TRIAD Drug Treatment Evaluation Project 

Six-Month Interim Report

Federal Bureau of Prisons

Office of Research and Evaluation

January 31, 1998

Project Director: Bernadette M. M. Pelissier, Ph.D.

Gerry Gaes

William Rhodes

Scott Camp

Joyce O’Neil

Sue Wallace

William Saylor

Do not use this paper without the permission of Thomas R. Kane, Assistant Director for

Information, Policy, and Public Affairs, Federal Bureau of Prisons.
TABLE OF CONTENTS

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Introduction 3

Paths to Treatment Service 4

Unit-Based Residential Treatment 5

Sample 6

Treatment Subjects 6

Comparison Subjects 7

Outcome Measures 7

Analyses 9

Findings — Residential Drug Abuse Treatment 10

Recidivism 10

Drug Use 10

CCC Failures 11

Post-Release Employment 11

Inter-Institutional Differences 11

Summary 11

CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Background 14

Organization of the Report 16

CHAPTER 2: A REVIEW OF RECENT STUDIES OF HIGH INTENSITY ADULT

CORRECTIONAL DRUG TREATMENT PROGRAMS — THE PROBLEM OF

SELECTION BIAS AND POSSIBLE SOLUTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Selection Bias and the Evaluation of Prison Drug Treatment Programs 19

A Model of Sample Selection Process 21

Methodological Solutions to Selection Bias 23

Additional Selection Concerns 27

A Critical Review of Prison Drug Treatment Research 28

Stay ‘N Out Program 29

Cornerstone Program 31

Key-Crest Program 33

New Vision In-Prison Therapeutic Community, Kyle Unit 35

Amity Right Turn Project 37

Summary of Research Literature 39

CHAPTER 3: DESCRIPTION OF DRUG TREATMENT PROGRAMS AND SERVICES . . 41

Paths to Treatment Service 41

Program Development by Location 41

Admission Criteria 42

Incentives for Program Participation 45
Program Design and Content 46

In-Prison Residential Drug Abuse Treatment Program (DAP) 46

Non-Residential Drug Abuse Treatment Services/Self-Help Groups 48

Drug Education Course 48

Community Corrections Centers 49

Transitional Services During Halfway House Placement 50

Supervised Release 51

Summary 52

CHAPTER 4: RESEARCH DESIGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Sample Selection Process 53

Research Subjects 55

A Chronological History of the TRIAD Subject Selection Process 56

Treatment Subjects 56

Non-Treatment Subjects 58

Data Collection Instruments 60

Data Collection Procedures 61

In-Prison Data 61

Community Corrections Center (CCC) Data 62

Post-Release Data 62

Description of Measures 62

Background Characteristics 63

Psychological/Attitudinal Measures 63

Treatment Received and Post-Release Supervision 65

Post-Release Behaviors 65

Probability-of-Volunteering Coefficient 66

Summary 67

CHAPTER 5: SUBJECT ATTRITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Introduction 69

Research Variables 69

Comparison/Control Subjects — History of Drug Use (HDU) Administration 70

HDU Refusals 71

Attrition of Identified Research Subjects 72

Subjects Who Were Missed as Research Subjects 73

Research Refusals 74

Intake1 Missing and Refusals 74

Refusal Rates for Intake1 Interview 76

Summary of Results for Subject Attrition 76

CHAPTER 6: DESCRIPTION OF SAMPLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

DAP Treatment Groups 77

Sample Demographics 78

Race/Ethnicity 78

Age 78
Education 79

Frequency of Drug Use During Heaviest Use Period 79

Drug and Alcohol Dependency 80

In-Prison Outpatient Treatment and Self-Help Group Participation 81

Alcohol and Drug Treatment History 82

Criminal/Incarceration History 82

Past Mental Health Treatment/Psychiatric Diagnoses 84

Employment 84

Motivation for Change 85

Other Variables 86

CCC Placements 87

Post-Release Behavior 88

Post-Release Offenses 89

Post-Release Drug Use 90

Violations of Supervision and Post-Release CCC Placements 90

Drug and Alcohol Treatment and Self-Help Participation 91

Employment and Cohabitation Status for Supervised Subjects 92

Summary 93

CHAPTER 7: ANALYSIS AND RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Outcome Measures 94

Analytic Strategies 96

Missing Data 99

Traditional Models of Arrest and Drug Use 101

Arrest as Failure 102

Arrest or Revocation as Failure 105

First Detected Drug Use as Failure 107

Bloom Models of Arrests and Drug Use 109

Arrest as Failure 109

Arrest or Revocation as Failure 110

First Detected Drug Use As Failure 111

Models of Arrest and Drug Use Controlling for Selection Bias 113

Arrest as Failure 113

Arrests and Revocations as Failure 115

First Detected Drug Use as Failure 116

CCC Failure 117

Traditional Model 117

Bloom Model 119

Heckman Model 119

Percent of Time Employed Full Time 120

Traditional Model 120

Bloom Model 122

Heckman Model 123

Inter-Institutional Comparisons of Outcome Variables 124

Arrests and Drug Use 125
Arrest as Failure 125

Arrest or Revocation as Failure 126

First Detected Drug Use as Failure 126

CCC Outcomes 127

Employment Outcomes 128

Summary of Results 129

Arrests 129

Arrests and Revocations 129

Drug Use 130

CCC Placement Failure 130

Employment 131

Inter-Institutional Comparisons 131

CHAPTER 8: SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

Selection Bias Reviewed 133

Traditional Approach 133

Bloom Comparisons 134

Modeling Selection Bias 135

Summary Findings 135

New Arrest as Post-Release Outcome 135

New Arrest or Revocation as Post-Release Outcome 136

First Detected Drug Test as Post-Release Outcome 137

CCC Placement Failure 137

Post-Release Employment 138

Inter-Institution Outcomes 138

Transitional Services 139

Conclusions 140

Future Research Efforts 140

Understanding the Treatment Component as a Continuum 141

Program Quality 142

Proximal Outcome 143

Gender 144

New Arrest vs. Revocation 144

A Final Note 145

TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

APPENDIX A: SUPPLEMENTARY TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

APPENDIX B: CODEBOOK OF VARIABLES USED IN TRIAD ANALYSES . . . . . . . . . 243

APPENDIX C: MODELS FOR DEALING WITH SELECTION BIAS IN AN EVALUATION

OF A PRISON-BASED DRUG TREATMENT PROGRAM . . . . . . . . . . . . . . . . . . . 249

Introduction 249
Recidivism: Survival Models That Are Based on the Lognormal and Exponential

Distributions 253

The Basic Recidivism Model 253

Introducing Selection Bias 256

Estimating the Probability of Selection Into Treatment 259

Determining PS, the Probability of Selection Into the Study Sample 260

A Probit Model of Halfway House Failures 262

A Two-Limit Tobit Model of Employment 263

Summary 265

GLOSSARY OF TERMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
Table Ex1. Type of Subject by Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table Ex2. Outcome Measure by Gender — 6 Months Post Release . . . . . . . . . . . . . . . . . . . . . 9

Table 1 Subject Attrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Table 2 Demographics — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Table 3 Demographics — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Table 4 Frequency of Drug Use During Heaviest Use Period — Male Subjects . . . . . . . . . . . 149

Table 5 Frequency of Drug Use During Heaviest Use Period — Female Subjects . . . . . . . . . . 150

Table 6 Estimated Log Odds of Drug and Alcohol Dependence — Male Subjects . . . . . . . . . 151

Table 7 Estimated Log Odds of Drug and Alcohol Dependence — Female Subjects . . . . . . . . 151

Table 8 Participation in In-Prison Outpatient Drug and Alcohol Treatment and 

Self-Help Groups — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Table 9 Participation in In-Prison Outpatient Drug and Alcohol Treatment and 

Self-Help Groups — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

Table 10 Drug and Alcohol Treatment History — Male Subjects . . . . . . . . . . . . . . . . . . . . . . 153

Table 11 Drug and Alcohol Treatment History — Female Subjects . . . . . . . . . . . . . . . . . . . . 153

Table 12 Criminal and Incarceration History — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . 154

Table 13 Criminal and Incarceration History — Female Subjects . . . . . . . . . . . . . . . . . . . . . . 155

Table 14 Psychiatric Diagnoses — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

Table 15 Psychiatric Diagnoses — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Table 16 Employment History — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Table 17 Employment History — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Table 18 Motivation for Change — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Table 19 Motivation for Change — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Table 20 CCC Placements, Failures, and Reasons for Failing — Male Subjects . . . . . . . . . . . 160

Table 21 CCC Placements, Failures, and Reasons for Failing — Female Subjects . . . . . . . . . . 161

Table 22 Transitional Services Received and Release Status — Male Subjects . . . . . . . . . . . . 162

Table 23 Transitional Services Received and Release Status — Female Subjects . . . . . . . . . . 163

Table 24 Drug and Alcohol Urinalysis During CCC Placement — Male Subjects . . . . . . . . . . 164

Table 25 Drug and Alcohol Urinalysis During CCC Placement — Female Subjects . . . . . . . . 165

Table 26 Post-Release Offenses — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Table 27 Post-Release Offenses — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

Table 28 Post-Release Offenses for Supervised Subjects — Male Subjects . . . . . . . . . . . . . . . 167

Table 29 Post-Release Offenses for Supervised Subjects — Female Subjects . . . . . . . . . . . . . 168

Table 30 Post-Release Drug and Alcohol Use Supervised — Male Subjects . . . . . . . . . . . . . . 169

Table 31 Post-Release Drug and Alcohol Use Supervised — Female Subjects . . . . . . . . . . . . 170

Table 32 Violations of Supervision and Post-Release CCC Placement — Supervised Male

Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

Table 33 Violations of Supervision and Post-Release CCC Placement Supervised — Female

Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

Table 34 Drug and Alcohol Treatment and Self-Help Participation Supervised — Male 

Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

Table 35 Drug and Alcohol Treatment and Self-Help Participation Supervised — Female

Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
Table 36 Employment and Cohabitation Status — Supervised Male Subjects . . . . . . . . . . . . . 173

Table 37 Employment and Cohabitation Status — Supervised Female Subjects . . . . . . . . . . . 174

Table 38 Traditional Models of Failure as Arrest: Supervised and Unsupervised Subjects . . . . 175

Table 39 Traditional Models of Failure as Arrest: Supervised Subjects Only . . . . . . . . . . . . . 176

Table 40 Traditional Models of Failure as Arrest or Revocation . . . . . . . . . . . . . . . . . . . . . . . 177

Table 41 Traditional Models of Failure as Evidence of Drug Use . . . . . . . . . . . . . . . . . . . . . . 178

Table 42 Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

Table 43 Arrests, Male and Female Subjects, Supervised Subjects Only

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Table 44 Arrests or Revocations, Male and Female Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

Table 45 Drug Use, Male and Female Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

Table 46 Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

Table 47 Arrests, Male and Female Subjects, Supervised Subjects Only

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Table 48 Arrests or Revocations, Male and Female Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Table 49 Drug Use, Male and Female Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Table 50 Arrests, Males and Females, Supervised and Unsupervised Subjects

Complete, Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

Table 51 Arrests, Males and Females, Supervised Subjects Only

Complete, Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

Table 52 Arrests or Revocations — Male and Female Subjects, Complete

Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

Table 53 Drug Use — Male and Female Subjects Who Had Urine Testing During Follow-Up,

Complete

Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

Table 54 Traditional Models of CCC Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Table 55 CCC Failure, Male and Female Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

Table 56 CCC Failure, Male and Female Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

Table 57 CCC Failure, Male and Female Subjects, Complete

Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

Table 58 Traditional Models of Employment Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

Table 59 Employment, Male Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Table 60 Employment, Female Subjects

Traditional Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

Table 61 Employment, Male Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Table 62 Employment, Female Subjects

Bloom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

Table 63 Employment, Male Subjects, Complete

Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Table 64 Employment, Female Subjects, Complete

Heckman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

Table 65 Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Treatment Subjects Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Table 66 Arrests, Male and Female Subjects, Supervised Subjects Only

Treatment Subjects Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Table 67 Arrests or Revocations, Male and Female Subjects, Treatment Subjects Only . . . . . 227

Table 68 Drug Use — Male and Female Subjects, Treatment Subjects Only . . . . . . . . . . . . . . 229

Table 69 CCC Failure — Male and Female Subjects, Treatment Subjects Only . . . . . . . . . . . 231

Table 70 Employment — Male Subjects, Treatment Subjects Only . . . . . . . . . . . . . . . . . . . . 233

Table 71 Employment — Female Subjects Treatment Subjects Only . . . . . . . . . . . . . . . . . . . 235

Table A1 HDU Refusals — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

Table A2 HDU Refusals — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

Table A3 Missed Research — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Table A4 Missed Research — Female Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

Table A5 Research Refusals — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

Table A6 Missed Intake 1 — Male Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
1

ACKNOWLEDGMENTS

Numerous Bureau of Prisons staff members, past and present, have contributed to the TRIAD

drug treatment evaluation project since its initiation in 1989. As project director I would like to

acknowledge the individuals who contributed in the past or who are currently involved with the

project. Sue Wallace manages the Community Corrections Center and post-release data collection

efforts, supervising both data collection staff and computer programmers. Joyce O’Neil manages

the field data collection efforts, which involves supervising field research staff, coordinating site

visits, and ensuring quality control. Prior to Joyce O’Neil’s involvement with this project, Connie

Hobbs performed this role. Initial plans for the project were developed in association with Dan

McCarthy, with additional assistance provided by Barbara Owen. Anita Arcidiaconco provided

the administrative leadership at the outset of the project.

Field researchers served as the backbone of the very intensive in-prison data-collection process. I

would like to thank the field researchers who spent many hours traveling to prisons to interview

staff and patiently administer surveys and lengthy interviews to the inmates serving as research

subjects. Researchers included Linda Adams, Tim Brooks, Solla Carrock, Karen Childress,

Christine Escobedo, Marty Hill, Anthony Iwaszko, Jamie Hart French, Neal Langan, Tom Marsh,

Natalie Merriweather, Holly Millner, Brenda Moore, Nicki Pate-Allen, Robin Pool-Harris, Connie

Stevens, Michelle Taylor, Sharon Tittemore Suppa, and Laurie Westman. Dana Cyra administered

the staff surveys and assisted with quality control procedures.

Equally important were the individuals involved in the collection of Community Corrections

Center and post-release data. Rhonda Parker coordinated all the data collection from Community

Corrections Centers and from the transitional services managers. Sharon Tittemore Suppa

coordinated the interviews of U.S. Probation officers, and Stacey Kappral coordinated the

collection of NCIC data (automated arrest records). Other individuals involved in post-release

data collection included Dana Cyra, Christine Escobedo, Jamie Hart French, Holly Millner,

Jennifer Pearson, Constance Stevens, and Laurie Westman.

The complex programming tasks for developing and updating tracking systems were coordinated

by Sue Wallace and John Cashwell. David Smith, Suzy Vanyur, Chris Lehman, Peter Brustman,

and Doug Yearwood wrote computer programs to manage the data collected for the project.

Jamie Hart French provided administrative support, with Deena Walter Heikkinen and David

Cramer previously responsible for such support. William Saylor provided statistical consultation

to all project staff during the last few years. Gerry Gaes and Scott Camp contributed both by

providing methodological advice and assisting in the data analysis and writing of this report. Sue

Wallace and Joyce O’Neil coordinated the staff efforts in obtaining information contained in this

report and wrote sections of this report. David Smith, Phyllis Newton, and Judy Gordon edited

this report. The data collection required for this report and the report itself could not have been

accomplished without the benefit of each staff member’s skills and the teamwork exhibited by all

of the persons cited above.
2

In addition, the invaluable contributions of the many Drug Abuse Treatment Program (DAP) staff

who helped coordinate the data-collection trips is greatly appreciated. Community corrections

staff, transitional services managers, and the numerous Probation officers nationwide who

provided the information on inmates after their release from institutions are also to be thanked for

their important contributions to this evaluation. 

