 

differential relationship between recognition 

Table 2. Summary of outpatient HLM problem 

recognition matching tests: Probability values 

associated with tests of during and after 

treatment client-treatment matching

Variable

Predicted 

match

Unplanned 

matching 

contrasts

MET vs. 

CBT&TSF

CBT– 

MET

CBT–

TSF

MET–

TSF

During treatment

PDA .40 .55 .75 .38

PDA linear .58 .76 .71 .51

PDA quadratic .14 .12 .59 .32

DDD .99 .87 .75 .88

DDD linear .61 .97 .42 .41

DDD quadratic .08 .05 .40 .27

After treatment

PDA .52 .69 .74 .48

PDA linear .04 .04 .70 .11

PDA quadratic .15 .93 .22 .21

DDD .72 .73 .96 .78

DDD linear .65 .81 .20 .31

DDD quadratic .53 .17 .11 .82
227

Alcohol Problem Recognition and Treatment Outcomes

and PDA by treatment condition at proximal followup. Probability values (a) associated with the 

unplanned paired contrasts in table 2 likewise 

indicate that problem recognition did not mediate treatment response in regard to PDA during 

or after treatment.

Parallel HLM analyses using drinking intensity (DDD) also failed to support the problem 

recognition hypothesis when time was collapsed 

during (p<.99) and after treatment (p<.72). 

When the contrast was modeled across months 

in linear or quadratic functions, no support was 

found either during (p<.61, linear; p<.08, quadratic) or after treatment (p<.04, linear; p<.53, 

quadratic) after Bonferroni correction for the two 

primary dependent measures (=0.05/2). None 

of the post hoc monthly contrasts supported the 

a priori prediction. Unplanned pairwise treatment contrasts also failed to indicate a problem 

recognition mediation effect.

Causal Chain Analyses

Figure 1 highlights one faulty assumption about the change mechanisms predicted 

to operate in the MET and combined CBT and 

TSF conditions. Inconsistent with our predication, the prognostic effect of client Recognition 

at intake was not stronger in the combined CBT 

and TSF conditions. This section examines the 

factors that may explain this breakdown in the 

Recognition matching hypothesis.

Figure 2 presents the standardized coefficients (β) for the separate path analyses for the 

Figure 1. Pretreatment Problem Recognition and 

proximal percentage of days abstinent in the outpatient sample: A priori matching hypothesis

MET and combined CBT and TSF conditions 

in the outpatient sample. Site effects were partialed out from all relationships shown in these 

figures. As predicted, pretreatment Recognition 

was significantly and positively related to therapist working alliance in the combined CBT and 

TSF conditions but not in the MET condition. 

Unexpectedly, therapeutic working alliance 

was not related to end-of-treatment problem 

Recognition in any group. Here, we anticipated 

positive relationships, with a significantly more 

positive relationship in the MET condition. We 

predicted a strong and positive relationship 

between pretreatment and end-of-treatment 

Recognition in the CBT and TSF condition, and 

a smaller relationship between these variables in 

the MET condition. This prediction was not supported. Finally, in the MET group, pretreatment 

Recognition was equally (if not more) prognostic 

of PDA and DDD both at proximal and distal followups as it was in the combined CBT and TSF 

groups. This finding is contrary to the prediction 

in our causal model.

The path analyses do not answer whether 

the corresponding slopes for the MET and the 

combined CBT and TSF conditions were statistically different from one another. These 

slope contrasts were accomplished using factorial MANCOVA. Here, variables specified in 

the causal chain (e.g., working alliance and 

end-of-treatment Recognition) were separately 

treated as dependent measures with pretreatment Recognition scores treated as a covariate. 

Standard tests of the homogeneity of regression coefficients (product term) indicated 

whether a differential relationship was 

present between Recognition and a causal 

variable when nested within treatment 

conditions. Also included as covariates in 

the analyses were baseline drinking (PDA 

and DDD), site, treatment, and pretreatment Recognition main effects. Because of 

the importance of the therapeutic working alliance construct to the matching 

hypothesis, these finer grained slope contrasts were conducted separately for each 

of the two subscales as well as the total 

score of the WAI. In addition, one variable 

representing client satisfaction with treatment was included in these analyses for 

exploratory purposes.
228

Part V: Motivation and Self-Efficacy

Figure 2. Outpatient Problem Recognition causal chain findings. Standardized coefficients are provided for proximal (above line) and distal (below line) followups. Results of regressing percentage of days 

abstinent on the Recognition measures (intake and end of treatment) are reported without parentheses, 

and results of regressing drinks per drinking day on the Recognition measures are reported in parentheses. *p<.05

Table 3 summarizes slope contrast findings. 

A reported significant interaction in the column 

labeled interaction with Recognition indicates 

that the relationship between client pretreatment Recognition and the causal chain variable 

differed reliably between MET and CBT plus 

TSF. Unstandardized slope coefficients below 

the columns headed MET and CBT plus TSF 

can be interpreted as describing the direction 

of the relationship of interest within the two 

treatment modalities. As our model predicted, 

pretreatment Recognition was significantly and 

differentially related to therapeutic working 

alliance by treatment condition. Pretreatment 

Recognition was unrelated to client report of 

alliance of treatment Goal and Task or to the 

total WAI score in MET. However, in the combined CBT and TSF conditions, pretreatment 

Recognition was significantly and positively 

related to the two WAI scales and the total WAI 

score.

No difference in slopes was found, however, 

for the treatment group nested relationship 

between pretreatment and end-of-treatment 

Recognition. The predicted situation wherein 

before and after treatment Recognition would be 

more strongly related in the combined CBT and 

TSF conditions was not realized. Unplanned 

slope contrasts indicated that client satisfaction with treatment (collected at week 12) was 

significantly and differentially related to pretreatment Recognition by treatment condition. 

Specifically, higher pretreatment problem recognition was modestly and negatively related to 

satisfaction with treatment in the CBT and TSF 

conditions. A modest and positive relationship 

between treatment satisfaction and pretreatment Recognition was obtained in the MET 

condition.

Slope analyses indicated several faulty 

assumptions in our predicted model in the 
229

Alcohol Problem Recognition and Treatment Outcomes

Table 3. Slope contrasts in the a priori causal 

chain model: Problem Recognition in the 

outpatient sample

Causal chain 

variable

Interaction 

with 

Recognition MET

CBT and 

TSF

WAI week-2 Goal p<.04 .05 .19

WAI week 2 Task p<.02 .03 .20

WAI week 2 total 

score

p<.02 .04 .19

End-of-treatment 

Recognition

p<.83 .43 .48

General satisfaction 

with treatment

p<.03 .07 -.09

NOTE: Coefficients are unstandardized.

outpatient sample. The next step therefore was 

to assess how, if at all, problem recognition 

changed during treatment. Table 4 provides 

the difference scores on the three SOCRATES 

scales between intake and end of treatment. 

In the ideal situation, and in both study arms, 

we predicted that Recognition would increase 

significantly more during treatment for clients 

assigned to MET. Heightened problem recognition was presumed, in turn, to improve 

treatment outcome. The basic assumption of 

increased gains in Recognition in the MET condition was not supported, and gains in Taking 

Steps to achieve abstinence actually was higher 

in the action-oriented CBT and TSF conditions 

(p<.05). The third and last columns in table 4 

show identically calculated difference scores, 

but only for those clients at intake with the 

lowest Recognition scores (lowest third of the 

distribution). Presented as a purer test of our 

assumptions, independent t-tests indicated 

that changes in motivation during treatment did 

not vary between MET and 

combined CBT and TSF on 

any of the three SOCRATES 

scales for low-Recognition 

clients assigned to MET versus CBT or TSF.

Did changes in client 

motivation during treatment

significantly add to prediction of proximal and distal 

PDA and DDD beyond that 

of pretreatment motivation? Using hierarchical 

multiple regression, and separately regressing proximal and distal PDA and DDD (four 

regressions), we entered baseline values of the 

two primary dependent measures (PDA and 

DDD), site effects, and treatment main effects. 

Baseline measures of client Ambivalence, 

Recognition, and Taking Steps were then 

entered. Finally, and of primary interest, the 

three SOCRATES scales collected at 3 months 

(end of treatment) were entered into the model. 

The 3-month SOCRATES measures accounted 

for a significant increment in variance above 

that associated with the baseline motivation 

measures for both proximal PDA (17-percent 

increment, F(3, 790)=70.64, p<.001) and proximal DDD (17 percent, F(3, 790)=59.91, p<.001). 

Separate t-tests of slope coefficients indicated 

that all three measures of motivation collected at 3-month followup—on both PDA and 

DDD—added significantly and uniquely (p<.05) 

to prediction of the frequency and intensity of 

proximal drinking, with the Taking Steps scale 

accounting for the largest unique contribution 

(β=0.42 for PDA and β=-0.41 for DDD).

At distal followup, changes during treatment in recognition accounted for a significant 

increment in PDA beyond that associated with 

pretreatment SOCRATES Recognition scales (8 

percent, F(3, 768)=26.86, p<.001) and in DDD 

(11 percent, F(3, 768)=33.45, p<.001). For the 

distal PDA measure, change in Ambivalence 

was significantly and negatively predictive (β= 

-0.11, p<.002), and change in Taking Steps was 

positively and significantly predictive (β=0.26, 

p<.001). Changes in problem recognition were 

unrelated to frequency of drinking at distal followup. For the distal DDD measure, problem 

recognition was not predictive, while changes in 

Ambivalence (β=0.14, p<.001) and Taking Steps 

Table 4. Mean (SD) comparison of pretreatment and end-oftreatment SOCRATES motivational scales: Difference scores by 

matching hypothesis for full and restricted outpatient samples

SOCRATES scale

Full sample Lowest third in distribution

MET CBT and TSF MET CBT and TSF

Ambivalence .14 (.35) .15 (.35) .10 (.30) .15 (.35)

Recognition (MV) 2.60 (4.96) 2.48 (5.42) .33 (4.02) .29 (5.99)

Taking steps 2.25 (6.92) 3.42 (6.63)* 2.14 (5.19) 3.56 (7.15)

MV=matching variable crease significantly more *p<.05
230

Part V: Motivation and Self-Efficacy

(β=-0.30, p<.001) uniquely predicted drinking 

intensity.

Aftercare Arm

Prognostic Effects of SOCRATES

The second-order partial correlations in table 

5 for the aftercare arm suggest that clients’ pretreatment Recognition was mostly unrelated 

to PDA and DDD at both proximal and distal followups, the one exception being a small 

but significant negative relationship between 

Recognition and drinking intensity during early 

followup. A more consistent pattern with the 

aftercare arm was found for the Taking Steps 

scale. Specifically, both PDA and DDD were 

significantly predicted by this scale at both 

followups.

The more conservative hierarchical multiple 

regression analysis (N=693) approach indicated that the three SOCRATES scales jointly 

contributed a significant increment in variance 

(2.0 percent) in proximal PDA above that attributable to the baseline drinking measures and 

treatment assignment (3.3 percent), F(3, 685)= 

4.89, p<.002. Single df tests showed that only 

the Taking Steps scale (β=0.13, p<.001) uniquely 

predicted PDA at early followup after controlling 

for the remaining SOCRATES scales and baseline PDA, DDD, and treatment assignment.

At distal followup, the three SOCRATES 

scales again added a significant increment in 

variance, accounted for in the PDA measure 

(3.0 percent), F(3,667)=3.53, p<.02. Single 

df tests indicated that the Taking Steps measure alone and uniquely predicted distal PDA 

Table 5. Prognostic main effect of SOCRATES 

scales on aftercare proximal and distal 

treatment outcome for both primary 

dependent measures1

SOCRATES 

scale

Proximal followup 

Months 4–9

Distal followup 

Months 10–15

PDA DDD PDA DDD

Ambivalence .04 -.10* .02 -.03

Recognition .07 -.09 .07 -.07

Taking Steps .13* -.15* .12* -.13*

1 Second-order partial correlations between SOCRATES scales 

and PDA and DDD, controlling for baseline PDA and DDD.

*p<.05

(β=0.11, p<.01). In the HLM analyses, which 

controlled for site effects and first-order interactions of Recognition with treatment and sites, 

no prognostic effect of the Recognition scale 

was identified with PDA (p<.57) nor with DDD 

(p<.90) across the entire 12-month followup 

interval.

Testing the Matching Hypothesis

Table 6 presents the unadjusted probability values (=0.05) of the HLM tests of the 

Recognition matching hypothesis reported 

in Project MATCH corporate papers (Project 

MATCH Research Group 1997b). Unplanned 

pairwise treatment contrasts are again provided as an aid to interpret how the Recognition 

matching variable may have unanticipated 

mediating effects.

No support was found for the Recognition 

matching hypothesis when time was collapsed 

during treatment (p<.10) or after treatment 

(p<.76). With time modeled, no linear function 

across monthly PDA values was found during 

(p<.41) or after treatment (p<.83). A significant 

Table 6. Summary of aftercare HLM Problem 

Recognition Matching tests: Probability values 

associated with tests of during and after 

treatment client-treatment matching

Variable

Predicted 

match

Unplanned 

matching 

contrasts

MET vs. 

CBT&TSF

CBT– 

MET

CBT–

TSF

MET–

TSF

During treatment

PDA .10 .14 .93 .19

PDA linear .41 .25 .43 .76

PDA quadratic .54 .44 .66 .77

DDD .08 .23 .49 .07

DDD linear .19 .16 .63 .40

DDD quadratic .55 .37 .49 .87

After treatment

PDA .76 .56 .56 .97

PDA linear .83 .63 .59 .93

PDA quadratic .005 .04 .41 .006

DDD .63 .65 .96 .71

DDD linear .69 .49 .54 .98

DDD quadratic .04 .11 .62 .05
231

Alcohol Problem Recognition and Treatment Outcomes

Recognition contrast by quadratic interaction 

(p<.005), however, was found using monthly 

PDA as the dependent measure. Contrary to 

prediction, post hoc monthly contrasts indicated that low-recognition clients assigned to 

the MET condition tended to drink more frequently than low-recognition clients assigned 

to the combined CBT and TSF groups during 

months 4 and 5, p<.01 and p<.05, respectively. 

This finding is depicted in figure 3 where, in 

proximal followup, the largest slope differences 

were during the first few months of early followup. No differences were found in monthly 

PDA values for high-recognition clients assigned 

to MET and the combined CBT and TSF group.

Replicating the HLM analyses using the 

drinking intensity measure (DDD) yielded 

similar findings. Specifically, the recognition 

contrast was not significant when time was collapsed during (p<.08) or after treatment (p<.63) 

nor when the contrast was modeled across time 

as a linear function during (p<.19) or after treatment (p<.69). In a quadratic context, however, 

the recognition contrast was significant (p<.04), 

and monthly tests showed that low-recognition 

clients assigned to the MET condition tended 

to drink significantly more alcohol per drinking 

occasion than low-recognition clients in CBT 

and TSF in the first month after the end of treatment (month 4, p<.02). This difference quickly 

faded, however, and no differences were found 

between high-recognition clients assigned to 

the different treatments.

Figure 3. Pretreatment Problem Recognition 

and proximal percentage of days abstinent in the 

aftercare sample: A priori matching hypothesis

Causal Chain Analyses

Contrary to our prediction, figure 4 shows a 

prognostic effect of pretreatment problem recognition in the MET condition and no prognostic 

effect of recognition in the combined CBT and 

TSF conditions. The proposed causal model was 

the same for aftercare and outpatient samples 

and is not repeated here.

Figure 4 shows the standardized coefficients (β) for the two path analytic models in 

the aftercare sample. All relationships of interest controlled for site effects. As predicted, 

pretreatment Recognition score was not predictive of therapeutic working alliance collected 

at Week 2 in the MET condition. Opposite to 

our predictions, pretreatment recognition for 

MET clients was significantly and positively 

related to end-of-treatment problem recognition, indicating that MET did not alter a client’s 

relative standing in the before and after treatment motivational distributions. Also, contrary 

to our predictions, therapeutic working alliance 

at Week 2 was unrelated to end-of-treatment 

problem recognition in the MET group. Finally, 

our prediction that the prognostic value of pretreatment recognition on PDA and DDD would 

be mitigated at proximal and distal followups in 

the MET condition was not supported.

In most aspects, the path analysis models 

were quite similar for the aftercare and outpatient MET conditions. Where the path analysis 

findings differed from outpatient to aftercare 

arms is in findings for the CBT and TSF conditions. As predicted for the MET conditions, 

pretreatment recognition was not predictive 

of proximal or distal PDA and DDD. Uniquely, 

aftercare CBT and TSF clients were the only 

ones who, as a group, reported a positive and 

significant relationship between therapeutic 

working alliance at week 2 and end-of-treatment problem recognition. This relationship 

had been predicted to occur only in the MET 

condition. Consistent with our predictions, and 

similar to the outpatient CBT and TSF conditions, a significant and positive relationship 

was found between pretreatment recognition 

and Week-2 therapeutic working alliance.

Factorial MANCOVA was used to conduct 

formal statistical contrasts of corresponding slopes illustrated in the two aftercare path 
232

Part V: Motivation and Self-Efficacy

Figure 4. Aftercare Problem Recognition causal chain findings. Standardized coefficients are provided for proximal (above line) and distal (below line) followups. Results of regressing percent days 

abstinent on the Recognition measures (intake and end of treatment) are reported without parentheses, and results of regressing drinks per drinking day on the Recognition measures are reported 

in parentheses. *p<.05

models. The rationale and analytical approach 

to these contrasts were described in the outpatient causal chain analyses and are not 

repeated. Table 7 presents the probability values () associated with the test of a differential 

relationship between a causal chain variable 

and pretreatment Recognition by treatment 

condition, labeled Interaction with Recognition. 

As predicted, pretreatment recognition was 

differentially related with Week-2 therapeutic 

working alliance, contingent upon treatment 

assignment. In the MET condition, as predicted, 

pretreatment recognition was not predictive of 

therapeutic relationship, whereas Recognition 

score was significantly and positively related 

to goal and total working alliance in the CBT 

and TSF conditions. No difference, however, 

was observed between treatment groups in 

the relationship between pretreatment and 

end-of-treatment problem recognition. Here, 

MET and combined CBT and TSF groups both 

Table 7. Slope contrasts in the a priori causal 

chain model: Problem recognition in the 

aftercare sample

Causal chain 

variable

Interaction 

with 

Recognition MET

CBT and 

TSF

WAI week-2 Goal p<.05 .02 .18

WAI week 2 Task p<.13 .04 .18

WAI week 2 total 

score

p<.05 .02 .19

End-of-treatment 

Recognition

p<.31 .32 .46

General satisfaction 

with treatment

p<.67 .07 .03

NOTE: Coefficients are unstandardized.
233

Alcohol Problem Recognition and Treatment Outcomes

reported significant and positive associations 

between before and after treatment Recognition 

scores. Pretreatment problem recognition was 

not differentially related by treatment condition 

with client satisfaction with treatment.

 Analyses indicated several points at which 

therapeutic change mechanisms did not differentially relate to the motivation matching 

variable. An important related question is 

whether the matching variable itself changed 

as predicted during treatment. One of the core 

assumptions of the recognition hypothesis was 

that the motivational focus of the MET condition would produce greater gains in Recognition 

and Taking Steps scores relative to the CBT 

and TSF conditions. Also implied was that MET 

would be associated with a greater decline in 

Ambivalence about change relative to the CBT 

and TSF conditions.

Table 8 presents the pretreatment and endof-treatment difference scores for the three 

SOCRATES scales. Difference scores were 

computed by subtracting pretreatment from 

end-of-treatment scores. Positive difference 

scores therefore suggest an increase in the 

measured attribute. As predicted, significantly 

greater gains in recognition of alcohol problems 

were made in the MET condition relative to the 

CBT and TSF conditions (p<.05). Unexpectedly, 

Taking Steps scores declined during the 12 

weeks of treatment, with no group differences 

observed in the magnitude of this decline. 

Ambivalence about change remained relatively 

stable from the beginning to end of treatment, 

with no treatment effect observed.

The third and fourth columns in table 8 compare before/after changes in SOCRATES scores 

for that portion of the aftercare sample with 

the lowest third of scores in the pretreatment 

Recognition distribution. 

This comparison is regarded 

as a purer test of the motivational impact of MET. 

Here, no mean differences 

were observed between the 

two comparison groups on 

any of the three SOCRATES 

scales.

Four hierarchical multiple 

regressions were conducted 

to examine if before/after 

changes in the three SOCRATES scales predicted proximal and distal PDA and DDD after 

controlling for site and treatment effects as 

well as baseline drinking (PDA and DDD) and 

SOCRATES scores. Three-month SOCRATES 

scales accounted for a significant increment 

in variance in both proximal PDA (13 percent, 

F(3, 605)=34.07, p<.001) and DDD (11 percent, 

F(3, 605)=27.32, p<.001). Problem recognition 

was not predictive of proximal PDA (p<.26) after 

controlling for baseline problem recognition. 

Three-month Taking Steps (β=0.37, p<.001) 

and extent of Ambivalence (β=-0.14, p<.01), 

however, uniquely predicted proximal PDA 

after controlling for baseline values. Regarding 

drinking intensity, all three SOCRATES scales 

uniquely and significantly predicted consumption patterns, with the Taking Steps scale 

(β=-0.34, p<.001) uniquely accounting for the 

bulk of variance in DDD.

End-of-treatment motivation scales added 

significant variance in the prediction of distal abstinence (6 percent, F(3, 590)=13.01, 

p<.001) and drinking intensity (5 percent, F(3, 

590)=12.23, p<.001) beyond that offered by 

baseline motivation alone. End-of-treatment 

Recognition score was not predictive of either 

the frequency (PDA) or intensity (DDD) of alcohol consumption after controlling for baseline 

measures. Ambivalence about change at the end 

of treatment was negatively and significantly 

predictive of distal frequency of abstinent days 

(β=-0.11, p<.01) and positively and significantly 

predictive of distal drinking intensity (β= 0.12, 

p<.006) after controlling for baseline measures. 

End-of-treatment Taking Steps was a strong 

predictor of distal alcohol consumption after 

controlling for pretreatment motivation. Here, 

Taking Steps was positively related with distal 

Table 8. Mean (SD) comparison of pretreatment and end-oftreatment SOCRATES motivational scales: Difference scores by 

matching hypothesis for full and restricted aftercare samples

SOCRATES scale

Full sample Lowest third in distribution

MET CBT and TSF MET CBT and TSF

Ambivalence .12 (.33) .11 (.32) .09 (.29) .16 (.37)

Recognition (MV) 3.65 (5.98) 2.47 (4.97)* 1.33 (6.37) .77 (5.40)

Taking steps -.61 (5.98) -.35 (5.80) -.44 (4.87) 1.22 (.06)

MV=matching variable 

*p<.05
234

Part V: Motivation and Self-Efficacy

abstinence (β=0.23, p<.001) and significantly 

and negatively predictive of drinking intensity 

(β=-0.22, p<.001).

Did We Choose the Wrong Scale?

The Recognition matching hypothesis was 

not supported in either the aftercare or the outpatient study, and causal chain analyses were 

only partially supported. Causal chain analyses 

highlighted the complex and poorly understood 

nature of readiness for change and how motivation for change influences and is influenced 

by the active ingredients of therapy. Especially 

important is gaining a better understanding of 

how the dimensions of motivation relate to one 

another, and how these relationships change 

across time. Figure 5, for example, shows the 

relationship between Recognition and Taking 

Steps before-and-after difference scores by 

study arm. Rationally, one would expect in a 

treatment-seeking population—and for clients 

actually engaged in therapy—a strong positive relationship between gains in awareness of 

problems and taking actions to eliminate these 

problems. This was the case for aftercare clients, but much less so for clients recruited in 

the outpatient sample. Why might this be so?

Analyses suggested that Taking Steps is prognostic of positive outcome, and in most cases, a 

stronger indicator of success than Recognition. 

We wondered whether the SOCRATES Taking 

Steps scale might be a client matching variable 

worthy of attention. To this end, we replicated 

most analyses reported scale as the matching 

Figure 5. Relationship between pre-post change in 

Problem Recognition and Taking Steps by Project 

MATCH study arm.

motivation variable. Classical multivariate 

repeated-measures analyses failed to support 

the Taking Steps matching hypothesis (same 

predictions as for Recognition) on either primary dependent measure.

The four Taking Steps path-analysis models differed from the Recognition models in two 

key respects. First, significant positive relationships were obtained between working alliance 

at Week 2 and end-of-treatment Taking Steps 

in all four models (r’s=0.20, 0.19, 0.17, and 

0.22). With the Recognition matching variable, 

this positive relationship was only present in 

the CBT and TSF aftercare conditions. Second, 

end-of-treatment Taking Steps was a significant 

predictor of both proximal and distal outcome, 

while the end-of-treatment Recognition measure 

partially predicted proximal outcome but generally failed to predict distal outcome reliably.

Table 9 shows the core slope contrasts in the 

causal chain model with the Taking Steps scale 

used as the motivational matching variable. 

Findings for the outpatient and aftercare samples are combined into a single table. Unlike the 

Recognition scale, product terms indicated that 

the relationship between pretreatment Taking 

Steps and the working alliance variables and 

end-of-treatment Taking Steps did not differ between MET and the combined CBT and TSF. In 

agreement with the path models using Taking 

Steps as the matching variable, strong positive 

relationships were found regardless of treatment assignment.

Discussion

The rationale for the predicted matching effect was that MET would differentially 

benefit clients with lower pretreatment 

levels of problem recognition by enhancing their motivation for change. Where 

did the hypothesis fail? Some early components of the predicted causal chain 

were confirmed, but later links were 

not. We did find the expected prognostic 

effect of pretreatment SOCRATES scores 

on treatment outcomes, but it occurred 

regardless of treatment assignment. 

Problem Recognition predicted both PDA 

and DDD throughout followup, but only 

among outpatients. The Taking Steps 
235

Alcohol Problem Recognition and Treatment Outcomes

Table 9. Slope contrasts in the post hoc 

causal chain analysis: SOCRATES Taking 

Steps as the matching variable in the 

total sample

Variable

Interaction 

with Taking 

Steps MET

CBT and 

TSF

Outpatient sample

WAI week-2 Goal p<.25 .08 .19

WAI week 2 Task p<.36 .11 .22

WAI week 2 total 

score

p<.42 .12 .22

End-of-treatment 

Taking Steps

p<.83 .17 .30

Aftercare sample

WAI week-2 Goal p<.28 .06 .19

WAI week 2 Task p<.25 .06 .19

WAI week 2 total 

score

p<.33 .07 .18

End-of-treatment 

Taking Steps

p<.31 .21 .25

NOTE: Coefficients are unstandardized.

subscale score, in contrast, predicted both outcome measures throughout followup in both 

arms of the trial. The more a client had already 

been taking steps toward change before beginning treatment, the better the outcomes.

Similarly, clients’ posttreatment motivation 

scores on SOCRATES were significantly predictive of both outcome measures at both proximal 

and distal followup intervals in both arms of the 

trial, even after removing variance accounted 

for by baseline drinking and motivation measures, treatment groups, and sites. Effects of 

change in Recognition were inconsistent, but 

in both arms of the trial, better outcomes on 

both dependent measures were consistently 

related to greater increases in Taking Steps and 

decreases in Ambivalence. That is, when treatment was associated with increased motivation 

(less ambivalence and more taking steps), outcomes were better throughout followup.

MET also showed a few of the predicted differential effects on intermediate variables. As 

expected, pretreatment Recognition scores predicted therapeutic alliance only in the CBT and 

TSF conditions and not in MET. That is, MET 

appeared to obviate the relationship between 

low problem recognition and poor working alliance that was observed in the action-oriented 

treatments. In the aftercare arm, change in 

problem recognition also occurred only in MET 

and not in CBT and TSF. The problem is that 

these intermediate variables, while differentially 

affected by MET, did not significantly influence 

drinking outcomes, and that is where the causal 

chain broke down.

So where does this leave us? Our findings do 

support the importance of motivation as at least 

a predictor, if not a determinant, of drinking 

outcomes. The Taking Steps score, both prior 

to and after treatment, was a good predictor of 

outcomes. In analyses from another multisite 

outcome study (Miller et al. 1996), we similarly 

found that Taking Steps toward change, as 

measured by the SOCRATES, was a strong predictor of continued improvement (rather than 

relapse) among alcoholics.

This is consistent with a larger literature 

linking higher client adherence or compliance 

to better outcomes (e.g., Pettinati et al. 1996; 

Robson et al. 1965; Fiorentine and Anglin 1996). 

Similarly, the degree of attendance or involvement in Alcoholics Anonymous is predictive of 

more favorable outcomes (Emrick et al. 1993; 

Montgomery et al. 1995). Even compliance with 

placebo medication has been found to predict 

more favorable treatment outcomes (e.g., Fuller 

et al. 1986). It appears that clients’ doing something toward change is a reliable predictor of 

better outcome.

The other measure of motivation in Project 

MATCH (URICA, DiClemente et al., this volume) 

also proved to be a good predictor of outcomes. 

It also appears to be the case that the more 

client motivation improves during treatment 

(on all three SOCRATES scales), the better the 

prognosis. Changes in motivation predict later 

changes in behavior.

An unanswered question is how this occurs, 

and more specifically, why Motivational 

Enhancement Therapy works at all (Miller 

1998). Even a single session of motivational 

interviewing has been found to improve substance abuse treatment outcomes substantially 

(e.g., Bien, Miller, and Boroughs 1993; Brown 

and Miller 1993; Saunders et al. 1995), and 
236

Part V: Motivation and Self-Efficacy

brief interventions more generally have been 

shown to trigger behavior change when offered 

as stand-alone treatments (Bien, Miller, and 

Tonigan 1993). Clinical descriptions of motivational interviewing have emphasized impact on 

cognitive/affective variables such as problem 

recognition, ambivalence, distress, and discrepancy (Miller 1983; Miller and Rollnick 1991), and 

findings from this study support that emphasis. 

Yet an intervention designed specifically to have 

impact on these variables largely failed to do so 

differentially.

This indicates a further need to separate 

prognostic from causal and intervention effects 

in clinical research. Motivational variables such 

as self-efficacy, alcohol expectancies, problem 

recognition, and readiness have been shown to 

predict outcomes. It does not necessarily follow, however, that interventions designed to act 

upon these variables will thereby improve outcomes. Data from both Project MATCH (reported 

here) and a prior multisite study (Miller et al. 

1996) question the mediating role of such cognitive variables and point instead to a key role 

of action and coping strategies. This would suggest that a treatment may succeed to the extent 

that it engages and retains clients in active personal efforts toward change.

In a prior study (Miller et al. 1996), we found 

that self-efficacy and other motivational variables did not contribute to variance accounted 

for in predicting relapse, once coping skills 

had been taken into account. It is possible 

that motivational variables to which causal 

importance has been attributed may represent 

instead more passive reflections of behavioral 

coping skills In any event, the causal mechanisms underlying the efficacy of motivational 

interventions remain to be explicated.

Acknowledgment

The authors gratefully acknowledge the assistance of Drs. Richard Longabaugh, Margaret 

Mattson, and Philip Wirtz in the preparation of 

this chapter. The authors also wish to acknowledge the support provided by the Project MATCH 

Research Group and the National Institute on 

Alcohol Abuse and Alcoholism (grant number 

U10–AA08435).

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239

Self-Efficacy as a Matching Hypothesis: 

Causal Chain Analysis

Carlo C. DiClemente, Ph.D., Joseph P. Carbonari, Ph.D., 

Jill Walker Daniels, Ph.D., Dennis M. Donovan, Ph.D., 

Lori E. Bellino, M.A., and Tara M. Neavins, M.S.

ABSTRACT

One of the matching hypotheses included in the Project MATCH trial involved 

Bandura’s concept of self-efficacy. Client efficacy to abstain from drinking was hypothesized to interact with the three MATCH treatments. Clients low in efficacy would 

do better in Cognitive-Behavioral Coping Skills Therapy (CBT) than in Motivational 

Enhancement Therapy (MET), with those high in efficacy doing better in MET than 

in Twelve Step Facilitation (TSF). However, those highly tempted to drink (i.e., those 

with high temptation to drink and low self-efficacy to abstain) would do better in TSF 

compared to those in MET. In the aftercare arm, low efficacy clients in CBT showed 

drinking outcomes superior to those in MET during the period of treatment, but these 

differences did not persist into the followup period. For highly tempted individuals, 

TSF demonstrated more abstinence than MET during the first few weeks of the treatment period but not during the followup period. These interaction effects on drinking 

during treatment in the aftercare arm were the only hypothesized matching contrasts 

that were supported. Analyses of the assumed causal mechanisms indicated that some 

interactions between the treatments and changes in self-efficacy over time supported 

aspects of the matching effects in the aftercare arm. However, overall, there was little 

support for treatment-specific effects on efficacy or treatment interactions with efficacy. 

Pretreatment self-efficacy was predictive of posttreatment drinking for outpatients but 

not for aftercare clients and predicted amount of drinking at the 3-year followup for 

these outpatients. Both for outpatient and for aftercare, clients’ abstinence self-efficacy 

assessed immediately posttreatment predicted client drinking during the 1-year followup period. Self-efficacy was distinct from measures of motivation, peaked during 

treatment, remained stable during followup, and played an important and interesting 

role in the cessation of drinking.

Self-efficacy is a construct developed by 

Bandura (1986) as a central element of 

his Social Cognitive Theory. Self-efficacy 

is a personal evaluation of the ability of an 

individual to perform a target behavior and differs from outcome expectancies, which involve 

expectations of the effects or consequences of 

performing the target behavior. Self-efficacy is 

assumed to influence motivation, information 

processing, personal effort, and thus, effective action. Various naturally occurring and 

treatment-specific activities and experiences 

are thought to influence levels of self-efficacy. 

Carlo C. DiClemente, Ph.D.

Professor and Chair

Department of Psychology

University of Maryland Baltimore County 

1000 Hilltop Circle

Baltimore, MD 21250

E-mail: diclemen@umbc.edu
240

Part V: Motivation and Self-Efficacy

Mastery experiences involving the target behavior are theorized to most strongly influence 

self-efficacy, followed by vicarious experience 

(modeling), verbal persuasion, and emotional 

arousal. Thus, self-efficacy is assumed to be a 

changeable, state variable associated with and 

predictive of specific behavior change.

Self-efficacy has long been considered a 

theoretically relevant construct for alcoholism treatment and recovery (DiClemente 

1986; Marlatt and Gordon 1985; Rollnick and 

Heather 1982). In Marlatt and Gordon’s (1985) 

Relapse Prevention model, efficacy to cope with 

high-risk situations is a central component of 

their conceptualization of the relapse process. 

Alcohol researchers have developed instruments to measure efficacy for coping with or 

resisting the urge to drink heavily (Annis 1986; 

Annis and Davis 1989) and efficacy to abstain 

from drinking (DiClemente et al. 1994) across 

a variety of high-risk relapse situations previously identified by Marlatt and colleagues. In 

order to evaluate the saliency or importance 

of these situations, many instruments include 

separate ratings of either the frequency or 

occurrence of these high-risk situations or the 

individual’s temptation to drink in these situations (DiClemente et al. 1995).

Previous research has found that self-efficacy 

evaluations change during treatment for alcohol 

problems (DiClemente et al. 1995). Treatments 

have been found to affect self-efficacy differentially; for example, specific skills training has 

been found to be superior to more generic interventions (Burling et al. 1989; Donovan and Ito 

1988). Moreover, differences between individuals’ experienced temptation to drink and their 

abstinence self-efficacy have been identified as 

an indicator of highly tempted individuals who 

have a sense of hopelessness about change 

(DiClemente and Hughes 1990).

Treatment-Specific Effects

Although self-efficacy is theorized to be 

a common pathway in all human behavior 

change (Bandura 1986), treatments may differ in their ability to engender self-efficacy. 