Members of the TRIAD advisory board provided suggestions during project implementation and

also provided useful feedback on earlier drafts of this report. The contributions from advisory

board members Doug Anglin, Helen Annis, Jeffrey Hoffman, Jim Inciardi, and Michael Maltz are

greatly appreciated.

Lastly, I would like to thank those who provided the administrative and financial support for this

project. Bureau of Prisons Director Kathleen M. Hawk and Assistant Director Thomas R. Kane

provided the administrative support within the BOP. They approved policies facilitating the data

collection and provided continued project funding. The National Institute on Drug Abuse (NIDA)

provided the initial funds that made possible a project of this magnitude. Bennett Fletcher from

NIDA has provided useful feedback and guidance throughout the project. 
 The Anti-Drug Abuse Act of 1986 laid the groundwork for the drug treatment programs 1

and the Anti-Drug Abuse Act of 1988 contained provisions for the funding of these programs.

3

TRIAD DRUG TREATMENT EVALUATION 

SIX-MONTH REPORT

EXECUTIVE SUMMARY

Introduction

The Federal Bureau of Prisons (BOP) has provided drug abuse treatment in various forms for

almost two decades. With the passage of the Anti-Drug Abuse Acts of 1986 and 1988 and an 1

increased emphasis on and resources for alcohol and drug abuse treatment, the BOP redesigned

its drug treatment programs. This design was completed after careful review of drug treatment

programs nationwide. The treatment strategy addresses an inmate’s drug abuse by attempting to

identify, confront, and alter the attitudes, values, and thinking patterns that led to criminal and

drug-using behavior. The current residential treatment program also includes a transitional

component that keeps inmates engaged in treatment as they return to their home communities.

The Bureau of Prisons (BOP) recently undertook an evaluation of its residential drug abuse

treatment program (DAP), designed to monitor inmates up to 3 years following release from BOP

custody. This interim report is based on inmates who had been released from BOP custody into

the community for 6 months. Findings suggest that the program is effective in reducing recidivism

and substance abuse. The evaluation, conducted with funding and assistance from the National

Institute on Drug Abuse, reveals that offenders who completed the drug abuse treatment program

and had been released to the community for a minimum of 6 months were less likely to be rearrested or to be detected for drug use than were similar inmates who did not participate in the

drug abuse treatment program. Specifically, among inmates who completed the residential drug

abuse treatment program, only 3.3 percent were likely to be re-arrested within the first 6 months

in the community compared to 12.1 percent of those inmates who did not receive such treatment.

In other words, treated inmates were 73 percent less likely to be re-arrested than untreated

inmates. Similarly, among inmates who completed the residential drug abuse treatment, 20.5

percent were likely to use drugs within the first 6 months in the community compared to 36.7

percent of those who did not receive such treatment, suggesting that those who received drug

treatment were 44 percent less likely than those who had not received treatment to use drugs

within the first 6 months.

The findings of this preliminary evaluation are noteworthy because prior research indicates that

the first 6 to 12 months of an offender’s release back to the community are particularly difficult

and often are critical to a successful reintegration. These findings, which suggest that drug abuse

treatment assists inmates during this initial resettlement period, offer encouragement for the

conclusion that another correctional program “works,” making a difference in the lives of

offenders and reducing the likelihood of future criminal conduct. In addition, the findings in this
4

study are strengthened due to the large sample size (1,800), a rigorous research design, and the

uniqueness of using a multi-site sample.

Paths to Treatment Service

This evaluation project addresses residential drug abuse treatment programs in the Bureau of

Prisons for inmates who receive a substance abuse diagnosis. However, any inmate interested in

drug abuse treatment can receive services through various means, ranging from drug education to

non-residential to residential depending upon individual inmate need. Treatment services are

primarily available at three different stages while in Bureau custody. Treatment can continue when

an inmate is released from Bureau custody to the supervision of U.S. Probation.

In the custody of the Bureau of Prisons:

Stage 1: Inmates participate in residential drug abuse treatment within the confines of a

designated drug abuse treatment unit generally for either 9 or 12 months (i.e., unit-based

treatment). The treatment strategies employed are based on two premises: the inmate is

responsible for his or her behavior, and the inmate can change his or her behavior.

Stage 2: Upon successful completion of the unit-based drug abuse treatment program, inmates are

required to continue drug abuse treatment for up to 12 months when returned to general

population. During this stage of institution drug abuse programming, known as institutional

transition, inmates meet with drug abuse program staff at a minimum rate of once a month.

Ordinarily, institution transition is conducted as a group activity consisting of relapse prevention

planning and a review of treatment techniques learned during the intensive phase of the residential

drug abuse program. 

Stage 3: All inmates who participate in the residential drug abuse program are required to

participate in community transitional services when they are transferred from the institution to a

Community Corrections Center (halfway house). In the community, the Bureau contracts with

community drug abuse treatment providers who provide group, individual, and/or family

counseling that meet the needs of the individual inmate. Generally, these contractors offer the

same type/philosophy of treatment offered in the institution. In addition, community transitional

services also are offered to inmates who have not completed any drug abuse treatment in the

institution or who have received treatment other than the residential program.

Out of Bureau Custody:

In addition, inmates leaving Bureau custody for supervision with the U.S. Probation Office may

remain in drug abuse treatment with the same treatment provider the Bureau used during the

community transition program. U.S. Probation provides a wide-range of treatment services for its
 This early release provision presents issues of disparity for Bureau inmates. The disparity 2

arises when, for example, two inmates convicted of the same offense receive different prison

terms because the inmate who has been diagnosed with a substance abuse problem receives a oneyear reduction on his/her sentence and the inmate without a substance abuse problem serves the

entire sentence. In effect, many perceive this one-year reduction as a reward for drug-abusing

behavior.

5

offender population, although not all releasees are required to participate in post-release

treatment.

Unit-Based Residential Treatment

This evaluation effort focuses on two types of residential treatment programs for alcohol and

other drug problems. The first offers 1,000 hours of treatment over a 12-month period with a

staff-to-inmate ratio of 1:12. The second offers 500 hours of treatment over a 9-month period

with a staff-to-inmate ratio of 1:24. Most of the subjects in this study participated in the 9-month

program.

All residential DAP’s are unit-based, that is, all program participants live together — separate

from the general population — for the purpose of building a treatment community. Each unit has

a capacity of approximately 100 inmates. Ordinarily, treatment is conducted on the unit for a halfday in two, 2-hour sessions. The other half of the day, inmates participate in typical institution

activities (e.g., work, school). During these times, as well as during meals, treatment participants

interact with general population inmates.

The goal of these programs is to attempt to identify, confront, and alter the attitudes, values, and

thinking patterns that led to criminal behavior and drug or alcohol use. Most program content is

standardized and the following modules comprise 450 hours of programming, both in didactic and

process groups: Screening and Assessment; Treatment Orientation; Criminal Lifestyle

Confrontation; Cognitive Skill Building; Relapse Prevention; Interpersonal Skill Building;

Wellness; and Transitional Programming. The remaining program hours are structured at the

discretion of each program.

All admissions to drug treatment are voluntary. At the outset of program implementation, there

were no incentives for residential drug treatment program participation. However, over time

various incentives were implemented. These included nominal financial achievement awards,

consideration for a full 6 months in a halfway house for successful DAP program completion, and

tangible benefits such as shirts, caps, and pens with program logos to program participants in

good standing. The incentives for drug treatment significantly changed with the passage of the

Violent Crime Control and Law Enforcement Act of 1994. This law allowed eligible inmates who

successfully complete the BOP’s residential drug treatment program to earn as much as a 1-year

reduction from their statutory release dates.2
 Typically, inmates enter a residential drug abuse treatment program 36 to 24 months 3

before release from BOP custody. This allows inmates to complete treatment and transition into

community-based treatment with minimal interruption to their treatment program.

 For purposes of this discussion, residential drug abuse program (DAP) refers only to 4

Stage One — the unit-based stage of the program. 

6

Sample

The preliminary results contained in this report relate to inmate subjects who were released no

later than December 31, 1995, and who were released to the community for 6 months or more.

Most of these inmates were within approximately one year of release from BOP custody when

they completed the program. This report includes data only for the first 6 months of release for 3

each inmate; the final report will cover a 3-year post-release period for all individuals. The sample

contained in this report includes 1,866 individuals — 1,524 men and 342 women — for whom

comprehensive data were available.

Treatment Subjects

Treatment subjects were sampled from 20 different institutions with a residential drug treatment

program. This represents approximately two-thirds of the institutions that currently operate

residential treatment programs. These institutions represent all security levels, except maximum

security, and serve both male and female populations.

The four types of residential DAP participants as they were categorized in the analyses are: 1) 4

inmates in residential drug treatment who completed that treatment, 2) inmates who dropped out,

3) inmates discharged from treatment for disciplinary reasons, and 4) inmates who, for a variety of

other reasons, did not complete the program. This “incomplete” category, in general, comprises

inmates unable to complete the residential, unit-based program because they were transferred to

another institution or to a halfway house (CCC), had their sentences shortened toward the end of

their incarceration, or spent an extended amount of time on writ or medical furlough. Table 1

provides a breakdown of inmate subjects by gender, treatment and comparison group

assignments, and individual categories within the treatment group.

Of the 719 male subjects who entered unit-based residential treatment, 73 percent completed the

treatment program, 5 percent voluntarily dropped out of the program, 8 percent were removed

for disciplinary reasons, and 14 percent constituted the “incomplete” subject type, as described

above.

Of the 180 female subjects who entered unit-based residential treatment program, 54 percent

completed the treatment program, 9 percent voluntarily dropped out of the program, 13 percent

were removed for disciplinary reasons, and 24 percent were of the “incomplete” category.
7

Anecdotal information collected during the study suggests that the lower percentage of treatment

“completers” among women than among men may be related to policy differences between

treatment sites and differential enforcement of program rules.

Comparison Subjects

Male and female comparison subjects were drawn from more than 30 institutions, some that

offered residential drug abuse treatment programs and some that did not. The comparison subjects

consisted of individuals who had histories of previous drug use and, therefore, would have met

the criteria for admission to the residential drug treatment programs. There were 805 male and

162 female comparison subjects.

Table Ex1. Type of Subject by Gender

MALE FEMALE

TYPE OF SUBJECT NUMBER PERCENT NUMBER PERCENT

Treatment 719 47.2% 180 52.6%

 12-month Program Graduate 122 8.0% 41 12.0%

 9-month Program Graduate 401 26.3% 56 16.3%

 Drop-out 37 2.5% 16 4.7%

 Disciplinary discharge 55 3.6% 24 7.0%

 Other reason - incomplete 104 6.8% 43 12.6%

Comparison 805 52.8% 162 47.4%

 TOTAL 1,524 100.0% 342 100.0%

Outcome Measures

Criminal recidivism and post-release drug use were the primary outcomes of interest in this

evaluation. The other outcomes examined were unsuccessful halfway house completion and

percent of post-release time the subject was employed full-time. Criminal recidivism was defined

two ways: 1) an arrest for a new offense, or 2) an arrest for a new offense or supervision

revocation. Revocation was defined as occurring only when the revocation was solely a technical

violation of one or more conditions of supervision (e.g., detected drug use, failure to report to

probation officer).
 A violation of a condition of supervision does not always result in a revocation. 5

 However, looking instead at inmate subjects who were employed either full- or part-time 6

during some or all of the post-release period, this trend changes, with 79 percent of women

working either full- or part-time and 82 percent of men working either full- or part-time.

8

Because much of the outcome information was obtained from interviews with U.S. probation

officers, the analyses for three of the outcome measures (arrest for new offense or revocation,

drug use, and employment) were conducted only for individuals released to supervision. The

analysis for arrest for new offense used both supervised and unsupervised subjects because arrest

information could be collected on unsupervised subjects from the FBI’s National Crime

Information Center (NCIC). The analysis concerning unsuccessful halfway house completion was

limited to those individuals who received halfway house placements.

Drug use as a post-release outcome refers to the first occurrence of drug or alcohol use. This

information consisted of four different categories of a violation of a supervision condition as

reported by U.S. probation officers: a positive urinalysis (u/a), refusal to submit to a urinalysis,

admission of drug use to the probation officer, or a positive breathalyser test. 5

Employment information was also obtained through interviews with U.S. probation officers. This

information was limited to employment occurring prior to any post-release arrest or revocation.

This outcome was defined as the percentage of post-release period that the subject was employed

full-time.

Halfway house placement failure, for those individuals who received such a placement before

release from custody, was reported by halfway house staff. Approximately two-thirds of the

subjects received a halfway house placement. Failure to complete a halfway house placement is

the result of a disciplinary infraction, whether for a violation of halfway house rules or for criminal

activity. 

Before examining the effects of treatment, it is important to look at the base rate of failure for

each outcome measure for both treatment and control inmates. This base rate of failure is

presented by gender in Table 2, and tells us, for example, that the base failure rate for arrest on a

new offense for all subjects (both those who received treatment and those who did not receive

treatment) is 14 percent for men and 6 percent for women. Overall, these results indicate that for

each outcome measure, the percentage with a successful outcome is lower for men with the

exception of employment. Forty-four percent of the male subjects were employed full-time during

the entire 6-month post-release period as compared to 28 percent of the women.6
9

Table Ex2. Outcome Measure by Gender

6 Months Post Release

 Male Female

ARREST FOR NEW OFFENSE 14% 6%

ARREST FOR NEW OFFENSE 21% 11%

 OR REVOCATION

DRUG USE 31% 20%

UNSUCCESSFUL HALFWAY 23% 15%

 HOUSE COMPLETION

EMPLOYED FULL-TIME ENTIRE 44% 28%

 POST-RELEASE PERIOD

Analyses

The analyses of the effects of unit-based drug treatment on the various outcome measures

controlled for a wide variety of background factors known to be related to recidivism and

treatment outcomes, including a number of factors related to drug-using populations that have not

been examined in previous evaluation studies. These background measures included drug use

frequency for each of the various drug types, drug and alcohol dependency, drug treatment

history, mental health treatment history, psychiatric diagnoses of depression and antisocial

personality, criminal history, age, race, ethnic status, employment history, motivation for change,

level of supervision (e.g., halfway house placements before and after release from custody, release

to supervision, frequency of urine testing), and post-release living situation. 

Three different methods of analyses were used to assess treatment effectiveness. Two of these

methods represent different approaches to control for self-selection into treatment, i.e., selection

bias. These methods represent alternative procedures to ensure that effects of treatment are not

confounded with effects of volunteering for treatment. One method compares all individuals who

had treatment available to those who did not have treatment available. The second method

directly controls for selection bias. The third, the traditional method, is similar to that used in

some previous evaluations but it does not control for selection bias. 

The assessment of treatment effects generally showed consistency in results for the various

outcome measures when comparing the differing methods of analyses. The results reported below

focus upon those using the strategy directly controlling for selection bias because this strategy

was felt to assess most reliably the effects of drug treatment. 
10

All analyses, except those for employment outcomes, were done 1) for males and females

combined and 2) for males only. Separate analyses of outcome measures other than employment

were not possible given the smaller number of women in the sample and the lower failure rate of

women (see Table 2).

Findings — Residential Drug Abuse Treatment

The effects of unit-based residential treatment on post-release outcomes described below are the

differences in outcomes between treatment and comparison groups after controlling for various

background factors and for self-selection into treatment. 

Recidivism

Arrest for New Offense — Individuals who had received unit-based residential treatment had a

lower probability of being arrested in the 6-month follow-up period than did comparison subjects.

The probability of arrest for individuals who entered and completed treatment was 3.3 percent as

compared to a probability of approximately 12.1 percent for untreated subjects (see Figure 1). In

other words, among inmates who completed residential drug abuse treatment, only 3.3 percent

were likely to be re-arrested within the first 6 months in the community compared to 12.1 percent

for non-treatment inmates; those who received treatment were 73 percent less likely to be rearrested than those who had not received treatment.

Arrest for New Offense Or Supervision Revocation — When outcome was defined as arrest for

new offense or supervision revocation, residential drug treatment effects also were found.

Questions arise when combining arrest and supervision revocation in the same outcome measure.

Therefore, this analysis should be

considered preliminary, with future

reports examining the relationship,

similarities, and differences in the two

measures of recidivism.

Drug Use

The results for drug use, like those for

arrests for a new offense, show that

individuals who participated in a

residential drug abuse treatment program

were less likely to have evidence of postrelease drug use than were comparison

subjects. Among inmates who completed

residential drug abuse treatment, 20.5
11

percent were likely to use drugs in the first 6 months following treatment completion compared to

36.7 percent among untreated inmates; that is, those inmates who completed residential drug

abuse treatment were 44 percent less likely to use drugs in the first 6 months following release

than those who did not receive treatment (see Figure 1). 

CCC Failures

Approximately two-thirds of the individuals received a halfway house placement (CCC) before

their release from BOP custody. Results indicate that treatment completion had no effect on

whether inmates successfully completed their halfway house stay.

Post-Release Employment

Individuals who participated in residential drug abuse treatment during their incarceration were no

more likely to be employed full-time for a greater percentage of the 6-month post-release period

than were individuals who did not participate in treatment. This finding was true for both men and

women. 

Inter-Institutional Differences

The preliminary data show that when controlling for differences in the composition of inmates in

the 20 different programs that were evaluated, there were few differences between programs in

effectiveness as measured by the five outcome measures. With larger sample sizes and other

information on program quality, inter-institutional differences can be examined more thoroughly

in the future.

Summary

The preliminary results of this initial look at residential drug abuse treatment programs suggest

important and exciting possibilities for the treatment of drug offenders. Despite what is thought to

be a difficult period of adjustment — the first 6 months following release from custody — inmates

who entered, received, and completed residential drug abuse treatment were 73 percent less likely

to be re-arrested than inmates who did not receive such treatment. This 73-percent reduction in

arrest rates, coupled with the 44-percent reduction in drug use for treated subjects, strongly

suggests that the Bureau of Prisons’ residential drug abuse treatment programs make a significant

difference in the lives of inmates following their release from custody and return to the

community.

This evaluation has been methodologically rigorous and has revealed significant positive effects on

arrest and drug use in post-release outcomes for a 6-month follow-up period. Because studies

have shown that recidivism rates are highest within the first year and, while lower after that, are

still high for another year or two, these results must be interpreted with caution. Future analyses
12

will evaluate whether these effects are sustained over a longer follow-up period (3 years after

release).
TRIAD is the acronym for “Treating Inmates’ Addiction to Drugs.” 7

13

CHAPTER 1: INTRODUCTION

In the late 1980’s, the Federal Bureau of Prisons (BOP) implemented a residential drug abuse

treatment program (DAP) designed to assist inmates struggling with drug problems. The

treatment strategy addressed inmate drug abuse by attempting to identify, confront, and alter the

attitudes, values, and thinking patterns that led to criminal and drug-using behavior. From that

initial effort, the program has grown to include an essential transitional component that keeps

inmates engaged in treatment as they return to their home communities.

To assess the effectiveness of its DAP program, the BOP in conjunction with the National

Institute on Drug Abuse (NIDA) initiated a drug treatment evaluation project, which has become

known as the TRIAD drug treatment evaluation project. The evaluation comprises a multi-site 7

study that compares inmates involved in the DAP (i.e., those who received in-prison residential

drug and alcohol abuse treatment) to inmates who did not receive treatment. While initial plans

called for an experimental design, we were not able to implement the necessary random

assignment procedures. Therefore, various approaches were used to control for any selection bias

resulting from such factors as non-random assignment.

This interim report focuses upon assessing the effectiveness of the BOP’s in-prison DAP, while at

the same time controlling for a variety of factors related to recidivism and treatment outcomes.

The preliminary results contained in this report relate to inmates who were released no later than

December 31, 1995, and who were in the community for 6 months or more. This report includes

data for the first 6 months of release for each inmate; the final report will cover a 3-year postrelease period for all research subjects, including those released after December 31, 1995. The

sample contained in this report includes 1,866 individuals — 1,524 men and 342 women — for

whom comprehensive data were available. In general, most results are reported for men and

women combined.

We sampled treatment subjects from 20 different institutions, which is approximately two-thirds

of the institutions that currently operate residential treatment programs. These prisons included all

security levels except maximum security, and they served both male and female populations. The

residential programs included two components of treatment — an in-prison component and a

transitional services component (as part of community placement and supervision). The treatment

programs consisted of two levels of intensity — 500-hour, 9-month programs and 1,000-hour,

12-month programs. 

Male and female comparison subjects were drawn from more than 30 institutions, some of which

had residential drug abuse treatment programs and some of which did not. The comparison

subjects consisted of individuals who had histories of previous drug use and, therefore, would

have met the criteria for admission to the residential drug treatment programs. 
 In the BOP, referred to as a Community Corrections Center (CCC) placement. 8

14

Results from this 6-month post-release study demonstrate positive effects on three of the five 

outcome measures: arrests, arrests and revocations, and drug use. That is, the evaluation shows

that the approximately 600 offenders who completed the drug abuse treatment program and had

been released to the community for a minimum of 6 months were less likely to be rearrested for a

new offense, to be rearrested or revoked, or to test positive for drug use than was a similar group

of inmates who did not complete the drug abuse treatment program.

The probability of rearrest was 3.0 percent for treatment completers as compared to a probability

of 12.7 percent for untreated subjects. The probability of rearrest or revocation was 4.7 percent

for treatment completers as compared to a probability of 17.4 percent for untreated subjects. The

probability of drug use was 20.5 percent for treatment completers as compared to a probability of

36.7 percent for untreated subjects. The findings are noteworthy because the first 6 months of an

offender’s release back to the community are particularly difficult and often critical to a successful

reintegration into society.

The fourth outcome measure, post-release employment, revealed no positive effects for those

completing treatment. When considering a fifth measure of effectiveness — successful completion

of halfway house placements — results were somewhat ambiguous. 8

The final report will provide results for a larger sample size — particularly for women — and will

contain outcome information on all subjects for a 3-year post-release period. We expect a sample

size of approximately 2,900 individuals for the final report — 2,300 men and 600 women. In

addition, future research efforts will identify the role of post-release treatment in successful

outcomes and will attempt to increase our understanding of the role that background and

psychological factors play in determining post-release outcomes. Future analyses will consider

whether any of these effects are sustained over a longer follow-up period. 

Background

A large proportion of State and Federal inmates have histories of substance abuse. A self-report

assessment of Federal inmates in 1989 indicated that between 30 and 44 percent had substance

abuse histories (Whittenberger, 1990). A survey of State inmates in 1991 indicated that more than

60 percent had used at least one illegal drug regularly (Beck et al., 1993). Considerable evidence

suggests that criminal behavior is amplified during periods of moderate and heavy drug use

(Anglin and Speckart, 1986; Anglin and Speckart, 1988; Ball et al., 1981; Nurco et al., 1985;

Nurco et al., 1988; Speckart and Anglin, 1985; Speckart and Anglin, 1986).

The BOP has provided drug abuse treatment in various forms for decades. Although the number

of drug treatment units in Federal institutions grew to a high of 33 in 1978, the number of
 The Anti-Drug Abuse Act of 1986 laid the groundwork for the drug treatment programs, 9

and the Anti-Drug Abuse Act of 1988 contained provisions for the funding of these programs.

15

programs began to decline in the early-to-middle 1980’s due to changes in the social and political

climate (Wallace et al., 1991). With the passage of the Anti-Drug Abuse Acts of 1986 and 19889

and an increased emphasis on and resources for drug abuse treatment, the BOP redesigned its

drug treatment programs after careful review of drug treatment programs nationwide.

The BOP’s renewed interest in prison-based drug treatment programs began in 1988 when thendirector J. Michael Quinlan organized a national drug conference. Immediately following this

conference, the BOP Executive Staff approved the establishment of five institution-based

residential drug abuse treatment programs. Similar programs were established throughout the

Federal prison system and, by December 1996, 34 residential programs were operational.

At about the same time, in 1989, the National Institute on Drug Abuse expressed interest in a

comprehensive evaluation of prison-based drug treatment programs, and the BOP submitted a

research proposal for evaluating drug treatment programs in the Federal system. That proposal

resulted in the signing of an interagency agreement between the BOP and NIDA in 1990 for a

multi-site evaluation of the BOP’s residential drug abuse treatment program, which eventually

emerged as the TRIAD project.

The objectives of the TRIAD evaluation project specified in the original evaluation proposal

submitted to NIDA in 1990 were:

 To conduct a process evaluation. This aspect of the evaluation would address the

following issues:

1) the nature of the services provided;

2) the characteristics of the service recipients; 

3) program staffing patterns; and,

4) implementation of residential drug treatment programs within a correctional

environment.

 To conduct an outcome evaluation. The most important objective was to assess the

extent to which in-prison residential treatment, reinforced by community-based

aftercare services, could reduce drug use and criminal behavior after release from

prison. Specific questions were:

1) Who are the program participants? Does the program serve the most serious

offenders?

2) What are the different types of substance abusing offenders?

3) What types of incarcerated offenders are more likely to volunteer for in-prison

drug treatment programs?
16

4) Are the 12-month (1,000-hour) drug treatment programs more effective than

the 9-month (500-hour) programs?

5) What role do services provided after release from prison play in preventing

relapse to drug use or criminal behavior?

6) Are there specific types of drug-abusing offenders who benefit more from

participation in the in-prison residential drug treatment?

7) What are the relative effects of pre-treatment characteristics, the treatment

program, and the post-release environment on the various outcomes?

Organization of the Report

This report is organized into eight chapters. Chapter 1 provides a brief introduction and

background to the long-term evaluation, as well as to this interim report. The chapter concludes

with synopses of the report’s remaining chapters.

Chapter 2 is divided into two sections. The first section discusses the nature of evaluation

research in an applied setting, with a focus upon what we view as the most significant

methodological problem — selection bias. Our goal is to represent the difficulties of applied

correctional research, to describe the organizational pressures that determine which inmates

receive treatment, to depict these influences in an understandable model of selection pressures,

and to offer potential solutions to these problems, both analytical and methodological. We discuss

the two different processes we used in our analyses to address the problem of selection bias.

In the second section of this chapter, we use our model of selection pressures to critique the

research design, analyses, and interpretation of results contained in the most commonly cited,

related studies. Overall, our review suggests that methodological problems associated with

evaluating residential drug treatment programs create important obstacles to interpreting the

results of this research. We believe that, for the most part, the research we reviewed suffers from

inferential problems associated with disentangling treatment effects from selection bias effects. We

argue that it would be prudent to temper strong conclusions about successful treatment outcomes

— which are often portrayed in the literature — with a bit of skepticism, born from a closer look

at the methodological problems.

Chapter 3 summarizes the evolution of the Bureau’s drug treatment programs from the beginning

of the TRIAD drug treatment evaluation project to the completion of the in-prison data collection

phase of the study. This summary details the nature and intensity of the services received and the

various pathways into treatment. That is, research subjects may have received drug treatment

services while in prison, while housed in a halfway house (if applicable), while under post-release

supervision (if applicable), or during some combination of the three. Chapter 3 also describes the

various components of the BOP’s in-prison drug treatment programs, as well as the treatment

services available following release from an institution. As is true for many major research efforts,
 It is important to note that these subject groupings are not equivalent to the subject 10

groupings used in the analyses of results or to control for selection bias. The groupings used for

data analysis are discussed in Chapter 4. 

17

changes in research design and data collection procedures occurred throughout the project in

order to adapt to changes in program implementation. 

Chapter 4 describes the research design. After a summary presentation of the basic research

design, we provide information on the procedures used for selecting research subjects, the

resulting sample, the data collection instruments, and the data collection procedures. We describe

the differences between the selection of treatment and comparison subjects, as well as the

methods used to collect information at the various stages of a subject’s criminal justice status —

at any given time, he or she is either in prison, in a halfway house, terminated from BOP custody

without supervision, or terminated from BOP custody with supervision by a probation officer.

Chapter 4 concludes with listings and descriptions (when necessary) of all the variables used in the

analyses.

Chapter 5 contains results of our subject attrition analyses. Some individuals identified as research

subjects failed to enter the sample pool due to logistical data collection issues. Other subjects

were unavailable for analyses because they refused to participate in the project. Although we

controlled for these factors in our analysis of outcomes, we felt it important to describe the

subject attrition process and to compare — on the variables available for both groups — those

included in the analyses with those lost to attrition.

Chapter 6 contains a description of the subject sample. This chapter provides univariate statistics

for each of the six subject groups used in our analytic strategies, and provides the reader with a 10

basic understanding of the background characteristics of the subject groups and the treatment

services received. These six subject groups are: 

inmates who completed a residential drug abuse treatment program; 

inmates who dropped out of a program; 

inmates discharged for disciplinary reasons;

inmates who did not complete a program through no fault of their own; 

inmates from drug treatment sites who did not volunteer for treatment; and, 

inmates who were housed at institutions at which there were no drug abuse treatment

programs offered. 

Chapter 6 should prove particularly relevant to service providers who want to know more about

the general nature of the drug abusing population being served by BOP drug abuse treatment

programs.

Chapter 7, which describes the analysis and results, begins with a description of our outcome

measures. This is followed by a description of our three analytic strategies and our method of
18

testing, hierarchically, the effects of adding blocks of variables. Two of our three analytic

strategies represent methods of addressing the problem of selection bias, and one strategy

represents an attempt to replicate the strategy frequently used by other researchers. We continue

with a presentation of the consideration of missing data. Because listwise deletion — the deletion

of a subject with a missing value on one or more data elements — can result in a biased sample

available for analyses, we either imputed the missing values or included the “missingness”

category in the effects vectors.

Results are first presented for three post-release outcomes: arrests only, arrests or revocations,

and drug use. We present the results for each of the three analytic strategies. We then present, by

analytic strategy, results for the two final outcomes: Community Corrections Center (i.e., halfway

house) placement failure and percent of time employed full-time after release. We conclude this

chapter with an effort to detect differences among the treatment programs for the five outcomes

previously discussed.

Chapter 8 provides a summary of the results. This discussion focuses upon the preliminary

conclusions we can make regarding the effectiveness of the Bureau’s drug abuse treatment

programs. We seek to identify consistencies in results across the various outcomes and, more

specifically, consistencies across the different analytic strategies for a particular outcome. Finally,

this chapter identifies the limitations of our conclusions, the issues to be addressed when data are

available for the entire pool of subjects, and the issues important to future analyses.
Throughout this Chapter we refer to the various selection pressures as a means of 11

describing the various elements of selection bias.