From a treatment-matching perspective, it is 

assumed that each treatment would influence 

self-efficacy in a differential manner depending 

on how the techniques employed by the treatment influenced efficacy evaluations of the 

client. According to the theory, there are several 

ways to influence efficacy evaluations that vary 

in the strength of their influence. Treatments 

that allow for mastery experiences would be 

expected to be more powerful in increasing perceived efficacy than would be those treatments 

that rely solely on vicarious experience or verbal 

persuasion. From Bandura’s social cognitive 

perspective, long-term behavior change would 

be the direct result of a treatment’s ability to 

increase the individual’s self-efficacy. In Project 

MATCH, the three chosen treatments were 

thought to influence self-efficacy differently.

Motivational Enhancement Therapy

One of the explicitly stated goals of 

Motivational Enhancement Therapy (MET) is to 

increase clients’ self-efficacy by allowing them 

to draw upon their own resources and make 

a change plan to reduce or eliminate alcohol 

consumption (Miller et al. 1992). MET provides 

feedback to the client about the problem and 

consequences of alcohol use, places the responsibility for change on the client, and emphasizes 

the client’s ability to change. However, the 

influence of MET on self-efficacy comes primarily from verbal persuasion and emotional 

arousal, which are considered potent but not 

the most powerful sources of efficacy enhancement (Bandura 1986). Therefore, individuals 

who enter treatment with low efficacy to abstain 

from drinking may not respond well to the techniques of MET and the fewer sessions in MET. 

Clients with higher initial self-efficacy would 

respond better to messages that they can be in 

charge of the change process and, thus, could 

be expected to do well in MET. Individuals low 

in self-efficacy could be expected to do better in 

a treatment that provides techniques to provide 

mastery experiences as well as verbal persuasion, such as in Cognitive-Behavioral Coping 

Skills Therapy (CBT).

Cognitive Behavioral Therapy

CBT has as one of its specific aims the development of skills that would increase the ability of 

the individual to cope with high-risk situations 

by teaching clients to manage their emotions 

and change their behavior in situations that 
241

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

have triggered drinking in the past (Kadden et 

al. 1992). This situation specificity and the concentration on the development of skills should 

contribute directly to an increase in alcohol 

abstinence self-efficacy. CBT teaches clients 

coping skills, uses role-plays to test these skills, 

and prescribes homework that would provide in 

vivo tests of these skills It could be assumed 

that over the 12 weeks of CBT, clients’ efficacy would increase, and this increase would 

translate into greater abstinence at the end of 

treatment and particularly during the followup 

periods. Although all individuals in CBT treatment would be expected to need additional help 

no matter what their level of efficacy, individuals higher in self-efficacy may not need as much 

assistance and may find the skills training 

somewhat redundant or more than they need. 

The most obvious match between self-efficacy 

and CBT appears to be with the low abstinence 

self-efficacy clients.

Twelve Step Facilitation

Twelve Step Facilitation (TSF) should have an 

interesting and potentially inverse relation with 

self-efficacy. TSF teaches clients that reliance 

on willpower alone will not achieve sobriety, but 

that reliance on and relinquishing of control to 

a higher power is necessary to achieve sobriety (Nowinski et al. 1992). TSF emphasizes that 

the client is powerless over alcohol and must 

abstain “one day at a time.” For individuals who 

have a high level of abstinence self-efficacy and 

want to rely on their own ability to abstain, TSF 

may be a mismatch (DiClemente 1993). These 

highly efficacious clients may have more difficulty seeking recourse to a higher power and 

renouncing their own sense of control. Thus, 

attrition from TSF may be higher among those 

clients reporting high self-efficacy. However, 

since they have higher efficacy to abstain from 

alcohol compared to other clients at the start 

of treatment, drinking outcomes may be less 

affected by this mismatch. Individuals with low 

self-efficacy, and particularly those with high 

levels of temptation to drink combined with low 

levels of efficacy to abstain across situations, 

may actually find the powerlessness message 

very compatible with their experiences and benefit most from TSF. The clearest match between 

TSF and self-efficacy seems to be with individuals having very high temptation levels and very 

low efficacy levels who are overwhelmed by the 

drinking habit.

Self-Efficacy Across Treatments

Since efficacy is considered a mediator of all 

behavior change, baseline and particularly posttreatment levels of efficacy should be predictive 

of drinking outcomes across all treatments. All 

treatments, if they produce non-drinking outcomes, could be expected to cause an increase 

in efficacy. Posttreatment efficacy should be 

negatively predictive of relapse, positively 

related to abstinence, and negatively related to 

levels of drinking posttreatment. Any matching effects would have to emerge in the context 

of these potential main effects. Since all three 

treatments could be expected to interact with 

either high or low levels of abstinence self-efficacy, only the most compelling contrasts were 

included in the matching hypotheses.

Matching Hypotheses

We predicted differential matching effects 

for clients who have higher and lower levels 

of self-efficacy with MATCH therapies. Several 

contrasts were examined. These contrasts made 

up the a priori matching hypotheses developed 

for Project MATCH and are reported elsewhere 

(Project MATCH Research Group 1997b). We 

articulated the following contrasts on theoretical and empirical grounds.

Hypothesis 1

Clients lower in self-efficacy will have better 

drinking outcomes in CBT when compared to 

MET. Conversely, clients higher in self-efficacy 

will have better drinking outcomes in MET when 

compared to CBT (figure 1). This second contrast, at the other end of the efficacy spectrum, 

is expected to have less influence on drinking 

outcomes, since CBT is specifically designed 

to remediate low self-efficacy to abstain from 

alcohol.

Hypothesis 2

Individuals higher in self-efficacy will have 

better outcomes in terms of abstinence from 

drinking in MET than TSF. This contrast bets on 

the mismatching effect between TSF and higher 

efficacy clients. It could also be hypothesized 
242

Part V: Motivation and Self-Efficacy

Figure 1. Hypothesized interactions between 

treatments and self-efficacy on abstinence 

outcomes

Figure 2. Hypothesized interactions between 

treatments and temptation minus self-efficacy 

on abstinence outcomes

that individuals lower in self-efficacy could get 

more support and assistance from TSF and 

have better outcomes than the low efficacy clients in MET. However, any effect at the lower 

end of the efficacy dimension may depend on 

how low is low self-efficacy. Since MET is also 

designed to increase self-efficacy, this contrast 

with TSF could be moderated among some of 

the lower efficacy clients. Therefore, with low 

self-efficacy clients, TSF may not show significant differences in drinking outcomes when 

compared with MET low efficacy clients. The 

best way to identify the low self-efficacy clients 

and test a contrast at the low end of perceived 

efficacy would be to use both experienced temptation as well as perceived efficacy as is done in 

hypothesis 3.

Hypothesis 3

A final contrast associated with self-efficacy 

compares individuals using a temptation minus 

efficacy difference score, where larger scores 

indicate that the client feels more overwhelmed 

by the alcohol problem. Highly tempted individuals with larger positive differences will do 

better with the intense support and the message delivered in the TSF treatment of needing a 

higher power than with the message of personal 

responsibility given in MET (figure 2).

Hypothesized Causal Chain

The self-efficacy hypotheses had a clear and 

measurable causal chain. Treatments were 

assumed to influence the clients’ efficacy to 

abstain from drinking and temptation to drink 

(figure 3). The differential ability of any of the 

treatments to produce positive changes in 

self-efficacy over the treatment period would 

be considered responsible for improved posttreatment outcomes. Self-efficacy was assessed 

multiple times throughout the trial: at baseline, 

immediately posttreatment (month 3), and at 

the 9- and 15-month followup assessments (6 

and 12 months posttreatment, respectively). 

Multiple assessments of perceived efficacy made 

it possible to examine changes in efficacy during and after treatment in order to determine 

if different treatments produced differential 

changes in efficacy and then to assess the effect 

of changes in efficacy on drinking outcomes. 

Lastly, we tested whether certain treatments 

performed better for clients with high or low 

levels of efficacy and positive or negative differences between temptation and efficacy at the 

baseline assessment.

In addition to the hypothesized direct effect 

of matching on the client’s efficacy at the end 

of treatment, treatment matching should also 

influence other process variables. For example, 

higher efficacy clients enrolled in TSF should 

show less compliance, measured in weeks of 

attendance at therapy, than high efficacy clients 

enrolled in MET. If CBT helped lower efficacy 

clients engage in greater use of behavioral processes of change to increase efficacy, then low 

efficacy CBT-matched clients should demonstrate greater use of behavioral process activity 

than those in MET. Active coping skills are the 

focus of the CBT treatment but not the focus 

of MET, and thus differences in behavioral processes should be seen particularly in the CBT 
243

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

Figure 3. Hypothesized causal chain for self-efficacy contrasts

versus MET contrast. Highly tempted individuals with low efficacy should report more AA 

attendance in TSF than similar clients in MET.

Measures

Alcohol Abstinence Self-Efficacy

For these hypotheses, both temptation to 

drink and self-efficacy to abstain were assessed 

using the Alcohol Abstinence Self-Efficacy Scale 

(AASE; DiClemente et al. 1994). This scale consists of 20 items with four 5-item subscales 

representing types of relapse precipitants: 

negative affect, social situations and positive 

emotions, physical and other concerns, and 

withdrawal and urges. In the efficacy scale, 

items are rated for “how confident are you that 

you can abstain from drinking” on a 5-point 

Likert-type scale (ranging from 1=not at all confident to 5=extremely confident). Item scores 

are averaged to form the mean level of abstinence self-efficacy across the 20 situations. In 

a separate form, the same high-risk situations 

are evaluated and rated for “how tempted are 

you to drink” with a similar 5-point Likerttype scale. Mean temptation scores are also 

computed across the 20 situations to evaluate 

the overall level of temptation to drink. Although 

subscale scores can be calculated using this 

scale, only total scores were used in the subsequent analyses.

Alpha reliabilities for the total self-efficacy 

scale were very good (0.92). They were a little 

lower for the subscales, but all were above 0.80. 

Similar alpha coefficients were found for the 

temptation scale. Initial evaluation of construct 

validity of the efficacy scale has been supported, 

with the scale demonstrating substantive correlation with related constructs (DiClemente et al. 

1994). The scale has good psychometric properties and produces a respectable range of scores 

with alcohol-dependent populations similar to 

those in Project MATCH. Although temptation 

and efficacy have been moderately correlated in 

prior research, they appear to be rather independent (DiClemente et al. 1995; Velicer et al. 

1990).

Drinking Outcomes

Our analyses used the main outcome variables from the MATCH trial, percentage of days 

abstinent (PDA) and drinks per drinking day 

(DDD). Analyses used transformed variables, 
244

Part V: Motivation and Self-Efficacy

and tables presented retransformed data for the 

PDA and DDD (Project MATCH Research Group 

1997a; Longabaugh and Wirtz, this volume, pp. 

4–17).

Processes of Change

Processes of change represent client coping 

activities, and measures of processes of change 

have been used with different addictive behaviors 

(DiClemente and Prochaska 1998; Prochaska et 

al. 1988; Snow et al. 1994). In Project MATCH, 

processes of change were assessed at posttreatment using a 40-item scale that contained 

subscales for each of 10 processes with 4 items 

for each process. Clients were asked to indicate 

on a 5-point Likert-type scale (ranging from 

1=never to 5=very frequently) how often they 

had experienced each of the activities or experiences listed. Twenty of the items comprised 

experiential (cognitive/affective) processes, and 

twenty items comprised behavioral processes. 

Both the 20-item subscales and the 40-item 

total scale have good psychometric properties, 

with Chronbach alphas of 0.90 for the experiential and 0.91 for the behavioral subscales 

(DiClemente et al. 1996). At posttreatment, 

clients completed the full 40-item version. A 

brief 8-item version of the processes of change 

measure was administered at the end of each 

therapy session as part of a Client Session 

Report. This measure contained four behavioral 

process items, two experiential process items, 

and two helping relationship items.

 Treatment Compliance

Treatment compliance was measured as 

the number of weeks of treatment attendance. 

Although MET consisted of only four sessions, 

the sessions extended over the 12 

weeks. Thus, the weeks of treatment 

provided an equivalent measure 

across treatments (Mattson et al. 

1998).

Results

Alcohol Abstinence 

Self-Efficacy

Clients in each arm of the trial 

were divided into three groups 

according to their baseline AASE 

scores in order to examine the distribution of 

efficacy scores between arms and among treatment conditions. The lowest third in each arm 

was the low efficacy group, the middle group 

in each arm was the medium efficacy group, 

and the highest third in each arm was the high 

efficacy group. The three groups in each arm 

were not exactly equivalent in number since cut 

points were never made within the same value.

Table 1 reports the means and standard 

deviations of the AASE scores at the baseline 

assessment for the trichotomized groups and 

the overall means for both the outpatient and 

the aftercare participants in Project MATCH. 

There were significant differences between the 

outpatient and aftercare groups, with the aftercare clients demonstrating higher overall levels 

of abstinence self-efficacy than the outpatients 

at both baseline and 3-month (immediately 

posttreatment) assessments. Since aftercare 

clients completed the baseline efficacy measure 

during the course of an inpatient or intensive 

day treatment episode, these higher evaluations 

are not surprising. Abstinence was achieved and 

supported by the aftercare environment. Thus, 

despite the fact that aftercare clients had significantly more baseline dependence symptoms 

and more consequences and problems related 

to drinking prior to the intensive treatment, 

they appeared more confident in their ability 

to abstain from drinking than their outpatient 

counterparts did at the initial assessment. This 

significant difference persisted but got smaller 

at 3 months and then disappeared at the final 

followup assessment. These differences were 

also reflected in the fact that the aftercare clients had greater levels of abstinence at the 

beginning of the MATCH treatments as well 

Table 1. Means and standard deviations for baseline 

abstinence self-efficacy groups (trichotomized)

Baseline abstinence self-efficacy

Low Medium High Overall

Outpatient arm n=322 n=295 n=307 n=924

Mean (SD) 2.13 (.38) 2.91 (.19) 3.84 (.49) 2.94 (.80)

Range 100–2.59 2.60–3.20 3.21–5.00 1.00–5.00

Aftercare arm n=244 n=248 n=246 n=738

Mean (SD) 2.02 (.56) 3.24 (.27) 4.31 (.41) 3.20 (1.03)

Range 1.00–2.78 2.80–3.70 3.75–5.00 1.00–5.00

NOTE: Efficacy items were scored from 1=not at all to 5=extremely.
245

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

as throughout the treatment period (Project 

MATCH Research Group 1997a, 1998a). These 

differences support the separate analysis of this 

variable by arm.

At the baseline assessment, client-perceived 

self-efficacy demonstrated low correlations with 

most of the other primary and secondary matching variables in both arms of the trial. Client 

efficacy was not correlated (r<0.10) with motivational readiness to change as measured by the 

University of Rhode Island Change Assessment 

scale (Carbonari et al. 1994) or the Stages of 

Change Readiness and Treatment Eagerness 

Scale, to Alcoholics Anonymous (AA) involvement in the prior 3 months, social support for 

drinking, or psychopathology. Efficacy was most 

related to Alcohol Involvement, as measured by 

the Alcoholics Anonymous Involvement Scale 

(Tonigan et al. 1996), in the outpatient (r=-

0.14) and aftercare (r=-0.23) arms of the trial. 

Efficacy evaluations appeared rather independent of most other client characteristics.

As expected, efficacy was highly related to 

the temptation minus efficacy measure (r=-0.82 

aftercare and -0.88 outpatient). Temptation 

minus efficacy was also unrelated to motivational readiness to change and AA involvement. 

However, this temptation minus confidence 

measure was more highly correlated with alcohol involvement (r=0.37 outpatient and 0.31 

aftercare) and with meaning seeking (r=0.27 

outpatient and 0.31 aftercare), social functioning (r=-0.25 outpatient and aftercare), and 

alcohol dependence (r=0.29 outpatient and 0.24 

aftercare). The high temptation measure, compared to the efficacy measure, was related more 

to physiological and psychological dependence 

and disrupted social functioning, as would be 

expected.

Matching Hypotheses 

Outpatient Arm

For the hypotheses that used baseline selfefficacy as the matching variable, there was 

little evidence for any matching effect or matching effect by time interactions with hypothesized 

treatment contrasts in the outpatient arm of the 

trial, using Bonferroni-adjusted levels of significance (table 2). This was true for drinking 

outcomes both during treatment and following the treatment period. Only one contrast 

(CBT versus MET) demonstrated an interaction 

effect on DDD in the posttreatment period that 

reached an unadjusted level of significance, 

p<.05. This finding indicated that low self-efficacy clients had fewer drinks per drinking day 

in MET than comparable clients in CBT, contrary to the hypothesized interaction.

There were no indications in the outpatient 

arm of any matching effect for the hypothesis 

about the highly tempted clients (temptation 

Table 2. Hierarchical linear modeling results 

for within treatment and posttreatment 

drinking in the outpatient arm for the 

abstinence self-efficacy matching hypotheses

MV × Tx 

Overall 

effect 

(F)

CBTMET 

(t)

CBTTSF

(t)

METTSF 

(t)

Within treatment

PDA 1.13 1.50 0.86 -0.68

DDD 0.81 -1.22 -0.33 0.92

Posttreatment

PDA 0.78 0.93 1.19 0.25

DDD 2.12 -2.00* -0.63 1.43

NOTE: MV=matching variable, Self-Efficacy; Tx=treatment. F

tests were used for the overall effect, and t-tests were used for 

pairwise treatment contrasts.

*p< .05

Table 3. Hierarchical linear modeling results 

for within treatment and posttreatment 

drinking in the outpatient arm for the 

temptation to drink minus abstinence selfefficacy matching hypotheses

MV × Tx 

Overall 

effect 

(F)

CBTMET 

(t)

CBTTSF

(t)

METTSF 

(t)

Within treatment

PDA 1.04 -1.40 -1.03 0.42

DDD 0.62 1.09 0.39 -0.75

Posttreatment

PDA 0.30 -0.50 -0.76 -0.24

DDD 0.83 1.12 0.04 -1.12

NOTE: MV=matching variable, Temptation to Drink minus 

Self-Efficacy; Tx=treatment. F tests were used for the overall 

effect, and t-tests were used for pairwise treatment contrasts. 

All effects were nonsignificant.
246

Part V: Motivation and Self-Efficacy

minus confidence) doing better in TSF as opposed 

to MET either during treatment or at posttreatment assessments (table 3).

Aftercare Arm

Several matching effects appeared during 

the within-treatment period but disappeared 

after treatment (table 4). No significant matching effects were found for any contrast during 

the posttreatment period. For drinking during 

treatment, however, the overall self-efficacy by 

treatment interaction was significant for the 

PDA outcome (p<.008). The specific CBT versus 

MET contrast during treatment demonstrated 

Bonferroni-corrected significant effects for 

the PDA outcome and unadjusted significant 

effects on the DDD outcome. As hypothesized, 

the means across the 3-month treatment period 

demonstrated that the low efficacy CBT clients 

had significantly more days abstinent during 

the treatment period than low efficacy MET clients (figure 4). Again during treatment, clients 

in MET with higher levels of self-efficacy were

Table 4. Hierarchical linear modeling results 

for within treatment and posttreatment drinking in the aftercare arm for the abstinence 

self-efficacy matching hypotheses doing better in terms of DDD and PDA than similar CBT 

clients, although these differences were not statistically significant (Project MATCH Research 

Group 1997b). In addition, there was an 

Table 4. Hierarchical linear modeling results 

for within treatment and posttreatment 

drinking in the aftercare arm for the 

abstinence self-efficacy matching hypotheses

MV × Tx 

Overall 

effect 

(F)

CBTMET 

(t)

CBTTSF

(t)

METTSF 

(t)

Within treatment

PDA 5.49** -3.26** -0.94 2.30*

DDD 2.67 2.23* 0.47 -1.73

Posttreatment

PDA 1.06 1.00 -0.51 -1.44

DDD 0.87 -1.30 -0.35 0.93

NOTE: MV=matching variable, Self-Efficacy; Tx=treatment. F

tests were used for the overall effect, and t-tests were used for 

pairwise treatment contrasts. *p<.05 **p<.008 (Bonferronicorrected level of significance)

unadjusted significant effect for the MET versus TSF contrast on the PDA outcome. Again 

as hypothesized, higher self-efficacy clients in 

MET had slightly more days abstinent than TSF 

clients did during the treatment period.

These results offer strong support for the 

first specific hypothesized contrast (CBT versus 

MET) and some support for the second specific 

hypothesized contrast between treatments (MET 

versus TSF) and client baseline levels of selfefficacy among aftercare participants. However, 

even the CBT versus MET hypothesis was not 

supported completely since the hypothesized 

effects were not significant after correction 

for both of the drinking outcomes. Moreover, 

these significant effects were limited to drinking 

outcomes during aftercare treatment and did 

not appear at all in the posttreatment period, 

where matching effects are traditionally sought. 

Nevertheless, for clients entering aftercare treatment, the level of their efficacy assessed during 

the more intensive prior therapy did have interesting interactions with the MATCH treatments.

There were no significant effects for the one 

hypothesized contrast between TSF and MET 

using the temptation minus confidence variable in the aftercare arm. One nonhypothesized 

interaction of treatment and baseline temptation minus efficacy emerged that reached an 

unadjusted level of significance. Similar to the 

results reported above, these effects were in the 

CBT versus MET contrast, indicating that highly 

tempted clients (i.e., those with high temptation to drink and low self-efficacy to abstain) 

did somewhat better in CBT than in MET. Once 

again, these effects occurred only within treatment and not during the posttreatment period 

(table 5).

In summary, there was support for the 

self-efficacy matching hypotheses only in the 

aftercare arm and only for abstinence from 

drinking within the period during which aftercare treatment was administered. However, 

these within-treatment aftercare effects were 

interesting be-cause clients had their efficacy 

assessed during an inpatient or intensive day 

treatment episode and most were abstinent at 

the beginning of the aftercare treatment. Thus, 

efficacy interactions with treatment during the 

aftercare treatment period represented the initial testing of the clients’ abstinence self-efficacy 
247

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

Figure 4. Within-treatment plots of percentage of days abstinent and drinks per drinking day showing the interaction between the three treatments and self-efficacy in the aftercare arm. The vertical 

axes represent predicted outcome scores and the horizontal axes represent baseline self-efficacy, 

with higher scores indicating higher self-efficacy. The triangles on the horizontal axes indicate the 

10th and 90th percentiles for self-efficacy in this study arm.

for immediate posthospitalization drinking 

outcomes as well as the success of various 

treatments in sustaining the gains made during 

the inpatient treatment. MET seemed to do well 

with the high self-efficacy aftercare clients, and 

there was evidence that CBT did better than 

MET with the less efficacious clients on drinking outcomes immediately after release from 

intensive treatment. Since these matching effects 

emerged and then disappeared in the aftercare 

arm, we examined the assumed causal linkages 

for within treatment and posttreatment interactions in order to evaluate how efficacy and other 

relevant variables were interrelated.

Efficacy, Temptation, and 

Drinking Outcomes

Outpatient Arm

For both the PDA and DDD outcomes over 

the 4- to 15-month followup periods, client 

abstinence self-efficacy predicted frequency and 

intensity of drinking in the outpatient arm of the 

trial (Project MATCH Research Group 1997b). 

Initial levels of abstinence self-efficacy played 

some role in drinking outcomes both during 

treatment and throughout the 1-year followup. 

Moreover, baseline efficacy was predictive of 

drinking intensity even at a 3-year followup 

(Project MATCH Research Group 1998b). Along 

with motivational readiness to change, abstinence self-efficacy was considered one of the 

most important overall predictors of drinking 

for outpatients (DiClemente et al., this volume; 

Project MATCH Research Group 1998b). As is 

illustrated in table 6, baseline abstinence selfefficacy had a small but consistent relationship 

Table 5. Hierarchical linear modeling results 

for within treatment and posttreatment 

drinking in the aftercare arm for the 

temptation to drink minus abstinence selfefficacy matching hypotheses

MV × Tx 

Overall 

effect 

(F)

CBTMET 

(t)

CBTTSF

(t)

METTSF 

(t)

Within treatment

PDA 2.91 2.41* 1.29 -1.16

DDD 1.24 -1.54 -1.02 0.55

Posttreatment

PDA 1.08 -1.35 -0.11 1.23

DDD 1.71 1.80 0.47 -1.34

NOTE: MV=matching variable, Temptation to Drink minus 

Self-Efficacy; Tx=treatment. F tests were used for the overall 

effect, and t-tests were used for pairwise treatment contrasts. 

*p<.05
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Part V: Motivation and Self-Efficacy

Table 6. Correlations between abstinence self-efficacy and temptation to drink at baseline and 

3, 9, and 15 months and PDA and DDD at baseline, week 12 of treatment, and 9 and 15 months: 

Outpatient arm

PDA DDD

BL Wk 12 9 mo 1 mo BL wk 12 9 mo 15 mo

ASE

Baseline .07* .14*** .14*** .10** -.07* -.03*** -.09** -.07

3 mo .35*** .29*** .25*** -.33*** -.26*** -.21***

9 mo .41*** .37*** -.39*** -.33***

15 mo .44*** -.39***

Temp

Baseline -.19*** -.20*** -.15*** -.11** .14*** .19*** .09** .12***

3 mo -.38*** -.33*** -.31*** .40*** .31*** .31***

9 mo -.55*** -.45*** .52*** .40***

15 mo -.54*** .53***

PDA

Baseline .01

Wk 12 -.76***

9 mo -.65***

15 mo -.64***

NOTE: PDA=percentage of days abstinent; DDD=drinks per drinking day. PDA and DDD are the average of PDA and 

DDD reported by clients during the prior 3 months. *p<.05, **p<.01, ***p<.001

with reported PDA at the end of treatment and 

at the 9- and 15-month drinking assessments. 

There was a smaller and less consistent relationship between efficacy and DDD during the 

posttreatment period.

Baseline self-efficacy was also related to 

baseline drinking in the 3 months prior to entry 

into treatment. As Bandura has indicated, efficacy appears to reflect past behavior as well 

as predict future behavior. This reciprocal 

relationship was also supported by the pattern of correlation between efficacy at one time 

point and concurrent and future drinking. The 

greater consistency in the pattern of correlation 

between efficacy and both drinking outcomes 

(PDA and DDD) posttreatment as compared 

to during treatment is related to the fact that 

PDA and DDD become more highly correlated 

after the initial evaluation, when abstinence is 

greater.

Client reports of temptation at baseline 

demonstrated a larger and more consistent 

relationship with drinking outcomes than did 

self-efficacy, both during treatment and throughout the posttreatment period. Posttreatment 

temptation appeared to have a stronger relationship with relapse than efficacy. There was 

a correlation of over 0.5 between temptation 

to drink reported at the 9-month assessment 

and client drinking in the 3 months prior to the 

assessment, indicating the influence of relapse 

on level of temptation. There was a correlation 

of 0.4 between temptation to drink reported at 

the 9-month assessment and drinking during 

the next 3 months that was reported at the end 

of the followup year (month 15), supporting the 

relationship between temptation and future 

drinking frequency and quantity.

Aftercare Arm

In the aftercare arm, there were no significant 

effects for initial baseline level of abstinence 

self-efficacy on either within-treatment or posttreatment drinking outcomes (Project MATCH 

Research Group 1997b). Posttreatment abstinence self-efficacy, however, presented a 
249

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

different picture (table 7). Efficacy assessed 

immediately posttreatment (AASE month 3) 

and efficacy to abstain assessed at the 9- and 

15-month followup periods were highly correlated with drinking outcomes. In these 

posttreatment assessments, the relationships 

between efficacy and drinking were similar to 

those found among outpatients. Once again, 

this supports the view that efficacy and temptation assessed during the intensive treatment 

were problematic and perhaps not as accurate 

as assessments made when clients were freer to 

drink.

Baseline levels of temptation demonstrated 

robust and significant relationships with posttreatment drinking. Temptation appeared to be 

a better measure of relapse potential for these 

aftercare clients than efficacy. Temptation may 

be a better way to assess craving and urges 

to drink that are more critical when they are 

present after the client achieves abstinence. 

Temptation evaluations are influenced by and, 

in turn, influence the client’s ability to be abstinent, as does self-efficacy.

In Summary

In the outpatient arm, baseline self-efficacy 

predicted both the intensity and quantity of 

drinking throughout the posttreatment period. 

This main effect on drinking outcomes of client 

efficacy to abstain from drinking is consistent 

with the theoretical assumption that efficacy is 

influenced by and also influences drinking outcomes. Baseline self-efficacy predicted drinking 

outcomes for the outpatient clients but not for 

the aftercare clients, who were assessed during a more intensive therapy. However, efficacy 

assessed during the posttreatment and followup 

assessments was significantly and highly related 

to drinking both for outpatient and aftercare 

clients. Temptation to drink, which represents 

clients’ acknowledgment of how highly tempted 

they are across a range of situations, was also a 

potent predictor of both intensity and quantity 

of drinking in both arms of the trial.

Table 7. Correlations between abstinence self-efficacy and temptation to drink at baseline and 

3, 9, and 15 months and PDA and DDD at baseline, week 12 of treatment, and 9 and 15 months: 

Aftercare arm

PDA DDD

BL Wk 12 9 mo 1 mo BL wk 12 9 mo 15 mo

ASE

Baseline .01 .02 .06 .01 -.10** -.02 -.12** -.07

3 mo .32*** .32*** .25*** -.35*** -.28** -.30***

9 mo .41*** .32*** -.40*** -.34***

15 mo .48*** -.47***

Temp

Baseline -.05 -.06 -.14*** -.11** .21*** .07 .18*** .14***

3 mo -.33*** -.28*** -.26*** .36*** .28*** .29***

9 mo -.46*** -.37*** .45*** .38***

15 mo -.55*** .53***

PDA

Baseline .14

Wk 12 -.85***

9 mo -.76***

15 mo -.73***

NOTE: PDA=percentage of days abstinent; DDD=drinks per drinking day. PDA and DDD are the average of PDA and 

DDD reported by clients during the prior 3 months. *p<.05, **p<.01, ***p<.001
250

Part V: Motivation and Self-Efficacy

Changes During Treatment

One of the central hypothesized causal 

mechanisms for the interaction of efficacy 

and treatment was that matched clients 

would demonstrate higher self-efficacy 

posttreatment compared to their pretreatment baseline levels. In both arms of the 

trial, there was a significant effect for time. 

Efficacy increased from baseline to the 

assessment immediately posttreatment 

and then leveled off at the 9- and 15-month 

assessments (table 8). It should be noted 

that differences between levels of efficacy 

immediately posttreatment and at the end 

of the 1-year followup represent a decline of only 

5 percent or less, indicating that, for the most 

part, overall efficacy stabilized posttreatment.

We examined the changes in self-efficacy 

from baseline to posttreatment by efficacy group 

and by treatment using a series of ANOVAs 

with efficacy changes and drinking outcomes 

as the dependent variables. There was a significant main effect for initial amount of perceived 

efficacy on changes in efficacy. The low self-efficacy group increased most during treatment, 

followed by the medium and then the high 

groups. High self-efficacy participants showed 

only slight increases, perhaps due to a ceiling 

effect.

There were no differences in before/after 

treatment changes in self-efficacy among the 

three MATCH treatments in the outpatient 

arm, although the MET clients showed the least 

Table 8. Means and standard deviations for alcohol 

abstinence self-efficacy over time by treatment 

assignment

Treatment 

assignment

Baseline 

M (SD)

3 mo 

M (SD)

9 mo 

M (SD)

15 mo 

M (SD)

Outpatient arm

CBT 2.92 (0.8) 3.47 (0.9) 3.36 (1.1) 3.41 (1.1)

MET 2.98 (0.8) 3.38 (0.9) 3.40 (1.1) 3.45 (1.1)

TSF 2.93 (0.8) 3.47 (1.0) 3.47 (1.1) 3.44 (1.1)

Aftercare arm

CBT 3.25 (1.0) 3.73 (1.0) 3.54 (1.2) 3.52 (1.2)

MET 3.22 (1.0) 3.48 (1.0) 3.41 (1.1) 3.46 (1.1)

TSF 3.10 (1.0) 3.56 (1.1) 3.47 (1.2) 3.44 (1.2)

NOTE: Efficacy items were scored from 1=-not at all to 5=extremely. 

No significant differences were found for treatment assignment 

except for aftercare arm at 3 months where MET < CBT (p<.05).

Table 9. Comparison of group means and standard 

deviations for change in abstinence self-efficacy 

(posttreatment – baseline) by treatment assignment 

in outpatient and aftercare arms

Change in 

Self-Efficacy

CBT MET TSF Tukey’s B

comparisons

Outpatient .63 (.98) .41 (1.2) .54 (1.1) ns

Aftercare* .59 (1.2) .30 (1.2) .43 (1.2) MET<CBT

NOTE: One-way ANOVAs were used. Post hoc comparisons 

were made using the Tukey’s B procedure. Pairwise comparisons that were significant are designated by a < symbol. 

*p<.05

average change (table 9). There was, however, 

a significant interaction between treatment and 

these changes in efficacy, F(4, 682)=2.62, p<.05.

 Low efficacy MET clients had a greater 

increase in efficacy (M=1.27) than did their 

counterparts in the other two treatments 

(CBT=1.04; TSF=1.07), contrary to the hypothesized mechanism. The opposite was true in the 

medium and high self-efficacy groups, in which 

MET clients in-creased less in efficacy than did 

those in CBT. The TSF clients at the upper levels of efficacy had changes that fell between the 

MET–CBT split.

In the aftercare arm, there was a significant overall difference between treatments in 

changes in before/after efficacy. MET clients 

demonstrated less change than the CBT clients 

did. In addition, at the 3-month posttreatment 

assessment, there was a significant difference 

between MET and CBT clients, with the CBT 

clients having significantly higher 

levels of efficacy. However, there were 

no significant interactions between 

levels of efficacy and treatments for 

changes in self-efficacy. Although 

nonsignificant, examination of the 

changes within the levels of efficacy 

revealed that low self-efficacy CBT 

clients had the most increase in selfefficacy, as hypothesized (before/after 

average difference=1.46 versus MET 

average difference=1.23). Differences 

between MET and TSF for clients at 

the higher levels of efficacy were not 

in the hypothesized direction (TSF 

before/after average difference=-0.22 

versus -0.42 for MET). Again, these 

interactions were not significant and, 
251

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

as noted above, clients with initial high levels 

showed little change or a slight decline.

 In summary, there was an overall increase 

in efficacy from baseline to posttreatment for all 

treatments and for both arms of the trial, indicating changes in efficacy during the treatment 

period. However, only a few changes in efficacy 

were related to differences among treatments 

or to the interaction of treatment and levels of 

baseline efficacy.

In the outpatient arm, there was a significant interaction between changes in efficacy 

and treatment condition, with MET doing better than CBT and TSF with low self-efficacy 

clients and not as well with the high efficacy 

clients. Contrary to our best guess, there was 

evidence that MET interacted with low levels 

of efficacy with outpatients to produce slightly 

more change in efficacy than the other treatments. There was also a lack of hypothesized 

matching effects in this arm, so that there was 

consistency between effects and mechanisms. 

On the other hand, among these outpatients, 

their baseline perceived efficacy was a significant predictor of drinking posttreatment across 

all three treatments.

In the aftercare arm, where a matching effect 

for the CBT and MET contrast was found during 

the treatment period, there were also treatment 

differences in the shifts in efficacy from baseline 

to posttreatment. Clients in CBT demonstrated 

greater positive changes in efficacy compared 

to clients in MET, but there was no significant 

interaction between treatments and level of efficacy on before/after changes in efficacy. Thus, 

CBT did better in fostering changes in self-efficacy across all levels of efficacy.