19

CHAPTER 2: A REVIEW OF RECENT STUDIES OF HIGH INTENSITY ADULT

CORRECTIONAL DRUG TREATMENT PROGRAMS — THE PROBLEM OF

SELECTION BIAS AND POSSIBLE SOLUTIONS

We focus our literature review on recent research studies that are most commonly cited. These

studies all conclude that prison-based drug treatment is effective. Before we examine these studies

in detail, we discuss the nature of evaluation research in an applied setting and what we view as

the most significant methodological problem — selection bias. Our goal is to represent the

difficulties of applied correctional research, to describe the organizational pressures that

determine which inmates receive treatment, to depict these influences in an understandable model

of selection pressures, and to offer potential solutions — both analytical and methodological — 11

to these problems. 

In the second section of this chapter, we use our model of selection pressures to critique the

research design, analyses, and interpretations of results contained in the most commonly cited

studies. Overall, our review suggests that methodological problems associated with evaluating

residential drug treatment programs create important obstacles in interpreting the results of this

research. We believe that, for the most part, the research we reviewed suffers from inferential

problems associated with disentangling treatment effects from selection bias effects. We argue

that it would be prudent to temper strong conclusions about successful treatment outcomes —

which are often portrayed in the literature — with a bit of skepticism, born from a closer look at

the methodological problems. We also describe different solutions for overcoming the problem of

selection bias. 

Selection Bias and the Evaluation of Prison Drug Treatment Programs

There is no question that conducting evaluations in an applied setting is very difficult.

Correctional systems are coercive by their very nature, and even when treatment is endorsed and

carried out by well-trained, motivated providers, there is typically a tension between the

necessities of custody practices and the goals of a therapeutic setting. Custody practices are

necessarily rigid and uniform, while treatment delivery must be personalized and flexible. 

The ideal model for any assessment is a clinical trial in which we can control the timing, dose

(amount of exposure to), and administration of treatment. Using random assignment allows us to

discount client characteristics when drawing inferences about the effects of treatment.

Unfortunately, there are very few situations in which it is practical to carry out a well- controlled,

random assignment design of drug treatment. In most correctional settings, control over who gets

treatment and when they get it rests with the treatment providers or some administrative
 For a technical discussion of sample selection bias, see Berk, 1983. 12

20

authority. Often there are policies that determine eligibility as well. Under these conditions, the

best we can achieve is a quasi-experimental design but even these will vary in their rigor. Our

emphasis is on the difficulty of in doing either random assignment or quasi-experimental designs in

a correctional setting. 

We raise these cautions because the internal and external validity of a study is compromised by

the vagaries of correctional environments and possible differences in the characteristics of the

clients involved in these studies. Rather than running away from these problems, we want to

address them and offer some solutions that we used in the current study. 

Fletcher and Tims (1992) have outlined the kinds of threats to internal and external validity that

can occur in evaluation studies performed in a correctional setting. Their critique is thorough, but

it does not give any color or texture to the scope of problems. In this chapter, we try to

characterize the nature of some of the problems that occur when a variety of administrative

decisions and local practices can contaminate the research design.

Rather than repeat the Fletcher and Tims critique, we focus on what we believe is the most

troublesome methodological problem in an applied setting, in general, and in the correctional drug

treatment literature in particular: understanding and controlling for selection bias. In a simple twogroup design, experimental versus control, we want to be able to assume that whatever effect we

observe is attributable to the treatment and not to differences in the characteristics of the subjects

in the two groups. Selection bias results from processes that change the composition of the two

groups in such a way that we are unable to make a clear inference as to whether the effects we

observe are due to the treatment or to the different group compositions.12

Adopting a skeptical perspective, we could conclude that the selection process prevents us from

drawing any conclusions about treatment effectiveness regardless of whether the original design

used randomization to assign offenders to treatment groups. From this perspective, program

terminations, both voluntary and involuntary, cause the treatment group to “boil down” to only

those participants who are ready and capable of succeeding when released to the community.

Thus, the “effect” of treatment may be nothing more than the process of “weeding out” those

more likely to fail from those more likely to succeed, and treatment has no additional value to

those who remain in treatment. 

A more sanguine view is that the selection process results in a motivated group of program

participants whose treatment results in even greater success than would be the case had no

treatment occurred. The problem becomes choosing a research design that can distinguish

between outcomes that are due solely to the selection process and those that are due to both this

selection process and treatment. Furthermore, the research design must be able to differentiate the

effects attributable to the selection process from those attributable to treatment. 
 One of the reviewers of our report asked us to address the issue of voluntary 13

participation in these in-custody criminal justice programs. All of the programs reviewed in this

section, as well as the drug treatment program within the Bureau of Prisons, were composed of

voluntary participants. We are unaware of the extent to which criminal justice-based drug

treatment programs are voluntary, mandatory, or “coerced.” Although there is some literature on

whether mandatory or coerced treatment can be successful, this is an aside for the present

purposes. Even if drug treatment were mandatory, the attrition process would still affect the

internal and external validity of the evaluation. Furthermore, the mandatory nature of drug

21

A simple conceptual device for understanding this problem is to treat it as an additive process. We

assume a baseline group of untreated comparison clients similar in background to our treatment

clients. For conceptual purposes, we can envision treatment subjects who “fall out” of treatment

and those who remain. We assume that those clients who remain, on average, would be more

successful than the comparison subjects even without treatment because they are a more select,

motivated subgroup. But, we also assume treatment has benefits, naturally, and that it “pushes”

the success of these motivated individuals higher than it would have been without treatment. The

inferential problem comes in identifying the “push” from motivation from the “push” from

treatment. In some cases, these causes may be so entangled that the separate influences are

extremely difficult to reconstruct. 

Although our discussion focuses on selection processes that bias results in favor of finding a

treatment effect, it is possible that selection processes can affect group composition in a manner

that biases results against finding a treatment effect. For example, there might be an incentive

structure that would encourage higher risk defendants, rather than lower risk defendants, to enter

treatment. Another possibility is that treatment selection is tightly controlled by providers who

reserve treatment beds for the most difficult cases.

A Model of Sample Selection Process

To understand the complexity of the problem, we have attempted to represent in Figure 2 the

most important selection processes that can occur in the research design when evaluating drug

treatment in a correctional setting. In this context, we use the word “selection” to describe the

processes that differentiate who enters treatment, as well as the processes that determine who

exits treatment prematurely. This latter process is also called “attrition.” Figure 2 indicates the

kind of selection pressures (filters) that operate within an environment in which treatment is

available and an additional selection process that occurs when researchers try to follow-up on

inmates who have been released to the community. There are four prominent in-custody selection

filters: self-selection, administrative — or clinical — selection, treatment selection, and

transitional treatment selection. The last selection pressure occurs when there are biasing

processes that determine which clients are lost to follow-up.

The first process, self-selection, is based on either internal motivational states or external

incentives that dispose some people to volunteer for treatment. The second process, 13
treatment may mean that the selection process is removed from the client and handed to an

administrator. 

 These selection pressures can come from external sources, such as judges who strongly 14

recommend candidates for treatment, or from internal sources, such as the pressures to fill

treatment beds in a crowded prison system.

22

Figure 2 — A Model of Selection Bias

administrative selection, reflects the clinical judgment exercised by treatment providers and other

administrators who determine whether someone is chosen for a program. The third process, 14

treatment selection, weeds out clients who cannot meet the program demands. As illustrated in

our review of the research on prison therapeutic communities, treatment selection can result in

high numbers of inmates failing the treatment. 
23

Another form of selection consists of weeding out participants during the transitional care phase

of a multi-phase treatment approach. Participants can be terminated by staff or they can withdraw

themselves. It appears from our study and some of the others that selection in this phase is usually

not very significant. However, for situations in which the conditions of supervision for treatment

subjects were very different from those for control subjects, this additional level of selection might

need to be considered. Finally, there may be conditions that affect which study participants are

lost to follow-up. For example, if follow-up interviews are voluntary, there may be self-selection

bias introduced by those characteristics that are correlated to the individual’s willingness or

unwillingness to be interviewed.

The problem of selection bias becomes readily apparent from Figure 2 when one focuses on the

end of the selection process and sees who remains in treatment. If the study design assesses only

those offenders who have made it through every selection filter, it is very difficult to construct a

legitimate control group composed of only those non-treated offenders who also would have

made it through the same selection process had they had the opportunity to do so.

What may not be readily apparent from Figure 2 is the way selection pressures can also affect the

comparison group. Let us assume we begin a study with a pool of drug dependent clients. From

this pool a sample of clients is selected into treatment. As we have already noted, the selection

pressures may operate in one of two ways. Clients in treatment may have characteristics that

dispose them to more successful treatment outcomes (case 1) or they may have characteristics

that dispose them to more unsuccessful outcomes (case 2). In both cases, if we have to draw our

comparison sample from the individuals remaining after others have been selected for treatment,

we may bias our design in a subtle way. 

In case 1, the residual group of untreated clients, on average, may be less disposed toward

successful post-release outcomes. In that case, we have “creamed” the treatment clients and the

residual pool is composed of the “sour” remnants. A sample drawn from the residual pool will

likely have less successful outcomes than will a randomly drawn sample of drug dependent clients

composed of both motivated and unmotivated individuals. In case 2, the residual group of clients

may be more disposed toward successful post-release outcomes. A sample drawn from this

residual pool will be more likely to have successful outcomes than will a randomly drawn sample

of drug dependent clients. Thus, in case 1, the residual comparison group will introduce bias in

favor of finding a positive effect, while in case 2 it will introduce bias against finding such an

effect.

Methodological Solutions to Selection Bias

There are several ways to attempt to handle the problem of selection bias in the absence of

random assignment. None may be completely satisfactory. The first is to assess all client

characteristics that can be used to adjust the treatment outcomes. Thus, if there are differences

between two groups due to selection pressures, we can control for these difference in a

multivariate analysis and adjust our outcomes accordingly. This approach will fail if there are
24

important unmeasured variables that distinguish the treated from untreated samples and those

differences affect recidivism. This approach also requires a thorough theoretical understanding of

the selection processes. We can speculate on some of the processes that may be affecting who is

selected (or self selected) to go into treatment. 

Client motivation or commitment may determine who volunteers for treatment. Perhaps a board

or group of administrators chooses clients based upon the seriousness of the subjects’ drug

dependencies and the extent to which the selecting personnel believe clients will benefit from

treatment. Attrition may also have its unique determinants. Perhaps some clients are unreceptive

to specific treatment approaches. Perhaps those inmates most entrenched in a criminal lifestyle or

most embedded in an inmate subculture are the most likely to withdraw from treatment. All of

these processes can affect outcomes. Many researchers in this domain have attempted to control

for client characteristics by measuring such variables as age, race, sex, criminal history, and drug

dependency and then use these variables in a multivariate analysis. However, unless these

variables control for the processes that affect both selection into treatment and recidivism, this

technique will fail to control for selection bias. Our argument is that considerable thought should

go into understanding and measuring selection pressures so that we can observe and control for

these processes.

A complementary approach to handling selection bias is to choose a comparison pool of clients

from sites in which no treatment is available or in which treatment is withheld. Based on the work

of Bloom (1984), Rhodes (personal communication, 1997) has shown how this approach can be

used to make inferences about treatment under conditions in which some clients accept treatment

and some decline. Figure 2 graphically shows the different flows of clients under these

circumstances. With just a few notations we can represent outcomes for the groups offered

treatment and for the groups not offered treatment.

We adopt the following notation:


: the effect of treatment at sites where treatment is offered.

F : the average proportion of clients recidivating at sites where treatment is treated population

offered.

F : the average proportion of clients recidivating at sites where treatment is untreated population

not offered.

Faccept: the proportion of clients who would recidivate if treatment were offered and they

accepted treatment.

Fdecline: the proportion of clients who would recidivate if treatment were offered and they

declined treatment.

P : the proportion of clients who accept treatment if treatment were offered. accept

P = (1-P ): the proportion of clients who decline if treatment were offered. decline accept
Ftreated population
Paccept(Faccept	
)(1	Paccept)Fdecline

Funtreated population
PacceptFaccept(1	Paccept)Fdecline



(Funtreated population 	 Ftreated population)/Paccept

T1
1/3F11/3F21/3F3

T2
1/3F11/3F21/3(F3
3)

T3
1/3F11/3(F2
2)1/3(F3
3)

25

Using this notation alone, we can represent the total average outcomes of clients offered

treatment, including those who accept and those who decline, as :

We can also show that the average effect for clients not offered treatment is :

With a little algebra, the treatment effect is :

In other words, if we know the total effect for those offered treatment and the total effect where

no treatment is available, we can find the treatment effect if we know the population proportion

that accepted treatment. Although we have represented this approach as if we are comparing two

sites, the technique generalizes to a multi-site evaluation. As an example, consider three sites. In

the first prison, no treatment is available. In the second prison, treatment is available and one-third

of the inmates accept it. In the third prison, treatment is offered and two-thirds of the inmates

accept treatment. Assuming the inmate populations are the same in all three prisons and using

similar notation as above, in the prison with no treatment, the average outcome is:

In the prison with one-third of the inmates treated, the average outcome is:

In the prison where two-thirds of the inmates are treated, the average outcome is:

By substitution, we can solve for the 
’s, and this allows us to evaluate whether the treatment

effect varies across the different sites. This approach generalizes to any number of sites. In the
 The derivation and computation of these parametric survival models with correction for 15

selection bias are not available in typical statistical packages. We engaged an econometrician

consultant, William Rhodes of Abt Associates, to help us derive the appropriate models and

estimate them using GAUSS.

26

current study, we used this approach in a regression context. We also adopted a third approach

because we were unsure of the homogeneity of our population across the different treatment and

non-treatment sites and we wanted a more general model that would allow us to build interaction

effects into the model. This is the technique we describe next. 

To adjust for selection directly, we developed a model that represents the selection process. This

model is used in conjunction with a model that represents the effect of treatment. By modeling

both processes simultaneously, this procedure allows us to estimate the treatment effect

conditional upon the processes that cause an offender to be selected into treatment. This

technique is described thoroughly in Appendix C.

Because selection bias is such a difficult problem, we decided to address the problem in two

different ways in our analyses of the Bureau of Prisons’ drug treatment programs. We measured a

number of background characteristics we believed were related to post-release recidivism and

drug relapse. However, because we were still unsure of the exact nature of all the selection

pressures operating in our study, we adopted a procedure analogous to the method we described

above. Our method compared clients from sites where treatment was offered to clients from sites

where no treatment was offered. When analyzing the post-release dependent variables using this

approach, we combined the outcomes of all treatment participants regardless of whether they

completed treatment, withdrew, or were terminated for disciplinary reasons. These data were

combined with data from the comparison subjects from DAP sites. Then we contrasted all of

these clients — together — with a sample of inmates from sites where no treatment was available.

We also used the model, described in Appendix C, that incorporates some information about the

selection process and uses that information to control for selection effects while simultaneously

testing for treatment effects. Selection bias adjustment was made to the survival function

associated with the time until an offender was arrested and the time until the offender had an

officially recorded drug relapse. By modeling selection bias explicitly, we were able to test

whether selection bias increased or decreased the survival time. If it increased the survival time,

this was evidence that there were pressures that selected lower-risk defendants into treatment. If

the selection bias parameter was negative, this suggested that there were pressures that selected

higher-risk defendants into treatment, which would in turn lower their survival times. If the

selection bias parameter was not significant, we could conclude there were no such selection bias

pressures operating.15

It is not unusual to find, in previous research, that program completers are more successful than

are controls, who in turn are more successful than are program terminators. If it was possible to

classify correctly control group offenders’ outcomes into hypothetical “completions” and
27

hypothetical “terminations,” and if we were to assume that treatment is effective, we would

expect to observe that those who completed treatment had more successful outcomes than those

in the control group who hypothetically completed the treatment. Further, we would expect to

observe that those who did not complete treatment had equivalent outcomes to their counterparts

in the control group who would hypothetically be expected not to complete the program.

However, in the absence of our ability to classify hypothetical “completers” and “terminators” in

the control group, the combined outcomes of program completers and terminators should be

significantly better than those of the control group.

Some treatment proponents might argue that even if treatment is, in fact, nothing more than a

weeding out process, this is still a useful result of the treatment process because it identifies the

individuals who are more likely to succeed. The problem with this logic is that the same goal

might be accomplished by simply improving our risk-classification devices in the absence of

treatment. Furthermore, it is important to know whether it is treatment per se, risk selection, or

both, that accounts for better outcomes in the treatment group. We cannot hope to improve

treatment or understand how it works if all we accomplish is the risk selection of inmates. 

Additional Selection Concerns

Figure 2 also can be used to conceptualize the selection bias issue by depicting the problem of

choosing an appropriate control group and the level of generalizability inherent in the research

design. It is clear from Figure 2 that treatment terminations, whether in-prison or communitybased, cannot be ignored if we are to make any sense of a program’s effects. Furthermore, it is

clear that by choosing a comparison group of volunteers, our level of generalization is restricted

to treatment for “motivated” treatment participants.

Another problem becomes apparent from Figure 2. Consider a design in which researchers choose

a control group (composed of drug abusers) that is a combination of volunteers and nonvolunteers. Unless one models the selection process and incorporates it into the analysis, the

outcome differences between a self-selected or administratively selected group and an

“unselected” group may be attributable entirely to the level of motivation of the volunteers and

have nothing to do with the treatment provided. 

Our procedures ensured that we collected follow-up data on all inmates who began treatment and

were selected into our “convenience” samples. Thus, regardless of whether an inmate completed

or was terminated from the program, data were collected on the individual’s post-release

outcomes. We have organized inmates in our treatment samples into 9-month completers, 12-

month completers, disciplinary terminations, program withdrawals, and treatment non-completers.

These last two groups of inmates completed some portion of their treatment but had to withdraw

for reasons beyond their control. For example, a significant number of inmates in these groups

were released earlier than expected. We also collected data from comparison subjects in both

DAP and non-DAP sites. These data collection procedures are described in great detail in

subsequent chapters. 
28

To recapitulate, depending on the analysis, we treated comparison subjects from DAP sites in

different ways. In the first analysis, to replicate previous research, we contrasted both (1) each of

the treatment groups (those who completed and those who did not complete for a variety of

reasons) and (2) the DAP comparison subjects from the treatment sites, to the non-DAP control

subjects from sites at which no treatment was available. If highly motivated inmates were entering

treatment at DAP sites, the pool of drug dependent comparison subjects who participated in our

study would have been composed of less motivated, perhaps more risky, clients. Under these

circumstances, the DAP comparison subjects may have had lower success than did the non-DAP

control subjects. Clearly, quite the opposite would happen if the residual pool of DAP comparison

subjects had been composed of more motivated inmates. Both of these hypothetical outcomes rest

on the assumption that the DAP comparison group is composed of inmates representative of all

the groups we have described and that they are represented in the same proportions. Thus, the

DAP comparison group is hypothetically composed of completers, disciplinary terminations,

program withdrawals, treatment noncompleters, and the residual comparison subjects. 

In the second analysis, we combined data from the treatment groups with data from comparison

subjects from DAP sites. We reasoned that the DAP comparison clients were inmates who had

treatment available but chose not to participate. We would expect that these same types of

inmates would be represented among our non-treatment site comparison subjects. Thus, research

subjects in the non-treatment sites should consist of all levels of inmates who would have

volunteered for treatment, as well as inmates who would have declined treatment. Thus, the

appropriate test of treatment in our design is the combined test of (1) all inmates who were

selected for treatment, as well as (2) the inmates who did not volunteer for treatment but who had

treatment available (were housed at a DAP site), contrasted with the control subjects who did not

have treatment available (were not housed at a DAP site).

Finally, in a third approach, we explicitly model the selection process, using non-treated subjects

from both DAP and non-DAP sites as “controls.” The difference between the two non-treated

groups is that comparison subjects from DAP sites were subject to selection bias while control

subjects from non-DAP sites were not. By explicitly modeling the selection process, we could

statistically capitalize on all comparison subjects to increase the power of our treatment versus

non-treatment contrast.

In the following sections we critically review the most commonly cited research on in-custody

therapeutic communities using our conceptual model of selection bias as a heuristic device.

A Critical Review of Prison Drug Treatment Research

Our review of the literature focuses on five programs that have received considerable attention in

recent reviews of the literature on prison residential drug treatment studies. These programs

include Stay ‘N Out, Cornerstone, Key/Crest, New Vision at Kyle Unit, and the Amity Right Turn

Project.
29

We examine the published and unpublished reports on these programs in some detail. Our general

conclusion is that all of these studies suffer from the inferential problems associated with

disentangling treatment effects from selection bias effects. Although we are somewhat critical of

the research in this domain, we realize how difficult it is to conduct a program evaluation and how

easily controls, intended to introduce rigor into the evaluation, are easily compromised. Our

critique attempts to assist future program evaluators in this research area and help them avoid

some of the same mistakes that both we and other evaluators have made. 

Stay ‘N Out Program

Wexler and colleagues have published a series of articles that report on the effectiveness of the

Stay ‘N Out drug abuse treatment program used by the Department of Corrections in New York

State (Wexler and Chin, 1981; Wexler, Falkin, and Lipton, 1988; Wexler, Falkin, and Lipton,

1990; Wexler, Falkin, Lipton, and Rosenblum, 1992; Wexler and Williams, 1986). We focus

primarily on the recidivism outcome results reported in Wexler, Falkin, and Lipton (1990) as

adapted and slightly modified for a National Institute on Drug Abuse Research monograph

(Wexler, Falkin, Lipton, and Rosenblum, 1992).

Their evaluation of the Stay ‘N Out therapeutic community (TC) contrasted male inmates who

participated in that program with inmates in two other drug treatment programs (milieu and

counseling treatment) and a control group of inmates who had volunteered for the therapeutic

community but were never admitted to the program because of time constraints. The volunteer

control group was used to minimize selection bias issues.

Female TC participants were contrasted with those in a drug counseling treatment group and

those in a control group composed of women who volunteered for the TC program but changed

their minds prior to admission into the program. Unlike the male control group, the female control

group could easily have been composed of unmotivated women who would be the least likely to

succeed following release and thus bias any contrast between program and non-program

participants.

Wexler, Falkin, and Lipton (1990) reported their results first by way of a series of group contrasts

among the mean differences in the outcome variables without controlling for background

differences, and then by using a multivariate analysis that controlled for background variables.

Although Wexler et al. argued that their study provided “convincing evidence that prison-based

TC treatment can produce significant reductions in recidivism,” (p. 89) we found several

shortcomings in the study’s analysis and methodology.

Female inmates were excluded from the multivariate analysis because, according to the authors,

there were too few to analyze. The outcome variables reported by Wexler et al. included the

percent of inmates arrested after release to parole supervision, the number of months before such
30

arrests occurred, and the percent having favorable parole outcomes. This last measure was based

on whether an inmate completed parole without a revocation, arrest, or rule infraction. 

Wexler et al. reported that their multivariate analysis of percentage arrested and parole outcome

produced no significant results. There was no effect tied to background characteristics or time-intreatment. Wexler et al. did find significant predictors in their multivariate analysis of time-toarrest, and they reported those results. We are puzzled by the fact that age and criminal history,

which were influential predictors of time-to-arrest, were not significant predictors of percentage

arrested or parole outcome. These variables typically are the most influential predictors of any

measure of post-release criminal recidivism (Harer, 1994). This is a minor point relative to their

interpretation of the multivariate analysis involving time-to-arrest.

In addition to the background characteristics of age and criminal history, Wexler et al. included

the following variables in their regression analysis: the duration of parole supervision, a dummy

code for each type of treatment, time-in-treatment for each of the treatments, the duration of

parole supervision, the amount of time an inmate spent in prison after completing treatment but

before release, and the square of the amount of time an inmate spent in the therapeutic

community.

 

In their analysis of time-to-arrest, Wexler et al. interpreted the linear and quadratic regression

coefficients for time-in-treatment. However, they failed to interpret the program participation

variables. Although only the dummy variable for the TC treatment was significant, all of the

treatment dummy variables demonstrated that regardless of the type of drug treatment given to

inmates, having any drug treatment shortened the period between release and arrest relative to the

control group. Thus, the treatment dummy codes demonstrated that inmates in treatment were

arrested sooner than were inmates in the control group. In addition to the fact that TC inmates

failed sooner than did control group participants, the relationship between TC treatment and

failure is quadratic. That is, time-to-arrest increased with the amount of treatment up to a point,

then declined thereafter. Wexler et al. emphasized this finding while disregarding the dummycoded treatment effects. 

The other major finding emphasized in this study, as well as in secondary sources that refer to this

study (see, for example, Lipton, 1995), was that when the treatment effect was examined without

accounting for the other background variables, male inmates were less likely to be arrested if they

participated in the TC drug treatment. For female participants, none of the group contrasts

reached conventional statistical significance. The percentage of male inmates arrested after release

from prison varied by treatment group. Among TC inmates, 26.9 percent were arrested after

release. For milieu and counseling inmates, 34.6 and 39.8 percent, respectively, were arrested.

Among no-treatment controls, 40.9 percent were arrested after release. In light of the fact that the

multivariate analysis of this outcome measure failed to reach statistical significance, we argue that

these results probably were attributable to differences in background characteristics of the groups

and not to a treatment effect. But there are other reasons why these group differences are possibly

not meaningful. 
31

The different groups had different risk periods, with the TC group having the shortest average

risk period (34.7 months). The other groups each received, on average, about 41 months of

parole supervision. Thus, each subject in the TC group, on average, had 6 fewer months of parole

supervision and, therefore, much less time in which to be arrested. Another difficulty with this

analysis of percent arrested by treatment group concerns the extent to which Wexler et al. should

have adjusted their findings for people who were censored. If inmates were technically violated,

rather than arrested, this would have removed them from the risk set. Thus, fewer arrests could

mean greater parole violations. Without an explicit explanation of the censoring process, we

cannot rule out this possibility. 

In general, when one is analyzing the time to an event — whether relapse, arrest, or conviction —

it is much more appropriate to use event history techniques that allow one to treat different risk

periods by censoring observations and removing them from the risk set. This is a problem not only

with this set of analyses, but with most other studies in this research domain as well.

Cornerstone Program

Field has published several studies evaluating the Cornerstone Program, a residential program for

alcohol- and drug-dependent inmates within the Oregon correctional system. A key component of

the Cornerstone Program as described by Field (1985) is that inmates who are admitted to the

program must be willing to commit to at least 6 months of follow-up treatment in the community.

Another program admission criterion requires that the inmates be granted minimum-security

status by the prison superintendent. At first glance, this would seem to be a very narrow selection

criterion that would exclude all but the lowest-risk candidates for drug treatment and would have

profound implications for possible selection bias effects. However, Field (1985) described the

treatment clients as having, on average, about 12 prior arrests, 6 prior convictions, and 6 years of

adult incarceration. Also, these clients were described as having chronic substance abuse histories.

In addition to the follow-up treatment in the community, Cornerstone graduates “have a job, a

place to live, and a drug-free support network before discharge” (Field, 1985, p. 52). Thus, the

community aftercare component of this program went far beyond focusing on drug relapse.

To compare program graduates, Field retrospectively chose three comparison groups. Group I

was composed of Cornerstone dropouts, Group II was composed of Oregon parolees with some

history of drug abuse, and Group III came from a follow-up study in Michigan that Field chose

because the study followed a “similar population over a similar time frame” (Field, 1985, p. 52).

There was a uniform 3-year follow-up period, and Field assessed recidivism in two ways.

Recidivism was defined as a return to prison within 3 years and, separately, as a conviction within

3 years. Among Cornerstone graduates, 29.2 percent returned to prison within 3 years. Among

the comparison groups, 74.1 percent of the dropouts were recommitted, 37.1 percent of the

group composed of Oregon parolees with a history of substance abuse were recommitted, and

about 43 percent of the Michigan release cohort were recommitted. Those reconvicted within 3

years consisted of 45.8 percent of the Cornerstone graduates, 85.2 percent of the Cornerstone
 Please note that pre-program data were not available in the 1985 study comparing 16

program graduates to the three comparison groups.

As M. Douglas Anglin, one of our reviewers pointed out, motivation is not constant over 17

time. Rather, it is episodic. Anglin argued that treatment outcome is determined by a host of

factors, including motivation, treatment retention, and type of services offered. These combine in

some complex way to influence outcomes. Nevertheless, it is still the case that the resultant

effects cannot be easily disentangled. 

32

dropouts, and 74.7 Oregon of the parolees with a substance abuse problem. No reconviction data

were available for the Michigan cohort.

There are three major problems with interpreting the results of this study. The first is that we have

no basis for comparing Cornerstone graduates with the three comparison groups on any variables

related to recidivism, such as age, criminal history, degree of substance abuse, and family social

support. Thus, we have no guarantee that the groups were equivalent with respect to their risk of

recidivism. Secondly, there are two significant program components to Cornerstone — the first is

institution based and the second is community based. Even if this program is influential in

reducing relapse and criminal recidivism, we cannot disentangle which program component was

the more important one. Finally, as the recidivism data showed for the program dropouts, and as

Field noted, program participants simply may have been highly motivated inmates who would

have succeeded with or without Cornerstone. 

Apparently, the dropout rate at Cornerstone was extremely high. Field (1992) enumerated the

dropout rate in a recidivism study of 220 inmates who had been admitted to Cornerstone over a

2-year period. Of those 220 admissions, 65 withdrew after spending one to two days in the

program, 58 withdrew after spending between 2 and 6 months in the program, 43 withdrew after

spending at least 6 months in the program, and 43 graduated. Thus, there was a far greater

number of dropouts than program graduates.

Field used these differential dropout rates to make a point about the duration of treatment. Field

(1992) reported on the criminal recidivism of these groups, showing that the longer an inmate was

in the program, the less likely he or she would be arrested, convicted, or recommitted to prison

following release from prison. Although Field acknowledged that the length of a subject’s

treatment may have acted merely as a proxy for his or her level of motivation, he argued that pretreatment incarceration data demonstrated that all four groups were equivalent in their preprogram arrest, conviction, and commitment rates. In other words, by controlling for pre- 16

program levels of criminal history, Field was satisfied that the dropout pattern represented

treatment effects and not motivation or other selection effects. Even though Field demonstrated 17

equivalence among the treatment groups (categorized by duration of treatment) with respect to

prior criminal history, we know there are a host of other variables that also could affect the group

outcomes in the absence of a treatment effect, none of which Field incorporated into his analysis.

Furthermore, self-selection probably represents, among other things, the level of motivation and
33

commitment one has to maintaining a drug-free lifestyle. Commitment to change may be quite

unrelated to one’s criminal history; in fact, it may even be inversely related.

As we argue below, program dropouts contaminate the interpretation of treatment effects in more

ways than one. Especially for programs in which the dropout rate is extremely high, there arises

the possibility that a program is simply selecting out high-risk-of-failure candidates rather than

changing or rehabilitating low risk candidates. Another way of viewing this potential selection

process is to approach it with a risk-assessment analysis. We consider two possible hypotheses.