Overall, CBT produced greater changes in 

self-efficacy than the MET treatment, consistent 

with its focus on skills and efficacy. This finding supported the matching effects on drinking 

within treatment. However, CBT did not produce 

significant shifts in self-efficacy differentially by 

level of baseline efficacy as would be expected 

from the matching effect, thus the specificity of 

this mechanism for lower self-efficacy clients 

has not been identified. In the aftercare arm, 

MET clients did not maintain efficacy as well 

as clients in the other treatments, irrespective of initial level of efficacy. Finally, TSF did 

not interact with changes in efficacy as was 

hypothesized. Changes in efficacy for TSF clients appeared to lie between the CBT and MET 

clients across levels of baseline efficacy. There 

was no support for a conflict between the messages of TSF and clients with higher initial 

levels of abstinence self-efficacy.

Efficacy and Processes of Change

A set of experiential and behavioral processes of change was assessed after each 

treatment session and at the end of treatment 

as described above. Client-reported process 

activity was examined for its relationship with 

baseline efficacy and treatment condition. 

Although not part of the putative causal chain, 

outpatient clients in CBT reported significantly 

less experiential process activity at Session 2 

than did those in TSF. However, this difference 

was not evident at the end-of-treatment assessment period. Also, in the outpatient arm, the 

low baseline self-efficacy group reported significantly more cognitive/experiential process 

activity at the end of treatment than did those 

in the high self-efficacy group (p<.05). Neither 

at Session 2 nor at posttreatment were there 

any significant interactions between efficacy 

and treatment condition on process activity, 

consistent with the lack of matching found in 

this arm.

In the aftercare arm, at the Session 2 assessment, clients in MET reported less cognitive/ 

experiential process activity compared to those 

in TSF. Again, there were no differences at the 

posttreatment assessment and no significant 

interactions between efficacy and treatment 

on process activity. In the aftercare arm, clients low in self-efficacy reported less frequent 

use of helping relationships than those in the 

high self-efficacy group at Session 2, but there 

were no differences between efficacy groups at 

the posttreatment assessment. Efficacy levels 

did not interact with behavioral process activity 

by treatment in order to produce any matching effect. The within-treatment matching effect 

found between CBT and MET and between TSF 

and MET did not have concomitant differential 

behavioral process activity. Thus, there was no 

support for a causal link between treatment 

and efficacy evaluations through the processes 

of change assessed during or after treatment.
252

Part V: Motivation and Self-Efficacy

Treatment Compliance and Efficacy

Compliance with treatment was another 

potential process link between efficacy and 

treatment. There was a small and clinically 

insignificant main effect of treatment on number of weeks in treatment. CBT had a slightly 

higher average number of weeks compared to 

TSF (8.8 versus 8.1) across both arms of the 

trial (Mattson et al. 1998). There were no differences in compliance due to baseline efficacy 

levels. Finally, there was no treatment by efficacy interaction on weeks in treatment in either 

arm of the trial. There was no evidence for an 

intermediary role for compliance in any of the 

aftercare within-treatment matching effects.

Efficacy and Attendance at AA

One final interesting relationship that could 

be involved in the interaction of efficacy with 

treatment involves the clients’ attendance at 

AA and its impact on efficacy evaluations. We 

examined this question with a simple correlation matrix of AA attendance reported for the 

3 months prior to an assessment point with 

the reported efficacy at that assessment. In 

the outpatient arm of the trial, the relationships between efficacy and AA attendance were 

small, with the largest correlation between AA 

attendance in the months 12 through 15 and 

abstinence self-efficacy assessed at month 15 

(r=0.11). Efficacy at the posttreatment assessment was significantly but minimally correlated 

with amount of AA attendance during treatment 

(r=0.10). Most relationships were nonsignificant, even with this large number of subjects.

In the aftercare arm, in which there was 

substantially more AA attendance compared to 

the outpatient arm, a greater number of correlations were significant. The absolute level of 

the correlations was moderate at best, with the 

highest correlation occurring between AA attendance from months 6 through 9 with abstinence 

self-efficacy assessed at month 9 (r=0.18). 

Relationships between perceived efficacy 

assessed at one time point and AA attendance 

in the months prior to that assessment were 

about the same as the correlations of current 

efficacy with future AA attendance, indicating a 

small, interactive relationship between the two 

constructs. However, it does not appear that 

the overall relationship between AA attendance 

and efficacy would confound our causal chain 

analysis since posttreatment efficacy was only 

minimally related to AA attendance.

To further clarify these relationships, we 

examined the correlations between AA attendance and efficacy within treatment groups 

since mean levels of attendance differed by 

treatment, with TSF showing the greatest 

attendance in both the outpatient and aftercare arms of the trial. Among outpatients, the 

correlations between AA and efficacy were minimal and not significant for clients in CBT and 

MET. Significant modest correlations, ranging 

from 0.16 to 0.21, were found for the TSF clients. Temptation to drink demonstrated a more 

modest association that was negative in sign. 

More temptation at one time point was associated with less AA attendance at the next, and 

more AA attendance in the prior months was 

associated with less temptation at the next 

assessment.

Among aftercare clients, similar low levels of 

association between AA attendance and efficacy 

were found for CBT. However, for MET clients, 

the relationships were a bit stronger, ranging 

from r=0.12 to 0.20. For TSF clients, the correlations between AA and efficacy ranged from 

r=0.15 to 0.23. Temptation to drink measures 

correlated less strongly and negatively as in the 

outpatient sample for all three treatments but 

were stronger (r=0.18 to 0.23) for both MET and 

TSF clients. Overall, the correlations between 

efficacy and AA attendance were stronger for 

TSF clients than for the other treatments. 

However, the actual correlations were small 

and supported a reciprocal interactive effect of 

efficacy on AA attendance and AA attendance 

on efficacy. The similar negative direction of the 

association between temptation and AA attendance across all three treatments indicated that 

there was no positive interaction of TSF with 

temptation to drink as was hypothesized.

Summary of Causal Chain Analyses

Although there was some support for a 

matching effect in the aftercare arm of the 

trial on drinking during treatment, there was 

little evidence to suggest that levels of self-efficacy related in any consistent manner to the 

proposed causal mechanisms. Efficacy levels 

demonstrated a few significant interactions 
253

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

with specific treatments on changes in self-efficacy during the treatment period. For example, 

CBT had significantly greater positive changes 

in self-efficacy during treatment compared to 

MET in aftercare, where there emerged matching effects during treatment.

In addition, as would be expected, the level 

of baseline efficacy was related to changes in 

self-efficacy during treatment, with low efficacy clients demonstrating the greatest change. 

However, there were no significant interactions 

between treatments and levels of efficacy on 

before/after changes in efficacy. In the aftercare arm, self-efficacy interacted with treatment 

immediately after discharge from the intensive 

treatment to affect drinking during aftercare 

treatment but did not do so during the posttreatment period, where matching effects 

disappeared. However, causal mechanisms 

were developed largely to predict posttreatment 

matching rather than during treatment matching. Specific proposed causal mechanisms were 

not supported as causal links in the withintreatment matching effects.

In the outpatient arm, there was little support for causal mechanisms as well. This would 

be expected, since there was no support for 

the hypothesized matching effects on drinking outcomes either during treatment or in the 

posttreatment period. There was one difference 

between treatments by self-efficacy groups on 

drinking outcomes. Baseline self-efficacy in the 

outpatient arm was a predictor of drinking outcomes, with high levels of efficacy predicting 

greater abstinence posttreatment. Level of efficacy also predicted changes in efficacy during 

treatment. However, there was no overall effect 

of treatment on changes in self-efficacy.

Although there was no matching, there 

was an interaction between efficacy and treatment on before/after changes in efficacy. MET 

showed the largest positive change in efficacy 

for low efficacy clients and the smallest change 

for the high efficacy clients compared to clients 

in both the other treatments. This interaction 

was opposite to that hypothesized but was consistent with the finding of no support for the 

matching hypotheses.

Discussion

The self-efficacy matching hypothesis that 

predicted a superior effectiveness of CBT for 

low self-efficacy participants and a problematic 

interaction of TSF with high self-efficacy clients 

received little support. Only within-treatment 

drinking in the aftercare arm demonstrated 

some effects for matching, but these were weak, 

and only one was in the hypothesized direction. 

However, these findings indicated that clients 

emerging from intensive treatments with higher 

levels of temptation and lower levels of efficacy 

would do better in the short term in the more 

extensive CBT treatment rather than in MET.

The causal chain analyses reported above 

yielded interesting information. There were 

clear differences in level and predictive ability of efficacy evaluations given by outpatients 

and those reported by clients assessed during 

intensive treatment, clearly supporting an effect 

of assessment setting (arm of trial) on efficacy 

levels. Aftercare clients’ assessments of selfefficacy prior to entry into aftercare were not 

predictive of outcomes and showed few interactions with process variables. We concluded that 

these efficacy evaluations taken during intensive treatment were less valid or, possibly, less 

accurate than those gathered from outpatients 

who had opportunity and freedom to drink 

when evaluating their confidence to abstain 

from alcohol.

However, aftercare clients’ efficacy evaluations showed some interesting interactions 

with treatments in terms of drinking outcomes 

during treatment. There were indications that 

CBT has some ability to increase efficacy during aftercare treatment better than MET does. 

CBT also produced more abstinence for low 

self-efficacy clients compared to MET, as was 

hypothesized. On the other hand, MET produced a bit more abstinence than CBT and TSF 

for high self-efficacy clients in the first couple 

of months of treatment and during the month 

immediately posttreatment for aftercare clients. 

No matching effect extended into the entire 

posttreatment period. Thus, there were matching interactions but no main effect for efficacy 

in the aftercare arm.

In the outpatient arm, there were fewer 

interactions but a clear ability of self-efficacy 
254

Part V: Motivation and Self-Efficacy

to predict drinking intensity and frequency. 

Baseline efficacy was a better predictor of future 

drinking with clients who were struggling to 

establish and maintain abstinence in this outpatient setting, where it can be assumed that 

the cues for drinking were more readily available 

than for those coming into an aftercare setting. 

There was some evidence that MET supported 

self-efficacy for the less efficacious but no 

evidence of a mismatching between high selfefficacy clients and the TSF treatment. Both of 

these findings are contrary to the hypothesized 

causal chain.

In terms of the three treatments, our 

assumed mechanisms of action were not supported to any great extent. Although supporters 

of CBT and MET could claim some differential 

effect of these treatments on efficacy, these differences were neither dramatic nor consistent 

enough to support an overall interpretation 

of the effect. The TSF treatment did not negatively interact with the high self-efficacy clients. 

Either the high efficacy clients found that the 

powerlessness message was not about abstinence self-efficacy or these clients ignored any 

personal powerlessness connotations and benefited from the support and modeling of the TSF 

and associated AA intervention. Whatever the 

mechanism, there were not many differences by 

level of efficacy for TSF clients in either arm of 

the trial. Relationships between AA attendance 

and efficacy were modest and positive as well as 

interactive in nature.

When studying efficacy, securing a sample 

with a broad enough range of efficacy scores to 

allow for an examination of the effects of efficacy 

would be important. In the Project MATCH samples, there was a broad range of efficacy scores, 

with the high efficacy clients very different from 

the low efficacy clients, who had mean scores 

hovering at 2 on a 1 to 5 scale. Additionally, 

efficacy levels had good variability within both 

the outpatient and aftercare arms. Low efficacy 

clients did worse in terms of drinking in the outpatient arm. Thus, there was enough diversity 

and breadth of efficacy scores to produce effects. 

Moreover, efficacy levels changed over time from 

before to after treatment differentially by level of 

initial perceived efficacy. These changes either 

supported an effect for the treatment or, at the 

very least, demonstrated that individuals taking action and achieving significant numbers of 

abstinent days in the posttreatment period had 

commensurate changes in their levels of abstinence self-efficacy.

More interesting insights follow from these 

analyses. Although efficacy evaluations were 

sensitive to setting (arm of trial) and clearly 

tracked the process of achieving abstinence 

from drinking, it is noteworthy that temptation 

to drink was as good, if not a little better, predictor of drinking outcomes in both the outpatient 

and aftercare arms. Change process activity, 

which was highly related to baseline motivation 

(DiClemente et al., this volume), was mostly 

unrelated to level of self-efficacy. In studies of 

smoking abstinence self-efficacy, the relationship between efficacy and processes of change 

varied according to the client’s stage of change 

(DiClemente et al. 1985). If clients had been 

differentiated according to stage of change in 

these analyses, then perhaps a more meaningful relationship between efficacy and processes 

of change might have emerged (DiClemente et 

al. 1992). Efficacy evaluations were not related 

to baseline motivational readiness to change 

and thus appeared to be independent of motivation, as has been discussed in a prior review 

of efficacy (DiClemente et al. 1995).

Although the three MATCH treatments were 

hypothesized to have differential impacts on clients with various levels of baseline abstinence 

self-efficacy, there was little evidence that the 

three treatments influenced efficacy in unique 

ways or had any unique influence on clients 

depending on their levels of efficacy. If future 

research were to seek self-efficacy matching 

effects, very different treatment parameters or 

more complex and multidimensional matching hypotheses should be considered. Several 

examples come to mind. A future study could 

match prospectively on efficacy levels to some 

type of self-help versus formal treatment. 

Another strategy would be to match different 

relapse prevention strategies to groups who 

have achieved abstinence but are high or low 

on efficacy. Finally, one could segment by stage 

of change and then attempt to match high and 

low efficacy participants with stage-specific 

interventions.

Matching treatments to efficacy levels is 

certainly more complicated than had been proposed in the literature. The analyses in this 
255

Self-Efficacy as a Matching Hypothesis: Causal Chain Analysis

chapter have provided one of the most indepth 

views of alcohol abstinence self-efficacy and 

its interaction with the process of treatment in 

a controlled trial. There is still much to learn 

about how treatments influence efficacy and 

how efficacy influences treatment outcomes. 

The reciprocity of the influence of efficacy on 

behavior and of behavior on attributions makes 

this a challenging enterprise. Our hope is that 

the analyses provided here contribute to this 

body of knowledge and assist in understanding 

this interesting and important self-evaluation 

called alcohol abstinence self-efficacy.

Acknowledgments

The authors want to acknowledge all the 

research assistants, therapists, and investigators who made this project possible. We 

particularly want to thank those at the Houston 

and Seattle clinical research units for their help 

in every aspect of the work. The research and 

publications efforts for this manuscript have 

been supported by the National Institute of 

Alcohol Abuse and Alcoholism (NIAAA) as the 

collaborative research award U10–AA08432 

called Project MATCH. We would like to thank 

Drs. Richard Fuller, John Allen, and Margaret 

Mattson from NIAAA for their untiring assistance throughout the project.

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259

Part V1: Interpersonal Functioning 

and Support

Network Support for Drinking

Prior Alcoholics Anonymous Involvement and 

Treatment Outcome

Social Functioning
260

Network Support for Drinking

Richard Longabaugh, Ed.D., Philip W. Wirtz, Ph.D., 

Allen Zweben, D.S.W., and Robert Stout, Ph.D.

ABSTRACT

Network support for drinking is prognostic of poorer drinking outcomes. To examine 

whether treatment can negate this effect, two a priori matching hypotheses involving 

network support for drinking were tested: Cognitive-Behavioral Coping Skills Therapy 

(CBT) will be incrementally more effective than Motivational Enhancement Therapy 

(MET) for clients who have pretreatment networks highly supportive of drinking, and 

Twelve Step Facilitation (TSF) will be incrementally more effective than MET for clients 

with networks highly supportive of drinking Results from the first 15 months indicated 

no support for a sustained matching effect for either matching hypothesis. However, 

for outpatient clients followed for 3 years from the end of treatment, the hypothesized 

support by TSF versus MET matching effect was observed for both percentage of days 

abstinent and drinks per drinking day. Causal chain analyses tested the assumptions underlying these two matching hypotheses. While increased drink refusal skills 

reported at 9 months among clients with network support for drinking predicted subsequent drinking, CBT did not result in greater self-reported drink refusal skills than did 

MET. Thus, the breakdown in the CBT causal chain was the failure of CBT to increase 

drink refusal skills more than MET did. Clients with pretreatment networks supportive 

of drinking who had networks less supportive of drinking 9 months after treatment 

initiation drank less often and less intensely subsequently than clients whose network 

support for drinking did not diminish However, TSF did not result in a greater reduction in network support for drinking by clients with pretreatment networks supportive 

of drinking than did MET. Thus, once again, the breakdown in the causal chain was 

attributed to the failure of treatment, in this case TSF, to have a differential effect on 

a hypothesized mediator of treatment outcome, network support for drinking. The failure of this causal chain, in the presence of a longer term TSF versus MET matching 

effect in the outpatient arm of the study, led to a search for another explanation, and 

participation in Alcoholics Anonymous by TSF clients during followup was identified as 

a partial mediator. AA participation by clients with pretreatment networks highly supportive of drinking improved their drinking outcomes. This matching effect was most 

pronounced for clients assigned to TSF and least apparent for those assigned to CBT.

Social support has had an inconsistent role 

as a variable in alcohol treatment outcome research (Beattie and Longabaugh 

1999). Conceptual ambiguity in the use of 

this construct contributes to this inconsistency (Longabaugh and Beattie 1985, 1986; 

Beattie et al. 1993; Longabaugh et al. 1993; 

Beattie and Longabaugh 1999). One important 

distinction needed is to differentiate general 

support from alcohol-specific support, that is, 

support for abstinence or drinking. Historically, 

these two constructs have been confounded. 

Richard Longabaugh, Ed.D.

Center for Alcohol and Addiction Studies

Brown University, School of Medicine

800 Butler Drive, Potter Building, Room 204 

Providence, RI 02906

E-mail: Richard_Longabaugh@Brown.edu
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Network Support for Drinking

Consequently, it has not been generally possible to identify the impact that each has on 

drinking outcomes of treatment-seeking clients. 

However, in the few instances in which alcoholspecific support has been compared with general 

support as predictors of drinking outcomes, 

alcohol-specific support has been found to be a 

better predictor (Beattie et al. 1991; Beattie and 

Longabaugh 1999; Karno and Longabaugh 1999).

Given this prognostic effect, an important 

question is whether treatments can be devised 

that will decrease social support for drinking and, by doing so, decrease drinking. Our 

prior research has shown that clients varying 

in alcohol-specific support will have different 

drinking outcomes as a function of assignment to treatments that vary in amount of 

relationship-based treatments that include a 

goal of increasing alcohol-specific support. This 

research also suggested that clients treated 

with extended cognitive-behavioral therapy will 

be less affected by an unsupportive social network (Longabaugh et al. 1995).

The Matching Hypotheses

These results stimulated the development of 

two matching hypotheses in the present study.

Cognitive-Behavioral Therapy Versus 

Motivational Enhancement Therapy

Cognitive-Behavioral Therapy (CBT) teaches 

coping skills for dealing with situations involving high risk for relapse (Kadden et al. 1992). 

These high-risk situations include those in 

which a client is exposed to interpersonal 

encounters where there is pressure to drink, 

either because people around the client are 

drinking or because the client is being offered 

alcohol or being subjected to more subtle pressures to drink.

Motivational Enhancement Therapy (MET) 

does not attempt to teach the client coping skills 

but rather how to utilize preexisting resources 

to set treatment goals and strategies relying 

on these preexisting skills (Miller et al. 1992). 

Clients with networks supportive of drinking 

would not be taught the skills for coping with 

this network. Therefore, we hypothesized that 

clients treated with CBT who had pretreatment 

social networks supportive of drinking would 

have better drinking outcomes than comparable 

MET clients. For clients having networks that 

were not supportive of drinking, we did not 

expect differential drinking outcomes. Thus, an 

ordinal interaction was hypothesized.

Twelve Step Facilitation Versus 

Motivational Enhancement Therapy

Twelve Step Facilitation (TSF) treatment, 

with its aim of involving the client in Alcoholics 

Anonymous (Nowinski et al. 1992), is conceptualized as a relationship-based intervention that 

will increase alcohol-specific support for the 

client. Attendance at AA meetings will expose 

the client to a large network of people who have 

a goal of maintaining abstinence and supporting one another in achieving this goal. The AA 

fellowship’s support was expected to assist 

clients in disengaging from elements of their 

pretreatment networks that were supportive of 

drinking. Therefore, TSF would promote support for abstinence both by the client’s getting 

involved in a mutual self-help group supportive 

of abstinence and by the client’s disengaging 

from a pretreatment social network supportive 

of drinking.

In contrast, AA involvement is not an important aim of MET. Rather, MET therapists are 

instructed to support a goal of AA involvement when raised by the client but not to 

initiate a discussion of this topic. We therefore 

hypothesized that to the extent that the client’s 

pretreatment social network was supportive of 

drinking, assignment to TSF versus MET would 

result in better drinking outcomes. Figure 1 portrays the two matching hypotheses as well as 

the anticipated prognostic effect of a network 

supportive of drinking.

Network Support for Drinking

Network support for drinking was measured 

prior to treatment and 9 months after treatment 

initiation by the Important People and Activities 

(IPA) instrument (Clifford and Longabaugh 

1991). The IPA is a structured interview that 

asks clients to identify important people in 

their networks with whom they have had frequent contact within the past 4 months. As 

implemented in Project MATCH, clients could 

identify up to 12 people over the age of 12. For 
262

Part V1: Interpersonal Functioning and Support

Figure 1. Hypothesized effects of network support for drinking on drinking outcomes.

each person so identified, the client is asked to 

identify the relationship (e.g., spouse, brother, 

friend, coworker), along with the duration of the 

relationship and the frequency of contact with 

the person. The client is also asked to assess 

the drinking behavior of each person: how 

often the person drinks, how much the person 

drinks on a drinking occasion, and the person’s 

overall drinking status (e.g., heavy drinker, 

moderate drinker, abstainer). Finally, the client is asked to select from this network the 

four people who are most important. For each 

of these four people, the client is asked to rate 

his or her importance (from totally important 

to unimportant), how much the client likes the 

person (totally like to dislike), and how the person behaves in relation to the client’s drinking 

and not drinking: Is the person supportive of 

drinking, accepting, neutral, not supportive, or 

nonaccepting? Is the person supportive of the 

client’s not drinking, accepting, neutral, nonsupportive, or nonaccepting?

The interview takes 20–30 minutes to administer. A summary measure of alcohol-specific 

network support derived from this instrument 

has previously been found to be prognostic of 

posttreatment drinking outcome at 1-year followup (Longabaugh et al. 1993). The version 

of the IPA used in Project MATCH was found 

to have test-retest reliability over a 2- to 3-day 

period. With a heterogeneous sample of 70 

heavy drinkers and clients who had received 

treatment, the summary index of overall support for drinking had a Shrout-Fleiss (1979) 

intraclass correlation of 0.80 and a product 

moment correlation of 0.95.

Because the version of the IPA used in Project 

MATCH was revised to suit the purposes of this 

study, it was necessary to develop a new single 

summary measure of alcohol-specific support 

based on this modified instrument. The single 

measure operationalized to test the alcoholspecific support matching hypotheses involved 

11 indices, each standardized to have a mean 

of 0 and a standard deviation of 1. The values 

assigned to each index are summed to yield an 

overall measure of network support for drinking. 

Three indices focus on the client’s investment 

in the network and eight focus on the network’s 

support of the client’s drinking (table 1).

The indices are correlated with one another 

in predicted directions, but the degree of association is quite modest, average r=0.256. Thus, 

the overall index is a composite of a fairly heterogeneous set of indices reflecting various 

dimensions of network support for drinking. 

However, because of the trialwide need to set 

the number of tests for each matching hypothesis to a minimum, this summary variable was 

utilized as the single measure of network support for drinking.

The client’s baseline score was used to test 

the matching hypotheses in each arm of the 

study. In the aftercare arm, the hypotheses were 

tested for two periods of observation: during the 

planned 12 weeks of treatment and during the 

12 months following planned treatment completion (months 4–15). In the outpatient arm, 

because clients were reinterviewed 39 months 

after treatment initiation, it was possible to test 

the matching hypotheses at this third period as 

well. In this interview, monthly drinking data 

were collected for months 37–39 using the Form 

90 (Miller 1996). Therefore, the sample population was the 806 outpatients who had complete 

outcome data, 84 percent of the 952 outpatients 

included in the original study.

Alcohol Consumption

Two measures of drinking were designated as 

primary to test the matching hypotheses: percentage of days abstinent (PDA) during a period 

of observation and drinks per drinking day 
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Network Support for Drinking

Table 1: Composite index of network support for drinking

Investment in the identified networks1

*Number of People in the 

Network

The square root of the number of members listed in the overall 

network, which can range from 0 to 12. The number is squared to 

provide a more normal distribution.

*Amount of Contact With 

One’s Network

The number of members within the overall network with whom the 

client has daily contact.

*Average Importance of 

Most Important People

The average value of “How important this person has been to you” 

among the people listed as most important.

Support for drinking2

Drinking Status of 

Network Members

The contact the client has with each member in the network multiplied by the drinking status ascribed to the network member by 

the client (ranging from abstainer or recovering alcoholic to heavy 

drinker) and averaged across the network.

Frequency With Which 

Network Members Drink

The frequency with which each person in the listed network drinks 

multiplied by the amount of contact the client has with that person, 

averaged across the entire network.

Maximum Drinking of 

Network Members on a 

Drinking Day

The value each person described in the network receives on the variable, “What is the maximum this person drinks on a drinking day” 

multiplied by the amount of contact the client has with this person.

Percentage of Heavy 

Drinkers in Network

The number of network members listed as heavy drinkers, divided by 

the total number of network members listed.

*Percentage of Abstainers 

and Recovering Alcoholics 

in Network

The number of network members who are recorded as abstainer or 

recovering alcoholic, divided by the total number of network members listed.

Most Support for Drinking 

Among Most Important 

People

The most supportive reaction to the person’s drinking, in response 

to the question: “How has this person responded to your drinking?” 

among the people listed by the client as most important.

*Least Support for 

Drinking Among Most 

Important People

The least supportive reaction to the person’s drinking in response 

to the question: “How has this person responded to your drinking?” 

among the people listed by the client as most important.

Average Support for 

Drinking Among Most 

Important People

The product of three values for each person listed as most important: 

how much the client likes the person, how important the person is to 

the client, and the person’s response to their drinking.

*The signs are reversed for indices with asterisks so that all indices have the same direction, with larger scores indicating more 

support for drinking. The indices are standardized to a mean of 0 and a standard deviation of 1, and a Composite Support Index 

is created by summing the standardized scores for each of the 11 indices.

1 Information concerning investment in the person’s network is drawn from two sections of the IPA: the client’s description of the 

overall network and of the four most important people in this network.

2 Information concerning support for the person’s drinking is also drawn from two sections of the IPA: the client’s description of 

the drinking behavior and status of the entire network and of the reactions of the most important people to the client’s drinking.
264

Part V1: Interpersonal Functioning and Support

(DDD). These measures were both transformed 

to reduce skewness (Project MATCH 1997a).

Data Analytic Procedures

As reported elsewhere, a hierarchical latent 

growth model was used to test for matching 

effects in each arm of the study for the initial 12-month followup period (Project MATCH 

1997a; Longabaugh and Wirtz, this volume, pp. 

4–17).

Analysis of the 37- to 39-month data for outpatients indicated the data could be combined 

into a single 3-month data point for the purposes of testing for matching effects present at 

3 years. Therefore, to test for the hypothesized 

matching effect during this followup period, 

ANCOVAs were conducted separately for each 

primary drinking variable—PDA and DDD.

To control for rival explanations for results, 

covariates included the two primary drinking 

variables measured during the 3 months prior 

to treatment, treatment site, treatment site by 

treatment assignment, and treatment site by 

treatment assignment by pretreatment support for drinking. Independent variables were 

treatment assignment, pretreatment network 

support for drinking, and their interaction term 

(Project MATCH 1998).

Results

Aftercare Arm

CBT Versus MET

No matching effects were observed for the 

CBT versus MET contrast that were independent of time (tables 2 and 3). While a quadratic 

time by matching effect for both PDA and DDD 

was observed during the within-treatment 

period, in no single week during this period 

was the matching contrast significant, with or 

without a Bonferroni correction. There was no 

support for the hypothesized matching effect of 

CBT and network support for drinking following 

treatment either.

TSF Versus MET

No matching effects were observed for the TSF 

versus MET contrast either during the treatment period or in the year following treatment. 

Table 2. Hierarchical linear modeling results 

for within-treatment drinking in the aftercare 

arm for the Network Support for Drinking 

matching hypothesis

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t .61 -1.21 .58 -1.11 -2.39 2.29

p .54 .22 .56 .26 .016 .022

CBT–

TSF

t 1.54 -1.36 .96 -.74 -1.03 1.22

p .12 .17 .34 .46 .30 .22

MET–

TSF

t .69 .08 .22 .51 1.56 -1.29

p .49 .93 .82 .61 .12 .20

Overall 

effect

F 1.19 1.16 .40 .66 2.86 2.65

p .30 .31 .62 .52 .057 .071

NOTE: MV=matching variable, Network Support for Drinking; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).

Table 3. Hierarchical linear modeling results 

for posttreatment drinking in the aftercare 

arm for the Network Support for Drinking 

matching hypothesis

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t -1.17 -.29 .79 .21 .27 .00

p .24 .77 .43 .83 .79 1.00

CBT–

TSF

t .12 - .85 -.07 .74 .56 -.38

p .90 .40 .94 .46 .57 .71

MET–

TSF

t 1.29 -.41 -.86 .40 .20 -.31

p .20 .68 .39 .69 .84 .76

Overall 

effect

F .92 .36 .41 .28 .16 .09

p .40 .69 .66 .75 .85 .92

NOTE: MV=matching variable, Network Support for Drinking; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).

Thus, there was no support for the hypothesized 

matching effect of TSF and network support for 

drinking in aftercare.
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Network Support for Drinking

Outpatient Arm 

CBT Versus MET

There were no significant matching effects 

observed for the CBT versus MET contrast 

within treatment, in the 1 year following treatment, or at the 3-year followup (tables 4 and 

5). In summary, there was no support for the 

CBT versus MET by network support for drinking contrast in either arm of the study at any 

data point.

TSF Versus MET

A matching effect was observed within treatment that changed over time (table 4). During 

the first 3 weeks of treatment for PDA and 

the first month of treatment for DDD, clients 

with networks supportive of drinking who were 

assigned to TSF were doing less drinking than 

those assigned to MET, while this was not the 

case for clients with networks unsupportive of 

drinking (figure 2). The matching effect was present during the first month of treatment for TSF 

clients with high network support for drinking, 

PDA=91 percent, versus MET clients, PDA=82 

percent; in contrast, clients with low network 

support for drinking assigned to TSF and MET 

did not differ in their PDA: TSF=85 percent, 

MET=87 percent. This effect was strong enough 

to survive the Bonferroni correction. However, 

this initial effect dissipated during the second 

month of treatment and had totally disappeared 

by the end of treatment. The effects observed for 

drinks per drinking day were comparable (not 

shown).

No matching effect was observed during 

the 1 year following treatment either (table 5). 

Rather, network support for drinking had a consistent prognostic effect on drinking outcome. 

Irrespective of treatment condition, clients with 

networks supportive of drinking had fewer days 

abstinent (F=9.74, p<.0018 for PDA and F=8.39, 

p<.0039 for DDD). Thus, it would appear that 

the temporary buffering effect that TSF provides 

outpatients during the first month of treatment 

is overwhelmed by the adverse effect of a network supportive of drinking.

At 3 years followup, however, the matching effect surprisingly reappeared. For PDA, 

p=.0057 (one-tailed test) and for DDD, p=.0036. 

As hypothesized, the effect was attributable to 

Table 4. Hierarchical linear modeling results 

for within-treatment drinking in the outpatient 

arm for the Network Support for Drinking 

matching hypothesis

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t .35 -.76 -1.88 1.91 - .88 .57

p .72 .45 .06 .06 .38 .57

CBT–

TSF

t -.42 .15 .92 -.54 -.57 .78

p .68 .88 .36 .59 .57 .44

MET–

TSF

t -.78 .92 2.85 -2.50 .31 .22

p .43 .35 .004* .012* .76 .82

Overall 

effect

F .31 .49 4.22 3.46 .39 .32

p .74 .61 .015 .032 .67 .72

NOTE: MV=matching variable, Network Support for Drinking; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise treatment contrasts. Reported p values are based on 

nondirectional tests (i.e., two tailed). *p<.0125 (refers to the 

Bonferroni-corrected level of significance for a one-tailed test)

Table 5. Hierarchical linear modeling results 

for posttreatment drinking in the outpatient 

arm for the Network Support for Drinking 

matching hypothesis

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t -.98 .50 -1.22 1.52 -.41 .43

p .33 .62 .22 .13 .68 .67

CBT–

TSF

t -1.71 1.62 -.84 1.75 .67 -1.17

p .09 .10 .40 .08 .46 .24

MET–

TSF

t -.71 1.11 .39 .21 1.15 -1.61

p .47 .27 .69 .83 .25 .11

Overall 

effect

F 1.47 1.38 .77 1.79 .69 1.39

p .23 .25 .46 .17 .50 .25

NOTE: MV=matching variable, Network Support for Drinking; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).

TSF clients with networks supportive of drinking having better drinking outcomes than 

comparable MET clients, while for clients with 

networks unsupportive of drinking, treatment 
266

Part V1: Interpersonal Functioning and Support

Figure 2. Percentage of days abstinent during 

first month of treatment: TSF versus MET clients 

with high and low network support for drinking.

assignment did not affect drinking outcome. 

Clients with high network support for drinking assigned to TSF had more days abstinent 

(83 percent) than those assigned to MET (66 

percent), whereas for those with low network 

support for drinking, there was no significant 

difference (TSF=80 percent, MET=83 percent).

In order to achieve a better understanding 

of why this matching effect reappeared 3 years 

after treatment, the average PDA and DDD 

(not shown) were graphed for the three treatments for clients with high 

and low network support for 

drinking throughout the entire 

period of observation starting 

from month 1 and continuing 

through month 39 (figure 3). 

Because the Form 90 (Miller 

1996) was not used to collect 

data during months 16 to 35, 

averages for this period could 

not be plotted.

The graph confirms that the 

matching effect was not present during this initial period of 

followup. The overall poor prognostic effect of network support 

for drinking is also evident, as 

those with low network support 

for drinking have higher PDA 

than those with high drinking 

support. Of note, among clients 

with high net-work support for 

drinking, the MET clients were 

doing less well than the TSF clients, who were 

doing about as well as clients with networks not 

supportive of drinking.

Apparently, between months 15 and 39, 

MET clients with networks supportive of drinking continued to decline, to 66 percent PDA by 

39 months, whereas comparable TSF clients 

maintained their level of PDA at 83 percent. A 

comparable pattern emerged for DDD.

A Posteriori Effects: TSF Versus CBT

Support for the hypothesis prompted us to 

examine post hoc the TSF versus CBT matching 

contrast. Our question was: Does pretreatment 

network support for drinking interact with TSF 

versus CBT in the same way as TSF versus 

MET?

A review of figure 3 suggests that CBT clients with networks supportive of drinking look 

like comparable MET clients, except during the 

first 3-month treatment period, when their PDA 

looks like that of comparable TSF clients. Thus, 

it appears that CBT is a protective factor for clients with networks supportive of their drinking 

during treatment. Table 4 supports this picture. 

The CBT versus MET by network support matching effect interacts with time and approaches 

a two-tailed unprotected significance level for 

both PDA (p<.06) and DDD (p<.06).

Figure 3. Percentage of days abstinent over entire followup period 

as a function of Network Support for drinking and treatment 

assignment.
267

Network Support for Drinking

Figure 4. CBT versus MET, causal chain analysis, increased drink refusal skills

Once treatment had ended, however, these 

CBT clients rapidly declined to a level of PDA 

comparable to the MET clients. Another look 

at table 5 provides some statistical support for 

a TSF versus CBT matching effect that is the 

result of this decline in MET clients. While the 

CBT versus MET matching contrast observed 

during treatment is no longer evident, the CBT 

versus TSF by network support posttreatment 

contrast has a two-tailed significance level of 

p<.09 for PDA and p=.105 for DDD. The lesser 

difference between CBT clients with high and 

low network support for drinking than between 

MET clients with high and low network support 

at 3 years suggests that CBT clients are less 

affected by network support for drinking than 

are MET clients.

Summary

Tests of the a priori matching hypotheses 

provided no support for matching aftercare clients to either CBT or TSF versus MET based on 

their network support for drinking

When these hypotheses were tested on the 

outpatient sample, CBT did not protect clients 

from the adverse effects of network support for 

drinking any more than did MET, except for a 

brief period during the beginning of treatment. 

Similarly, it initially appeared as though TSF 

protected clients from the adverse effects of 

network support for drinking only during the 

first month of treatment. However, drinking reported 3 years later indicated that there was 

a reemergence of the matching effect for TSF 

clients who had pretreatment networks supportive of drinking. In contrast to comparable 

clients in the other two treatments, TSF clients 

with networks supportive of drinking were able 

to maintain the abstinence they had achieved 

by 1-year followup for the subsequent 2 years. 

In contrast, MET and CBT clients with networks 

supportive of drinking continued to decline over 

this 2-year period.

These provocative findings point to the need 

for an analysis of the causal links underpinning 

the success and failure of the two matching 

hypotheses.
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Part V1: Interpersonal Functioning and Support

Causal Chains

CBT Versus MET

Figure 4 diagrams one putative causal chain 

underlying the hypothesized superior effectiveness of CBT versus MET for clients with 

net-works supportive of drinking. It was anticipated that by teaching clients cognitive and 

behavioral skills for coping with interpersonal 

pressures to drink, CBT would provide clients 

with coping mechanisms to reduce the risk of 

drinking in such situations. Specifically, it was 

hypothesized that clients assigned to CBT would 

be taught drink refusal skills which they would 

then utilize in high-risk social situations, resulting in more days abstinent and fewer drinks per 

drinking day. As clients with networks supportive of drinking would more often be exposed to 

these high-risk social situations, they would 

be more likely to benefit from this skill acquisition than clients with networks unsupportive 

of drinking prior to treatment. Change in drink 

refusal behavior from pretreatment to posttreatment was measured by self-report items on the 

self-efficacy confidence and temptation instrument (DiClemente et al., this volume).

Tests of the drink refusal causal chain 

occurred in two steps. First, examination of 

the relationship between drinking and change 

in drink refusal skills indicated that increased 

drink refusal skills from baseline to 9 months 

was significantly related to decreased drinking 

frequency (p=.0001) and intensity (p=.0001). 

However, in the second step tested, there was no 

relationship between increase in drink refusal 

skills and CBT versus MET treatment assignment. Thus, the causal chain indicated that the 

failure was in CBT’s not increasing drink refusal 

skills more than did MET. Those who reported an 

increase in confidence in using these skills, in 

fact, did have fewer drinking days and less intensive drinking on drinking days.

TSF Versus MET

The A Priori Causal Chain

Figure 5 displays the putative causal chain 

for TSF clients. Because of the TSF therapist’s 

support for client involvement in AA, TSF clients would be more likely to be involved in 

AA than would MET clients. This greater AA 

involvement, in turn, would lead to a decrease 

in network support for drinking posttreatment 

(measured at 9 months by a readministration of 

the IPA). This reduction in network support for 

drinking would in turn result in decreased PDA 

and DDD by months 13–15. Because network 

support for drinking is problematic for clients, 

those with networks highly supportive of drinking prior to treatment would benefit more from 

this reduction in network support for drinking 

than would those whose networks were unsupportive of drinking.

This causal chain was tested in two steps. 

First, the change in network support for drinking from pretreatment to 9 months was related 

to the two drinking measures during months 

Figure 5. Causal chain for TSF versus MET by network support for drinking matching effect
269

Network Support for Drinking

13–15. The results indicate a highly significant relationship between change in network 

support for drinking and change in drinking. 

Clients whose network support for drinking 

had declined between baseline and 9 months 

reflected significantly greater decreases in drinking intensity and frequency than those whose 

network support for drinking had increased.

However, in the second step, which tested 

the relationship between treatment assignment 

to TSF versus MET and change in network 

support for drinking, we found no association 

between these two measures. TSF did not lead 

to a greater decrease in overall network support 

for drinking than did MET.

Thus, as was the case with the CBT causal 

chain, treatment assignment failed to lead to 

differential changes in the putative mediating 

variable. And, as was also the case with the 

CBT causal chain, clients who reported positive 

change in the mediating variable—in this case, 

a network less supportive of drinking—did in 

fact also report better drinking outcomes. The 

failure of the matching hypotheses during this 

period appears to be attributable at least to the 

failure of the CBT and TSF treatments to bring 

about the greater changes anticipated.

The A Posteriori Causal Chain

Given the evidence for the long-term support for the treatment matching effect, our 

focus turned to identifying a causal chain that 

mediated this effect. How does it happen that 

TSF clients with networks supportive of drinking have increasingly better drinking outcomes 

than either MET or CBT clients with networks 

supportive of drinking, whereas for those with 

pretreatment networks unsupportive of drinking, such a differential effect is not evident? 

What does TSF have that CBT and MET lack 

that would differentially affect clients with high 

and low network support for drinking? What 

TSF ingredient would increase in impact as the 

time between formal treatment completion and 

followup observation increases?

The most obvious candidate for a mediating 

variable is AA participation itself, one of the two 

goals for TSF treatment not shared by either 

CBT nor MET. If a client does indeed become 

involved in AA, exposure to this social network 

is in itself highly supportive of abstinence, 

irrespective of any wider impact that AA involvement might have on the broader social network 

of the client.

We therefore revised the causal chain, as follows: First, clients with networks supportive of 

drinking prior to treatment will have fewer days 

abstinent after treatment than clients with networks unsupportive of drinking. Second, clients 

having networks supportive of drinking will 

also be less likely to participate in AA. Third, 

however, because of the primary aims of TSF, 

clients assigned to TSF would be more likely 

than clients assigned to either MET or CBT to 

participate in AA, irrespective of pretreatment 

network support for drinking. Fourth, we stipulate that clients participating in AA will have 

greater support for abstinence than those who 

do not participate.

Therefore, AA participation will reduce the 

negative impact of network support for drinking 

on posttreatment abstinence. This effect will be 

greatest for clients with pretreatment networks 

more supportive of drinking. Therefore, the 

greater AA participation of such clients in TSF 

will mediate the observed matching effect of 

the combination of TSF treatment assignment 

and network support for drinking on drinking 

outcome.

To conduct this analysis, we included a measure of AA participation, namely, the number 

of days the client reported going to meetings 

throughout the initial 3 months of treatment 

and during the year that followed. These data 

were collected by self-report through the Form 

90 (Miller 1996). Number of days of attendance 

were summed and divided by the number of 

days within the period of observation to yield a 

percentage of available days within a period in 

which the client attended meetings.

For purposes of the causal chain analysis, 

the variable was dichotomized into high and low 

AA participation. Prototypically, a high AA participant attended AA on more than 20 percent of 

the days during the treatment period and about 

16 percent during the year following treatment. 

In contrast, low AA participants attended fewer 

than 20 percent of days during the treatment 

period and stopped attending AA in the year 

following. Measures of attendance have been 

criticized as a poor proxy for operationalizing 

AA involvement (Tonigan et al. 1996). For this 
270

Part V1: Interpersonal Functioning and Support

reason, we also used a measure of involvement 

in AA provided by the Alcoholics Anonymous 

Involvement (AAI) questionnaire (Tonigan et al. 

1996). The results of this analysis have been 

reported elsewhere (Longabaugh et al. 1998). 

Only small differences were observed between 

the results of using these alternative measures 

of AA involvement.

Results

Network support for drinking is a prognostic 

indicator of fewer posttreatment days of abstinence and more drinks per drinking day during 

months 37–39 (PDA, p=.03; DDD, p=.01, onetailed tests). Thus, the first step in the causal 

chain is supported.

Table 6 displays AA participation as a function of pretreatment network support for 

drinking and treatment assignment. Network 

support for drinking decreased the participation of a client in AA: 46 versus 54 percent 

(p<.0003), as hypothesized in the second step 

of the causal chain. Nevertheless, also as predicted, assignment to TSF resulted in greater 

AA participation than assignment to MET or 

CBT: TSF=75 percent versus MET, 38 percent, 

or CBT, 35 percent (also highly significant). 

Most pertinent, for those with high network 

support for drinking, TSF resulted in 70 percent AA participation versus only 37 percent 

for MET clients and 28 percent for CBT clients. 

Thus, the third step in the causal chain was 

supported. TSF leads to higher AA participation 

by clients with networks supportive of drinking 

than does either CBT or MET.

The next link in the causal chain was to test 

whether greater participation in AA by TSF clients with high network support for drinking 

accounted for the matching effect that high network support for drinking clients assigned to 

TSF have better drinking outcomes at 3-year 

followup.

The test was conducted by a series of multiple 

regression analyses. In the first analysis, after 

entering the appropriate covariates, the product 

term of TSF versus MET was entered into the 

predictor equation, along with the network and 

treatment variables. In the second analysis, a 

third-order interaction term was created by the 

product of network support for drinking, treatment condition, and AA participation. This term 

was added to the prior variable set. If AA participation were mediating the observed matching 

effect, we would expect to find that partialing 

out this effect would reduce or de-crease to 

nonsignificance the strength of the relationship 

between the treatment assignment for these clients and their drinking outcomes.

As can be seen from table 7, when the effect 

of differential AA participation is partialed out 

of the relationship between treatment assignment and PDA for each of the three groups, only 

one group is markedly affected. As anticipated, 

the PDA of clients with high network support for 

drinking who were assigned to TSF was reduced 

by 7 percent when the effect of their AA participation was partialed out. In all other groups, 

there was only a small change. Once the effect of 

AA participation was removed from this group, 

their PDA was significantly reduced. (The onetailed p value dropped from .0053 to .04.) Thus, 

AA participation by clients with networks highly 

supportive of drinking was a partial mediator 

of the observed matching effect. That the p

value remained significant, at a reduced level, 

Table 6. AA participation months 1-15 as a 

function of network support for drinking and 

treatment assignment

MV/AA 

participation

CBT

N

MET

N

TSF 

N

Total 

N

Low support

Low 83 92 33 208

High 62 61 125 248

% high 

participants

42.6% 39.9% 79.1% 54.4%

High support

Low 105 95 48 248

High 40 55 114 209

% high 

participants

27.6% 36.7% 70.4% 45.7%

All participants

Low 188 187 81 456

High 102 116 239 457

% high 

participants

35.2% 38.3% 74.7% 50.0%

Total 290 303 320 913

NOTE: MV=matching variable, Network Support for Drinking
271

Network Support for Drinking

Table 7. Average percentage of days 

abstinent in month 39, before and after 

partialing out the effects of AA participation

MV/AA 

participation

Treatment

TSF MET CBT

High support

Included 83 66 70

Partialed out 76 68 73

Difference -7 +2 +3

Low support

Included 80 83 74

Partialed out 79 84 75

Difference -1 +1 +1

NOTE: MV=matching variable, Network Support for Drinking.

TSF/network support verses MET/network support contrast: 

p value reduced from .0053 to .04.

indicates that other partial mediators, still to be 

identified, were also operative.

The difference in PDA for those with networks supportive of and nonsupportive of 

drinking who did and did not participate in AA 

was largest for clients assigned to TSF. Clients 

with networks highly supportive of drinking who 

nevertheless participated in AA averaged 90 percent PDA at 39 months. Those not participating 

in AA had a PDA of only 61 percent, a 29-percent 

difference. In contrast, for those with networks 

unsupportive of drinking, AA participants had a 

PDA of 81 percent, while those not participating 

in AA had a PDA of 76 percent, only a 5-percent 

difference.

The same pattern held for MET clients. For 

clients with networks supportive of drinking, 

those who participated in AA had an average 

39-month PDA of 80 percent, whereas those 

who did not averaged a low PDA of only 55 

percent, a difference of 25 percent. Again, in 

contrast, for those with networks unsupportive 

of drinking the difference was less, with those 

participating in AA having a higher PDA of 90 

percent than those who did not, 77 percent, a 

13-percent difference.

Finally, for clients assigned to CBT, the relationship was also apparent. CBT clients with 

networks supportive of drinking who participated in AA had a 39-month PDA of 82 percent, 

while those not participating had a PDA of 65 

percent, a 17-percent difference. In contrast, 

clients with networks unsupportive of drinking 

differed less in their PDA as a function of AA 

participation. AA participants had an average 

PDA of 80 percent, while nonparticipants had 

an average PDA of 70 percent, only a 10-percent 

difference.

In summary, the causal chain developed to 

explain the long-term TSF versus MET/CBT 

matching effect was supported. Clients with 

networks supportive of drinking prior to treatment are less likely to become involved in AA 

than clients with networks unsupportive of 

drinking. However, TSF increased the probability of clients being involved in AA. This was true 

irrespective of pretreatment network support 

for drinking. Participation in AA, in turn, was 

associated with more abstinent days. Partialing 

out this effect from the relationship of pretreatment network support by treatment matching 

effect to drinking at months 35–37 reduced the 

significance of the relationship, thus indicating 

that AA participation is a partial mediator of 

this matching effect.

Given the causal chain support for the TSF 

versus MET matching hypothesis at 3-year followup, the obvious questions to be addressed 

are:

■ Why did this matching effect not appear 

earlier in the outpatient group?

■ Why did this matching effect not appear 

in the aftercare sample?

In order to examine these questions, we tested 

the AA participation causal chains for the earlier posttreatment periods separately for clients 

in the outpatient and aftercare arms, substituting the last 3 months of posttreatment drinking 

measures (months 13–15) for the 37–39 month 

measure indexing the 3-year outcomes.

Outpatient 1-Year Posttreatment

When the TSF versus MET by network support for drinking matching effect was tested 

for months 13–15, no evidence for matching appeared. When regression analyses were 

conducted to include the product term of AA 

participation by treatment by network support 

for drinking, this product term had no main 

effect on PDA, thus confirming that AA participation was not interacting with treatment and 

support to enhance the outcomes of TSF clients 
272

Part V1: Interpersonal Functioning and Support

with networks supportive of drinking. With AA 

participation excluded from the analysis, the 

negative effect of network support for drinking 

on MET clients was slightly but nonsignificantly 

greater than for TSF clients. When AA participation was factored in, both MET and TSF 

clients with networks supportive of drinking did 

equally well when AA participation was high. 

When AA participation was low, both treatment 

groups did increasingly poorer with increasing 

network support for drinking. Thus, the higher 

order product term had no mediating effect.

Aftercare 1-Year Posttreatment

These analyses were repeated for the aftercare sample. As above, we found no evidence 

that PDA during months 13–15 was affected by 

matching network support for drinking to TSF 

versus MET treatment. When the product term 

factoring in AA participation was entered, those 

with high AA participation in both treatments 

did equally well and better than those with low 

AA participation. MET clients tended to have 

better PDA than TSF clients when network support for drinking was high.

Discussion

The failure of the CBT versus MET matching 

hypothesis is consistent with the breakdown 

in its underlying causal chain. While clients 

reporting greater drink refusal coping skills in 

social situations had better drinking outcomes, 

assignment to CBT did not result in a reported 

greater utilization of these pertinent skills at 6 

months after treatment completion. It would 

thus appear that increased social coping skills 

is not a unique contribution of CBT. This finding is consistent with that recently reported by 

Finney and colleagues (1998). These investigators conducted a naturalistic study of Veterans 

Administration treatment programs that had 

either a 12-step orientation, a CBT orientation, or were eclectic. Results showed that TSF 

and eclectically treated clients were as likely to 

report increased coping skills as those treated 

in CBT. It then follows that clients especially 

in need of such skills will not incrementally 

benefit from CBT therapy. This conclusion is 

consistent with a review of CBT’s putative active 

ingredients as mediators of CBT effectiveness 

with alcohol-dependent clients (Morgenstern 

and Longabaugh 2000). These investigators 

found that increased social skills did not mediate the effectiveness of CBT versus treatments 

against which it has been compared.

In contrast, a TSF by network support for 

drinking matching effect was observed to 

emerge over an extended period of followup 

for outpatients. Even though TSF clients did 

not show a differential change in their everyday social network’s support of their drinking, 

they did participate in AA to a greater extent 

than did their MET and CBT counterparts. For 

clients with pretreatment networks supportive of drinking, participation in AA eventually 

reduced the influence of this network on their 

drinking Thus, AA participation was implicated 

as at least one of the active ingredients mediating this matching effect. Therefore, to the 

extent that a treatment is successful in getting 

outpatient clients with high network support 

for drinking involved in AA, their drinking outcomes should be improved.

AA participation is an important correlate of 

successful drinking outcomes of clients with 

pretreatment networks supportive of drinking 

across all three treatment conditions but is less 

influential in producing good drinking outcomes 

for those with social networks unsupportive of 

drinking prior to treatment. Here, AA participation is positively associated with good drinking 

outcomes but much less strongly so.

Further Research 

Questions

While a host of further research questions 

arise from these findings, five are especially 

pressing.

1. Methodological review. In order to test the a 

priori matching hypothesis, it was necessary 

to combine a multidimensional construct of 

support for drinking into a single measure. 

The indices which go into making up this 

overall index are only moderately correlated, 

and it is possible that some dimensions of 

support for drinking are more important 

than others for measuring support and its 

influence on drinking outcomes.

While two indices of drinking have served 

to measure treatment outcome, it is important to assess whether any of the matching 
273

Network Support for Drinking

effects reported for these two drinking measures generalize to other dimensions of 

outcome.

In measuring AA involvement, we confined 

this report to a measure of AA attendance 

summed over the initial 15 months. We 

have elsewhere reported (Longabaugh et al. 

1998) the results of using a more sophisticated measure of AA involvement developed 

by Tonigan and colleagues (1996). Much 

to our surprise, the effect of this more elegant measure of AA involvement, while 

also observed to be a partial mediator of 

the matching effect, was not as robust. AA 

involvement measured by the AAI scale 

incorporates two dimensions of AA affiliation, working the program and participation 

in the Fellowship. AA attendance may be a 

purer measure of social support than AA 

involvement, which confounds affiliation 

and working the Twelve Steps. As the AAI 

scale measures both of these dimensions, 

further analysis should discern whether 

these two components contribute differentially to mediating this matching effect.

2. Within the present analysis, AA participation 

was measured as a single variable covering the entire period of followup. It will be 

important to separate participation into different blocks of time in order to isolate which 

periods of participation have the largest 

impact on outcome and whether incremental gains are made by the addition of other 

time blocks. While the effects of AA involvement were apparent 3 years after treatment, 

AA participation itself did not differ for the 

three treatment groups at 3 years (Tonigan 

et al. in press). An important question to 

address therefore is how long AA participation must continue in order to reap these 

beneficial effects, especially for those clients 

with networks supportive of drinking. As 

Tonigan has reported elsewhere (in press), 

AA participation is highest during treatment 

and diminishes as time from treatment 

completion increases. Our matching finding 

would suggest that while AA participation is 

diminishing as time from treatment completion increases, the benefit to be gained by 

clients with networks supportive of drinking is nevertheless increasing for those who 

have participated in AA.

3. Still another question to be addressed 

is that of gaining a more comprehensive 

understanding of clients affected by the 

matching process. What other client characteristics increase the likelihood of those 

with networks supportive of their drinking 

becoming involved in AA? We have observed 

that clients assigned to TSF were most likely 

to participate in AA. Drinking outcomes 

were best for those who did participate, but 

those who did not participate in AA, despite 

the TSF push, had the poorest outcomes. In 

contrast, clients who were assigned to MET 

and CBT had much less likelihood of participating in AA, but the drinking outcomes 

for those who did not participate were not 

as bad as those observed among the high 

network support for drinking/low AA participants in TSF.

If we can identify other characteristics 

besides network support for drinking that 

reduce the likelihood of clients utilizing AA, 

we could plan treatments particularly suited 

to these types of clients. This would improve 

overall treatment effectiveness by triaging 

AA-aversive clients with networks supportive of drinking to other kinds of treatment 

interventions.

4. We also need to examine the causal chain in 

greater detail in order to identify what in the 

experience of the client leads to the emergence of this matching effect 3 years after 

treatment. It is tantalizing that a matching effect observed during the first month 

of treatment disappears only to reemerge 3 

years later. This suggests that some kind 

of dynamic process is active which unfolds 

over time. Our initial hypothesis is that clients with networks supportive of drinking 

are thrown into great conflict when assigned 

to TSF with its goal of AA client involvement. 

Perhaps they do so during the first month of 

treatment, but then the everyday influence 

of their preexisting network pulls them away 

from this support group. They may subsequently relapse or gradually increase their 

alcohol involvement over time. After sufficient adverse effects from drinking recur, 

some of these clients may return to AA as 

a posttreatment resource to assist them in 

regaining a more trouble-free lifestyle.
274

Part V1: Interpersonal Functioning and Support

5. Finally, as differential AA participation does 

not fully explain the observed TSF versus 

MET matching effect for clients with high 

network support for drinking, what other 

factors are involved? What other causal 

chains may be identified?

Conclusions

The TSF versus MET by network support for 

drinking matching hypothesis was confirmed 

at 3 years posttreatment. This matching effect 

accounted for a 17-percent difference in PDA 

between clients with networks supportive of 

drinking who were assigned to TSF and MET. 

Not hypothesized but evident in the results, 

CBT compared as unfavorably for clients with 

networks supportive of drinking as did MET.

Causal chain analyses revealed that this 

matching effect did not emerge earlier during 

the posttreatment period because TSF clients 

with networks unsupportive of drinking were 

as helped by AA attendance as those with networks supportive of drinking. Additionally, in 

aftercare, MET clients participated in AA to 

nearly the same extent as did TSF clients, no 

doubt because of the influence of the preceding 

inpatient or day hospital treatments that had 

already exhorted the client to become involved 

in AA.

Because AA is a partial mediator of this 

matching effect, it can be incorporated as an 

active ingredient into treatments other than 

TSF, with the likelihood that client outcomes in 

these other treatments will be enhanced.

It is notable that this matching effect was one 

of the few observed in Project MATCH. Because 

the present matching hypothesis was also one of 

the few that were predicated on a causal chain 

that involved variables outside of the therapy 

itself, this suggests that if matching effects are 

to be observed, they need to take into account 

the social context in which treatments occur.

In contrast, the CBT matching hypothesis 

relied upon changes that were to occur within 

the treatment itself which were anticipated to be 

generalized to in vivo experience. However, evidence for this matching effect was only a trend 

and limited to the within-treatment period in 

the outpatient arm, which reinforces the belief 

that to be successful, treatment must go beyond 

changes that may take place only in treatment.

Acknowledgments

This project was supported by grant number U10–AA08443 from the National Institute 

on Alcohol Abuse and Alcoholism as part of the 

Cooperative Agreement on Matching Patients 

to Alcoholism Treatments. We thank Deborah 

Mendes for her assistance in preparing this 

manuscript.

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276

Prior Alcoholics Anonymous 

Involvement and Treatment Outcome

J. Scott Tonigan, Ph.D., William R. Miller, Ph.D., and 

Gerard J. Connors, Ph.D.

ABSTRACT

This chapter addresses the relationship between clients’ prior exposure to Alcoholics 

Anonymous (AA) and their response to three psychosocial treatments for alcoholism: Cognitive-Behavioral Coping Skills Therapy (CBT), Motivational Enhancement 

Therapy (MET), or Twelve-Step Facilitation Therapy (TSF). It was predicted that clients 

with higher levels of previous AA involvement would have better outcomes in the TSF 

treatment condition because of that treatment’s AA orientation. Weaker relationships 

between prior AA involvement and outcome were predicted for the CBT and MET treatments. Preliminary analyses showed that AA involvement prior to treatment overall 

was not systematically related to posttreatment percentage of days abstinent or drinks 

per drinking day among either the outpatient or aftercare populations sampled. Tests 

of the matching hypothesis provided no support for the predicted match among either 

the outpatient or aftercare clients. Analyses exploring the causal chain presumed to 

underlie the hypothesized matching effect provided little support for the proposed 

causal chain links. These findings suggest that the efficacy of these three treatments 

is not significantly altered by clients’ prior exposure to AA.

Alcoholics Anonymous (AA) is the most 

popular mutual-help program for people experiencing alcohol problems, and 

estimates of current 12-step meeting attendance rates in North America range from 1 7 

million (Alcoholics Anonymous 1990) to 3.5 million (Room 1993) per year. Historically, AA has 

influenced the conduct and goals of professional 

treatment of alcoholism in the United States 

and, in turn, AA membership has increased 

because of professional referral to AA (Makela 

1996). The extent of interplay between AA and 

professionals is probably best evidenced in the 

current and lively discussion about whether AA 

is an adjunct to formal treatment wherein gains 

made in formal treatment are sustained by continued AA affiliation or, instead, AA should be 

regarded as offering unique benefits essential to 

recovery from alcoholism (Freimuth 1996).

In the context of the mutually beneficial 

albeit sometimes tense relationship between 

professionals and AA, it is surprising (and disappointing) that so little effort has been made 

to understand how, if at all, congruity in therapeutic orientation and prior exposure to AA 

principles may influence drinking outcome. 

Emrick and colleagues (1993), for example, 

found virtually no relationship between prior 

AA attendance and drinking outcome after 

formal treatment (r weighted=0.05), but their 

combining of findings from 12 studies ignored 

the nature of the professional treatment clients 

received in each study. Congruity between client expectations about what treatment ought 

to be (or ought not to be) may influence client 

J. Scott Tonigan, Ph.D.

Center on Alcoholism, Substance Abuse, 

and Addictions (CASAA)

2350 Alamo S.E.

Albuquerque, NM 87106

E-mail: jtonigan@unm.edu
277

Prior Alcoholics Anonymous Involvement and Treatment Outcome

treatment satisfaction and outcome. The extent 

to which clients received what they wanted (at 

intake) has been found to predict better alcoholism treatment outcomes, whereas the delivery of 

unwanted services may have little or no impact.

Pragmatically, treatment providers encounter 

individuals with prior AA exposure. Here, exposure refers most immediately to AA attendance, 

although the term is inclusive and may involve 

such activities as reading AA literature. While 

exact estimates of the percentage of clients with 

AA exposure immediately before treatment are 

problematic to derive, the percentage of clients 

with prior lifetime AA exposure is certainly high 

(estimated at 77 percent by the Project MATCH 

Research Group 1997). Thus, the importance of 

AA for professionals is not restricted to referral and encouragement to meetings. The AA 

membership survey, for example, reported that 

62 percent of AA members (sampled) reported 

seeking formal therapy after becoming members 

of AA and achieving sobriety (AA Membership 

Survey 1997).

One matching hypothesis in Project MATCH 

(1993, 1997) specifically addressed the congruity of professional treatment approaches 

with clients’ prior AA exposure. We predicted 

that clients with higher levels of previous AA 

involvement would fare better in a Twelve Step 

Facilitation (TSF; Nowinski et al. 1992) treatment 

condition because of its AA orientation. In terms 

of the two primary dependent measures in the 

Project MATCH trial, we predicted that posttreatment percentage of days abstinent (PDA) and 

prior AA involvement would be positively related 

for those clients assigned to the TSF condition, 

and that drinks per drinking day (DDD) would 

be negatively related with prior AA involvement 

for TSF clients. Within the other two treatments 

of the Project MATCH trial—Cognitive-Behavioral 

Coping Skills Therapy (CBT; Kadden et al. 1992) 

and Motivational Enhancement Therapy (MET; 

Miller et al. 1992)—we predicted that a weaker 

relationship would be found between prior AA 

involvement and drinking outcomes.

Causal Chain

Our predictions were based on the following 

assumptions:

■ Higher prior AA engagement would be 

associated with more positive client 

attitudes about the goals and tasks in 

the TSF treatment condition.

■ Increased acceptability of the TSF modality would also be reflected in stronger 

client-therapist relationship bonding.

■ Greater commitment to the TSF therapeutic process would, in turn, result in 

higher rates of both treatment session 

completion and AA attendance during 

treatment.

■ Heightened compliance with the TSF 

protocol and AA would translate into 

higher rates of posttreatment AA attendance, which, in turn, would result in 

more favorable drinking outcomes.

Operationalization of the 

Matching Variable

Central to the formulation of our hypothesis 

was the development of a conceptual framework 

to define AA involvement, followed by selection 

of a measure with known psychometric characteristics. Our intent was to measure the extent 

of prior commitment to AA, rather than just AA 

attendance. The core literature of AA (Alcoholics 

Anonymous 1976, 1981) specifies two domains 

of AA experience. On the one hand, the program of AA, compactly summarized in the 12 

Steps and Traditions of AA, includes prescriptions for achieving sobriety and for conducting 

one’s life. On the other hand, the practice of AA, 

often described as the AA fellowship, includes 

the ways in which AA members relate to one 

another as well as how AA group interactions 

are perceived by members. Montgomery and 

associates (1993) found that AA groups differ 

significantly in perceived group dynamics (fellowship), and Tonigan et al. (1995) reported 

that differences in AA group dynamics were predictive of the extent to which the 12 Steps of AA 

were discussed in meetings (program).

Composite measures of AA involvement seem 

to have more utility than single-item measures 

but often have sampled a small range of behaviors. Snow and colleagues (1994) emphasized 

social relationships as a measure of AA commitment and largely ignored progress in working 

the AA steps, a central element of the AA program. On the other hand, Gilbert (1991) ignored 

social support within AA and measured progress 
278

Part V1: Interpersonal Functioning and Support

in AA step work, thus excluding consideration 

of the AA fellowship. It seems sensible to sample involvement in both the AA program and 

fellowship when measuring the construct of AA 

involvement.

Instrumentation

The AA Involvement (AAI) scale developed for 

Project MATCH consists of 13 items designed 

to measure lifetime and more recent participation in AA. The inventory includes some items 

pertaining to the AA program (e.g., step work) 

and others reflecting commitment to the AA fellowship. In a test-retest substudy of the Project 

MATCH trial, the AAI was found to have good 

internal consistency and test-retest reliability (Tonigan et al. 1996) and, based upon the 

intake Project MATCH sample (N=1,726), support was found for the multidimensional nature 

of engagement in AA.

As a validity check of our composite AAI 

measure, we plotted by study arm lifetime and 

recent AA involvement (based on intake AAI 

data) against reported attendance at AA meetings in the 90 days prior to study recruitment, 

as reported in the Form 90 interview (Miller 

1996). Figure 1 shows the nature of the relationship between these measures at intake for 

the aftercare and outpatient samples. Recent 

AA attendance was a reasonable proxy of more 

general AA involvement under conditions of low 

to moderate involvement, but AA involvement 

plateaued at higher levels of attendance—above 

40 percent of days (or about 3 meetings a week). 

Not shown, this same significant quadratic relationship was found at each followup point in 

both study arms of Project MATCH, although 

with time, the nature of the quadratic relationship became somewhat shallower.

Results

About 7 percent (n=69) of Project MATCH 

outpatients did not provide sufficient information to compute a composite intake AAI score, 

with a somewhat lower percentage of missing 

cases (5 percent, n=33) in the aftercare arm. No 

between-treatment mean differences were found 

in intake AAI scores within either arm but, on 

average, aftercare clients reported significantly 

higher prior AA involvement (M=5.37, SD=2 .33) 

Figure 1: Relationship between self-reported AA 

attendance and involvement at intake: Project 

MATCH aftercare and outpatient samples

than outpatient clients (M=3.38, SD=2.48), 

p<.001. At least some lifetime AA attendance 

was reported by 64 percent of the outpatient 

and 91 percent of aftercare clients.

Aftercare Sample 

Prognostic Effects

The prognostic effects of the AAI secondary matching variable were also assessed in a 

hierarchical linear modeling (HLM) context that 

controlled for study site, treatment condition, 

and linear and quadratic time main effects as 

well as their interactions with each other and 

with AAI. Four analyses were conducted, one 

for each primary dependent measure (transformed PDA and DDD) and separately within 

study arm (outpatient and aftercare). A more 

detailed description of the analytic strategy is 

provided by the Project MATCH Research Group 
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Prior Alcoholics Anonymous Involvement and Treatment Outcome

(1997) and Longabaugh and Wirtz (this volume, 

pp. 4–17).

For aftercare clients, AA involvement prior 

to treatment was unrelated to abstinence 

(PDA) during 12 months of followup, with some 

variation in the relationship between AAI and 

PDA across the five aftercare sites (p<.06). 

Examination of scatter plots indicated that in 

two after-care sites the relationship between 

intake AAI and followup PDA was positive 

(r=0.19 and 0.10), whereas at the other three 

sites it was negligible or negative (r=-0.05, 

-0.08, and -0.11). The prognostic effect of prior 

AA involvement on intensity of drinking (DDD) 

during the 12 months of followup approached 

statistical significance (p<.06) in the direction 

opposite to our prediction. Specifically, clients with more prior AA involvement reported 

higher levels of drinking intensity during early 

followup. Variability across aftercare sites 

(p<.054) and time (p<.01) was again observed. 

At three aftercare sites, for example, the relationship between prior AA involvement and 

intensity of drinking during the first 6 months 

of followup was significant and positive, ranging 

from r’s=0.29 to 0.19, while at the remaining 

aftercare sites the relationship was negligible,

r’s ranging from 0.03 to 0.01.

Matching Hypothesis

The analytic strategy for testing of the matching hypotheses and protection of type-1 error 

rate are described elsewhere in this volume 

(Longabaugh and Wirtz, pp. 4–17). Succinctly, 

HLM models similar to those for testing the prognostic effect of matching variables were applied, 

and three statistical tests of slopes were evaluated in possible rejection of the null hypothesis. 

These were: (1) an overall matching effect collapsing across time, (2) a matching effect by 

linear time interaction, and (3) a matching effect 

by quadratic time interaction. We predicted a 

more positive slope for AAI and outcome during 

the 12 months of followup within the TSF condition than within the combined CBT and MET 

conditions.

Inspection of table 1 shows that the a priori 

overall matching effect was not supported either 

during treatment or across the 12 months of 

followup (all p’s >.10). During the 12 weeks of 

treatment, however, an AAI by treatment by 

linear time interaction was found on both dependent measures (PDA and DDD), but this 

effect was opposite to the prediction: clients 

assigned to TSF with higher AAI scores tended 

to have increasingly fewer abstinent days per 

week and drank more heavily as treatment continued than comparable clients assigned to CBT 

and MET conditions. This finding did not persist into the posttreatment phase of the study. 

Inspection of the unplanned pairwise contrasts 

in table 1 likewise indicated no presence of an 

AAI moderating effect on treatment response.

Table 1. Summary of aftercare HLM of 

Alcoholics Anonymous Inventory tests: 

Probability values associated with tests 

during and after treatment

Predicted 

match

Unplanned 

matching 

contrasts

Variable

TSF vs. 

MET/CBT

TSF– TSF– CBT–

CBT MET MET

During treatment

PDA

PDA linear

PDA quadratic

DDD

DDD linear

DDD quadratic

After treatment

PDA

PDA linear

PDA quadratic

DDD

DDD linear

DDD quadratic

.63

.03*

.65

.13

.01*

.21

.65

.18

.34

.81

.15

.90

.47

.06

.36

.09

.04

.27

.82

.29

.12

.96

.17

.60

.90

.05

.91

.37

.03

.29

.58

.21

.90

.65

.26

.76

.56

.87

.30

.43

.78

.99

.74

.83

.16

.61

.83

.40

* opposite direction 

Causal Chain Analyses

Where did our predictions fail? To explore 

this question we examined the causal chain 

underlying our hypothesis. Figure 2 shows 

the path analysis testing our causal model for 

the two aftercare groups contrasted in the AAI 

matching hypothesis. Partial correlation coefficients are provided beside each arrow, and each 
280

Part V1: Interpersonal Functioning and Support

coefficient controls for relationships of equal or 

prior temporal order (left to right).

Hierarchical multiple regression analyses 

were conducted to determine whether parallel 

links (slopes) in the two models in figure 2 were 

different from one another. These analyses controlled for site variation as well as for the main 

effect of treatment and the matching variable. 