The first is that dropouts are more likely to have background characteristics that predict criminal

recidivism. The second is that they are equivalent to “stayers” on objective measures of risk;

however, by observing treatment subjects closely or by testing their motivation in a controlled,

closely monitored environment, staff can further “weed out” higher risk inmates.

Key-Crest Program

The Key-Crest Program is a drug treatment intervention occurring in three phases. The Key

component is a prison TC for inmates in the Delaware corrections system. Crest, the second

component, involves inmates released to a community work-release center where they maintain

jobs in the community but live in a facility where they continue their drug treatment in a modified

TC. In the final component, offenders are released to the community, either under parole or some

other form of supervision. In this stage, drug treatment consists of outpatient counseling and

group therapy. 

Four groups were evaluated. The first was composed of 43 inmates — selected by correctional

counselors — who volunteered to participate in the prison-based TC. Because the Crest program

had not yet been implemented, these inmates were the only Key program participants who did not

subsequently participate in the Crest stage. The second group consisted of Key-Crest inmates who

participated in both stages. Virtually all Key graduates were allowed to participate in Crest after it

was implemented. The third and fourth groups were composed of inmates who had drug abuse

problems, had not participated in Key, and were given the opportunity to participate in the Crest

work-release program. On a random basis, half of these volunteers (the Crest-only group) were

provided the Crest program, while the other half (the comparison group) participated in workrelease in the absence of residential drug treatment. Thus, the comparison group for these

analyses was inmates who had drug abuse problems, had volunteered for Crest, and had not

received in-prison TC drug treatment but had received AIDS/HIV prevention education.

There were two selection bias processes operating in the Key-Crest design. The first selection

process involved selection into the Key and Key-Crest groups. For one, it appears that the

selection involved staff evaluation of candidates for the program. The second selection process

occurred as a result of the way baseline data were gathered. These data were gathered just prior

to inmate releases from prison. Baseline data were collected on Key graduates, but not on Key

terminations. Thus, only Key graduates were followed in the longitudinal design. Data were

gathered on Crest and comparison subjects at baseline, in the absence of any knowledge about
34

potential future attrition in these two groups. Thus, both the Key and Key-Crest groups were

composed of inmates who were motivated enough to graduate from the Key component of this

program. 

Even though Key-Crest participants had the opportunity to drop out of the program while they

were in the Crest stage, this group already was composed of a very select group of motivated

individuals. As noted in Deleon, Inciardi, and Martin (1995), the Crest-only group was composed

of some clients who “displayed negative attitudes toward the treatment program, which generally

led to their quitting or being discharged from the Crest program” (p.88). However, all inmates in

the Crest-only groups were still followed even though some had dropped out of the program

(Inciardi, 1997, personal communication). 

The Key-Crest program is being evaluated by Inciardi and his colleagues (Martin, Inciardi, and

Saum, 1995; Martin, Butzin, and Inciardi, 1995; Inciardi, Martin, Butzin, Hooper, and Harrison,

1997). Martin, Butzin, and Inciardi (1995) reported data based on interviews conducted 6 months

after the inmates were released from prison. Most inmates who had participated in the Crest stage

were probably still under supervision at the time of this 6-month interview. Thus, the results at

this stage should be interpreted with a great deal of caution. Based on inmate self-reports, the

data showed that 97 percent of the Key-Crest group and 84 percent of the Crest-only group said

they had not been arrested within 6 months of release from prison. Among the Key-only

participants, 74 percent reported they had not been arrested, while 60 percent of the comparison

group claimed no arrests. The proportions reporting drug use were similar. When these

proportions were adjusted for background characteristics, including time-in-treatment, the same

ordinal relationship was obtained. Key-Crest participants were the least likely to self-report arrest

and drug use, followed by Crest-only, Key-only, and comparison subjects. 

An 18-month follow-up of the program (Inciardi, Martin, Butzin, Hooper, and Harrison, 1997)

showed that 77 percent of Key-Crest participants reported being arrest-free at 18 months, while

57 percent of Crest-only, 43 percent of Key-only, and 46 percent of the comparison group

reported being arrest-free. Drug use was measured by combining results of self-reports and

urinalysis tests. The drug-free pattern corresponded to the arrest-free pattern. However, there is

no indication that there was any attempt to check the veracity of the self-reported arrests. 

Although several papers written by Inciardi and his colleagues have emphasized that offenders

should be receiving aftercare while they are under supervision, at the time of their study there was

no formal aftercare (Inciardi, 1997, personal communication). Apparently, this study has no

selection bias and no attrition operating in the Crest-only and comparison groups, although the

authors have never reported the extent to which inmates withdrew or were terminated from the

Crest program. Therefore, the reductions in self-reported arrest and actual drug relapse may be

entirely attributable to the effects of transitional treatment. However, the Key-only and Key-Crest

groups are composed of offenders who were either selected into treatment or who selected

themselves out of treatment. Reductions in self-reported arrest and actual drug relapse in those

groups are still potentially contaminated. 
35

New Vision In-Prison Therapeutic Community, Kyle Unit

The New Vision In-Prison Therapeutic Community in Kyle, Texas, is only one component of a

comprehensive Texas criminal justice initiative to treat criminal drug abusers. The Kyle unit is

being evaluated by a team of researchers affiliated with Texas Christian University. There have

been several reports of the evaluation conducted by Simpson and his colleagues (Simpson,

Knight, Chatham, Camacho, and Cloud, 1994; Knight, Simpson, Chatham, Camacho, and Cloud,

1995; Knight, Simpson, Chatham, and Camacho, 1997). Outcomes are available for inmates who

had been released for 6 months.

The program’s evaluation compared a control group to a treatment group composed of inmates

who participated in a 9-month prison-based TC, followed by 3 months of community-based

residential treatment, followed by a year of outpatient treatment. Program graduates agreed to

provide urine samples for drug testing on a monthly basis. 

The selection process for participants in the drug treatment program began with a drug-use

screening mechanism given to all inmates who entered Texas Department of Corrections facilities.

A treatment referral committee reviewed the inmates’ records, which included self-reported drug

use. Inmates who had less than 9 months remaining on their sentences or who had committed an

aggravated offense were excluded from further referral. Inmates who qualified for treatment had

their cases forwarded to the Texas Parole Board for the final decision on placement in a drug

program. Both comparison and treatment subjects in this study completed the initial referral

process. However, the Parole Board rejected a certain number of inmates for treatment while still

granting parole to these inmates. The reasons for these decisions were not specified by the

authors. Thus, we have an initial selection process that differentiates treatment and comparison

subjects. As it turned out, based on a composite risk assessment, treatment subjects were at

higher risk for recidivism than were comparison subjects. Nevertheless, Parole Board members

used their “clinical judgment” to further refine the selection process based on some unknown set

of “clinical” criteria. 

Also, treatment subjects were sent to halfway houses. There was no indication that comparison

subjects were assigned to halfway houses after release from prison; nor was there any

measurement of their level of release supervision (including whether they were tested for drug

use). As the authors indicated, in addition to drug treatment, halfway houses fulfill other social

service needs and provide assistance in locating employment. Thus, potential differences between

the treatment and comparison groups could be attributable to in-prison treatment, halfway-house

drug treatment, halfway-house transitional assistance, the drug testing and close supervision of

parolees in the treatment plan, or any combination of these factors. Although there does appear to

be a selection process operating in the Kyle Unit evaluation, Simpson and his colleagues have

described that process more thoroughly than has any other study we reviewed.

A possible, but significant, measurement problem with this study is that the risk sets for the

treatment and comparison groups were quite different. Outcome assessment occurred at 6 months
36

and will occur at 12 months after release from prison. However, for treatment subjects, 6 months

after release from prison was only 3 months after release from the halfway house. That is, the 6-

month risk set for treatment subjects included 3 months of halfway house placement and 3 months

of parole supervision, while the risk set for comparison subjects included 6 months of parole

supervision. In their future analysis, the risk set for the treatment group will consist of 3 months

of halfway house and 9 months of parole supervision, contrasted with 12 months of supervision

for the comparison group. Treatment outcomes will be severely biased in the direction of a more

positive treatment effect, because halfway house supervision decreases the probability of arrest

relative to parole supervision. Thus, differences between treatment and comparison groups may

merely reflect differences in the level of supervision and thus level of arrest risk for the two

groups, rather than any effect of treatment.

The attrition process for this evaluation was described comprehensively and provides a good

indication of how difficult it is to conduct follow-up interviews for this population. Of 482

treatment referrals, 386 (80 percent) graduated; 29 inmates (6 percent) were transferred for

medical reasons, outstanding warrants, or inappropriate classification of drug problems); and 67

(14 percent) were terminated for program non-compliance. Unfortunately, no attempt was made

to follow-up on the program terminations. Also, there was attrition among those who completed

the program and those who constituted the control group, because inmates were not available at

the time the 6-month follow-up data was collected. By that time, only 222 of the original 386

treatment graduates could be interviewed, and 75 of 121 control group inmates released to parole

could be interviewed. Attrition was due to offenders who moved out of the area accessible to

interviewers, who were recommitted to prison, who could not be located, or who refused to be

interviewed. It is not clear why inmates who were recommitted to prison were not interviewed

and did not enter into the outcome results. However, there was an equal percentage of

recommitment for the treatment and comparison groups — about 10 percent. Not only was the

attrition rate extremely high, there was no attempt to collect follow-up data on the program

failures; thus, the results could be severely biased.

It is interesting to note that — at least in a set of univariate comparisons — program terminations

and graduates were similar in background characteristics. Program graduates were equivalent to

program terminations in terms of age, education, marital status, type of commitment offense, and

recidivism risk score. Whites were more likely to be removed from the program than were African

Americans. It would be useful to know whether graduates and dropouts were comparable in a

multivariate analysis. One of the limitations of this kind of research is the failure to learn what

distinguishes program graduates from program failures. The more we can understand about this

process, the better we might be in selecting participants for the program in the first place and the

more we will understand the selection process. Further, it will aid us in tailoring programs to meet

the individual and group needs of the participants.

Knight et al. also reported 6-month post-release outcomes without controlling for the many

background characteristics they measured. Official Texas arrest records indicated that 7 percent

of the treatment group members had been arrested, compared to 16 percent of the comparison
37

group members. Treatment clients self-reported that they had engaged in illegal activities during

an average of 11 days in the 6 months since their release from prison, while comparison inmates

reported an average of 28 days. In reporting these comparisons, Knight et al. acknowledged how

dissimilar the risk sets were for these two groups. The drug relapse data they reported were

problematic for this same reason. 

The dissimilarity in risk sets was acknowledged by Knight et al. in 1996, although no adjustments

were made to the data. Knight et al. reported on considerable background data, including

information on sociodemographic characteristics, criminal background, drug-use history,

HIV/AIDS risk behaviors, ratings of social and psychological functioning, ratings of treatment

experience, clinical assessments of attention-deficit disorders, hopelessness, depression, and

symptom reports. These data should have been analyzed with multivariate techniques. 

If we ignore the many methodological problems with this study and assume that at the end of the

12-month post-release arrest period the treatment group had a lower drug relapse and lower

criminal recidivism rate, the strongest conclusion we can make is that while offenders are in

treatment, they are less likely to recidivate and return to drugs. To assess what happens to these

offenders after treatment, Simpson and his colleagues must follow the treatment and control

groups for a period after the outpatient counseling has ended. 

Amity Right Turn Project

The Amity Right Turn program combines prison- and community-based therapeutic communities

for inmates who volunteer for treatment. This program is funded by the California Department of

Corrections in the R. J. Donovan medium security Correctional Facility in San Diego. The

program is being evaluated by Wexler and his colleagues. Wexler, DeLeon, Thomas, Kressel, and

Peters (1997) have written an initial report on their evaluation of the program using

reincarceration of subjects in the California prison system as their primary outcome.

Reincarceration included a commitment for either a new offense or a technical violation of parole. 

The researchers divide the subjects into five groups, with inmates who either had volunteered to

be treated, had a drug problem, or were within 9 and 14 months of their parole release composing

a waiting list of eligible participants. From this pool, inmates were randomly selected to

participate in the prison TC. 

There were a total of 715 research subjects. Inmates who were eligible but could not be treated

prior to their release composed the control group (n=290). The remaining four groups consisted

of the inmates who had been randomly selected for treatment in the prison TC. The composition

of the four study groups depended upon whether they volunteered for post-release communitybased treatment and whether they completed the prison or community-based program. Thus, the

first study group was composed of inmates who volunteered for the prison program but who were

terminated (prison treatment dropouts, n=95). The second study group consisted of those inmates
 In their report, Wexler et al. did not provide the actual percentages of inmates who 18

were reincarcerated for the prison dropout, prison completion, and prison completion/communitybased dropout groups. We had to estimate these percentages.

38

who completed the prison drug program but did not volunteer for the community-based program

(prison treatment completions, n=193). The third study group included inmates who volunteered

and completed prison drug treatment and who volunteered and were terminated from the

community-based program (prison treatment completions/community-based dropouts, n=45). The

fourth study group was composed of inmates who volunteered and completed the prison and the

community-based program (prison completions/community-based completions, n=92).

Wexler et al. reported that the no-treatment control group had significantly higher reincarceration

proportions at both 12 and 24 months after release from prison than did all of the other study

groups combined. The 12-month comparison showed that the control group had 49.7 percent

recidivism and that the combined study groups had 33.9 percent recidivism. At 24 months, these

percentages were 59 and 42.6, respectively. When the combined result is separated into the

control and four study groups, the five groups had the following reincarceration percentages at 12

months: control group, 49.7; prison treatment dropouts, 45; prison treatment completions, 40;

prison treatment completions/community-based dropouts, 40; and prison treatment

completions/community-based completions 6.5. 18

Wexler et al. also reported the number of days until reincarceration; however, for some reason

these data were only compiled on 256 releasees for the 12-month follow-up and on 166 releasees

for the 24-month follow-up period. Generally, the time-to-recidivism data mirrored the 12- and

24-month reincarceration data. A logistic regression of background factors, in conjunction with

the treatment effect, indicated that reincarceration was 42 percent less likely for the combined

treatment groups than for the control group. The background factors included age, ethnicity,

criminal history, IQ, childhood problems, anti-social DSM-III-R diagnosis, distress, and social

achievement. Unfortunately, there was no multivariate analysis that combined all of the

background factors with dummy-coded representations of the different study groups. This may

have given some indication that the combined effect was primarily attributable to the inmates who

completed both the prison and community-based programs.

Wexler et al. acknowledge that their results were confounded by the fact that, during the postrelease period, inmates who were receiving treatment in the community-based TC were at much

lower risk than were other releasees simply by their residence in the TC. This would also affect

the 24-month outcomes. If the risk periods were defined as beginning the day after release from

the community-based facility or the day after release from prison for clients who did not

participate in the community-based facility, the “risk environment” would have been more

comparable for the different groups involved in the evaluation. It is clear from the analysis of the

individual study groups that the dramatic differences between the combined study group and the

control group were attributable to, primarily, the prison treatment completion/community-based

completion group. Although no analysis was presented, there were much more modest differences
39

between the control group and the three study groups composed of inmates who spent little or no

time in the community-based aftercare facility. 

There is another limitation to this study as well. In order to control for selection bias, the

researchers used treatment volunteers exclusively. This not only limits their generalizations to

volunteers (as it does in most of these studies), but it also gives us no indication how treatment

results compare to outcomes of drug dependent prisoners who are unwilling to volunteer for

treatment. Secondly, as the authors acknowledged, while they were able to control for selection

bias at the prison treatment phase, they were unable to control for selection bias at the

community-based treatment phase. The clearest conclusion that can currently be drawn from this

study is that the longer an inmate volunteers and stays in treatment, the less likely is his or her

reincarceration. Whether prison drug treatment was effective was ambiguous in this study, and

whether community-based drug treatment was effective after release was largely untested. 