None of the three therapeutic alliance measures (goal compatibility, task compatibility, 

relationship bonding) supported our prediction 

of a differential relationship (all interaction p 

values >.05). For clients assigned to TSF and 

the combined CBT and MET conditions alike, 

there was an equivalent negative relationship 

between prior AA involvement and client agreement on therapeutic tasks. Extent of prior AA 

involvement was virtually unrelated to therapist 

bonding in both groups, and agreement with 

therapeutic goals was modestly and positively 

related to prior AA exposure in both groups.

Six interactions were tested in determining 

whether the slopes between the three therapeutic measures and AA meeting attendance and 

percentage of therapy attended variables were 

different between the TSF and combined CBT 

and MET path models. While substantial variability was found in parallel links (e.g., 0.13 

versus -0.10), none of these slope contrasts 

exceeded chance variation (smallest obtained 

p value=.28). As predicted, AA meeting attendance during treatment was a strong predictor 

of posttreatment AA attendance which, in turn, 

predicted both primary dependent measures 

at both proximal and distal followup periods. 

Not anticipated, this prediction was manifest in 

about the same magnitude in both the aftercare 

TSF and combined CBT and MET conditions 

(nonsignificant slope contrast).

Outpatient Sample

Prognostic Effects

Among outpatients, no support was 

found for a prognostic main effect of 

the AAI on posttreatment outcome. 

Considering the frequency-of-drinking 

measure (PDA), there was no overall 

main prognostic effect (p<.23). Possible 

variation by quadratic time (p<.09) and 

the interaction of quadratic time with 

sites (p<.02) suggested complex relationships associated with site-specific 

factors. Inspection of bivariate relationships by site indicated a positive 

and significant relationship (r=0.16) 

between prior AA and PDA (months 

1–6) at one site while this relationship 

was not present at the other outpatient 

sites. For the drinking intensity measure (DDD), prior AA involvement was 

unrelated to posttreatment drinking 

(p<.48), with little evidence that site or 

time factors confounded the relationship of interest (all p values >.05).

Matching Hypothesis

Table 2 presents the probability 

values associated with HLM tests of 

the prospective AAI matching hypothesis. None of the overall tests of the 

AAI matching hypothesis reached 

Figure 2. AAI causal model for aftercare TSF and combined CBT and MET conditions

1 Proximal outcome defined as months 4–9 (first 6 months after 

end of treatment).

2 Distal outcome defined as months 10–15 second 6 months after 

end of treatment).
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Prior Alcoholics Anonymous Involvement and Treatment Outcome

Bonferroni-protected statistical significance on 

either PDA or DDD during or after formal treatment. During the 12 weeks of treatment, there 

was a trend in the linear time effect in the predicted direction using the frequency of drinking 

measure (PDA, p<.08) such that prior AA 

engagement and PDA were positively related for 

clients assigned to TSF while prior AA and PDA 

were unrelated in the combined CBT and MET 

conditions. After treatment, a linear time trend 

was found on the drinking intensity measure 

(DDD, p<.09) such that clients assigned to the 

combined CBT and MET conditions drank more 

heavily with greater extent of prior AA exposure, 

while no relationship was present between prior 

AA and drinking intensity for TSF clients.

Unplanned pairwise matching contrasts 

indicated that prior AA involvement moderated 

treatment outcome in the context of contrasting CBT with MET. Post hoc analyses showed 

that PDA and prior AA were positively related 

both during and after treatment for clients 

assigned to the MET condition. Oppositely, 

prior AA was modestly and negatively related 

with PDA during treatment, and prior AA was 

positively related with DDD during treatment 

for CBT clients. These overall matching effects 

were unplanned and hence should be interpreted 

cautiously. Nevertheless, several of these overall 

MET versus CBT matching contrasts exceeded 

Bonferroni-corrected probability values used to 

test a priori contrasts. Future work should examine the potential moderating effects of prior AA 

when outpatient treatments paralleling CBT and 

MET are offered and evaluated.

Causal Chain Analyses

The planned matching AAI hypothesis was 

not supported. Attention was thus directed to 

determining the reasons for the failure of our 

hypothesis. Figure 3 shows the proposed causal 

chain (and standardized path coefficients) 

for the AAI hypothesis separately for the two 

groups included in the AAI matching contrast. 

Parallel to causal chain analyses in the aftercare sample, multiple regression analyses were 

conducted to determine if corresponding slopes 

for the two diagrams in figure 3 differed beyond 

chance fluctuation. Similar to findings in the 

aftercare sample, the proposed mechanisms 

producing the matching effect failed at the first 

causal link of our model: prior AA involvement 

did not differentially predict a stronger therapeutic alliance in TSF and relatively weaker 

associations in the combined CBT and MET 

conditions (smallest obtained p value=.30).

Partial support was found for subsequent 

proposed mechanisms in the AAI causal chain 

in the outpatient sample. In particular, client 

agreement with therapeutic tasks was positively 

predictive of AA meeting attendance during 

treatment and therapy attendance in TSF. 

These relationships were either nonexistent or 

negative in the combined CBT and MET conditions (slope contrasts all p<.01). Further, it was 

predicted that during treatment AA attendance 

would predict posttreatment AA attendance 

which, in turn, would predict more frequent 

abstinent days and fewer drinks per drinking 

day. This prediction was supported in both TSF 

and the combined CBT and MET conditions 

such that AA attendance for the first 3 months 

after treatment predicted positive outcomes on 

both primary dependent measures at proximal 

and distal followup periods.

Not specified in the causal model, prior 

involvement in AA was a significant and positive 

Table 2. Summary of outpatient HLM of 

Alcoholics Anonymous Inventory tests: 

Probability values associated with tests 

during and after treatment

Variable

Predicted 

match

Unplanned 

matching 

contrasts

TSF vs. 

MET/CBT

TSF–

CBT

TSF–

MET

CBT–

MET

During treatment

PDA .99 .27 .26 .02

PDA linear .08 .18 .09 .69

PDA quadratic .76 .66 .32 .13

DDD .70 .15 .45 .02

DDD linear .50 .63 .48 .82

DDD quadratic .97 .66 .71 .40

After treatment

PDA .69 .50 .17 .03

PDA linear .83 .99 .70 .69

PDA quadratic .12 .47 .04 .17

DDD .53 .16 .76 .08

DDD linear .09 .11 .16 .87

DDD quadratic .56 .58 .64 .94
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Part V1: Interpersonal Functioning and Support

Figure 3. AAI causal model for outpatient TSF and 

combined CBT and MET condition

1 Proximal outcome defined as months 4–9 (first 6 months after 

end of treatment).

2 Distal outcome defined as months 10–15 (second 6 months 

after end of treatment).

predictor of AA attendance of outpatients during treatment (p<.001), but the magnitude of 

this effect differed significantly between TSF 

and the combined CBT and MET groups. Once 

again contrary to our prediction, the positive 

relationship was stronger in the CBT and MET 

combined conditions.

Discussion

In sum, the prospective AAI matching 

hypothesis was not supported in either the 

aftercare or outpatient samples. In both samples, the predicted causal chain was weakest at 

the initial link of the causal model where prior 

AA was expected to be more closely related to 

the therapeutic alliance in TSF than in CBT 

and MET. Consistent with expectations, therapeutic alliances (task compatibility) among the 

out-patient clients were more positively associated with subsequent AA attendance and 

treatment compliance in the Twelve 

Step Facilitation condition than in CBT 

and MET. Yet this did not translate 

into differentially better outcomes. The 

expected prognostic effect of prior AA 

involvement appeared, if anywhere, in 

the two treatment groups not based on 

AA principles.

Prior AA involvement was differentially predictive of treatment response 

during treatment on both PDA and DDD 

(p’s<.02) and, to a lesser extent, on both 

PDA (p<.03) and DDD (p<.08) during 

followup when contrasting CBT and 

MET, a contrast not specified in the AAI 

matching hypothesis. Exact reasons for 

prior AA engagement benefiting clients 

assigned to MET while not benefiting 

CBT clients are unclear. Certainly, this 

finding awaits prospective testing and 

possible replication.

We posited that prior AA involvement 

would prepare clients, through familiarity, for better outcomes in TSF. It could 

also be predicted plausibly that greater 

prior AA involvement would be associated with poorer outcomes in TSF, in 

that it represents “more of the same” 

for clients who in one sense could be 

considered AA failures. Neither assertion was supported by project MATCH 

findings, suggesting that a 12-step approach is 

neither indicated nor contraindicated by virtue 

of prior engagement with AA. The efficacy of the 

next round of treatment—be it with TSF, CBT, 

or MET—seems simply unrelated to the extent 

of clients’ previous experience with AA. In contrast, involvement with AA during treatment 

modestly predicted better outcomes in all three 

treatment conditions.

Practical Implications

What practical implications can be drawn 

from the prospective matching findings? First, 

clients who have had greater AA exposure 

prior to presenting for treatment (and might 

be considered, in this sense, AA “failures”) fare 

at least as well in treatment approaches that 

are not focused on AA principles. Said another 

way, higher prior AA involvement does not 
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Prior Alcoholics Anonymous Involvement and Treatment Outcome

contraindicate or undermine the efficacy of cognitive-behavioral or motivational-enhancement 

approaches nor does it predict a better response 

to 12-step-oriented treatment. Although a 

Twelve Step Facilitation treatment may be initially more familiar and comfortable, this does 

not translate into differentially more (or less) 

favorable outcomes. This is reminiscent of the 

finding that although alcoholics may feel more 

bonded to and understood by a therapist who 

is in recovery, recovering therapists are neither 

more nor less effective than other therapists 

when it comes to treatment outcome (McLellan 

et al. 1988; Project MATCH Research Group 

1997).

Second, the effect of AA involvement may vary 

depending upon the outcome measure used. 

We found that outpatients with greater prior 

AA exposure tended, after treatment, to consume somewhat more when drinking. Marlatt 

has cautioned against an abstinence violation 

effect inherent in the popular AA slogan, “One 

drink, one drunk,” such that once an alcoholic 

slips there is no expectation of restraint. In 

another multisite study, we found that pretreatment endorsement of beliefs consistent with a 

disease model of alcoholism was predictive of 

a higher risk of relapse through a year of followup (Miller et al. 1996). Brandsma and his 

colleagues (1980) similarly found that offenders sentenced to attend AA meetings were more 

likely to show binge drinking during followup, 

relative to those assigned to cognitive-behavioral treatment. It is noteworthy, however, that 

the magnitude of such effects is generally small 

and might be compensated by higher rates of 

abstinence with a 12-step approach (Project 

MATCH Research Group 1997).

Third, different measures of AA engagement 

are not always linearly related. We found, for 

example, that AA involvement reached a peak 

around three meetings per week and did not 

increase further (even decreasing in some contexts) at higher levels of attendance.

Finally, the relationship between AA involvement and treatment outcome is complex. 

Across aftercare sites in this study, different 

sites showed positive, negative, or no relationship between outcomes and prior AA exposure. 

Montgomery and associates (1995) found 

that whereas AA attendance did not predict 

treatment outcome, there was a significant 

relationship between better outcomes and a 

higher level of actively “working the steps” of 

AA as reported on their General AA Tools of 

Recovery (GAATOR) scale. The AA involvement 

scale used in this study represents yet another 

way of conceptualizing AA engagement. It 

appears that the prognostic value of AA engagement depends heavily on how it is measured 

and may also vary substantially across different contexts.

Acknowledgments

The authors gratefully acknowledge the 

labor and guidance of the editors of this 

monograph, Drs. Richard Longabaugh and 

Margaret Mattson. The authors also wish to 

thank the Project MATCH Research Group and 

the National Institute on Alcohol Abuse and 

Alcoholism (grant number U10–AA08435) for 

their support in the development and application of a novel Alcoholics Anonymous-related 

measure that takes into consideration the multifaceted nature of mutual-help exposure and 

participation.

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285

Social Functioning

Richard Longabaugh, Ed.D., Philip W. Wirtz, Ph.D., and

Christopher Rice, Ph.D.

ABSTRACT

It was hypothesized that social functioning would be matched to Cognitive-Behavioral 

Coping Skills Therapy (CBT) versus Twelve Step Facilitation (TSF) and Motivational 

Enhancement Therapy (MET) such that clients with lower levels of social functioning 

would have incrementally better drinking outcomes when treated in CBT relative to TSF 

or MET. Results in the aftercare arm of treatment failed to support this hypothesis. In 

the outpatient arm of the study, the matching effect was opposite that hypothesized, 

that is, the lower the clients’ level of social functioning, the worse their drinking outcomes in CBT relative to TSF and MET. Causal chain analyses were conducted to 

identify where the theory underlying the treatment broke down. Clients who reported 

greater drink refusal skills after treatment also reported drinking less often and less 

intensely on days in which they drank. However, CBT failed to increase coping skills 

of poorly functioning clients more than did MET or TSF. This suggests a failure in CBT 

implementation, or alternatively, that the other treatments also produced this change. 

The fact that poorly functioning clients did best in TSF suggests that this treatment 

has active ingredients, yet to be identified, that are especially helpful to these clients.

Social functioning has been found to be 

a fairly consistent prognostic indicator 

among clients treated for alcohol problems in that those with higher social functioning 

prior to treatment are more likely to have good 

treatment outcomes. Early reviews supported 

this relationship (e.g., Gibbs and Flanagan 

1977). Subsequent individual studies have continued to support this finding (e.g., Miller et al. 

1996). In the absence of treatment-matching 

effects, social functioning can be expected more 

often than not to be a variable prognostic of better drinking outcomes.

The challenge then becomes the development 

of interventions that can reduce the adverse 

prognostic effects of poor social functioning. 

If treatments can be devised that eliminate 

the disadvantage of poor social functioning, 

the average outcomes of such clients will be 

enhanced, thus increasing the overall effectiveness of alcohol treatments.

 Cognitive-Behavioral 

Therapy

Social skills training was adapted to treat 

alcoholics (Monti et al. 1989). This precursor 

of cognitive-behavioral therapy (CBT) had two 

points of origin. In Australia, Oei and Jackson 

(1980) developed a general social skills training program for alcoholics. These investigators 

assumed that alcoholics had general social 

skills deficits and because of these used alcohol 

as an alternative behavior. The hypothesis was 

that the learning of skills to reduce these social 

deficits would reduce alcohol consumption.

Richard Longabaugh, Ed.D

Center for Alcohol and Addiction Studies 

Brown University, School of Medicine

800 Butler Drive, Potter Building, Room 204 

Providence, RI 02906

Phone: 401-444-1835, Fax: 401-444-1888
286

Part V1: Interpersonal Functioning and Support

At the same time, Chaney and associates 

(1978) developed a treatment program that 

focused on teaching social skills for coping with 

situations in which the alcoholic was at risk to 

drink. The rationale for this approach was derived from the work of Marlatt (1978; Marlatt 

and Gordon 1985), who found that relapsing 

alcoholics identified certain situations that were 

highly likely precipitants of relapse to drinking. 

The social skills training program developed by 

Chaney et al. (1978) taught relapse prevention 

skills to be used in these situations.

These early studies demonstrated clinical 

effectiveness for these two variations of skills 

training. In the following 20 years, social skills 

training programs have mushroomed and have 

been modified to incorporate cognitive as well 

as behavioral coping skills. A body of clinical research has demonstrated that cognitive 

behavioral therapy is an effective treatment for 

alcohol dependence (Miller, Brown et al. 1995).

The question that arises from these studies is 

whether CBT is especially effective for clients with 

deficits in social coping skills. Client-treatment 

matching studies have been conducted to test 

this hypothesis. Kadden and colleagues (1992) 

found support for this hypothesized matching 

effect: clients treated in group-administered 

CBT who were rated as having less skill in a 

drink refusal role play situation prior to CBT 

were more likely to be abstinent at the end of 

treatment than were clients treated in a Yalombased interactional group therapy.

Of interest, clients high in drink refusal 

skills prior to treatment did less well in CBT 

than when treated in the Yalom-based interactional group therapy. The implication from this 

study is that those who have deficits in relapserelated social skills will especially benefit from 

CBT, while those without such deficits may be 

mismatched to a CBT treatment that focuses 

primarily on these deficits. However, a study 

carried out by Rohsenow and associates (1991) 

failed to demonstrate the anticipated matching 

effect for clients with greater social skills deficits prior to treatment with social skills training 

versus mood management. Thus, it is not clear 

under what set of circumstances CBT will be 

matched to social skills deficits.

As implemented in Project MATCH, based 

on the work of Monti and associates (1989), 

CBT involved a menu of skills training modules from which to select. However, all clients 

were required to receive a core set of eight modules developed to teach skills for dealing with 

situations in which risk for relapse is normatively reported as high, as these core skills are 

believed to be most critical (Kadden et al. 1992; 

Monti et al. 1990, 1993; Rohsenow et al. 1991). 

Modules developed for dealing with more general skill deficits were optional.

As the modal CBT MATCH client attended 

about eight sessions, the core sessions were 

the only set of modules received by the average CBT client. From this information, it would 

be inferred that the CBT client most apt to be 

matched to this treatment would be one who 

had coping skill deficits in these high relapse 

situations, rather than clients who were generally deficient in social skills, because the 

average CBT client would not be exposed to 

these general skill deficit sessions.

Twelve Step Facilitation

Twelve Step Facilitation (TSF; Nowinski et al. 

1992) is very much a socially focused treatment. 

A proximally successful outcome for TSF gets 

the client involved in AA. However, we assume 

that sustained participation in AA involves having sufficient social skills to be accepted within 

the AA Fellowship. The Fellowship is likely to 

have greater tolerance for deficient social skills, 

relative to a client’s larger social network (“your 

problems are attributable to your disease of 

alcoholism, not to the fact that you’re a bad 

person”). Nevertheless, if treatment success is 

predicated upon acceptance in AA, clients with 

social skills deficits would be less likely to succeed in TSF than in a therapy such as CBT that 

totally relies upon the sessions with the therapist, a person trained to have tolerance for, and 

able to effectively deal with, such clients. As 

the CBT therapy is assumed to be a sufficient 

active ingredient for change, its success is not 

expected to be contingent upon acceptance by 

A.A.

This line of reasoning led us to expect that 

clients with poorer social functioning would 

do worse in TSF than would clients with better 

social functioning.
287

Social Functioning

Motivational Enhancement 

Therapy

Motivational Enhancement Therapy (MET; 

Miller et al. 1992) is intended to activate clients 

to use their own preexisting resources. The 

therapy neither provides the opportunity for 

training social skills to those with such deficits 

(as does CBT) nor does it focus especially on 

promoting the client’s involvement in a support 

system that might reject the person because of 

social deficits (as would TSF). We would expect 

that the outcomes of MET clients would be 

consistent with the general prognosis for alcohol treatment-seeking clients. The better their 

social functioning prior to treatment, the better 

their drinking outcomes.

Matching Hypotheses

Given these considerations, we hypothesized 

the following matching effects:

■ The lower the social functioning of the client, the greater the incremental effect of 

being treated in CBT versus TSF or MET.

■ The greater the social functioning of the 

client, the greater the incremental effect of 

being treated in TSF or MET versus CBT.

Figure 1 portrays the anticipated effects. 

Overall, the greater the clients’ social functioning, the better their drinking outcome. 

Assignment to CBT, however, will reverse this 

effect, such that those with poorer social functioning will have better drinking outcomes in 

CBT than those with better social functioning. This effect is hypothesized because of our 

assumption that CBT will be especially effective 

for clients with greater deficits in social functioning but will be irrelevant for clients who 

already had good social functioning prior to 

treatment.

The causal chain postulated to underlie these 

matching effects is that clients with poorer 

social functioning prior to treatment will incrementally improve in this domain when treated 

with CBT skills training This pretreatment to 

posttreatment improvement in social functioning will be predictive of reduced drinking. Thus, 

when the effect of before to after improvement 

in social functioning is partialed out of the 

Figure 1. Hypothesized effects of matching 

social functioning to treatment

hypothesized interaction effect, this improvement will be shown to be the variable mediating 

this effect.

Clients assigned to TSF who are unable to 

sustain participation in AA because of their 

poor social skills will do as poorly as will MET 

clients with low social functioning

Measurement of Social 

Functioning

Measurement of social skills deficits and 

functioning was not a high priority for the 

MATCH study. The development of matching 

hypotheses such as the present one followed 

after the selection of the assessment battery. 

This necessitated a post hoc review and selection from the assessment battery already in 

place. Two instruments were judged, in combination, to yield an adequate assessment of 

social functioning.

Social Behavior Scale

The Psychosocial Functioning Inventory (PFI) 

was developed to provide a brief but comprehensive measurement of clients’ self-reported 

functioning and well-being (Feragne et al. 1983). 

One scale from this battery is self-reported 

social behavior, which involves 10 questions, 

each having 4-point scales measuring the relative frequency (almost daily, at least once a 

week, less than once a week, not at all) of potentially problematic social behaviors within the 

prior month (table 1). The items are aggregated 
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Part V1: Interpersonal Functioning and Support

to provide a single social-behavior score. This 

variable has been used in prior studies of 

psychiatric (Longabaugh et al. 1983a) and alcoholic populations (Cooney et al. 1991; Kadden 

et al. 1989; Fink et al. 1985; Longabaugh et 

al. 1983b; McCrady et al. 1986), where social 

behavior was found to significantly improve 

from before to after treatment. However, it has 

not been sensitive to matching effects (Cooney 

et al. 1991; Kadden et al. 1989).

Negative Consequences of Drinking

The Drinkers Inventory of Negative 

Consequences (DrInC) was developed by 

Miller,Tonigan, and Longabaugh (1995) to measure negative consequences that the client 

attributed to drinking alcohol. One of the five 

subscales of this self-report inventory measures 

the negative inter-personal consequences the 

client attributes to alcohol consumption. This 

10-item scale is summed to yield an overall 

score of negative interpersonal consequences 

attributable to alcohol (table 2).

All clients were administered the DrInC prior 

to treatment initiation and reported on lifetime 

negative consequences they attributed to alcohol. Posttreatment, they were administered an 

alternate form which asked them to report on 

negative consequences experienced during the 

prior 3 months.

In order to equally weight both poor social 

functioning in general as well as poor social 

functioning that the client attributed to alcohol consumption, both the PFI and the DrInC 

scales were converted into indices that varied between 0 and 1 and then were averaged 

to yield an overall index that varied from 0 to 

1, with higher scores indicating higher social 

functioning.

A contrast of outpatient and aftercare clients 

on this composite measure of 

social functioning indicated that, 

as might be expected, outpatients averaged a higher level of 

social functioning prior to treatment (M=0.51, SD=0.17) than 

did aftercare clients (M=0.44, 

SD=0.17), p<.001. Within arms, 

there were no between-treatment 

differences in social functioning 

prior to treatment assignment.

Results

Outpatient Arm

Tables 3 and 4 report the 

results of the overall testing of 

the matching hypothesis during 

the within-treatment and posttreatment periods. There were 

no in-treatment matching effects 

for either dependent variable, 

percentage of days abstinent 

(PDA) or drinks per drinking 

day (DDD). Within treatment, 

the overall F for PDA=0.95 and 

for DDD, F=0.88, both p>.05. 

Interpretation of these statistics 

is facilitated by figures 2 and 

3, which present plots of the 

expected mean PDA and DDD 

Table 1. Items that comprised the Psychological 

Functioning Inventory

IN THE PAST MONTH Almost 

Daily

At least 

once a 

week

Less 

than 

once a 

week

Not at all

1. Did you avoid talking with 

family members or friends? 1 2 3 4

2. Did you have to rely on others 

to make your decisions for 

you?

1 2 3 4

3. Did your family or friends 

upset you? 1 2 3 4

4. Did you have heated arguments with other people? 1 2 3 4

5. How often were you upset, 

angry, or disappointed with 

the way people did things?

1 2 3 4

6. Did you feel your family or 

friends did not trust you? 1 2 3 4

7. Did you feel anxious or afraid 

when you were with other 

people?

1 2 3 4

8. Did you demand that others 

do things your way? 1 2 3 4

9. Did you do things that upset 

you family and friends? 1 2 3 4

10. Did you do things when you 

were in public that other 

people did not like? (belching, 

spitting, wearing inappropriate clothing, etc.)

1 2 3 4
289

Social Functioning

Table 2. Drinker Inventory of Consequences

Instructions: Here are a number of events that drinkers sometimes experience. Read each one carefully 

and circle the number that indicates whether this has 

EVER happened to you (0 = No, 1= Yes). If an item 

does not apply to you, circle zero (0).

Has this EVER happened to you? 

Circle one answer for each item. No Yes

1.

2.

3.

4.

5.

6.

7.

8.

9.

10. 

My family or friends have worried or 

complained about my drinking.

My ability to be a good parent has 

been harmed by my drinking.

While drinking, I have said or done 

embarrassing things.

While drinking, I have said harsh or 

cruel things to someone.

My marriage or love relationship has 

been harmed by my drinking.

My family has been hurt by my 

drinking.

A friendship or close relationship has 

been damaged by my drinking.

My drinking has damaged my social 

life, popularity or reputation.

I have lost a marriage or a close love 

relationship because of my drinking.

I have lost a friend because of my 

drinking.

0

0

0

0

0

0

0

0

0

0

1

1

1

1

1

1

1

1

1

1

Source: Miller, Tonigan, and Longabaugh 1995

Table 3. Hierarchical linear modeling results 

for within-treatment drinking in the outpatient 

arm for social functioning

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t 1.16 -1.32 0.20 -0.02 0.06 -0.11

p .25 .19 .84 .98 .95 .91

CBT–

TSF

t 1.25 -0.73 0.18 -0.95 0.42 0.88

p .21 .47 .86 .34 .67 .38

MET–

TSF

t 0.09 0.62 -0.03 -0.96 0.37 1.01

p .93 .54 .96 .34 .71 .31

Overall 

effect

F 0.95 0.88 0.02 0.61 0.11 0.61

p .39 .42 .98 .54 .90 .54

NOTE: MV=matching variable, Social Functioning; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).

Table 4. Hierarchical linear modeling results 

for posttreatment drinking in the outpatient 

arm for social functioning

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t 2.19 -2.24 -0.06 0.84 -0.44 -0.82

p .03 .04 .95 .40 .66 .41

CBT–

TSF

t 3.24 -3.20 -0.79 0.90 0.60 -0.56

p .002 .002 .43 .37 .51 .57

MET–

TSF

t 1.08 -1.00 -0.74 0.05 1.13 0.27

p .28 .32 .46 .96 .26 .79

Overall 

effect

F 5.40 5.33 0.40 0.50 0.64 0.35

p .005 .005 .67 .61 .53 .70

NOTE: MV=matching variable, Social Functioning; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).

for clients dichotomized into high (75th percentile) and low (25th percentile) social functioning 

groups over the entire 15-month period, including the 3 months during which treatment was 

delivered.

However, during the posttreatment period, 

strong interaction effects were observed for both 

PDA and DDD. Those low in social functioning 

did most poorly relative to those high in social 

functioning when treated in CBT. This relationship between social functioning and drinking 

was less pronounced in MET and was reversed 

in TSF, where low functioning clients had better

outcomes than those with high social functioning.

Further, as TSF clients with low functioning entered the posttreatment followup period, 

they started with a higher percentage of days 

abstinent than comparable MET and CBT clients, and they preserved this level of abstinence 

throughout the followup period. In contrast, both 

CBT and MET low functioning clients began and 

ended followup with a lower percentage of days 
290

Part V1: Interpersonal Functioning and Support

Figure 2. Percentage of days abstinent as a function of 

treatment modality and social functioning: Outpatient 

arm

Figure 3. Drinks per drinking day as a function of treatment modality and social functioning: Outpatient arm

abstinent. However, even MET low functioning 

clients had more abstinent days than comparable CBT clients for most of the followup period. 

For high functioning clients, CBT appeared to 

have a nonsignificant initial advantage during the treatment period, which lasted into the 

beginning weeks of the posttreatment phase 

but then rapidly diminished, so that by the end 

of followup, PDA was no different for this group 

than the other high functioning groups.

A comparable posttreatment interaction

effect was present for the measure of drinking 

intensity. However, in this instance, the low 

functioning CBT clients were drinking significantly more drinks on a drinking day than high 

functioning clients in CBT and more than both 

high and low functioning clients in TSF and 

MET.

In summary, in the outpatient arm of the 

study, an unexpected matching effect 

was observed. Lower levels of social 

functioning were associated with particularly poor drinking outcomes for 

clients treated in CBT and particularly 

good outcomes for comparable clients 

treated in TSF. CBT clients with high 

social functioning had better drinking 

outcomes than those with a lower level 

of social functioning.

Aftercare Arm

In the aftercare arm, there was no 

strong evidence for an interaction effect 

that was independent of time, either 

in the direction predicted or opposite 

the direction predicted. During treatment, as is apparent in table 5, there 

was no suggestion of interaction effects. 

During the posttreatment period (table 

6), the CBTTSF contrast in slopes 

changed over time for PDA (p=.01) but 

not for DDD (p=.15). The percentage of 

days abstinent by high social functioning CBT clients remained high during 

the followup period, while those with 

low social functioning initially did well 

but then deteriorated, so that they were 

doing worse than the high functioning 

CBT clients for most of the followup (see 

figure 4).

Table 5. Hierarchical linear modeling results 

for within-treatment drinking in the aftercare 

arm for social functioning

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t -0.41 1.46 -0.35 -0.50 1.26 -0.11

p .68 .14 .73 .62 .21 .91

CBT–

TSF

t -0.09 0.86 -1.34 0.71 1.52 -0.90

p .93 .39 .18 .48 .13 .37

MET–

TSF

t 0.32 -0.57 -0.99 1.19 0.29 -0.79

p .75 .57 .32 .23 .78 .43

Overall 

effect

F 0.09 1.08 0.95 0.71 1.34 0.47

p .91 .34 .39 .49 .26 .62

NOTE: MV=matching variable, Social Functioning; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).
291

Social Functioning

Figure 4. Percentage of days abstinent as a function of treatment modality and social functioning: 

Aftercare arm

Causal Chain Analyses 

The A Priori Causal Chain

Our a priori matching hypothesis was that 

poor social functioning clients would have better 

drinking outcomes when treated in CBT, while 

those with high functioning would do poorly 

when treated in CBT. This hypothesis was predicated on the assumption that those with poor 

social functioning would benefit from the social 

skills training provided for high relapse situations in CBT. For those already having high 

social functioning, we assumed that such skills 

training would be superfluous, thus the focus 

of CBT treatment would be misguided.

Evidence to support this causal chain would 

be provided if it were found that CBT improved 

the social functioning of low functioning clients more than did either TSF or MET and that 

improved social functioning was associated with 

better drinking outcomes. If both of these conditions were true, then further analysis would be 

conducted to see whether the improved social 

functioning accounted for the hypothesized 

CBT versus TSF by social functioning matching effect. However, the hypothesized matching 

effect was not observed.

To test the first part of the purported causal 

chain, a general linear models procedure was 

used to analyze the relationship between 

treatment assignment and social functioning 

immediately following treatment after controlling for social functioning at baseline, by arm. 

For both arms, the relationship was found to be 

nonsignificant.

To test the second part of the purported 

causal chain, a general linear models procedure 

was used to separately analyze drinking at each 

of three points in time as a function of social 

functioning at that time, controlling for baseline 

drinking and baseline social functioning scores, 

by arm. Three posttreatment observation points 

were used: end of treatment (3 months), 6 

months after treatment completion (9 months), 

and 1 year after treatment completion (15 

months). These data points were selected 

because they were the three in which posttreatment overall social functioning scores could be 

computed (data points at which both the DrInC 

and the PFI social behavior scale were administered). In every case (3-, 9-, and 15-month data 

points), posttreatment social functioning was 

strongly associated with posttreatment drinking (p’s all <.0001) after controlling for baseline 

drinking and baseline social functioning in both 

the outpatient and aftercare arms

In summary, causal chain analysis revealed 

where the chain broke down. While the 

relationship between posttreatment social 

functioning and PDA was supported, the relationship between treatment assignment and 

posttreatment functioning was not.

Table 6. Hierarchical linear modeling results 

for posttreatment drinking in the aftercare 

arm for social functioning

MV × Tx

MV × Tx

MV × Tx 

× T

MV × Tx × 

T2

PDA DDD PDA DDD PDA DDD

CBT–

MET

t 0.63 0.24 1.21 0.18 -0.63 -0.50

p .53 .81 .23 .86 .53 .62

CBT–

TSF

t -1.21 1.19 2.72 -1.45 -2.22 1.52

p .22 .23 .01 .15 .03 .13

MET–

TSF

t -1.81 0.95 1.53 -1.62 -1.59 2.00

p .07 .34 .12 .10 .11 .05

Overall 

effect

F 1.69 0.79 3.72 1.57 2.59 2.16

p .19 .46 .02 .21 .08 .12

NOTE: MV=matching variable, Social Functioning; 

Tx=treatment; T=linear time; T2=quadratic time. F tests were 

used for the overall effect, and t tests were used for pairwise 

treatment contrasts. Reported p values are based on nondirectional tests (i.e., two tailed).
292

Part V1: Interpersonal Functioning and Support

Discussion

The results in the outpatient arm were opposite those hypothesized. The effect appears to 

be quite robust and unlikely to have been produced by chance alone. Causal chain analyses 

indicated where the initial theory broke down 

CBT was not more effective than the other two 

treatments in increasing social functioning. So 

even though those who reported better social 

functioning had better drinking outcomes, CBT 

clients were not disproportionately represented 

in this group. Thus, CBT’s failure to incrementally improve the drinking outcomes of clients 

with poor interpersonal skills is accounted for.

What is not accounted for is why low functioning clients should do significantly worse with 

CBT than with the other treatments, especially 

TSF. The theory underlying CBT is that alcohol 

clients lack coping skills, leading them to use 

drinking as a dysfunctional coping mechanism. 

Learning adaptive coping skills, taught in CBT, 

should address the problem. Why should clients with poor social functioning skills do worse 

than when treated with TSF or even MET?

One speculation offered to account for this 

poor showing is that, contrary to theory, to be 

helped by cognitive behavioral skills training, 

clients may need to have a modicum of skills 

already available in their repertoire in order 

to make use of further training. For those not 

having the prerequisite starting skills, the 

costs involved in learning and utilizing them 

may preclude practice, acquisition, mastery, 

or generalization of these behaviors to real 

(as opposed to role play) situations. Certainly, 

Project MATCH CBT did not include any provision for determining whether clients were 

actually using the skills that they rehearsed in 

therapy.

The nonenduring tendency for CBT clients 

with high social functioning to be doing better 

than their counterparts during the treatment 

phase and immediately following is consistent 

with this conception that those who are likely 

to benefit from CBT are those who already have 

good enough skills to make further acquisition 

easier. However, the dissipation of the initial 

gain suggests that use of these skills may have 

been discontinued not long after the end of 

therapy.

Future Research Directions

One question for further research is why clients with poor social functioning did best in 

TSF. One speculation that might be researched 

is that the involvement in AA that was a goal of 

TSF might have been of special help to clients 

with especially poor interpersonal functioning. As stated earlier, AA views the person’s 

dysfunctional lifestyle as being attributable to 

the disease of alcoholism. Thus, poor interpersonal functioning may be attributed to factors 

believed to be outside of the person’s voluntary 

control, which only a long period of sobriety and 

working the 12 steps of AA can rectify. In such 

a circumstance, there may be a very broad tolerance for deviance among other AA members. 

The availability of unconditional regard, as long 

as the AA member seeks to stop drinking, may 

provide the opportunity for a person to regain 

the self-esteem that might otherwise not be 

forthcoming from the client’s own social network. An increase in self-respect in turn might 

provide the client with the hope and motivation 

to remain sober and begin to deal with the negative consequences of alcohol consumption.

A priority for future investigation with these 

data is a causal chain analysis to identify the 

process that leads these TSF low functioning 

clients to fare better than would otherwise be 

predicted

Conclusion

Results from the outpatient arm of the study 

contradict the a priori hypothesis that CBT will 

be especially effective in improving the drinking 

outcomes of clients with the poorest interpersonal functioning. Causal chain analysis 

identifies the source of failure for CBT—such 

clients do not utilize coping skills more than 

do comparable clients in other treatments. 

However, unexplained is why such clients do 

best in TSF treatment.

Acknowledgments

This work was supported by grant number 

U10–AA08443 from the National Institute on 

Alcohol Abuse and Alcoholism as part of the 

Cooperative Agreement on Matching Patients to 

Alcoholism Treatments. Our appreciation goes 
293

Social Functioning

also to Deborah Mendes who assisted in the 

preparation of this manuscript.

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295

Part VII: Conclusions and Implications

Methodological Assessment and Critique 

Substantive Review and Critique
296

Methodological Assessment and 

Critique

Philip W. Wirtz, Ph.D., and Richard Longabaugh, Ed.D.

ABSTRACT

The preceding chapters have as a common goal the identification of what went right 

and what went wrong in the theories leading to the matching hypotheses. The authors 

of these chapters took a number of distinctly different approaches in meeting this 

common goal, with varying levels of success. In the present chapter, we review some of 

these alternative approaches with an eye toward identifying the strengths and weaknesses of each. In the process, we address some of the difficulties inherent in the shift 

from testing the mediation of main effects to testing the mediation of moderator (or 

interaction) effects (as is a primary objective in matching studies). We consider in this 

chapter the manner in which some of these difficulties can be overcome and some of 

the challenges introduced in the causal testing of latent growth models. We conclude 

with prescriptive guidance which we hope will inform the field as it continues the fruitful pursuit of causal chain analysis.

 Testing Causal Chains

The procedure for testing mediation has 

been formalized by Baron and Kenny (1986) 

and further explicated by Holmbeck (1997). The 

procedure is based upon empirical tests of four 

simple conditions. Following these guidelines, 

in order to conclude that variable B mediates 

the relationship between variables A (independent) and C (dependent; see figure 1), four 

conditions must prevail: (1) A and C must be 

related in the hypothesized direction, (2) A and 

B must be related in the hypothesized direction, 

(3) B must be related to C (in the hypothesized 

direction) after controlling for A, and (4) the 

relationship between A and C must be smaller 

after controlling for B than it is before controlling for B. In practice, the first three conditions 

require the relationship between the two variables to be directionally statistically significant 

at some preordained level of a (conventionally, 

0.05). Condition 4 is satisfied if the parameter 

estimate obtained by regressing C on A (controlling for B) is smaller than the parameter 

estimates obtained by regressing C on A without controlling for B.

Note that the Baron and Kenny formulation is just as important for models in which 

mediation is hypothesized and is not found as 

it is for models in which mediation is hypothesized and is found, because strict adherence 

to the four steps provides an indication of where 

the purported causal chain broke down if the 

hypothesized mediation cannot be empirically 

verified. Relying on figure 1 again for example, 

if A is found not to be significantly related to 

B (and/or if C is found not to be significantly 

related to B after controlling for A), it provides 

an immediate indication of the locus of a logical flaw in the hypothesized causal chain. Thus, 

Philip W. Wirtz, Ph.D.

Department of Management Science 

George Washington University

2115 G Street NW, #403

Washington, DC 20052

Email: pww@gwu.edu
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Methodological Assessment and Critique

Figure 1. Model of a mediated effect, where B is 

a purported mediator of the relationship between 

A and C.

Baron and Kenny (1987) and Holmbeck (1997) 

understate the value of the four-step procedure to the process of causal chain analysis: in 

addition to providing a rigorous test of whether 

mediation occurs, these four steps also provide 

an indication of where the purported model 

failed when hypothesized mediation is found 

not to occur.

One implication of this four-step procedure 

is that relationships which might appear to be 

mediational are not. Consider the case where 

we know a priori that some factor A is a strong 

causal factor for C, and it does so through 

only one mechanism, which is to induce variation in B, with A being the sole cause of B. 

For example, suppose that there was an exact 

dose response between amount of smoking in 

a 6-month period (A) and buildup of plaque on 

arteries (B). Second, let’s assume that there 

is a strong dose response between the thickness of the plaque in the arteries and resting 

blood pressure (C), although this is not the only 

cause of C. Suppose this leads to a correlation 

between A and B of 1, between B and C of 0.5, 

and between A and C of 0.5. If we calculate the 

semipartial correlation of B and C, controlling 

for A, it will be zero. Initially, this might seem to 

be an example where full causal mediation is in 

place, but condition 3 does not hold, because B 

is not related to C after controlling for A.

The problem here is that the standard that 

was set in answer to the question of “what constitutes mediation?” is lower than that required 

by Baron and Kenny. Under this example, Baron 

and Kenny would insist that before B (plaque 

buildup in the arteries) is called a mediator, it 

is necessary to establish that it is a causal factor of C (high blood pressure). When A is the 

sole causal antecedent of B (as in this example), 

then while it might be the case that B (plaque) 

is a causal factor of C (high blood pressure), it 

might alternatively be the case that smoking is 

the true causal factor and that plaque is just 

“along for the ride”. Essentially, Baron and 

Kenny require that B be established as a true 

causal factor of C before it is called a mediator, 

and this is not possible if A and B are perfectly 

(or very highly) correlated.

To drive the point to an absurdity, suppose 

that there is a fourth variable (D) which is highly 

correlated with both A and B in this example: 

for instance, suppose that D is “percentage of 

friends who are smokers”. Now, suppose we 

have measured only A, D, and C, and we wish 

to know if D mediates the relationship between 

A and C. If condition 3 was merely bivariate and 

did not control for A, it would allow us to reach 

the dubious conclusion that percentage of 

friends who are smokers mediates the relationship between smoking and blood pressure. (This 

is a dubious conclusion because it is doubtful 

that having a greater percentage of friends who 

smoke causes high blood pressure.) By controlling for A, condition 3 would eliminate this 

variable as a potential mediator, because it has 

not been proven that percentage of friends who 

are smokers (D) is causally related to high blood 

pressure. In the same way that D is eliminated 

as a potential mediator by Holmbeck’s condition 3 under this model, B would be eliminated 

as a potential mediator under the model of the 

original example—and for the same reason.

The difference here is one of could be versus 

is a mediator. In the example, plaque buildup 

could be a mediator but we are not sure it is 

causally linked to high blood pressure, so we 

do not call it a mediator under the Baron and 

Kenny standard. Under Baron and Kenny, a 

higher standard needs to be met in order to 

establish mediation: there needs to be evidence 

of a causal link between the putative mediator and the outcome before mediation can be 

established.

A potential weakness in this four-step 

approach lies with its rather cavalier treatment 

of variables outside the model which could 

influence the outcome. For example, the underpinning of the condition-2 requirement that A 

and B be related lies with the logical assertion 
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Part VII: Conclusions and Implications

that, in order for B to mediate the relationship 

between A and C, B has to be caused by A. 

However, merely establishing that A and B are 

related provides a necessary but totally insufficient empirical test for the causal assertion 

because it does not account for the possibility 

that A and B are not causally linked but are 

rather both consequences of some antecedent 

variable. Thus, a much stronger test of condition 2 than is found in the typical application of 

the Baron and Kenny formulation would require 

A and B to be related after controlling for other 

variables which might represent alternative 

explanations for the existence of the relationship.

Similarly, although it is not frequently cited in 

applications of the Baron and Kenny formulation, condition 3 would be much stronger if, in 

addition to controlling for A, additional control 

variables were added to the model which would 

refute potential threats to internal validity.

A fully comparable alternative approach for 

testing mediation employs structural equation 

modeling. Under this approach, the direct A–C 

effect is initially estimated by omitting B from 

the model. Following this, a full model containing both the direct (A–C) and indirect (A–B–C) 

linkages is tested. Mediation occurs when (1) 

the A–C effect in the initial model is directionally significant, (2) the A–B and (3) B–C effects 

in the second model are directionally significant, and (4) the A–C effect in the second model 

is less than the A–C effect in the first model. 

These conditions are exactly analogous to the 

Holmbeck (1997) explication of Baron and 

Kenny’s (1986) formulation.

The choice between these two alternatives 

is often driven by whether the investigator has 

multiple indicators for A, B, and/or C. The two 

approaches are fully equivalent when each of 

the variables is measured by a single indicator. 

Multiple indicator models dictate the selection 

of a structural equation modeling procedure as 

the analytical procedure of choice.

While these two approaches have been widely 

adopted for testing simple mediation hypotheses, the formal test of a matching causal 

chain is one level more sophisticated, in that 

it requires testing for mediation of a moderator

(i.e., interaction) effect rather than of the main

effect that is addressed under the Baron and 

Kenny formulation and by a structural equation 

modeling approach. The additional challenge 

imposed by a matching hypothesis is further 

heightened under a structural equation modeling approach, where detection of interactions 

involving latent constructs has proven particularly daunting.

Even under the single-indicator Baron and 

Kenny formulation, the purported causal path 

can take any of several different forms. The 

potential for these different forms imposes a 

considerably greater burden on the investigator, who must explicate the model to be tested 

in advance of formally testing any causal chain 

hypotheses. As described subsequently, the 

application of the Baron and Kenny formulation 

for single-indicator models to mediated moderation models is relatively straightforward, given 

that the investigator has adequately explicated 

the postulated model in advance.

Some authors in this monograph approached 

the mediated moderation question by testing each treatment condition separately, 

similar to the “within-groups” approach critiqued by Finney and associates (1984). Such 

an approach carries the advantage of being 

arguably simpler to understand and to execute 

than following the Baron and Kenny four-step 

formulation. Unfortunately, however, the use of 

this approach does not permit either a formal 

test for the existence of mediation or a reflection 

of the locus of failure in a failed causal chain. 

The anger matching hypothesis, for example, is 

predicated on the presumption that pretreatment anger leads to resistance, and that MET 

more effectively deals with resistance than does 

(for example) CBT. One approach for testing 

such a hypothesis would require the assessment 

of pretreatment anger and posttreatment resistance. A causal diagram of this hypothesized 

model is presented in figure 2. While separate 

testing of an anger→posttreatment resistance 

link for MET versus CBT clients might reveal 

a significant relationship among CBT clients 

and a nonsignificant relationship among MET 

clients (as hypothesized), the magnitude of the 

between-treatment difference in the strength 

of the link might be small and nonsignificant—especially in the case where CBT reflects 

marginal significance (i.e., p slightly below ) 

and MET reflects marginal nonsignificance (i.e., 

p slightly above ). Furthermore, if separate 
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Methodological Assessment and Critique

Figure 2. Example of a purported mediated 

moderator relationship

testing of MET versus CBT clients revealed that 

both groups reflected a significant relationship 

between anger and posttreatment resistance (as 

would also be consistent with the theory), there 

would be no mechanism using this approach 

for determining whether the anger-resistance 

link was significantly weaker for MET clients 

than for CBT clients. Thus, separate analysis of 

the treatments can actually be counterproductive, incorrectly suggesting (in some situations) 

a mediating effect that does not exist and 

incorrectly failing to validate the existence of a 

mediating effect when it does exist.

There is a deceptive attraction of analyzing 

treatment groups separately for the purposes 

of determining the locus of causal chain breakdowns. If the results of separate-group analysis 

fail to reveal a relationship (e.g., between anger 

and resistance) in a group where it was hypothesized, or if the relationship is found in a group 

where it was not hypothesized, this would at 

first glance seem to provide an indication of 

where the causal chain failed. However, the 

former case requires the methodologically incorrect practice of accepting the null hypothesis. 

The “failure to reveal” a relationship (at some 

preordained level of certainty, as manifested in 

the investigator-specified type 1 error level) is 

not the logical equivalent of “determining with 

certainty that the relationship does not exist.” 

The latter case is also logically flawed: any nonzero relationship can be found to be statistically 

significant if the group size is large enough, 

and therefore a finding of an unanticipated 

“significant” relationship may be nothing more 

than an artifact of sample size. Thus, the practice of separately analyzing treatment groups 

in order to determine the locus of failures in 

the hypothesized causal chain is fraught with 

methodological shortcomings.

How, then, does a researcher who wishes to 

investigate the loci of a causal chain failure (or 

to statistically validate a hypothesized causal 

chain) proceed in the context of a matching 

(or, more generally, moderator) hypothesis? 

The answer lies in a joint analysis of all treatment groups using an extension of the Baron 

and Kenny formulation. In the anger-resistance 

example, because anger is hypothesized to lead 

to resistance, the interaction between anger

and treatment is hypothesized as a distal reflection of the more proximal interaction between 

resistance and treatment. Phrased more formally, the resistance-treatment interaction is 

hypothesized to mediate the anger-treatment 

interaction. This provides a slightly more sophisticated application of the Baron and Kenny 

formulation, where A in figure 1 represents the 

anger-treatment interaction, B represents the 

interaction between resistance and treatment, 

and C represents drinking behavior (figure 3).

With these simple representations in place, 

the four-step Baron and Kenny formulation (as 

elucidated by Holmbeck) can now be applied in 

order to obtain a formal statistical test of the 

hypothesis of mediation. The only challenging 

part of this formulation lies in step 2 (establishing a relationship between A and B), because 

B is now an interaction. If the hypothesized 

Figure 3. Testing a purported mediated moderator relationship
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Part VII: Conclusions and Implications

interaction involves only two groups, B is a 

vector composed of the product of the two constituent variables. In this case, step 2 of the 

Baron and. Kenny formulation can be implemented under a standard general linear model. 

If the hypothesized interaction involves more 

than two groups, B is an array composed of 

k-1 product variables (where k is the number of 

groups specified in the hypothesis). In this case, 

step 2 of the Baron and Kenny formulation can 

be implemented under a standard multivariate 

general linear model.

We noted earlier that the Baron and Kenny 

formulation is just as important in determining where a purported mediational model failed 

as it is in determining the empirical plausibility of a hypothesized mediational model. The 

quintessential importance of this dual applicability is no less salient in the extension of the 

Baron and Kenny formulation to the mediation 

of moderator models than it is to the base applications referenced by Baron and Kenny and by 

Holmbeck. The simple substitution of interaction terms for A and for B in the Baron and Kenny 

formulation underscores two critical issues for 

investigators wishing to employ causal chain 

analysis. First, it is crucial that the investigator rigorously specify, in advance, the specific

causal chain(s) that is (are) hypothesized, and 

then proceed to test exactly that chain. Second, 

in matching (or attribute-by-treatment) models, 

where the focus is on hypothesizing and testing 

group differences in the relationship between 

an attribute and an outcome variable, analyzing 

the groups separately will lead to a suboptimal 

(and, in all likelihood, inconclusive) result.

Canonical Models and the 

Testing of Causal Chains

As described earlier (pp. 21–26), the expansion of the Baron and Kenny (1986) formulation 

to include the mediation of moderator (interaction) effects carries with it the concomitant 

expansion of the types of models that can be 

investigated. Figure 2, for example, represents 

a model in which the purported matching effect 

is hypothesized to occur as a result of the surrogation of a matching variable (anger) for a more 

proximal variable (resistance) which interacts 

with treatment. Alternatively, the investigator 

might hypothesize that the treatment leads to 

a proximal outcome which interacts with the 

matching variable vis-a-vis drinking behavior. 

The Typology Hypothesis Team, for example, 

hypothesized that CBT and TSF would differ 

from MET in the amount of structure inherent 

in the treatment modality, and that degree of 

structure would interact with Typology (Type A 

vs. Type B) in affecting drinking behavior.

It should be emphasized that the value of 

distinguishing between canonical models is 

primarily descriptive. Whereas Canonical Type 

1 focuses on a proximal effect of treatment to 

explain the hypothesized interaction, Canonical 

Type 2 focuses on a proximal effect of the matching variable. The empirical test of a Canonical 

Type 1 model, however, continues to follow 

the four-step Baron and Kenny (1986) formulation. In the case of Typology, for example, A 

would be replaced by the Typology-Treatment 

interaction, and B would be replaced by the 

Typology-Structure interaction (see figure 1).

A third canonical form, which we identify 

as Canonical Type 3A, is exemplified in the 

motivational readiness causal chain. Here, the 

interaction between treatment and readiness 

to change was hypothesized to affect alcohol 

abstinence self-efficacy (the putative mediator) which, in turn, was hypothesized to affect 

drinking outcome. In terms of the Baron and 

Kenny (1986) formulation, the formal testing of 

this type of model is slightly simpler than the 

other two canonical forms we have introduced: 

A is replaced by the original matching interaction (e.g., treatment-readiness to change), and 

B is replaced by the putative mediator (e.g., 

alcohol abstinence self-efficacy).

The fourth canonical form, which we identify 

as Canonical Type 3B, is tested in the same way 

as model 3A. What is different is that the content of the putative mediator in 3B is a change 

in the therapeutic implementation hypothesized 

to occur as a result of the original matching 

interaction.

Inherent Difficulties

We have argued above that the formal procedure for testing a putative causal chain involving 
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Methodological Assessment and Critique

a matching effect is not, if applied with sufficient 

rigor, arduously more difficult than the formal 

procedure for testing the type of mediational 

model described by Baron and Kenny (1986). 

However, there are analytical considerations 

which make the detection of interaction effects 

considerably more difficult than the detection of 

main effects.

Although interaction effects are frequently 

found in experimental studies, they are notoriously more difficult to detect in field settings. 

A number of reasons for this difficulty have 

been cited, including the covariance of the 

interaction term with its component variables, 

differences in measurement error, the use of 

nonlinear scales, and differential residual variances of interactions once the component main 

effects have been partialed out. McClelland and 

Judd (1993) have shown that tests of interactions in field studies will often have less than 20 

percent of the efficiency of optimal experimental 

tests. Centering the component variables prior 

to creating a product-term interaction may 

reduce the problem (see Aiken and West 1991), 

although the usefulness of this procedure in 

significantly reducing the problem remains 

unclear (see Finney et al. 1984). In general, 

the powerful detection of interaction effects in 

field settings remains a highly elusive goal, the 

careful design and large sample size of Project 

MATCH notwithstanding.

Statistical Considerations

It has been noted with some alarm that the 

number of Project MATCH a priori hypotheses 

which were confirmed empirically is substantially less than what would be expected by 

chance (using a type 1 error rate of 0.05). Does 

this reflect negatively on our ability to understand the complex relationships involved in 

treatment matching for alcoholism? The preceding chapters of this monograph would certainly 

underscore just how complex some of these 

relationships can be. But it is also possible that 

statistical issues reduced the power of the study 

to identify hypothesized matching relationships. Three issues are of particular interest: 

the nature of the outcome measures, the inability to capitalize on the virtues of a latent growth 

model, and the possibility of undiscovered site 

differences. We consider each of these in turn.

The Nature of Outcome Measures.

Alcohol data are notorious in their departure from the assumptions of the general linear 

model. There are often clusters of “extreme” values (e.g., abstinence), the observations are rarely 

normally distributed around the regression 

line, and the relationships may be curvilinear. 

In contrast, most of the analytical procedures 

employed in alcohol research require nicely distributed mound-shaped data for which p values 

are reasonably accurate.

In order to bridge the gap between data as we 

would like them to be and data as they present 

themselves, it is not uncommon to attempt to 

induce normality and homoskedasticity through 

some sort of transformation. For example, a log 

transformation can often be helpful when the 

data are severely skewed to the right; a negative 

log transformation can be employed when the 

data are severely skewed to the left. Square root 

transformations are frequently of use when the 

skew is not quite so severe.

Transforming data presents two principal 

problems. First, interpreting the transformed 

data can often be challenging in the absence of 

obvious theoretical relevance, and therefore the 

results frequently have to be “back translated” 

by employing a reverse (or reciprocal) transformation in order to bring the units back into an 

interpretable metric. This is particularly challenging with alcohol treatment outcome data, 

where the nature of the skew is likely to change 

across time. For example, prior to treatment, 

drinking frequency measures (such as percentage of days abstinent) are likely to be positively 

skewed, with most individuals reflecting comparatively low values and some outliers reflecting 

comparatively high values of abstinent days. 

However, posttreatment (and particularly immediately posttreatment), these same measures 

are likely to be negatively skewed, with most 

individuals reflecting comparatively high values 

and some outliers reflecting comparatively low 

values. This nonnormality problem then poses 

a challenge to the researcher: should the same 

transformation be applied both at baseline and 

posttreatment (in order to preserve the integrity of the interpretation of the transformed 

variable), or should different transformations 

be applied to the same measure taken at different times in order to respond to the requisite 
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Part VII: Conclusions and Implications

assumptions under the general linear model?

In Project MATCH, the former approach was 

ultimately selected, although the question was 

thoroughly debated. In the end, employing different transformations on the same variable at 

different points in time was viewed as indefensible. However, this decision necessarily moved 

the analysis plan away from the assumptions 

necessary for the correct interpretation of the 

p values that were critical to the interpretation 

of the results. Alternative operationalizations 

of the primary outcome measures were considered (e.g., a dichotomization such as abstinent/ 

nonabstinent or heavy drinking/not-heavydrinking), but the loss of information in such 

alternative operationalizations was considered 

too great to base the entire trial on it.

A second problem associated with transforming the data to conform to the requisite 

assumptions of the general linear model was 

that, even after transformation, the data did 

not conform very well to these assumptions. 

When you have a large group of heavy drinkers 

(at the beginning of the trial) or of light drinkers 

(immediately following the end of treatment), 

normality cannot be even reasonably approximated in measures such as percentage of days 

abstinent. Thus, the p values on which the trial 

results were based were predicated on untenable assumptions. The exact effect of these 

departures from assumptions is unknown, but 

is likely to have contributed at least somewhat 

to the inability to detect a large number of “statistically significant” matching effects.

It should also be recognized that the primary 

intensity outcome measure (drinks per drinking 

day) possesses a property that limits its interpretability. In order to assign a value to this 

variable for all subjects in the trial, it was necessary to define a value for this indicator to take 

on when the individual did not drink at all during the period under study. After considerable 

debate, the Project MATCH Steering Committee 

decided to retain this variable as one of the two 

primary outcome variables and to assign the 

value zero to anyone whose number of drinking days during the period was zero. Thus, low 

scores on this variable could indicate either of 

two things: a truly low intensity or no drinking 

days at all (and therefore no basis for computing 

the intensity). The potential multidimensionality associated with this variable may also have 

been a contributor to the dearth of significant 

findings. (In fact, fewer matching effects were 

observed for the drinks per drinking day outcome variable than for the percentage of days 

abstinent matching variable.)

Latent Growth Modeling

This study presented one of the largest 

opportunities to introduce the features of latent 

growth modeling to the domain of randomized 

clinical trials. Latent growth modeling provides a feature not available in more traditional 

forms of the general linear model: the ability to 

remove random between-subject variation from 

the error term when testing hypotheses about 

fixed effects (such as the matching hypotheses). 

The approach is particularly useful when the 

individuals in a well-defined subgroup (such as 

those who receive a certain form of treatment 

modality) follow a similar pattern of increase or 

decline in drinking behavior across time. When 

this is the case, removing the between-subject 

variation from the error term of the F test of 

an a priori contrast can greatly increase the 

power of the analysis to detect hypothesized 

relationships.

Unfortunately, the subjects in Project MATCH 

revealed remarkable heterogeneity in drinking 

behavior across time, even among subjects who 

were in identical treatment modalities, identical sites, similar baseline drinking levels, and 

similar demographic profiles. While a clear-cut 

overall decrease in drinking was evident across 

the 12-week treatment period, and a slow 

regression toward more drinking was evident 

across the posttreatment followup period, there 

was little evidence to suggest that well-defined 

subgroups of individuals followed a similar (and 

distinguishable) growth trajectory.

After extensive analysis, it became clear that 

the best theoretically grounded model to fit 

these data was quadratic (allowing for a curvilinear growth pattern across time), but even 

under such a model the between-subject heterogeneity was quite high. As a result of this 

heterogeneity, there was little to commend this 

approach over the more traditional multivariate 

analysis of covariance procedure—a fact which 
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Methodological Assessment and Critique

was born out in the highly similar results across 

the two procedures.

Do these results suggest that latent growth 

analysis (also known as hierarchical linear 

modeling) has a limited (if any) role in longitudinal studies of drinking behavior? Probably not: 

the promise of latent growth is still very real. 

The failure here was likely attributable in large 

part to the previously noted aberrational distributional characteristics of the primary outcome 

measures. With measures which provide clearer 

growth patterns of individual change across 

time, it is likely that latent growth analysis will 

prove to be considerably more powerful than 

the classical multivariate analysis of covariance approach. But with outcome measures 

as heterogeneous as the Project MATCH primary outcome variables, there is little apparent 

justification for more sophisticated analytical 

techniques such as latent growth analysis.

Undiscovered Site Differences

The Project MATCH analysis plan was centered around the assumption of a common 

model of drinking behavior influences for all outpatient subjects (and a distinct common model 

for all aftercare subjects), with site adjustments 

for baseline and demographic differences. This 

assumption provided a large pool of subjects 

(with commensurately large power) for testing 

the primary matching hypotheses.

The common-model assumption is, however, a double-edged sword: while it provides 

the basis for powerful detection of hypothesized 

matching effects, it also opens up the possibility 

of considerable within-sample heterogeneity. In 

essence, after minor adjustments for cross-site 

differences in baseline drinking and demographic characteristics, the assumption views 

the data as if the subjects were all selected from 

a single source, which they were not.

An extensive analysis provided no indication 

of systematic cross-site differences which, if not 

accounted for, would influence the results of 

the study. Nevertheless, the question remains 

whether focusing the analysis on individual 

sites would have reduced the heterogeneity to 

a sufficient level to overcome the resulting loss 

in power.

Conclusion

Why the matches believed to be so promising 

investigated in Project MATCH were not supported remains an uncertainty. Although those 

hoping for recommendations on robust clinical 

matching algorithms were disappointed, a great 

deal of guidance and wisdom regarding the 

design and analysis of subsequent matching 

studies has been achieved. A decade ago, when 

MATCH was conceptualized and implemented, 

the methodology developed by Baron and Kenny 

(1986) for testing for mediators was not well 

understood and had not been disseminated to 

the alcohol treatment research community. By 

the time Holmbeck (1997) published his paper 

clarifying this methodology, MATCH had been 

completed. Now that mediator analysis has 

become well known to the field, future studies 

will benefit from these clarified procedures. The 

MATCH causal chain analysis, despite its early 

inception, provides direction to those seeking 

to undertake mediation analyses of moderator 

variables.

Five lessons learned from Project MATCH 

pertain to the conduct of future treatment outcome studies.

First, it is absolutely critical that the investigator be clear not only about what is being 

hypothesized but also about why that matching 

hypothesis is being proposed. Advancement of 

theory is not well served by being right for the 

wrong reason.

Second, it is equally important that the 

research test both the hypothesis itself and 

the purported causal chain in a statistically 

defensible manner. As we examined each of the 

causal chains presented in this monograph, 

these were the two tenets of causal chain testing protocol that were commonly violated. Even 

when the hypothesized chain was structurally 

sound and well defended, the failure to follow 

the four steps of the Baron and Kenny formulation (or their equivalents under a structural 

equation model) often led to a disappointing 

inability to identify where the chains failed. 

Although requiring minor modification for nonadditive models, the seminal foundation laid by 

Baron and Kenny for testing mediational models 

provides a solid framework for not only determining whether the purported causal model of 
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Part VII: Conclusions and Implications

a matching hypothesis is tenable, but also for 

identifying the locus of failure in those situations where the hypothesized causal model is 

not supported.

In our review of the preceding chapters, we 

found two tenets of the causal chain testing 

protocol that were frequently violated. The first 

was the failure to rigorously specify, in advance, 

the specific causal chain(s) that was (were) 

hypothesized and then to proceed to test exactly 

that chain. The second was the failure to follow 

the four steps of the Baron and Kenny formulation (or their equivalents under a structural 

equation model), often leading to an inability to 

identify where the chains failed.

Third, particularly in alcohol research (where 

the outcome measures often defy requisite 

distributional assumptions of the analytical 

technique), the distributional characteristics of 

the data are important factors in considering 

the choice of analytical technique. Sophisticated 

analytical approaches such as latent growth 

analysis will be of little value in the context of 

data which do not support the requisite distributional assumptions.

Fourth, the above-mentioned point notwithstanding, the promise of latent growth analysis 

looms quite large in longitudinal alcohol research where (1) the outcome measures are not 

characterized by gross skewness and (2) individuals are likely to be characterizable by their 

unique growth pattern. Project MATCH demonstrated the feasibility of latent growth analysis 

in a multisite randomized clinical trial.

Fifth, while a larger number of subjects 

means greater power to detect hypothesized 

effects, larger is not always synonymous with 

better especially when the subjects come from 

multiple sites. The increased heterogeneity may 

offset the gains in power. To the extent that a 

smaller sample accurately reflects the systemic 

behavior of a smaller definable population of 

theoretical or practitioner interest, the investigator might be well advised to move in this 

direction rather than focusing on a larger, multisite, sample.

Some have criticized Project MATCH because 

it did not support large numbers of matching 

hypotheses. While this was disappointing to 

investigators and interested parties alike, we 

believe that the number of lessons learned from 

this study have moved the field forward. We 

do not agree with those who view the negative 

results from this trial as a widespread reflection 

of the futility of matching studies. With the lessons learned from this trial in hand, we believe 

that the future of alcohol matching studies has 

been considerably strengthened.

Acknowledgments

This project was supported by a series of 

grants from the National Institute on Alcohol 

Abuse and Alcoholism as part of the Cooperative 

Agreement on Matching Patients to Alcoholism 

Treatments. The authors wish to extend their 

thanks to George Howe and Paul Poppen for 

their comments on the mediation section of this 

document.

References

Aiken, L., and West, S. Multiple Regression: 

Testing and Interpreting Interactions. 

Newbury Park: Sage Publications, 1991.

Baron, R.M., and Kenny, D.A. The moderatormediator variable distinction in social 

psychological research: Conceptual, strategic, and statistical considerations. Journal 

of Personality and Social Psychology

51:1173–1182,1986.

Finney, J.W.; Mitchell, R.E.; Cronkite, R.C.; 

and Moos, R.H. Methodological issues in 

estimating main and interactive effects: 

Examples from coping/social support and 

stress field. Journal of Health and Social 

Behavior 25:85–98, 1984.

Holmbeck, G.N. Toward terminological, conceptual, and statistical clarity in the study of 

mediators and moderators: Examples from 

the child-clinical and pediatric psychology literatures. Journal of Consulting and 

Clinical Psychology 65: 599–610,1997.

McClelland, G.H., and Judd, C.M. Statistical 

difficulties of detecting interactions and 

moderator effects. Psychological Bulletin 

114(2):376–390, 1993.
305

Substantive Review and Critique

Richard Longabaugh, Ed.D., and Philip W. Wirtz, Ph.D.

ABSTRACT

The first part of this chapter summarizes all of the client-treatment interactions 

observed in Project MATCH, irrespective of whether they were hypothesized. For those 

hypothesized, we examine the extent to which their underlying causal chains were 

supportive or still remain unknown. For emergent interactions, we evaluate their plausibility as matching hypotheses to be tested in future investigations. We conclude 

that MATCH detected evidence for several small interaction effects. We then address 

the lessons learned from the causal chain analyses. In the second part, we examine 

implications of our findings, first for future matching research and then for alcohol 

treatment research more generally. We conclude that Project MATCH’s investigation of 

the mediation of matching effects has provided guidance for future alcohol treatment 

research.

We are now reaching the completion of 

our journey. The aims of this volume 

were first to present the rationale for 

each of the a priori matching hypotheses, the 

hypotheses themselves, and results. The second 

aim was to present the theory underlying each 

of the matching hypotheses and the results of 

testing these theories by causal chain analysis. For unsuccessful predictions, the causal 

chain analysis identified how the theory failed. 

For supported matching predictions, the causal 

chain analyses tested whether the underlying 

theoretical assumptions were the mechanisms 

through which the hypothesized interaction 

occurred. These analyses and results have now 

all been reported. The preceding chapter provides a methodological critique of the various 

approaches taken to testing the causal chains 

and matching predictions.

In the first part of this chapter, we attempt 

an integration of the overall effort, incorporating all data from the observed client-treatment 

interactions and causal chain analyses. Next, 

we examine the unique contribution of the 

causal chain analysis to understanding Project 

MATCH results. In the second part, we offer 

our recommendations and perspectives on 

treatment matching specifically and future 

treatment research more generally.

 Summary of Observed 

Client Attribute-Treatment 

Modality Interactions

Before undertaking this review, introductory comments are in order. The presentation 

is organized by arm of study, and within arm 

of study, by phase of treatment—within treatment followed by posttreatment effects. Within 

each section, first the a priori hypothesized 

interactions that were supported are addressed 

in detail. Here our assumption is that these 

hypotheses have been supported. Therefore, our 

focus is in critiquing how well we understand 

the theory underlying the matching prediction. Next we describe the observed interactions 

Richard Longabaugh, Ed.D.

Center for Alcohol and Addiction Studies 

Brown University, School of Medicine

800 Butler Drive, Potter Building, Room 204 

Providence, RI 02906

E-mail: Richard_Longabaugh@Brown.edu
306

Part VII: Conclusions and Implications

that were not hypothesized. Here our focus is 

on exploring the plausibility of the interaction. 

Is it simply a chance occurrence or is it possible that it reflects a true effect? Causal chain 

analysis is used to evaluate the plausibility of 

the observed interaction. As the probability of 

finding both an observed interaction and a supporting causal chain by chance alone would be 

extremely low, this observation would suggest 

further research may be warranted.

It should be noted that in many instances 

observed interactions were not involved in an a 

priori contrast. Consequently, pertinent causal 

chain analyses were not conducted. In other 

instances, however, the treatment contrasts 

were involved in an a priori hypothesis, but the 

observed outcome was opposite that predicted. 

Here causal chain information is more likely to 

be available and pertinent.

Finally, given our evaluation in the last chapter that few of the underlying theories for the 

a priori matching hypotheses were both conceptually explicit and clearly and correctly 

operationalized, inferences drawn from the 

causal chain analyses are necessarily speculative. No supporting causal chain analysis was 

carried through to the last step of ruling out 

competing interpretations for the observed 

matching effect. Further, causal chain analyses 

of failed matching hypotheses were not required 

to identify all of the linkages in which the causal 

chain was unsupportive—only one failed link 

was needed. Thus, complete information necessary to critique each matching hypothesis is not 

available. Despite these limitations, we believe 

there is sufficient information from the causal 

chain analyses to enrich our understanding 

of the client attribute-treatment interactions 

tested.

The Observed “Hit Rate”

Considering the large number of interactions 

tested, it is entirely possible that all of the interactions observed may be attributed to chance. 

Leaving aside interactions involving time, only 

3 percent of the a priori hypothesized contrasts were supported at the Bonferroni level of 

adjustment. Twelve contrasts met our criteria 

for significance out of 400 tested (2 dependent 

variables × 40 hypothesized contrasts, each 

tested 5 times—aftercare within- and posttreatment and outpatient within- and posttreatment 

and months 37–39).

This suggests two possibilities. Perhaps 

all a priori hypotheses are untrue, with those 

appearing to be supported being attributable 

to chance alone. Another possibility is that we 

made our criteria for rejecting the null hypothesis too stringent. The familywide Bonferroni 

correction may have led us to underdetect valid 

client-treatment interactions.

In order to provide a context for assessing 

these alternative interpretations, it is useful 

to provide the studywide hit rate for tests of 

interactions. Had we specified a studywide 

uncorrected alpha level of 0.05, we would expect 

on average 5 percent of all nonexistent interactions tested to meet our criteria by chance. 

Overall, across the 2 study arms, 2 dependent 

variables, 21 matching variables, and 3 treatment contrasts per matching variable (CBT vs. 