Summary of Research Literature

From our close reading of these studies, we have found fundamental problems in the designs,

analyses, and interpretations of results. However, the researchers who have conducted these

studies have referred to each other’s work as mounting evidence that in-prison drug treatment,

especially in combination with post-release community-based treatment, can produce dramatic

results. Furthermore, secondary references to these studies (see, especially, Lipton, 1995)

minimize or fail to mention the methodological problems inherent in these studies and, instead,

continue to report what appears to be a consistent set of results across different settings. 

The clearest finding comes from the program being evaluated by Inciardi and his colleagues in the

state of Delaware. By virtue of random assignment and a comprehensive follow-up of those who

dropped out of the transitional care component of the program, we can have confidence in the

finding that offenders receiving transitional care in the absence of in-prison treatment are less

likely to recidivate and relapse to drug use. Replication of this finding in other settings by other

researchers could be very compelling. Further analyses by Wexler and his colleagues of the Amity

Right Turn Project may also indicate that community based aftercare is an important drug

treatment dimension; a redefinition of their risk period may lead to that conclusion. However, the

community-based study groups were composed of volunteer inmates, and even redefining the risk

periods cannot circumvent the bias introduced by this self-selection. 

Although all of the other results are suggestive of effective treatment, this may merely reflect the

culmination of a selection process that demonstrates that drug treatment — whether in prison or

in the community — is a winnowing process. By the end of that process, only those most likely to

succeed remain in treatment.

In summary, although we found the evidence on drug treatment effectiveness to be less than

compelling, after reviewing the recent literature on in-prison therapeutic communities and
40

conceptually examining the processes that lead to subject selection and attrition, we developed a

research design that we felt would address and rectify the major methodological problems. We

acknowledge that it is extremely difficult to conduct random-assignment research designs in an

applied setting. In the absence of random assignment, statistical techniques — such as those we

adopted — are technically difficult, depend upon a great many assumptions, and may not always

solve the problem. Our complete design is presented in Chapter 4. 
 The Federal fiscal years run from October 1 through September 30. 19

41

CHAPTER 3: DESCRIPTION OF DRUG TREATMENT PROGRAMS AND SERVICES

This chapter describes the nature of the BOP’s drug treatment programs and the changes that

occurred since the inception of the TRIAD evaluation project. In addition, we provide a brief

description of the post-release treatment services available for inmates released with conditions of

supervision. 

Paths to Treatment Service

Inmates interested in drug treatment receive services through various means. Treatment services

for BOP inmates are available at three different stages: while incarcerated, during a halfway house

placement, and while under supervision by a Probation officer. Services can be offered during the

latter two stages only if the individual received a halfway house placement or was released with a

condition of supervision. Treatment services for study subjects ranged from none to services at all

three stages. The treatment available while incarcerated consisted of the residential DAP in

combination with non-residential outpatient services and self-help groups. Treatment provided

during a halfway house stay is referred to as “transitional services” and consists of outpatient

counseling services. Transitional services were required for all DAP graduates. 

Treatment provided while an individual was under supervision is referred to as post-release

treatment. This treatment consists of a wide range of services, including both outpatient and

residential/inpatient services, with an emphasis on outpatient services. It also includes

participation in self-help groups.

Program Development by Location

The residential Drug Abuse Treatment Programs offered treatment for alcohol and other drug

problems, and were implemented in two distinct categories: pilot programs and comprehensive

programs. Later, all programs were referred to as residential Drug Abuse Treatment Programs.

Pilot programs offered 1,000 hours of treatment over a 12-month period, with a staff-to-inmate

ratio of 1:12. Comprehensive Drug Abuse Treatment Programs provided 500 hours of treatment

over a 9-month period, with a staff-to-inmate ratio of 1:24.

The first eight programs were approved for activation in FY’s 1989 and 1990. During FY’s 19

1989 and 1990, the BOP implemented its first three residential Drug Abuse Treatment Pilot
 FMC refers to a Federal Medical Center and FCI to a Federal Correctional Institution (see 20

Glossary of Terms).

 A comprehensive history of DAP development between 1988 and 1995 is contained in an 21

unpublished report — “BOP Residential Drug Treatment Program Development: 1988 to 1995 ”

— available upon request. A narrative description summarizing the selection of the research sites

is contained in Chapter 4 under the section entitled “A Chronological History of the Selection of

Subjects.”

42

Programs at FMC Lexington, FCI Butner, and FCI Tallahassee. By the end of FY 1996, 39 20

programs had been approved for implementation. Figure 3 shows when each DAP was approved

for activation and indicates which of the programs were included in this study and which were

eliminated as research sites during the study.21

Admission Criteria 

All admissions into the BOP’s residential Drug Abuse Treatment Programs were voluntary.

Initially, residential programs required inmates to have (1) a drug problem and to have completed

the BOP’s Drug Abuse Education Course; (2) no outstanding legal concerns to interfere with

Community Corrections Center (CCC) placement; (3) no serious medical or mental health

problems; (4) no violent behavior within the last 12 months; and (5) between 24 and 36 months

remaining on their sentences. By the time the first policy was issued, however, a number of these

criteria had changed.

 

Inmates could apply for program admission at any time, with priority given to those inmates with

less time remaining on their sentences. All program participants had to have at least 15 months

remaining until their release dates (18 months for pilot program participants). An inmate was

accepted into a program if:

 the inmate had a history of moderate to severe drug abuse, as reflected in the

psychological assessment score on the Inventory of Substance Use Patterns

(ISUP) administered by Psychology Services (or as reflected in the presentence

investigation report);

 the inmate had no history of violence or assaultive behavior during the current

 incarceration;

 the inmate was fluent in the English language;

 the inmate had no serious medical, psychiatric, or psychological problems that would

interfere with full program participation;

 the inmate was not a State boarder;
 As will be noted in Chapter 4, this site was dropped between 1991 and 1993. 22

43

Figure 3

Chronological History of DAP’s and DAP Research Sites 

DAP Research Site FY Approved for Activation Date Selected as Date Discontinued as 

Research Site Research Site

Butner FCI 1990 03/90 06/96

Fairton FCI 1990 03/90 06/96

Lexington FCI* 1989 03/90 02/94

Oxford FCI 1990 03/90 06/96

Rochester FCI 1990 03/90 08/91

Seagoville FCI 1990 03/90 06/9622

Sheridan FCI 1990 03/90 06/96

Tallahassee FCI 1990 03/90 06/96

Danbury FCI 1991 03/93 06/96

Dublin FCI* 1991 08/93 06/96

El Reno FCI 1991 not selected not selected

Englewood FCI 1991 not selected not selected

Leavenworth USP 1991 not selected not selected

Marianna FCI 1991 02/92 06/96

Phoenix FCI 1991 03/93 08/93

Bastrop FCI 1992 not selected not selected

La Tuna FCI 1992 03/93 02/93

Bryan FPC* 1992 not selected not selected

Atlanta USP 1992 not selected not selected

Lompoc USP 1992 not selected not selected

Lompoc FCI 1992 03/93 06/96

Allenwood FPC 1992 not selected not selected

McKean FCI 1992 03/93 06/96

Alderson FPC* 1992 03/93 06/96

Morgantown FCI 1992 03/93 06/96

Yankton FPC 1992 03/93 06/96

Terminal Island FCI 1992 03/93 06/96

Terre Haute USP 1992 not selected not selected

Three Rivers FCI 1992 03/93 06/96

Talladega FCI 1992 not selected not selected

Fort Worth FCI 1994 not selected not selected

Dublin FPC* 1995 not selected not selected

Sheridan FPC 1995 not selected not selected

Fort Dix FCI 1995 not selected not selected

Cumberland FPC 1996 not selected not selected

Talladega FPC 1996 not selected not selected

Texarkana FPC 1996 not selected not selected

Florence FCI 1996 not selected not selected

Milan FCI 1996 not selected not selected

Notes: sites having female inmates are denoted by an asterisk (*). Also, Tallahassee and Danbury converted to all-female

institutions in 1995, and Lexington converted to all-male in 1994.
 Bureau of Prisons, Operations Memorandum 132-90 (5330), September 20, 1990. Inmate 23

Drug Abuse Program.

 Self-reported drug use does not qualify as a verifiable, documented problem. 24

44

 the inmate did not have a State or Immigration and Naturalization Service (INS)

detainer or pending charges, and the inmate qualified for Community Corrections

Center placement where transitional services would be provided;

 the inmate was willing to sign an agreement to participate in the Residential Drug Abuse

Treatment Program; and

 the inmate successfully completed the Drug Abuse Education Program (described

below).23

After several programs had admitted cohorts of inmates to drug treatment, the admission criteria

were reviewed and modified in a number of ways. Inmates with detainers, State boarders, and

inmates ineligible for Community Corrections Center placement became eligible to participate in

residential Drug Abuse Treatment Programs, as did inmates who spoke Spanish (as more bilingual

staff became available).

In October 1993, new BOP policy dictated a further modification in the admission criteria.

Individuals now had to meet Diagnostic and Statistical Manual of Mental Disorder (DSM-III-R

— American Psychiatric Association, 1987) criteria for substance abuse or dependence.

After the passage of the 1994 Violent Crime Control and Law Enforcement Act (VCCLEA), drug

program policy required a number of changes as the VCCLEA made demands on the BOP’s

residential Drug Abuse Treatment Programs. The VCCLEA provided the Bureau with an

incentive for inmate participation: the BOP Director was allowed to provide up to a 1-year

sentence reduction for non-violent inmates who successfully completed a residential Drug Abuse

Treatment Program. In addition, the VCCLEA required that by the end of FY 1997 the Bureau

provide residential drug abuse treatment for all inmates who were “eligible.”

In May 1995, the BOP revised its policy in accordance with the VCCLEA. Additional admission

criteria required inmates to have a verifiable, documented drug abuse problem. This criterion 24

was established to prevent inmates who did not have drug problems from volunteering for drug

treatment solely to obtain early release from prison. In addition, while inmates were always taken

into the program with priority placement given to those with the least amount of time to serve, the

time frame was generally limited to 36 months before release to account for a potential 1-year

reduction in custody.

The policy statement issued in May 1995 also implemented more specific criteria for program

expulsion. While in the residential Drug Abuse Treatment Program, an inmate could be expelled if

he or she was found to have used or possessed alcohol or drugs, exhibited violence or threatened

violence against staff or another inmate, committed a serious rule infraction, or exhibited
 We note that financial incentives can also be viewed as having imposed a contingency 25

management situation into the treatment process. The effect of this particular aspect of the

treatment process cannot be disentangled from other aspects of the treatment process. 

45

disruptive behavior related to the program. Much of the greater specificity in discharge criteria —

especially those related to disruptive behavior in the program — was the direct result of

VCCLEA. Drug treatment administrators believed it necessary to define clearly expulsion criteria

because program expulsion was accompanied by loss of eligibility for an early release.

It should be noted that some institution residential Drug Abuse Treatment Program administrators

did not always apply the admission criteria as dictated by policy. Clinical judgment used as one of

the selection filters into treatment is a process that must be acknowledged by researchers. This

process, which we referred to in the literature review as the administrative selection process, can

affect the profile of individuals being admitted to the treatment program. The question is, “to what

extent do clinicians reject individuals who meet the admission criteria?” Although there were no

systematic data available to shed definitive light on this process, some information was available

from the field notes of researchers located at six of the initial research sites. While there were

some programs with twice as many applicants as admitted individuals, the primary reasons for

rejection were because staff had made referrals for inmates who did not volunteer or because the

inmates did not meet the admission criteria. However, there was evidence that staff occasionally

would reject an applicant due to a lack of motivation, because the inmate was disliked by staff, or

because the individual was considered a management problem. 

Incentives for Program Participation 

At the earliest implementation, there were no incentives for DAP participation. However, initially

low numbers of DAP volunteers despite increased funding and scrutiny by external agencies and

Congress led the BOP Executive Staff to approve residential Drug Abuse Treatment Program

incentives in October 1991.

Financial achievement awards were approved as a means of overcoming the “disincentive” of 25

pay losses incurred by inmates who, by participating in treatment, were no longer able to work

full-time. Achievement awards were dispensed quarterly and were based on program performance

— no unexcused absences from program activities, a 95-percent promptness rate for all scheduled

program activities, no guilty findings for disciplinary infractions, and successful completion of all

program assignments (including readings, homework, and self-evaluations).

The second incentive approved by the Executive Staff was consideration for a full 6 months in a

Community Corrections Center for all successful residential Drug Abuse Treatment Program

graduates. 
 This early release provision presents issues of disparity for Bureau inmates. The disparity 26

arises when, for example, two inmates convicted of the same offense receive different prison

terms because the inmate who has been diagnosed with a substance abuse problem receives a 1-

year reduction in his or her sentence and the inmate without a substance abuse problem serves the

entire sentence. In effect, many perceive this 1-year reduction as a reward for drug-abusing

behavior.

46

The third incentive involved tangible and intangible benefits granted to treatment participants by

local institution staff. Wardens received the latitude to offer such items as shirts, caps, and pens

with program logos to program participants in good standing. Other local incentives included the

assignment of participants to preferred living quarters and to units with washer/dryer access,

special recreation privileges, and special dining privileges.

The incentives for drug treatment changed with the passage of the Violent Crime Control and

Law Enforcement Act. This law allowed eligible inmates who successfully completed the

Bureau’s residential Drug Abuse Treatment Program to earn as much as a 1-year reduction from

their statutory release dates (the qualification for early release was limited to inmates who had not

committed a “crime of violence”). Successful completion of drug treatment was defined as 26

completion of all phases of the drug treatment program — the residential program, the

institutional aftercare program (when applicable), and the transitional services component

received while housed in a Community Corrections facility (described below).

A final change in DAP incentives came in 1995, with the discontinuation of tangible incentives for

residential Drug Abuse Treatment Program participants. This was modified as part of an overall

BOP policy to reduce the quantity of inmates’ personal belongings.

Program Design and Content

In-Prison Residential Drug Abuse Treatment Program (DAP)

All residential DAP’s are unit-based; that is, all residential Drug Abuse Treatment Program

participants live together — separate from the general population — for the purpose of building a

treatment community. Each unit has a capacity of approximately 100 inmates, based on a staff-toinmate ratio of 1:12 or 1:24. Ordinarily, treatment is conducted on the unit for a half-day in two

2-hour sessions. During the other half day, inmates participate in typical institution activities (e.g.,

work or school). During these times, as well as during meals, treatment participants interact with

general population inmates.

Program specifications originally were geared toward the 9-month residential Drug Abuse

Treatment Programs, with the 12-month programs following the same guidelines but adding

increased flexibility in terms of hours not devoted to required program content. At the outset, the

9-month programs were to include 40 hours of comprehensive assessment and treatment-plan
 Full-team reviews include all members of the unit team (unit manager, case manager, and 27

case counselor), as well as representatives from Education and Psychology Services. During

these meetings, the following items are discussed: custody and security classification, work

assignment and performance, leisure time activities, overall institutional adjustment, education and

other program activities, plans for release, and Financial Responsibility Program involvement.

 Although priority was placed on admitting individuals near release from custody, individuals 28

with time left to serve after program completion were initially admitted in order to fill the DAP

treatment beds. 

47

development, 280 hours of group/individual counseling, 100 hours of wellness lifestyle training,

and 40 hours of study devoted to transitional-living issues. The individual/group therapy focused

primarily on behavioral-skill building, cognitive-skills development, family issues,

vocational/educational issues, criminal-thinking confrontation, pro-social values development, and

relapse prevention. The program also provided support groups and elective self-help groups.