MET, CBT vs. TSF, MET vs. TSF), 504 tests of 

attribute by treatment modality interactions 

were conducted (excluding those involved in 

either a linear or quadratic interaction with 

time). Of these, 38 were observed to be significant at p≤05, 7.5 percent of those tested. This 

hit rate is somewhat greater than that expected 

by chance alone.

A possible implication of this comparison is that our attempt to capitalize on what 

we thought we already knew to develop a priori predictions actually impeded our ability to 

uncover evidence for matching. Had we been 

less confident of our predictions, we might have 

conducted an exploratory hypothesis-seeking 

analysis with one portion of the data and used 

the other as a holdout sample upon which to 

test the emergent hypotheses. The Steering 

Committee considered this alternative but ultimately rejected it because of our (in retrospect 

unfounded) confidence in the predictions that 

were developed.

While the MATCH requirement of a priori 

hypotheses had the great virtue of sharpening 

the focus of study, it also precluded examination of emergent interactions that had not been 

predicted. Now that all of the hypotheses have 

been tested, exploratory investigations of other 

observed interactions are appropriate. With this 
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Substantive Review and Critique

perspective in mind, we examined the tables in 

the separate chapters reporting the results for 

interactions tested though not hypothesized. 

We extracted any interaction that met the p≤.05 

alpha level and incorporated these results into 

appendices 1 and 2. These show all interactions 

that occurred, whether hypothesized or not, 

including those involving linear and quadratic 

time. Appendix 1 summarizes the outpatient 

arm, appendix 2 the aftercare arm.

As this information, though of potential significance, is too vast to address, table 

1 presents for both arms of study only those 

interactions that did not involve a time dynamic 

(i.e., an interaction of the matching effect with 

time). Treatment contrasts involving 14 of the 

21 matching variables met the criteria specified. While several of the interactions involving 

change over time are quite interesting and 

even comprehensible (i.e., the matching effect 

increases or decreases over time), we have not 

yet identified the tools necessary to undertake a 

causal chain analysis that could explain these 

dynamics.

Outpatient Arm

Within-Treatment Interactions

No matching prediction was observed during the within-treatment period that met the 

Bonferroni level of adjustment. Four interactions were observed with p’s ≤05.

Psychopathology and Psychiatric Severity.

During treatment, there was evidence supporting an a priori matching hypothesis that CBT 

would be more effective than MET for more psychologically impaired clients. Not hypothesized 

but observed, TSF was also more effective than 

MET for more impaired clients. This suggests 

either that CBT and TSF each may have a distinct component that is helpful to more impaired 

participants which MET lacks, or that CBT and 

TSF may share an active ingredient lacking in 

MET that may help high psychopathology clients. One obvious active ingredient shared by 

CBT and TSF was the greater number of treatment sessions available (12 vs. 4). This may 

have accounted for this beneficial effect during 

the treatment period (rather than CBT’s hypothesized greater focus on psychopathology).

Gender. It had been hypothesized that 

women would do better in CBT than in TSF, relative to men. In fact, the opposite was observed. 

Women treated in TSF had more abstinent days 

during treatment than when treated in CBT. 

Causal chain analysis revealed that most of 

the differences related to instrumentality and 

expressiveness upon which the gender matching 

hypothesis was predicated were in fact present. 

Where the causal chain appeared to fail was 

in CBT’s inability to influence these characteristics as expected. Instead, one of the causal 

chains tested (involving both canonical models 

1 and 2 (see pp. 21–26)) showed that females in 

CBT reduced their psychiatric severity less than 

did males in CBT, and less than either males or 

females in TSF. This would suggest that CBT’s 

relative failure to diminish women’s psychiatric severity may have accounted for their lesser 

improvement in drinking while in treatment. 

This causal chain is not completely supportive 

of the theory, however, as posttreatment psychiatric severity did not predict days abstinent 

during treatment for women. Thus, the puzzle 

remains. It should be noted that several of the 

hypothesized causal chains were not directly 

tested, so the mediating mechanism for TSF’s 

superiority for women may yet be detected.

Sociopathy. It was hypothesized that CBT 

would be more effective than MET for clients 

high in sociopathy. Instead, during treatment, 

clients with high sociopathy fared equally well 

in CBT and MET. However, clients low in sociopathy had a higher percentage of days abstinent 

(PDA) when treated in CBT than their MET 

counterparts. This would suggest that CBT is 

either mismatched to high sociopathy, or MET 

is mismatched to clients low in sociopathy.

While the well-articulated causal chains 

did not attempt to account for drinking during 

treatment, indirect support for the interaction 

was observed in one of the causal chains conducted to account for posttreatment drinking. 

This type 3A canonical chain involved the working alliance. CBT clients low on sociopathy were 

more likely to have a better working alliance, 

and a better working alliance was related to better PDA. For MET clients, working alliance was 

unrelated to drinking outcomes. The linkages 

in the causal chain are thus consistent with the 
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Part VII: Conclusions and Implications

Table 1. Observed interactions

Matching 

variable

Study 

arm1

Observation 

period2

Hypothesized 

contrast3

Observed 

contrast4 Predicted5

Dependent 

variable6

Causal chain 

supportive?7

Psychiatric 

Severity OPT Treatment CBT>MET CBT>MET yes* PDA no

Gender OPT Treatment CBT>TSF TSF>CBT opposite PDA no, partially 

tested

Sociopathy OPT Treatment CBT>MET MET>CBT opposite PDA yes 

Prior AA OPT Treatment MET>CBT no PDA, DDD not tested

Psychiatric 

Severity OPT year 1+ CBT>TSF CBT>TSF yes PDA no 

Anger OPT years 1, 3 MET>TSF, 

CBT MET>TSF yes PDA, DDD no 

MET>CBT yes PDA, DDD no

Self-Efficacy OPT year 1 MET>CBT CBT>MET opposite DDD yes 

Social 

Functioning OPT year 1 CBT>MET MET>CBT opposite PDA, DDD no 

CBT>TSF TSF>CBT opposite PDA, DDD no 

Prior AA OPT year 1 MET>CBT no PDA not tested 

Interpersonal 

Dependency OPT year 1 CBT>MET no PDA no 

year 3 CBT>MET no DDD no 

year 3 MET>TSF TSF>MET opposite DDD no

Network 

Support OPT year 3 TSF>MET TSF>MET yes PDA, DDD yes

A vs. B 

Typology OPT year 3 MET>TSF TSF>MET opposite DDD no 

MET>CBT CBT>MET opposite DDD no

Self-Efficacy AFT Treatment MET>CBT MET>CBT yes* PDA, DDD no 

Treatment MET>TSF MET>TSF yes PDA, DDD no 

ASPD AFT Treatment CBT>TSF CBT>TSF yes* DDD not tested 

Temptation AFT Treatment CBT>MET no PDA not tested 

Readiness AFT Treatment CBT>MET MET>CBT opposite PDA no

Readiness AFT year 1 TSF>CBT no PDA not tested 

Alcohol 

Dependence AFT year 1 TSF>CBT TSF>CBT yes PDA, DDD yes

1. OPT = outpatient, AFT = aftercare

2. Treatment = months 1–3, year 1 = first year after treatment completion (months 4–15), year 3 = 37–39 months after treatment 

initiation

3. A priori

4. p≤.05, 2-tailed test

5. yes = difference in slopes same as hypothesized, opposite = observed contrast in slopes opposite what was predicted, no = no 

a priori contrast hypothesized

6. PDA = percentage of days abstinent, DDD = drinks per drinking day

7. yes = causal chain supports the observed interaction (irrespective of what was hypothesized), no = there is not a complete 

linkage supporting the observed interaction even though one or more was tested, not tested = a complete linkage of the causal 

chain to the observed interaction was not tested.

* = hypothesized contrast but significant only when unprotected (p≤.05)

+ = There was both an attribute by treatment interaction and a time by attribute by treatment interaction. The contrast was 

significant at months 5–11. The finding is included because of the fact of the attribute by treatment interaction.
309

Substantive Review and Critique

direction of the observed sociopathy-treatment 

modality interaction. This supports the credibility of the interaction observed despite its 

variance from the original hypothesis.

Prior AA. No matching effect had been hypothesized for the interaction of prior involvement 

in Alcoholics Anonymous (AA) and MET versus 

CBT treatment assignment. However, it was 

observed that for those with prior AA involvement, MET is predictive of higher PDA and fewer 

drinks per drinking day (DDD), while for CBT 

clients, prior AA is predictive of poorer drinking 

outcomes. As this contrast was not a candidate 

for causal chain analysis, only speculation can 

be offered. MET encourages clients to develop 

their own change plans, which may lead to utilization of AA during and after treatment. In 

contrast, CBT, with its own prescriptions for 

client change, may inadvertently work against 

whatever predilections some clients would have 

for invoking AA prescriptions for recovery.

Posttreatment Interactions

Psychiatric Severity and Psychopathology. An 

ordinal interaction effect was hypothesized for 

psychopathology. It was expected that for low 

levels of psychopathology, treatment assignment would not make a difference, but as 

psychopathology increased, clients assigned to 

CBT would have better drinking outcomes than 

those assigned to either TSF or MET. An ordinal 

interaction was observed; however, contrary to 

expectations, clients with low psychopathology 

had better drinking outcomes in TSF than in 

CBT. For those with high psychopathology, CBT 

was neither better nor worse than TSF.

Despite the unanticipated implication for 

clinical triaging—assign low psychopathology 

clients to TSF—the theoretical premise underlying the hypothesized interaction was supported. 

As CBT client psychopathology increased, 

drinking outcomes improved. As this was not 

so for TSF, where clients outcomes were unaffected by their level of psychopathology, it could 

be concluded that CBT was helping clients with 

greater psychopathology more than it was helping those with less. However, CBT’s increased 

efficacy for clients with higher psychopathology raised their level of outcome sufficiently 

only to equal that of comparable TSF clients. 

This suggests that TSF has one or more active 

ingredients more helpful to all alcohol clients 

than does CBT. CBT’s increased efficacy for the 

subset of clients with high psychopathology 

appears to compensate for the absence of these 

unidentified active ingredients.

The matching effect observed was no longer 

significant after the tenth month of posttreatment, suggesting that the differential effect of 

CBT versus TSF on clients varying in their psychopathology fades over time.

Multiple attempts to identify one or more 

underlying supportive causal chains for this 

interaction, both a priori and post hoc, were 

unsuccessful. These attempts, when decomposed, involved canonical models 1 and 3A. The 

causal chains indicated that, contrary to what 

was hypothesized, there was neither greater 

attention to client psychopathology in CBT nor 

did CBT clients experience a greater reduction 

in psychopathology following treatment. The 

mechanisms by which this interaction is produced are yet to be identified.

Anger. Client anger was observed to interact 

with MET versus TSF/CBT treatment modalities to produce a disordinal interaction during 

followup. This matching effect was the most 

consistent one observed in the entire study. 

Evidence for the matching effect was present at 

all followup points—at 3-year followup as well 

as during the first year of followup. However, the 

theoretical underpinnings for this effect could 

not be identified in the causal chain analyses 

conducted, which involved canonical models 

type 2 (for taking steps and problem recognition) and type 3 (for working alliance).

Also problematic, the a priori matching 

hypothesis proposed was ordinal: that angry 

clients would have better drinking outcomes 

in MET than in either CBT or TSF. The theory 

underlying this matching hypothesis was that 

high anger clients would be more resistant to 

treatment than would low anger clients. It was 

expected that MET, with its nonconfrontational 

stance, would reduce the client’s resistance 

to treatment, and by doing so, produce better 

drinking outcomes. Unanticipated was that low 

anger clients would have poorer drinking outcomes in MET than in CBT/TSF.

Nevertheless, a review of treatment research 

findings for other psychological dysfunctions 

indicates that a disordinal matching effect 
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Part VII: Conclusions and Implications

similar to this one has often been observed. If 

the state-trait anger variable is considered to 

be an index of reactance (Brehm 1976; Brehm 

and Brehm 1981), it has been found that psychotherapy clients with high resistance (also an 

index of reactance) are most effectively treated 

with a supportive, nonconfrontational, low 

directive therapy, while those low in reactance 

(or resistance) are most effectively treated with 

a more directive therapy (Beutler et al. 2000).

The hypothesis team was at a disadvantage 

regarding the selection of the putative mediators 

of this matching prediction. The assessment 

battery had already been decided upon by the 

time Secondary Matching Hypothesis Teams 

had completed development of their causal 

chains. The Anger Matching Hypothesis Team 

selected five indices hypothesized to characterize an absence of client resistance—two 

measures from the SOCRATES readiness scale: 

Problem Recognition and Taking Steps for 

Change, and three measures from the Working 

Alliance: Agreement on Treatment Goals, Tasks, 

and Bonding.

A first problem with these indices was that 

only one, Working Alliance Goals, was negatively 

related to client anger as would be expected and 

necessary for the underlying causal chain to be 

supportive. The two other indices of working 

alliance were unrelated to client anger. Problem 

Recognition was actually significantly related to 

client anger in the opposite direction predicted. 

Thus, there was a breakdown in the causal 

chain in the first linkage.

While the hypothesis team observed that the 

indices of working alliance partially mediated 

the relationship of the anger-treatment matching variable to drinking, the analysis conducted 

included working alliance variables as both 

main effects and product terms. As the separate effects were not reported, their status as 

putative mediators of the moderating effect is 

con-founded with their main effects.

As discussed in the preceding chapter, the 

fact that the relationships were reduced from 

significant to nonsignificant when these mediators were partialed out might also be attributable 

to the greater number of degrees of freedom utilized in the mediator analyses.

The one putative mediator of the matching 

effect that appears to be operative is Problem 

Recognition, which reduces the relationship of 

the treatment-anger variable to PDA to nonsignificance and is also related to client anger. 

However, this relationship is positive rather 

than negative. This suggests that angry clients 

are more likely to report problem recognition, 

which when paired with MET as opposed to 

CBT and TSF, leads to better PDA.

This causal chain involving problem recognition has migrated considerably from the causal 

chain from which the hypothesis team started. 

Waldron et al. (this volume) conclude that the 

data at hand do not provide an adequate operationalization of Resistance. They expect that 

tape ratings of therapy sessions may pro-vide 

such an index in the future. Beutler’s re-search 

is supportive of this interpretation. He and his 

colleagues have found that observer ratings of 

therapist directiveness and client resistance 

produce the anticipated disordinal matching 

effect (Beutler et al. 2000; Karno et al. in press). 

Thus, despite the lack of a supporting causal 

chain, the disordinal matching effect is highly 

credible.

Network Support for Drinking. As hypothesized, clients with network support for drinking 

prior to treatment had better drinking outcomes at 3-year followup than those assigned 

to MET. Although hypothesized to be ordinal, 

the interaction actually gave indication of being 

disordinal, as clients with little support for 

drinking prior to treatment had significantly 

(but only slightly) better outcomes in MET than 

in TSF.

The causal chain model tested was type 1. 

The analyses indicated that participation in 

AA was in part responsible for the observed 

attribute-treatment interaction. Clients treated 

in TSF were more likely to participate in AA, 

which in turn was differentially associated with 

better drinking outcomes, with those high in 

network support benefiting the most. However, 

the significance of the matching effect was only 

substantially reduced, not eliminated, when the 

effect of AA participation was partialed out. So 

other mediators, as yet unidentified, also contribute to this attribute-treatment interaction.

Another point of interest here is that the 

mediator variable itself turned out to be a moderator variable, the product term of network 

support for drinking and AA participation and 
311

Substantive Review and Critique

treatment modality. The enhanced benefit of AA 

participation for clients with networks supportive of drinking was most apparent in TSF. This 

suggests that the compatibility of treatment 

modality and self-help group belief system is 

likely to be an important factor to consider in 

matching. Research by McCrady and colleagues 

(1999) bolsters this speculation. These investigators found that a cognitive-behavioral Alcohol 

Behavior Couples Therapy followed by AA 

involvement was less helpful than a cognitivebehavioral relapse prevention aftercare model.

Finally, two other aspects of this attributetreatment modality interaction have yet to be 

satisfactorily accounted for. First, why did clients with networks unsupportive of drinking 

actually do somewhat better in MET than in 

TSF? No explanation or analysis was offered to 

account for this tail of the disordinal interaction. 

Second, it is not clear why the interaction that 

eventually emerged for TSF’s superiority with 

clients having networks supportive of drinking did not do so earlier in the followup period. 

Presumptively, participation in AA after treatment completion explains the delayed matching 

effect, but this has not been tested.

Nonpredicted Outpatient Interactions

Nonhypothesized interactions involving five 

matching variables were observed during the 

posttreatment period: Prior AA, Self-Efficacy, 

Social Functioning, Interpersonal Dependence, 

and Typology.

Prior AA. The effects of prior AA involvement in 

the MET and CBT treatment contrast observed 

during treatment continued into the first year 

of posttreatment. MET clients who had been 

involved in AA prior to MATCH treatment continued to have more abstinent days than those not 

so involved, while for CBT clients, the reverse 

was observed. The pervasiveness of this interaction into the posttreatment period strongly 

suggests that further study is warranted.

Self-Efficacy. It was hypothesized that selfefficacy would interact with treatment modality 

such that clients low in self-efficacy would do 

better in CBT than in MET. In the outpatient arm 

(in contrast to the aftercare arm), an interaction 

occurred that suggested the opposite. Low selfefficacy clients treated in MET had fewer drinks 

per drinking day than those treated in CBT.

The causal chain analysis supported this 

finding. Employing a canonical model 3A, an 

interaction effect involving change in self-efficacy in CBT versus MET was tested. It was found 

that low self-efficacy MET clients had a greater 

increase in self-efficacy before to after treatment 

than did CBT clients. This increase in self-efficacy predicted end of treatment drinking which 

in turn predicted self-efficacy at 9 months and 

drinks per drinking day in the year following 

treatment. Thus, the causal chain analysis, by 

supporting the observed interaction, strengthens the credibility of this finding, despite its 

opposition to what had been hypothesized.

Social Functioning. It was hypothesized that 

CBT would be more effective than either TSF or 

MET for clients who had poor social functioning. In fact, the opposite effect was observed. 

Clients with poor social functioning who were 

assigned to CBT had fewer abstinent days and 

more drinks per drinking day throughout the 

first year of followup. Clients with high social 

functioning initially did better in CBT, but this 

superiority dissipated as the year went on.

The causal chain analysis was limited to 

determining where the a priori theory had broken down. While it was found that, as expected, 

posttreatment social functioning predicted 

posttreatment drinking, CBT did not improve 

social functioning more than did MET or TSF. 

This explained why CBT was not superior to 

MET and TSF for poorly functioning clients. 

However, it does not explain why it should be 

significantly worse. Post hoc causal chain analyses are necessary to explore this finding.

Interpersonal Dependency. No a priori predictions were made regarding contrasting 

effects of CBT and MET on clients varying in 

their interpersonal dependency. Nevertheless, 

interactions were observed for PDA in the year 

following treatment and for DDD at the 3-year 

mark. During the year following treatment, MET 

clients with higher dependency had fewer days 

abstinent than those with low dependency. At 

3 years out, higher dependency in CBT was 

associated with fewer drinks per drinking day. 

Thus, in one case, high-intensity treatment was 

beneficial to more dependent clients, and in the 

other, low intensity treatment was beneficial to 

those with low dependency.

At 3 years, as had been hypothesized, TSF was 
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Part VII: Conclusions and Implications

associated with fewer drinks per drinking day 

for more highly dependent clients. Combined 

with the results for CBT, this suggests that 

interpersonal dependency is predictive of fewer 

drinks per drinking day in higher intensity 

treatments, but unrelated to DDD in low intensity treatments. The causal chain analyses, 

involving treatment completion and treatment 

satisfaction in a canonical model 3A do not provide any clues for these observed differences. 

Interpersonal dependency is unrelated to both 

treatment completion and satisfaction in all 

three treatments.

A Versus B Typology. It was predicted that 

clients having a type A typology would have better drinking outcomes in CBT and TSF than in 

MET, while the reverse would be true for type B 

clients. At 3-year followup, the opposite effect 

was reported. Three causal chains were tested. 

The first tested amount of therapeutic structure 

and cognitive change in a canonical model 1. 

The second and third were type 3A canonical 

models and tested working alliance and change 

in psycho-pathology as potential mediators. 

The models indicated various links in which the 

causal chains broke down but did not provide 

any clues as to why the unpredicted interactions occurred. Given the gap in time between 

treatment and 3-year followup and the absence 

of a connecting causal chain, it is likely that 

this finding is spurious.

Summary of Outpatient Arm

During the within-treatment period, none 

of the a priori predictions was supported at a 

protected level of confidence. Therefore, nothing 

definitive can be said about matching clients to 

treatment modalities during outpatient treatment. If all observed interactions are taken into 

account, a multidimensional typology would be 

necessary to develop useful hypotheses about 

which clients would do better and worse in these 

treatments. For example, it might be hypothesized that for women, those who are high in 

psychopathology would do best in TSF and less 

well in CBT. For men high in psychopathology 

and low in sociopathy, CBT might be optimal, 

while MET could be contraindicated.

In the posttreatment period of observation, 13 attribute-treatment interactions were 

observed. Four of these were predicted, involving 

psychiatric severity, anger (in two treatment 

comparisons), and network support for drinking. Six, involving four matching variables 

self-efficacy, social functioning, interpersonal 

dependency, and typology—were opposite the 

a priori predictions. Three others emerged in 

the absence of any predictions. One interaction involving prior AA that emerged during 

treatment persisted through the first year of follow-up. Two other interactions, involving anger 

and interpersonal dependency, persisted from 

the first year of posttreatment followup through 

to the third.

Whether the criterion be a priori predictions 

or observed interactions, evidence is sufficient 

for posttreatment matching effects in the outpatient arm of study.

Aftercare

Within-Treatment

Self-Efficacy. The self-efficacy matching hypothesis was strongly supported in the 

aftercare arm during treatment. As the effect 

disappeared after treatment had been completed, the practical advantage to be gained for 

clinical purposes is diminished. Nevertheless, it 

has theoretical significance.

It was hypothesized that MET would be more 

effective than either CBT or TSF for clients with 

higher self-efficacy. However, with lower selfefficacy, it was predicted that CBT and TSF 

would be more effective. Results indicated that 

client self-efficacy made a difference in MET, 

such that the lower the client’s abstinence selfefficacy, the poorer the drinking outcomes. In 

contrast, client self-efficacy did not appear to 

affect treatment success in either CBT or TSF. 

The net result of these effects was that low selfefficacy MET clients did more poorly during 

treatment than their counterparts in CBT and 

TSF. At higher levels of self-efficacy, treatment 

modality did not differentiate drinking patterns 

during treatment.

The causal chain underlying the hypothesis 

anticipated that treatment modality and pretreatment self-efficacy would interact to affect 

self-efficacy during treatment (canonical model 

3A). This differential change in self-efficacy 

would in turn interact with pretreatment selfefficacy to influence drinking. The causal chain 
313

Substantive Review and Critique

failed in that CBT increased the self-efficacy of 

all clients more than did MET, thus failing to 

account for the matching effect.

Nonpredicted Within-Treatment 

Interactions

In addition to the predicted interaction involving self-efficacy and treatment modality, three 

other client variables were observed to interact 

with treatment modality: motivational readiness, antisocial personality disorder (ASPD), 

and temptation minus confidence.

Antisocial Personality Disorder. It was 

hypothesized that CBT would be more effective than TSF in treating clients with ASPD. 

For drinks per drinking day, this contrast was 

significant at p<.024 (a value larger than what 

was required to meet the Bonferroni correction). 

As causal chain analyses were conducted only 

with the related variable, sociopathy, no pertinent information is available to evaluate this 

attribute-treatment modality interaction.

Temptation Minus Confidence. The construct 

of temptation minus confidence (closely related 

to self-efficacy, which did show the hypothesized 

interaction during treatment) was involved in 

an interaction contrasting CBT and MET during treatment. Highly tempted CBT clients had 

more abstinent days than did their MET counterparts. As this contrast was not involved in an 

a priori hypothesis, no causal chain was developed or tested for this effect.

Motivational Readiness. Motivational readiness was hypothesized to interact with CBT 

and MET because clients with low readiness 

were expected to respond to MET more than 

to CBT. It was expected that highly motivated 

clients would respond equally well to the two 

treatments. Thus, an ordinal interaction was 

hypothesized. The interaction observed was 

opposite that predicted. Clients with low motivational readiness apparently achieved higher 

PDA when treated in CBT versus MET. For 

those with high motivation, treatment assignment made less of a difference. Causal chain 

analyses failed to support the observed interaction, reducing its credibility.

Posttreatment Interactions

During the 1-year posttreatment period, 

one hypothesized interaction was observed, 

involving alcohol dependence. A second, involving motivational readiness, emerged in the 

absence of a prediction.

Alcohol Dependence. A hypothesized disordinal matching effect was observed. The higher 

the clients’ alcohol dependence, the more likely 

they would achieve a higher percentage of abstinent days and fewer drinks per drinking day 

when treated in TSF versus CBT. Conversely, 

those with lower alcohol dependence would 

achieve more PDA and fewer DDD when treated 

in CBT versus TSF. This disordinal interaction 

met all criteria for matching—clients high on 

dependence had significantly better drinking 

outcomes when treated with TSF, while those 

low on dependence had significantly better 

drinking outcomes when treated with CBT.

Two causal mechanisms were hypothesized 

to be responsible for this interaction—therapist 

emphasis on abstinence and client participation in AA. In both cases, the canonical causal 

chain model tested was type 1. Only therapist 

emphasis on AA was supported. Lack of therapist emphasis on abstinence, associated with 

CBT, was found to explain the superiority of 

CBT for clients with low alcohol dependence. 

As alcohol dependence increased, the superiority of CBT diminished, so that at high levels 

of dependence, the treatments were not distinguishable in their effectiveness.

Left unexplained by this mediator analysis 

is why TSF clients with high dependence had 

better drinking outcomes than comparable CBT 

clients. It would be expected that as dependence 

increases, therapist emphasis on abstinence 

would enhance drinking outcomes. From figure 

5 in the Cooney and Babor chapter, it can be 

seen that this was not the case. While lack of 

emphasis on abstinence enhanced PDA for those 

less dependent, emphasis on abstinence did not 

enhance outcomes for those more dependent. 

The implication is that some other active ingredient, associated with TSF, was responsible for 

increasing the PDA of highly dependent clients. 

This ingredient is yet to be identified.

Nonpredicted Posttreatment Interactions

Motivational Readiness. During the year following treatment, MET and TSF were observed 

to interact with motivational readiness. No 

hypothesis had been offered for this contrast. 
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Clients with low motivation treated in TSF had 

fewer drinking days than those treated in MET. 

As motivation increased, the treatment differences in PDA decreased. As this did not pertain 

to a hypothesized contrast, no pertinent causal 

chain analyses were conducted. It is of interest 

to note, however, that in both the within-treatment contrast with CBT and the posttreatment 

contrast with TSF, clients with low motivation 

who were treated in MET had more drinking 

days. This is inconsistent with the notion that 

MET is helpful because it increases the motivation of less motivated clients.

Summary of Aftercare Arm

Five interactions were observed during the 

aftercare treatment. Three were predicted a 

priori (although only one achieved a protected 

level of significance), one was opposite that predicted, and one was observed in the absence of 

a prediction. Within-treatment matching effects 

occur as often in aftercare treatment as in 

standalone outpatient treatment. Of note, however, of the aftercare interactions observed, four 

of the five involved variables as much reflective 

of state as trait (i.e., readiness, self-efficacy, and 

temptation minus confidence). It is plausible 

that client states would be more responsive to 

ongoing aftercare treatment than would traits.

In contrast, only two matching effects following aftercare treatment were observed. It 

may be that the confounding of more intensive 

treatment with the MATCH aftercare treatment 

diluted posttreatment matching effects that otherwise might have occurred had MATCH been a 

standalone treatment.

Commentary

In summary, this review of observed client 

attribute-treatment modality interactions calls 

attention to several that had not been previously addressed because either:

■ Although they were hypothesized a priori, they did not achieve a familywide 

protected level of significance using a 

Bonferroni adjustment.

■ They were not hypothesized a priori.

■ The results observed were in the direction opposite that hypothesized.

Review of all 21 matching variable candidates points to 7 that were not involved in an 

interaction affecting drinking outcomes (excluding interactions involving a time dynamic): 

alcohol involvement, psychiatric comorbidity, 

cognitive impairment, conceptual level, meaning seeking, religiosity, and problem recognition. 

However, some of these variables had prognostic value, unaffected by treatment assignment: 

alcohol involvement, meaning seeking, religiosity, and problem recognition (see appendix 

3). In such instances, it can be concluded that 

their prognostic effect was not moderated by 

this set of treatment modalities, posing a challenge to those trying to develop more effective 

treatments.

Only two client variables, conceptual level 

and cognitive impairment, appear to be irrelevant for prognostic or treatment assignment 

purposes. Thus, it would appear that all of the 

variables included in Project MATCH’s selection as candidates for matching are, in at least 

some very small degree, likely to be pertinent to 

assessing the effectiveness of treatment.

Implications of Matching 

Results

Having completed our review of all attributetreatment interactions not involving a time 

dynamic (i.e., an interaction with time), what 

implications can be drawn for the idea of matching clients to treatments? Does client-treatment 

matching affect drinking outcomes? If so, is it 

possible to conduct treatment outcome research 

that will demonstrate these matching benefits?

Does Client-Treatment Matching 

Occur?

Does client-treatment matching occur? 

Intuitively, the answer is yes. At the most general level, clients treated for one disorder who 

in fact have a different disorder are less likely to 

respond to the treatment. Confining our scope 

to clients treated for alcohol problems, there is 

evidence that treatment service matching to client profile affects outcomes (e.g., McLellan et al. 

1983). The scope of MATCH, however, was more 

narrowly focused. Our aim was to compare 

three individually delivered, manual guided 

and structured psychosocial therapies in their 

capacity to be matched or mismatched to 21 client attributes, mostly traits and a few states.
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Substantive Review and Critique

Within this limited scope, what do we conclude about matching in Project MATCH? 

Having reviewed all of our matching results, 

either there is little evidence for matching at this 

level of specificity or there is ample evidence for 

multiple small matching effects.

Argument for No Evidence for Matching

The results observed in Project MATCH may 

indicate that client attribute-treatment interactions do not occur in a way that would affect 

client drinking behaviors in a predictable way. 

We failed to find more than 3 percent of the 

hypothesized interactions significant at the 

protected level. Moreover, those that were significant accounted for small portions of the 

variance. No interaction was significant at all 

periods of observation nor was any hypothesized interaction significant in both arms of the 

trial. There is no indication in these data that 

strong matching effects exist, indicating that 

the matching paradigm may have outlived its 

heuristic value.

Argument for Multiple Small Matching 

Effects

An alternative conclusion is that MATCH 

results suggest the presence of several small 

matching effects between single client attributes and these three treatment modalities. As 

the number of observed interactions may exceed 

what could be attributed to chance alone, it is 

likely that something is going on, but we have 

not been able to comprehend what that something is.

Our inclination is to accept this interpretation of multiple small matching effects. But 

then, why was this pattern of multiple small 

interactions observed rather than several strong 

matching effects? One explanation may lie in 

the design. Three treatments developed for all 

patients with alcohol dependence or abuse were 

compared with one another for their matching effects on clients contrasted in 21 different 

ways. As each of these client characteristics 

was believed to be involved in one or more credible matching hypothesis, in effect this meant 

that they were competing with one another for 

the same outcome variance.

As only three treatment contrasts were possible, TSF versus CBT, TSF versus MET, and 

MET versus CBT, on average seven matching 

variables were involved in each of these contrasts. Given their a priori plausibility, how 

likely is it that any one matching variable would 

show a strong matching effect if the others were 

also influential? If the matching variables were 

moderately correlated with one another (as 

most were), it is conceivable that they may have 

been proxies for one another or for more general 

underlying constructs.

Under these conditions we might expect 

to see the pattern observed: small matching 

effects involving alternative matching variables 

in different interactions observed during different periods of observation and in different 

arms. When it is noted (as we discuss later) that 

many of the matching predictions relied upon 

the same putative mediator variables to explain 

the matching effect, this explanation becomes 

more likely.

Finally, some matching hypotheses assumed 

main effects of treatment modality or client 

matching variable on mediating mechanisms 

(canonical models 1 and 2, respectively), while 

others assumed and found these same mediating 

mechanisms being affected by modality-matching variable interactions (models 3A and 3B). 

The latter effects would undermine the assumptions of the former. If one matching hypothesis 

assumes treatment modality has a consistent 

effect on treatment process, whereas results 

from testing the causal chain of another matching hypothesis shows the treatment process to 

be affected by the second matching variable, 

the conditions necessary for mediation of the 

first matching prediction may be precluded.

This said, it is nevertheless clear that if 

matching research is to be productive at this 

level of specificity, a major shift in approach 

is necessary. MATCH represented the best 

effort that could be put forth using this type 

of approach where single client characteristics were expected to interact with two or more 

treatments to affect drinking outcomes.

Causal Chain Analyses

A comprehensive review and critique of the 

causal chain analyses is clearly beyond the 

scope of this chapter. Instead, we limit our 

comments to a few of the more salient points to 

emerge.
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Part VII: Conclusions and Implications

First, Project MATCH’s use of causal chain 

analysis increased our understanding of why 

hypothesized interactions emerged, as in the 

case of alcohol dependence and network support for drinking. As importantly, the causal 

chain analysis threw into question assumptions we had as to why other observed matching 

effects that were hypothesized did in fact occur: 

anger, psychiatric severity, self-efficacy.

The causal chain analyses also helped to 

clarify our understanding of why so many of 

the hypothesized interactions failed to emerge. 

Causal chain analyses also provided plausibility for a few observed interactions that were not 

hypothesized.

Finally, prior to undertaking this review, we 

suggested that it was possible for a causal chain 

to emerge in the absence of an interaction. 

This too, in fact, appears to have occurred. For 

example, in both arms of the study, the amount 

of structure in therapy interacted with sociopathy to affect drinks per drinking day, and in the 

outpatient arm, TSF was found to have more 

structure than MET. Thus the causal linkage 

appears to be complete, despite the fact that 

TSF versus MET did not affect drinks per drinking day when clients high and low in sociopathy 

were contrasted. (The analytic approach used 

to test these causal chains precluded a test of 

the significance of the interaction between the 

two treatment groups. Thus, this inference is 

tentative).

The most serious limitation apparent from 

our critique of the causal chain analysis was 

that all too often the underlying theory was 

either not made sufficiently explicit so that it 

could be operationally tested, or if conceptually clear, its operationalization was either not 

totally accurate or was incomplete. It is now 

apparent that mediator analysis of moderating 

effects is a significant challenge. Nevertheless, 

once beyond these front-end limitations, the 

causal chain analyses conducted yielded considerable information.

Mediator Variables

The variables hypothesized to act as mediators turned out to be a surprisingly small set 

of indicators of the treatment process and a 

somewhat larger number of client responses to 

treatment.

Treatment Processes

Only six constructs characterizing treatment 

were tested as hypothesized mediators. Working 

alliance, amount of structure in treatment, the 

amount of treatment offered or received, and 

AA involvement and/or attendance were each 

tested in the causal chains for several of the 

hypotheses. Two indices of treatment content 

were each used once, treatment emphasis on 

psychopathology and emphasis on abstinence.

Working Alliance. Working alliance was the 

most frequently tested measure of treatment 

process. The factors thought to affect it varied across matching hypotheses. And, in fact, 

analyses indicated that it was affected by treatment modality and client matching variable and 

the interaction of treatment modality and client 

matching variable. For example, in the outpatient arm, MET enhanced clients’ experience of 

the working alliance more than did CST or TSF 

in one causal chain analysis, while in another, 

TSF clients reported better working alliance 

indicators than did CBT or MET clients.

Such inconsistencies become understandable when the evidence indicates that working 

alliance is also influenced by the interaction of 

treatment modalities and some matching variables (cognitive impairment, religious beliefs 

and background, meaning seeking, and problem recognition). Finally, working alliance 

is also affected directly by client matching 

variables (sociopathy, anger, motivational 

readiness). Given these complexities, it is clear 

that a matching effect predicated upon working alliance being solely an effect of but one of 

these three sources of variance is inherently 

weakened.

While it was expected that motivational readiness, prior AA, typology, anger, and problem 

recognition each would interact with treatment 

modality to affect working alliance, this turned 

out to be so only for problem recognition, which 

interacted with MET versus CBT/TSF in both 

the outpatient and aftercare arms.

There was evidence that working alliance 

was directly affected by client matching variables more often in the outpatient treatments 

(sociopathy, anger, and readiness) than in 

aftercare (readiness). Finally, working alliance 

was observed to be directly affected by treatment modality in both outpatient and aftercare 
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Substantive Review and Critique

treatments. For example, MET led to a better 

working alliance than TSF or CBT, and TSF 

appeared to lead to better agreement on the 

task of therapy than did either CBT or MET.

Working alliance was also conceptualized as 

having an effect on drinking outcome. In the outpatient arm, working alliance did indeed have 

a main effect on drinking outcome. However, 

its effect was also observed to be moderated by 

treatment modality (CBT vs. MET) and client 

matching variable (motivational readiness and 

typology). In the aftercare arm, working alliance 

did not have a main effect on drinking outcome 

but did interact with treatment modality (CBT 

vs. MET) and matching variable (motivational 

readiness) to affect drinking outcomes.

Readily apparent from these results, working alliance is influenced by treatment modality 

and matching variables in multiple ways and 

in turn affects drinking outcomes either directly 

or in combination with treatment modality or 

matching variables.

Treatment Structure. Structure, also invoked 

as a mediator to explain matching predictions, 

proved to be less of a discriminator between 

treatment modalities than expected. For the 

most part, MET proved not to be less structured 

than either CBT or TSF.

In the outpatient arm, structure was not 

affected by treatment modality. In the aftercare arm, structure was reported to be affected 

by the interaction of treatment modality (MET 

vs. TSF and CBT) and client typology (A vs. B). 

This finding indicates that, contrary to our best 

intentions, the delivery of treatment modality was influenced by client characteristics in 

the aftercare arm (canonical model type 3B). It 

appears that type B clients influenced MET to 

become more structured than when delivered to 

type A clients.

When structure is viewed as a factor affecting drinking outcome, it appears that in some 

instances it directly affects drinking (PDA in 

outpatient), while it may also interact with sociopathy to influence drinking outcome (DDD in 

outpatient, PDA in aftercare).

AA Attendance. As a final example of the 

complex ways in which putative mediators are 

both influenced by client matching variables, 

treatment modalities, and their interactions 

and in turn influence drinking outcomes, we 

cite AA attendance. In the outpatient arm, AA 

attendance was influenced by treatment modality (TSF vs. CBT, MET), client matching variable 

(alcohol dependence and network support 

for drinking), and the interaction of treatment modalities and client matching variables 

(TSF vs. MET and/or CBT with network support, religiosity, meaning seeking, and alcohol 

dependence).

AA attendance, in turn, had both a direct 

effect on drinking outcome and a moderating 

effect in combination with TSF versus MET 

and/or CBT. In the aftercare arm, AA attendance was affected by treatment modality (TSF 

vs. MET and CBT), client attribute (gender and 

alcohol dependence), and the interaction of 

treatment modality with client attribute (TSF 

vs. MET and CBT with meaning seeking). AA in 

turn sometimes affected drinking outcome but 

in other analyses did not do so.

In aggregate, these examples indicate that 

the putative active ingredients of treatment are 

themselves influenced by multiple and complex 

factors and in turn influence drinking outcomes 

in variable and complex ways. Given this level 

of complexity, it is not surprising that matching 

predictions based upon simplifications of these 

complex dynamics would not be supported.

Client Response to Treatment

In contrast to causal chains involving treatment processes, those involving client response 

to treatment present a simpler picture. It was 

generally expected that improvement in the 

matching variable from before to after treatment would be predictive of better drinking 

outcomes. In fact, this usually turned out to 

be the case. Increased self-efficacy, readiness 

to change, and taking steps to change generally were predictive of better drinking outcomes, 

as was decreased temptation to drink. Anger 

reduction, a decrease in network support for 

drinking, increased social functioning, and a 

reduction in psychiatric symptoms were also 

predictive of better drinking outcomes

Nevertheless, when the effect of the interaction of improvement in the matching variable 

and baseline level of the matching variable was 

tested, it was found that the effect of the posttreatment score on drinking outcomes could be 
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Part VII: Conclusions and Implications

moderated by the client’s baseline score. For 

example, while anger reduction was predictive of 

decreased drinks per drinking day in the outpatient arm, the effect of anger reduction on PDA 

was moderated by the client’s baseline level of 

anger. In the outpatient arm, anger reduction’s 

effect on both PDA and DDD was moderated by 

the client’s baseline level of sociopathy. Thus, 

causal chains that did not incorporate the 

interaction of the baseline level of the matching variable into the affect of the posttreatment 

level on drinking would be more likely to fail.

If, for example, it is theorized that clients 

with networks supportive of drinking will benefit from a treatment that decreases their 

network’s support of drinking, then it would 

be expected that those who had networks most 

supportive of drinking prior to treatment would 

benefit the most from this intervention. If baseline level of network support is not included as 

part of the interaction term for predicting drinking outcome, then the causal chain is simply 

predicting that those who have networks less 

supportive of drinking will have better drinking 

outcomes. The logic is, however, that those with 

networks supportive of drinking would be more 

affected by a change in network support from 

pretreatment to posttreatment.

While posttreatment response was usually 

predictive of drinking outcomes, the predictors 

of posttreatment response remain a mystery. 

Neither the matching variable itself, treatment 

modality, treatment process, nor the interaction 

of any combination of these variables predicted 

posttreatment response with any regularity.

Presumptive Causal Chains

A review of all tested causal chains suggests 

that perhaps as many as 15 may have successfully linked the interaction of treatment 

modality and matching variable to drinking outcome through a presumptive mediator. (It is not 

possible to conclude this with certainty because 

of various limitations in the causal chain analyses.) Again, a few summary comments are in 

order.

First, all canonical models (1, 2, 3A, and 3B) 

were represented in completely linked causal 

chains. Canonical model 3, which conceptualized an intervening variable as the consequence 

of treatment modality and client treatment 

matching variable, was most often successfully 

linked. Canonical model 2, which conceptualized a matching variable predictive of a client 

characteristic that interacted with a treatment 

process variable to affect drinking outcome, was 

least often successfully linked. Model 1, which 

conceptualized a treatment modality leading to 

a treatment process that interacted with the 

client matching variable, was intermediate in 

success between models 2 and 3.

A second observation is that, as often as not, 

the causal chain was opposite the direction predicted. Of significance, when the causal chain 

was opposite that predicted, usually so was the 

matching effect that it was developed to explain 

(e.g., for sociopathy, self-efficacy, and gender). 

This consistency suggests that the matching 

effect was real; what was at variance with reality was the a priori theorizing.

A third observation, of considerable importance, is that (irrespective of whether they were 

hypothesized) most of the causal chains that 

appeared to be successfully linked occurred in 

the outpatient arm of study (13) rather than in 

aftercare (2). This is important because outpatient was a standalone treatment, whereas 

aftercare followed a more intensive treatment 

experience. It would be expected that a standalone treatment would be more likely to be 

amenable to a successful examination of mediators than would a treatment that was only the 

latter part of the whole treatment experience of 

the client. The greater robustness of the outpatient causal chain analyses corresponds with 

the greater robustness of the outpatient matching effects.

This concordance suggests that research utilizing standalone treatments is more likely to be 

informative.

Research Recommendations

Our interpretation of MATCH matching 

results is that we have observed several small 

single attribute by treatment modality interactions. From this conclusion, it follows that 

treatment strategies that rely on consideration 

of client attributes will need to find a better 

empirical foundation for their justification. We 

suggest some (nonmutually exclusive) avenues 

for future matching research.
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Substantive Review and Critique

Dedicated Treatments for Identified 

Clients

Treatment modalities can be developed to 

treat clients with singular outstanding characteristics, such as gender. Rather than expecting 

that CBT would be superior to TSF because of 

assumptions regarding its active ingredients 

that might affect women more than men, a 

treatment would be designed specifically to treat 

female alcohol abusers. Such treatments have 

been developed but have not been subjected 

to rigorous study as to whether they enhance 

treatment outcomes.

Matching Therapy Process to Client 

Attributes

The results of the causal chain analyses indicated that while treatment modality often did 

not relate to putative active ingredients in the 

treatment process as anticipated, these treatment process variables were themselves often 

predictive of client changes, including client 

drinking. This finding suggests the need to go 

beyond the “brand name” of the modality to 

identify differences in therapy behaviors that 

interact with different client attributes.

For example, in studying tapes of therapy sessions, Karno and colleagues (in press) 

found that over and above treatment modality, 

therapist behaviors interact with client characteristics to affect drinking outcomes. For clients 

who are assessed as high in emotional arousal, 

therapist behaviors that seek to focus on that 

arousal, as opposed to dampening the arousal, 

lead to better drinking outcomes. Similarly, for 

clients who are characterized by low emotionality, therapist behaviors that do not seek to 

enhance emotional arousal appear to lead to 

better drinking outcomes than therapist behaviors that seek to induce emotional arousal.

Karno et al. also found that therapist directiveness interacted with client resistance to 

affect drinking outcomes, such that clients 

with high resistance had poorer outcomes with 

directive therapists, while those with low resistance had poorer outcomes with less directive 

therapists. These interactions accounted for 

more variance than did treatment modality 

and suggest that variability in therapist behavior unrelated to treatment modality interacts 

with client characteristics to affect drinking 

outcomes. Karno is currently conducting a replication study of these findings with MATCH 

audiovideo tapes (Karno and Longabaugh 2000).

Matching Multimodal Therapies to 

Multiple Patient Characteristics

Another approach to matching treatment 

to client attributes is to build decision trees 

into the therapy that will modify the treatment 

modalities to be received by the client on the 

basis of a multidimensional characterization 

of the client. This approach is illustrated by a 

clinical trial under way (Davidson et al. 2000). 

Gulliver and Longabaugh (2000) have developed 

a broad spectrum treatment that selects modules to be delivered to the client on the basis of 

assessments of functioning and alcohol-specific 

support in five domains: cognitive impairment, 

family relationships, occupation, residential 

stability, and social network. Dual classification 

of the client’s level of functioning and support 

for abstinence in each of these domains leads 

to triage to different configurations of treatment 

modules.

A related approach is to match modules 

to clients on the basis of client choice. This 

approach is being used in Project COMBINE 

(2000). Here, a combined behavioral intervention employing the principles of motivational 

interviewing (Miller and Rollnick 1991) leads 

clients to develop their own change plans 

and then select from a large number of modules available to facilitate achievement of this 

change plan (Miller 2001). In this approach, 

client choice is viewed as the result of the interaction of client self-assessment and therapy 

alternatives offered.

Hierarchical Algorithms for Selecting 

Treatment Options

If we look outside the field of alcohol treatment to therapy for psychiatric problems more 

generally, a more elaborate theory of systematic 

treatment selection nests matching principles within a hierarchy of decision trees to be 

applied to a given patient’s treatment. The systematic treatment selection model developed by 

Beutler and associates (2000) describes such 

an approach. For purposes of illustration, we 

will superimpose this model on our study.
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Part VII: Conclusions and Implications

Beutler and colleagues have proposed 

empirically supported principles for systematic 

selection of treatment for depression. To date, 

they believe that six dimensions are important 

in guiding selection of treatment for the patient: 

functional impairment, subjective distress, 

experienced social support, problem complexity/chronicity, level of resistance, and coping 

style. They find empirical support for relationships between these variables and treatment.

Seven variables of treatment believed to be 

important are treatment intensity/duration, 

emotional focus, interpersonal focus, insight 

versus behavioral focus, breadth of treatment 

focus, directiveness of therapy, and the extent 

to which therapy is symptom focused. They have 

observed that present treatment modalities are 

only loosely associated with these dimensions. 

Consequently, they believe that in order to find 

lawful relations between these treatment dimensions and patient characteristics, it is necessary 

to tailor treatment behaviors (rather than treatment modalities) to patient characteristics.

Project MATCH matching variables can be 

grouped within the six characterizations of 

patients by Beutler et al. Functional impairment subsumes alcohol involvement, alcohol 

dependence, psychiatric severity, cognitive 

impairment, social dysfunction, and by inference, lack of confidence in maintaining 

abstinence and temptation to drink. Subjective 

distress may subsume motivational readiness, 

alcohol problem recognition, and meaning 

seeking. Experienced social support could subsume network support for drinking, religious 

background and beliefs, and prior AA involvement. Problem complexity is poorly represented 

in the MATCH domain, perhaps indexed by 

Axis I-comorbidity. Level of resistance encompasses anger, interpersonal dependence, and 

as used in MATCH, gender. Finally, coping style 

(inwardly directed vs. externally focused) maps 

readily on to sociopathy, ASPD, and A versus 

B typology. In this conceptualization, MATCH 

variables within each of these domains could 

be considered alternative or overlapping proxy 

measures for each construct.

As anticipated, however, mapping the three 

MATCH treatment modalities into Beutler 

et al.’s seven dimensions of treatment is not 

possible. Only a few correspondences can be 

estimated with any confidence. Treatment 

intensity/length can be mapped. Inpatient 

plus aftercare treatment is more extensive and 

intensive than standalone MATCH outpatient 

treatments. Within each treatment arm, MET is 

less extensive and intensive than either CBT or 

TSF. Because TSF aims to involve the client in 

AA during and after treatment, while CBT does 

not, TSF could be considered to be more intensive than CBT.

Treatment directiveness can also be mapped, 

with CBT and TSF considered to be more 

directive than MET. Symptom focus might be 

captured in the contrast of CBT versus MET, 

with CBT being more symptom focused. The 

three treatments cannot be differentiated with 

any confidence on the remaining dimensions.

Mapping Project MATCH interactions on to 

Beutler et al.’s interface of treatment intensity 

with social support and problem impairment 

supports their hypothesis that greater impairment and lesser support require greater 

treatment intensity. The interface of treatment 

directiveness and client resistance supports the 

MATCH interactions involving anger and interpersonal dependency, and MET versus CBT and 

TSF supports the hypothesis that greater resistance requires less directiveness. The interface 

of symptom focus with patient coping style is 

less clear, but MATCH interactions between 

CBT and sociopathy, ASPD, and typology were 

observed.

In summary, hypotheses derived from the 

Beutler et al. systematic treatment selection 

model appear to explain many of the interactions observed in MATCH. Characterization 

of the treatments by actually observed therapist behaviors (Karno 2000) will put several 

of Beutler’s matching predictions to a direct 

empirical test.

Commentary

Irrespective of the theoretical approach taken 

to matching, it is clear that any model that rests 

on single client attribute-treatment interactions 

will not suffice. Rather, if matching is going to 

enhance treatment outcomes, multidimensional 

matching algorithms will be needed. It is likely 

that such models must encompass nonlinear 

relationships as well. Matching effects may not 

be simply additive. For example, if a client is 
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Substantive Review and Critique

mismatched with treatment on one important 

characteristic this may well nullify matching 

synergies on a number of other dimensions. 

Whether treatment research will ever reach this 

level of sophistication remains to be seen.

Recommendations for Alcohol 

Treatment Research

Up until now we confined our commentary to 

recommendations regarding treatment matching and treatment matching research. In this 

section, we close with a series of recommendations concerning alcohol treatment research 

more generally.

Need for Study of the Treatment Process

The causal chain analyses conducted to test 

the theory underlying the matching predictions 

indicated that we have little idea what the active 

ingredients of treatment are nor how they affect 

patients more generally as well as patients with 

specific attributes. The “black box of treatment” identified by Moos and Finney 20 years 

ago (Moos et al. 1980) still remains pretty much 

a mystery. While we have been able to identify 

some general elements of treatment (e.g., working alliance) as modest predictors of enhanced 

outcome, as of yet we know little about the process that brings about this better alliance.

The examination of putative mediators in 

the present volume revealed that ingredients 

of a treatment that we expected to impact differentially on clients with certain attributes 

frequently failed to do so in the ways anticipated. This might be regarded as simply a 

shortcoming of matching theory. However, an 

investigation of the putative active ingredients 

of Cognitive-Behavioral Therapy (Morgenstern 

and Longabaugh 2000) indicated that the active 

ingredients of this popular and well studied 

therapy were also yet to be identified. So our 

ignorance of how treatments work extends 

beyond matching theories to our theories of 

treatment.

As the present volume has amply demonstrated, our theories about how treatment 

works need to be operationalized and tested. 

Not to do so permits us to forge ahead while 

operating under false assumptions. That is no 

way to build a knowledge base about treatment.

So, the strongest recommendation we have 

regarding treatment research is that the treatment process itself has to be studied, through 

mediator analysis or other means, and related 

to treatment outcomes. We need to know what 

the mechanisms are that give rise to better 

outcomes. To do so will markedly affect the 

sophistication of theories about treatment.

Need for Study of the Treatment Context

As has been said on numerous occasions, 

treatment is but one small event in the life of an 

alcohol troubled person. Treatment takes place 

in a context: What factors bring the client to 

treatment, what is going on in the client’s life 

while the treatment is delivered, and what environmental context awaits the client completing 

treatment? Until we understand these treatment context variables and bring them into our 

treatment models, we will be unable to account 

for major variance in treatment outcomes.

In the present study, such effects were 

implied by the disappearance of within-treatment matching effects after treatment was 

completed as well as the treatment by time 

interaction effects observed while treatment was 

going on. Also pertinent, the one matching effect 

that appeared 3 years after treatment identified variables outside of the treatment itself 

as a moderator (network support for drinking) 

and a mediator (attendance at AA meetings). 

Environmental context and interface needs to 

be brought directly into our models and either 

controlled for or systematically varied.

Measure Outcomes at the End of 

Treatment

In research on psychosocial treatments we 

usually designate “outcome” as a sustained 

period following treatment completion. We do so 

in the belief that the changes that occur because 

of treatment are sustaining and will enable the 

client to successfully cope with drinking during 

this posttreatment period. How long treatment 

effects last is an important question, but it is 

not the only important question. Also important 

is: Does treatment affect change in the client 

while it is ongoing or by its completion? If this 

does not happen, clearly the theory of treatment 

is either incorrect or has not been implemented 

as conceptualized. Thus, unless the theory 
322

Part VII: Conclusions and Implications

underlying the treatment clearly specifies that 

its effects are not to become apparent until a 

specified time after treatment has been completed, the posttreatment observation is not a 

direct test of the theory.

This expectation of sustaining benefits of 

treatment is not present in most studies of 

pharmacotherapies. Here, the question is usually whether the client is changed while taking 

the medication. Whether the client continues to 

drink less after the completion of a trial of naltrexone, for example, is viewed as an interesting 

question but not one that speaks to the efficacy of the drug itself. Why is it that we should 

expect so much more from psychosocial therapies than from pharmacotherapies?

Studies of mechanisms should be designed 

to test as directly as possible the effects of these 

mechanisms on the designated dependent variable. Once mechanisms have been identified, 

they can be continued or terminated as part 

of the treatment. Studies of behavioral therapies (like studies of pharmacotherapies) suggest 

that their effects are likely to last as long as the 

treatment is ongoing. If that is so, what is it that 

precludes our conceiving of psychosocial treatments as having maintenance phases which 

might go on indefinitely? Treatment so conceived might well prove to be more cost effective 

than repeated “acute phase” treatments.

Measuring Outcome

In alcohol treatment outcome studies, the 

primary dependent variable is typically a measure of drinking (frequency, intensity, total 

volume, or some composite of these). Secondary 

measures of outcome may include measures 

of functioning, subjective well-being, service 

utilization, and the like. Project MATCH was 

prototypic in this regard. Yet many of our treatment theories do not specify a direct impact of 

the treatment on drinking per se. In MATCH, for 

example, it was expected that TSF clients would 

stop drinking as a consequence of working the 

12 steps that require major cognitive, attitudinal, and behavioral changes. CBT was expected 

to improve the client’s cognitive-behavioral coping skills, while MET was focused on increasing 

client motivation to change. Assuming these 

foci of treatment are accomplished, a change in 

drinking is expected to follow.

Depending upon the underlying theory of 

treatment, drinking may not be conceived of 

as the primary dependent variable. For example, drinking may be seen as one mediator of 

enhanced quality of life. If people drink less, 

they are expected to experience fewer negative consequences from drinking and positive 

consequences from not drinking. However, if 

cessation of drinking does not lead to one, the 

other, or both of these effects, enhanced quality 

of life will not occur. Thus, change in drinking status is one link in the supporting causal 

chain.

In contrast is a theory that relies on enhanced 

quality of life as a mediator of change in drinking. In this scenario, the treatment leads to 

increased quality of life that in turn results in 

a reduction in drinking. If quality of life does 

not improve, drinking will not diminish. Thus, 

depending upon the underlying causal chain, 

drinking may be a mediator variable, a dependent variable, or both.

Our interest in alcohol treatment is in reducing or eliminating the negative consequences 

of drinking rather than ending drinking itself. 

Thus, the negative consequences from drinking should be the primary measure of outcome. 

Measures of drinking may be important or 

critical to testing various parts of the theory of 

treatment, but they are neither necessary nor 

sufficient as primary measures of alcohol treatment outcomes.

The Need for Inclusion of Multiple 

Treatment Sites

If Project MATCH has taught us only one 

thing, it is the risk involved in conducting singlesite studies. Had we not included multiple sites 

and different study arms and had con-duct ed 

the study in just one treatment site, we would 

have found an effect that we assumed could be 

generalized. Instead, subtle and as yet unidentified variables led to inconsistencies of effects 

across sites and settings. In order to begin to 

test for generalizability of effects beyond a single 

treatment site, it would appear that, despite the 

additional cost, inclusion of at least two sites in 

a treatment outcome study are necessary.
323

Substantive Review and Critique

Conclusion

Our predictions as to how treatments would 

be distinctive in ways that would differentially 

impact clients with specific attributes were woefully inadequate. A major conclusion is that we 

do not know yet how our treatments work. The 

theories developed to support matching predictions were not adequate for the task.

Several caveats are in order. While the 

development and testing of the a priori matching hypotheses was exceedingly rigorous and 

analytically appropriate, in hindsight, the development and testing of causal chains was not. 

There are several reasons for this.

First, when Project MATCH was initiated 

(1989), mediational analyses were not widely 

known to the alcohol treatment research community. The seminal paper by Baron and 

Kenny had only been published in the Journal 

of Personality and Social Psychology in 1986. 

Holmbeck’s paper clarifying some of the ambiguities in testing for mediational effects was not 

published until 1997, after the MATCH causal 

chain analyses had been completed. Thus, from 

an historical perspective, the causal chain analyses were undertaken in Project MATCH before 

the methodological and statistical techniques 

were fully developed. Even to the present, analyses have been confined largely to testing for 

mediation of main effects. In Project MATCH, 

we sought to push the envelope by initiation 

of mediational analyses of moderator effects, a 

novel topic even a decade later.

Compounding this difficulty, the senior scientists selected to conduct the MATCH trial 

were trained in an era before mediational analysis had become prominent. As a group, we were 

not especially well trained in these new methodologies. Further, there were differences among 

MATCH investigators regarding the importance 

of a priori theory development. Some investigators believed, in reflection quite accurately, that 

elaborate theory development was premature 

and expected that the facts would emerge in 

due course.

Because we were breaking new ground, there 

was not a consensus on the best analytic tools to 

test the causal chains for moderating variables. 

Consequently, each matching hypothesis team 

was free to adopt whatever analytic tools made 

sense to them for testing their causal chain. The 

variety of approaches adopted is quite apparent 

as one reads across the chapters.

Because of the need to implement Project 

MATCH on a timeline close to that projected 

and funded, the study was implemented before 

the theories underlying the matching predictions had been fully elucidated. This meant 

that matching hypotheses were guided by a 

theoretical orientation rather than derived from 

a completed theory of which they were a part. 

Clearly, many of the assumptions underlying 

the matching predictions proved to be incorrect. Had theories been fully explicated earlier, 

it would have been apparent that many involved 

assumptions that would be untenable or at 

least previously untested.

There was also the necessity of relying on 

the assessment battery that had been adopted 

and was already being implemented prior to the 

completion of causal chain development. This 

resulted in proxy indices for pivotal mediators 

for many of the causal chains tested. Further, 

the number of measures of treatment process 

that transcended the three treatments was limited by assessment time and MATCH priorities. 

As so many matching hypotheses were to be 

tested, the allocation of resources to assessing any one was necessarily limited. Finally, 

while the three treatments developed for testing matching hypotheses were believed to be 

distinctive from one another, they were not 

developed to be distinctive in their relations to 

the matching variables, which were still to be 

decided upon.

It is our hope that the Project MATCH study of 

mediational analyses of moderator effects, now 

concluded, will provide guidance to treatment 

researchers as they develop theories and test 

interventions to enhance treatment outcomes.

Acknowledgment

This project was supported by a series of 

grants from the National Institute on Alcohol 

Abuse and Alcoholism as part of the Cooperative 

Agreement on Matching Patients to Alcoholism 

Treatments.
324

Part VII: Conclusions and Implications

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Beutler, L.E.; Clarkin, J.F.; and Bongar, B. 

Guidelines for the Systematic Treatment of 

the Depressed Patient. New York: Oxford 

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Brehm, S.S. The Application of Social Psychology 

to Clinical Practise. New York: Halstead 

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Brehm, S.S,. and Brehm, J.W. Psychological 

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Control. New York: Academic Press, 1981.

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June 2000.

Davidson, D.; Gulliver, S.; Longabaugh, R.; 

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the child-clinical and pediatric psychology literatures. Journal of Consulting and 

Clinical Psychology 65: 599–610, 1997.

Karno, M., and Longabaugh, R. Psychotherapy 

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Karno, M.P.; Beutler, L.E.; and Harwood, T.M. 

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McCrady, B.S.; Epstein, E.E.; and Hirsch, L.S. 

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1999.

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O’Brien, C.P.; and Druley, K.A. Increased 

effectiveness of substance abuse treatment: 

A prospective study of patient-treatment 

“matching.” Journal of Nervous and Mental 

Disease 171:597–605, 1983.

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325

Appendix 1Appendix 1. Observed interactions, Outpatient Arml Client variable Within treatment One year posttreatment Months 37–39 posttreatment CBT vs. MET TSF vs. MET CBT vs. TSF CBT vs. MET TSF vs. MET CBT vs. TSF CBT vs. MET TSF vs. MET CBT vs. TSF

Alcohol 

Dependence DDD

PDA

Alcohol 

Involvement DDD

PDA

Cognitive 

Impairment DDD

PDA

Psychiatric Severity/

Psychopathology

PDA CBT > MET* TSF v METT,Dx CBT v TSF* TSF v METT CBT > TSF*f5-11

DDD

Sociopathy/ASPD PDA MET > CBT TSF v METT DDD TSF > MET*T11,12A

A–B Typology DDD PDA CBT > MET TSF > MET

Anger PDA MET > CBT* MET > TSF* MET > CBT* MET > TSF* DDD MET > CBT* MET > TSF* MET > CBT* MET > TSF*

Conceptual Level DDD PDA TSF v METT

Meaning Seeking DDD PDA

Religiosity DDD PDA CBT v METT

Dependency

Interpersonal PDA CBT > MET

DDD CBT > MET TSF > MET

(Female) Gender PDA TSF > CBT DDD

Readiness

Motivational PDA CBT v TSFT MET > CBT*T15 TSF > CBTTDDD CBT v TSFT TSF > CBTTProblem Recognition PDA CBT v METT DDD CBT v METT

Self-Efficacy DDD PDA MET > CBT

Temptation minus 

confidence DDD

PDA

Network Suppport 

for Drinking

PDA TSF v MET*T1-4 TSF v MET*

DDD TSF v MET*T1-5 TSF v MET*

Prior AA PDA MET > CBT MET > CBT TSF > METT DDD MET > CBT
326

Part VII: Conclusions and Implications Client variable Within treatment One year posttreatment Months 37–39 posttreatment CBT vs. MET TSF vs. MET CBT vs. TSF CBT vs. MET TSF vs. MET CBT vs. TSF CBT vs. MET TSF vs. MET CBT vs. TSF Social Functioning PDA MET > CBT TSF > CBT DDD MET > CBT TSF > CBT 1 Positive entries in the table are derived from results reported in the four Project MATCH publications (Project MATCH, 1997a, 1997b, 1998a, 1998b) and from the tables provided in the chapters in this volume. All entries indicate that the observed p value for the test was p≤05. v Indicates an interaction of time, treatment modality, and client attribute in which the direction of the treatment contrast by client attribute interaction is unknown, either because it was not reported or the interaction involved quadratic time, precluding easy description. >	 When the dependent variable is percentage of days abstinent, indicates that the slope of the first treatment is significantly more positive than the slope of the second treatment (even under the condition that both slopes are negative (decreasing with increasing values of the client matching variable)). When the dependent variable is drinks per drinking day, > indicates that the slope of the first treatment is more steeply negative. * Indicates that the direction of the contrast was hypothesized a priori. No asterisk indicates either that the result was opposite that hypothesized or that no a priori hypothesis had been offered regarding the observed interaction. T	 Indicates an interaction of client attribute, treatment, and time that reached p.05 level. Numbers following the T indicate the weeks (if a within-treatment effect) or months (if a

posttreatment effect) in which the contrast was significant at ps.05. If no numbers follow the T, either differences between the contrasts were not tested for specific intervals,were not reported, or tests were conducted and none of the intervals contrasted were significant at the p level, despite evidence of an overall time by treatment by client attributeinteraction.

+ indicates the effect is increasing with time. 

- indicates the effect is decreasing with time.

A 	 Indicates that the contrast entered was significant for antisocial personality disorder but not for sociopathy. Entries for this construct not accompanied by an A were significant for

sociopathy or for sociopathy and antisocial personality disorder.

Dx 	 Indicates that the contrast was significant for DIS Axis I personality disorder but not for ASI psychiatric severity. Entries for which a Dx is not entered are significant either for both

ASI psychiatric severity and DIS Axis I diagnosis or for ASI psychiatric severity alone.

f Accompanied by numbers indicates that an attribute by treatment interaction was observed as well as an attribute by treatment by time interaction. The numbers indicate the 

intervals during which the contrast was significant.
327

Appendix 2

Appendix 2. Observed interactions, Aftercare Arm1

Client variable Within treatment One year posttreatment

CBT vs. MET TSF vs. MET CBT vs. TSF CBT vs. MET TSF vs. MET CBT vs. TSF

Alcohol 

Dependence

PDA TSF > CBT*

DDD CBT v METT TSF > CBT*

Alcohol 

Involvement

PDA

DDD TSF v METT

Cognitive 

Impairment

PDA CBT > MET*T

DDD CBT > MET*T CBT > TSF*T

Psychiatric Severity/

Psychopathology

PDA

DDD

Sociopathy/ASPD PDA CBT > TSF*A,T

DDD CBT > TSF*A CBT > MET*A,T4” CBT > TSF*A,T4,5

A–B Typology PDA

DDD MET > CBT*T

Anger

PDA

DDD

Conceptual Level

PDA

DDD

Meaning Seeking PDA TSF > MET*T TSF > CBT*T

DDD

Religiosity

PDA

DDD

Interpersonal 

Dependency

PDA

DDD

(Female) Gender PDA TSF v METT

DDD TSF v METT

Motivational 

Readiness

PDA CBT > MET CBT > TSF

DDD

Problem Recognition PDA CBT > METT4,5 TSF > METT4,5

DDD TSF > METT4

Self-Efficacy PDA CBT > MET* TSF > MET*

DDD CBT > MET*

Temptation minus 

confidence

PDA CBT > MET

DDD TSF > MET*T1-3

Network Suppport 

for Drinking

PDA CBT v METT

DDD CBT v METT

Prior AA PDA MET > TSFT+

DDD MET > TSFT+

Social Functioning

PDA TSF>CBTT4,5,14-15e

DDD

1 Positive entries in the table are derived from results reported in the four Project MATCH publications (Project MATCH, 1997a, 1997b, 

1998a, 1998b) and from the tables provided in the chapters in this volume. All entries indicate that the observed p value for the test was 

p≤05.

v Indicates an interaction of time, treatment modality, and client attribute in which the direction of the treatment contrast by client attribute 

interaction is unknown, either because it was not reported or the interaction involved quadratic time, precluding easy description.

>	 When the dependent variable is percentage of days abstinent, indicates that the slope of the first treatment is significantly more positive

than the slope of the second treatment (even under the condition that both slopes are negative (decreasing with increasing values of the 

client matching variable)). When the dependent variable is drinks per drinking day, > indicates that the slope of the first treatment is more

steeply negative.

* Indicates that the direction of the contrast was hypothesized a priori. No asterisk indicates either that the result was opposite that hypothesized or that no a priori hypothesis had been offered regarding the observed interaction.
328

Part VII: Conclusions and Implications

T	 Indicates	an	interaction	of	client	attribute,	treatment,	and	time	that	reached	p.05	level.	Numbers	following	the	T	indicate	the	weeks	(if	a	

within-treatment	effect)	or	months	(if	a	posttreatment	effect)	in	which	the	contrast	was	significant	at	ps.05.	If	no	numbers	follow	the	T,	

either	differences	between	the	contrasts	were	not	tested	for	specific	intervals,	were	not	reported,	or	tests	were	conducted	and	none	of	

the	intervals	contrasted	were	significant	at	the	p	level,	despite	evidence	of	an	overall	time	by	treatment	by	client	attribute	interaction.

 + indicates the effect is increasing with time. 

- indicates the effect is decreasing with time.

A		 Indicates	that	the	contrast	entered	was	significant	for	antisocial	personality	disorder	but	not	for	sociopathy.	Entries	for	this	construct	not	

accompanied	by	an	A	were	significant	for	sociopathy	or	for	sociopathy	and	antisocial	personality	disorder.

Dx		 Indicates	that	the	contrast	was	significant	for	DIS	Axis	I	personality	disorder	but	not	for	ASI	psychiatric	severity.	Entries	for	which	a	Dx	is	

not	entered	are	significant	either	for	both	ASI	psychiatric	severity	and	DIS	Axis	I	diagnosis	or	for	ASI	psychiatric	severity	alone.

e Estimated
329

Appendix 3

Appendix 3. Prognostic effects of matching variables, Outpatient and Aftercare 

Arms, on percentage of days abstinent and drinks per drinking day

Client variable

Outpatient Arm Aftercare Arm

4–15 months 37–39 months 4–15 months

PDA DDD PDA DDD PDA DDD

Alcohol Depedence + + × + ×

Alcohol Involvement +T+15 –T-15 + – –T +

Cognitive Impairment

Psychiatric Severity/Psychopathology × –T+ +T+

Sociopathology – × T- +T- × ×

A-B Typology + + × ×

Anger × × × ×

Conceptual Level ×

Meaning Seeking + ×

Religiosity – × – +

Interpersonal Dependency × ×

Gender (female) + –T+

Motivational Readiness + × – × + –

Problem Recognition + × – + – × ×

Self-Confidence + – × –

Temptation minus Confidence – + + – +

Network Support for Drinking – + × + × – +

Prior AA +

Social Dysfunction + × × + ×

	 +	 =	positive relationship between client variable and drinking variable (ps.05)

	 –	 =	negative relationship between client variable and drinking variable (ps.05)

	T+	 =	An Interaction with Time such that the prognostic effect of the client variable increases with Time (ps.05)

	T–	 =	An Interaction with Time such that the prognostic effect of the client variable decreases with Time (ps.05)

× =	An Interaction between Treatment and client attribute observed at ps.05 level (two directional) during this period. (Includes attribute by

treatment by linear time only)