Individualized treatment plans were required, based on assessments of the subjects’ needs. Fullteam reviews were scheduled every 90 days, with a treatment plan review every 30 days.27

Urinalyses were to be conducted more frequently than was the case with the general population.

In July 1991, residential Drug Abuse Treatment Program content became standardized. All

residential Drug Abuse Treatment Program coordinators were brought together, and they agreed

that residential Drug Abuse Treatment Programs would include specific core components,

including screening and assessment, treatment orientation, criminal-thinking confrontation,

cognitive skill building, relapse prevention, interpersonal skill building, wellness, and transitional

programming.

Together, these components accounted for 350 hours of programming, both in didactic and

process groups. The remaining program hours were to be divided at the discretion of the

individual coordinators.

In FY 1993, a workgroup chaired by the BOP national clinical coordinator developed the

residential “Drug Abuse Treatment Program Handbook,” standardizing 450 hours of the required

500 hours of treatment. These manuals were distributed during staff training conducted in the

summer of 1994. As a result of this handbook — and modifications required by VCCLEA — the

BOP policy, issued in May 1995, required post-testing of each (REPHRASE ) module covered in

the handbook.

Finally, due to the changing admission criteria, and because not all inmates were released to

Community Corrections Centers or from custody shortly after completing the program, an 28

institutional transition program was established in 1992. The program originally required 25 hours

of “refresher” treatment in the last 4 months prior to an inmate’s release from the institution.

However, in 1995 that policy changed and required each successful residential Drug Abuse
48

Treatment Program graduate to receive no less than one hour of individual or group counseling

per month for the first 12 months out of the residential unit or until transfer to a Community

Corrections Center or release, whichever came first.

Non-Residential Drug Abuse Treatment Services/Self-Help Groups

Although a few non-residential programs existed from the start, these programs were not defined

clearly in drug treatment program policies. By June 1992, non-residential programs were better

defined and it became mandatory to make these programs available in every BOP institution. This

level of programming now provides individual and group counseling to inmates with substance

abuse histories. Non-residential programs provide alcohol and other drug abuse treatment services

to inmates who are not eligible or not interested in residential Drug Abuse Treatment Programs or

who may have overriding mental health problems that preclude the inmate’s full residential Drug

Abuse Treatment Program participation. Non-residential drug abuse treatment also provides

inmates with institutional transitional services. Self-help groups are available in all types of drug

abuse treatment in the BOP, but they are most often associated with non-residential drug abuse

treatment. However, self-help groups alone do not constitute non-residential drug abuse treatment

as defined in BOP policy.

Drug Education Course

Drug Abuse Education is the only drug abuse program service that is mandated by BOP policy.

Inmates are required to participate in this program if they meet any of the following criteria:

 there is evidence in the presentence investigation report (PSI) that alcohol or other drug

use contributed to the commission of the offense for which the inmate is currently

incarcerated;

 alcohol or other drug use was a reason for a violation of supervised release — including

parole — or BOP community status (CCC placement) for which the inmate is

currently incarcerated; or

 the inmate was recommended by the sentencing judge for drug programming during the

current incarceration.

Participants in the 40-hour drug abuse education course receive information about alcohol and

drugs, as well as the physical, social, and psychological impact of these substances. Participants

must complete an assessment of their lives, including an accounting of the costs that their drug

use has had on their health, on the lives of their families, and on the community. 

Inmates required to take the Drug Abuse Education course who refuse, or who fail to complete

the course successfully are remanded to the lowest pay-grade for the remainder of their

incarceration and are ineligible for community programs. It should be noted, however, that

inmates may also volunteer for this course.
 This program involves a system of deductions from an inmate’s pay in order to meet the 29

requirements of court-ordered fines (e.g., child support, restitution).

49

Community Corrections Centers

Ordinarily, inmates are transferred to a Community Correction Center (i.e., a “CCC,” or halfway

house) prior to their release to the community or release to supervision. CCC placements provide

inmates with structured environments in which to find a job, reunite with their families, and

receive vocational and behavioral counseling.

Approximately 9 months before an inmate’s probable release date, BOP staff determine an

inmate’s eligibility for CCC placement. A recommendation for CCC placement is based on the

inmate’s needs for services, the consideration of public safety, and the proper management of the

BOP inmate population as a whole. An inmate may be referred to a CCC for as many as 180 days,

but the average length of stay for all inmates is approximately 4 1⁄2 months.

An inmate will most likely be determined ineligible for a CCC placement if he or she meets any of

the following conditions:

 is a deportable alien;

 is serving a sentence of less than 6 months;

 has pending charges or detainers;

 requires psychological or psychiatric treatment or inpatient care;

 refuses to participate in the Inmate Financial Responsibility Program;29

 is deemed an aggressive sex offender; or

 poses a significant threat to the community.

Home confinement is another community option available to the BOP. In cooperation with the

Federal Corrections and Supervision Division (probation services) of the Administrative Office of

the U.S. Courts (AO), some inmates may be allowed to be placed at home while remaining under

BOP custody. Home confinement provides inmates with increasing responsibility while remaining

under supervision. Inmates on home confinement status are allowed to work, but are required to

stay at home during non-work hours of the day. Where available, electronic monitoring equipment

is used to ensure compliance with these conditions. The length of home confinement placement is

limited to the last 10 percent of an inmate’s sentence or 6 months — whichever is less. 

Individuals receiving a CCC placement may spend some of their time in home confinement.

When an individual is arrested for a new offense or is found guilty of a serious disciplinary

infraction, he or she may be sanctioned and transferred to a local jail or to a Federal correctional

facility and thus not successfully complete his or her CCC placement. 
 The Transitional Services treatment would thus build upon the core components of the 30

residential in-prison treatment program and provide continuity of care.

50

Transitional Services During Halfway House Placement

At the outset of program planning in 1989, transitional services were to consist of two phases.

The first phase, pre-release services, would include 6 months in a CCC, with specialized

programming provided either by a contractor or directly by BOP staff. The second phase —

aftercare services — would consist of 6 months during which community services would be

coordinated jointly by the BOP and the requisite U.S. Probation or Parole office, or provided

directly by CCC staff if community resources were unavailable.

This initial plan was not implemented. Rather, in working closely with the AO’s Federal

Corrections and Supervision Division, in July 1992, a Memorandum of Understanding (MOU)

was signed between the BOP and the AO concerning the provision of transitional care. The

agreement specified that for individuals with CCC placements, a planning conference involving

CCC contract staff, a community-based drug counselor, and the inmate would be held within the

first week of the inmate’s arrival at the CCC. Because most Probation offices had communitybased treatment contracts for offenders under Federal supervision, the MOU allowed the BOP to

use the same contractors — in other words, to “piggyback” on the probation services agreements.

This “piggyback” effort ensured that inmates would continue to receive treatment services from

the same providers as they moved from BOP custody to Probation supervision.

Transitional services generally include community-based treatment with philosophies similar to

those of institution-based treatments. Initially, the intensity of transitional services was to be 30

standardized, with each individual receiving 4 hours of services per week during his or her stay at

the Community Corrections Center. However, soon after implementation, the community-based

treatment provider began to direct the individual’s course of treatment and, typically, now

Transitional Services inmates receive, on average, 2 hours of services each week.

At the outset, transitional services were granted only to graduates of the of the DAP. In early

1993, the range of inmates who could receive transitional services during CCC placement was

expanded to include any inmate in a CCC who was identified as needing drug treatment, even if

he or she had not participated in an in-prison residential drug treatment program. This expansion

resulted, in part, from recruitment problems in the early residential Drug Abuse Treatment

Programs, which left funding available for an expanded community-based treatment population.

Although some transitional services participants who had not been DAP participants received

these services voluntarily, most did not. Most of these latter transitional services participants

became involved as a result either of community corrections staff recommending treatment as a

condition of the CCC placement or of two new community corrections programs initiatives

implemented in 1994. The first initiative provided for the creation of Comprehensive Sanction

Centers (CSC’s), which were CCC’s designed to offer more gradual and structured release
 In the event that the necessary services are not offered by one of the contract agencies, these 31

services may be provided by a non-contract agency or the Probation officer.

51

experiences to individuals who might not be appropriate for the traditional CCC experience. All

CSC residents were required to be screened for drug treatment needs and then referred, if

appropriate, to Transitional Services. CSC’s had been fully implemented at 12 different sites in the

country by January 1994, and they housed 4 percent of all the Transitional Services participants.

The second initiative was called the Enhanced Transitional Services (ETS) project. Within each of

the six BOP regions, at least one CCC facility (not already designated as a CSC) was selected as

an ETS site. ETS sites were similar to CCC’s but contained special provisions for transitional

services. ETS participants were required to participate in community-based treatment (i.e.,

transitional services) if they were identified by a BOP community corrections manager as having a

substance abuse problem and were determined by a community-based treatment provider to need

treatment. Four ETS programs were implemented between January and March 1994, and another

four began between April and August 1994. In the beginning of 1994, 26 percent of all the

Transitional Services participants were in ETS programs.

As of the end of 1996, with the implementation of the VCCLEA initiative, 62 percent of BOP

inmates receiving transitional services during their CCC placement were residential Drug Abuse

Treatment Program graduates.

Supervised Release 

Approximately 80 percent of the TRIAD research subjects were released from BOP custody with

provisions for supervision by a U.S. Probation officer. The system of selecting individuals who

were to receive treatment services as part of post-release supervision varied among each of the 94

judicial districts. Individuals with an identified history of drug abuse may have been required to

receive treatment services while under supervision. Individuals under the supervision of a U.S.

Probation officer may have undergone urinalysis tests and have had drug treatment services

provided under the “Contract Services Program Plan” when the services were required as a

condition of supervision. However, urine testing was required of most individuals flagged as 31

having a drug problem.

Urine testing involves a combination of regularly scheduled collections (fixed-interval testing) and

unscheduled collections (random testing). Many, but not all, of the Probation offices follow a

phase program for urine testing, with the following three phases: 

 Phase I — This phase involves six urinalysis (UA) collections monthly with at least two

unscheduled collections. During this phase, the treatment contractor, when

requested, should provide four 30-minute counseling (or alternative treatment)

sessions each month. This phase usually lasts 6 months.
52

  Phase II — When an individual completes 6 months of Phase I satisfactorily, he or she

moves on to Phase II, where the urine collections are reduced to four per month,

with at least two of these being unscheduled. During this phase, if treatment

services are required, there are three counseling sessions of at least 30 minutes

each month. This phase generally lasts 3 months.

 Phase III — This phase reduces the monthly urine collections to two unscheduled urine

tests. Counseling sessions are reduced to two sessions of at least 30 minutes each

month. Usually, the type of treatment provided by a contractor consists of either

individual or group counseling. However, when necessary, intensive outpatient

counseling, detoxification services, and residential services are provided.

Individuals who have positive urinalyses or violate other conditions of supervision, including

being arrested for a new offense, may be revoked and transferred to a Federal prison or other

correctional facility. Revocations are made at the discretion of the Probation officer and the

judicial official presiding over a revocation hearing. Thus, in some districts an individual will be

revoked for one positive urinalysis whereas in other districts an individual may have several

positive urinalyses before being revoked. There are however, a few acts, such as possession of a

firearm and possession of a controlled substance, that call for mandatory revocation.

Summary

In summary, it is clear that throughout the TRIAD drug treatment evaluation effort various

components of the BOP’s drug treatment programs underwent changes. The number of programs

grew from 8 in fiscal years 1989 and 1990 to 39 in fiscal year 1996. The incentives for program

participation changed from financial incentives to offset the loss of pay resulting from program

participation to a 1-year reduction in sentence for successful program completion. Admission

criteria became more stringent, eventually requiring an official DSM-III-R diagnosis of drug abuse

or dependence that was verifiable and documented. Program content became more standardized

over time. 

Transitional services provided during a CCC placement were initially limited to individuals who

had completed the in-prison residential drug treatment program. Later these services became

available to other drug-abusing individuals. Furthermore, several new community corrections

initiatives mandated such services for individuals with histories of drug use. 

Some of the program changes did not affect the research design for the TRIAD evaluation.

However, the rapid growth in programs did notably affect the research design. Chapter 4 provides

a description of the research design and how it was affected by the program changes.
53

CHAPTER 4: RESEARCH DESIGN

The primary purpose of the TRIAD project was to conduct a multi-site evaluation of the

effectiveness of residential drug abuse treatment, including in-prison treatment and its extension to

post-release treatment. We used a quasi-experimental design in which inmates with histories of

drug use volunteered to participate in treatment designed around the therapeutic community

concept. These subjects composed the treatment groups. Comparison subjects were drawn from

research volunteers at DAP and non-DAP sites.

The study was prospective in that inmates were identified at the beginning of their drug treatment,

or, if they were comparison subjects, at a point within a year prior to their releases. Once an

inmate was identified as a treatment subject, he or she was included in the study regardless of his

or her ultimate disposition. Thus, inmates who dropped out of the treatment program, were

terminated, or failed in a halfway house were still included in the treatment groups.

The longitudinal design calls for measurement of background and intervening variables. For those

in treatment, some of these variables were measured prior to and after treatment. In the final

report, post-release outcomes will include results of measures carried out, for each inmate, during

a period of 3 years following his or her release from custody. In this interim report, however, we

cite results for those treatment and comparison inmates who had been released to the community

for at least 6 months. 

Sample Selection Process

At the outset of this project, treatment volunteers were intended to be assigned randomly to either

a treatment or comparison group, thus circumventing problems with selection bias. Once the

treatment programs began, however, we realized the infeasibility of such an implementation. First,

there were insufficient numbers of treatment volunteers, which resulted in all volunteers being

given treatment slots. This situation wouldn’t work for us because random assignment requires

that there be more volunteers than there are available treatment slots. In addition, treatment staff

exerted tremendous pressure to control the treatment assignment process, making it impossible

for researchers to exercise that same control. Thus, the TRIAD project had to adopt a quasiexperimental design to address the issue of selection bias.

As we discussed in the section on “Selection Bias and the Evaluation of Prison Drug Treatment

Programs” (see Chapter 2), we used two approaches to minimize selection bias and to test the

effect of treatment tainted by selection pressures. Our first approach combined all treatment

outcomes, regardless of whether inmates completed treatment, and is referred to as the Bloom

approach. Our second approach, which was implemented by William Rhodes, an econometrician

at Abt Associates, modeled selection bias and tested for treatment effects following statistical

procedures outlined by Heckman (1979) and Maddala (1983).
54

In order to test explicitly for selection bias effects, some comparison subjects were selected from

sites in which treatment was available. Theoretically, if selection pressures compel more

motivated volunteers to participate in drug treatment programs, this would diminish the number

of motivated clients remaining in the comparison pool from DAP sites. Under this assumption, the

comparison subjects drawn from DAP sites should have been less motivated than were treatment

participants, and perhaps they would have had characteristics associated with a higher risk of

recidivism.

It also is possible that selection pressures, such as external incentives, compelled less motivated

inmates to participate in drug treatment programs. Under this assumption, comparison subjects

drawn from DAP sites should have been more motivated than were their treatment counterparts

and may have had background characteristics associated with a lower risk of recidivism.

It is important to note that comparison subjects drawn from DAP sites (i.e., sites with residential

treatment programs) have some probability of volunteering for treatment even if that probability is

extremely low. However, control subjects drawn from sites where no treatment was available,

non-DAP sites, have a zero probability of volunteering for treatment.

William Rhodes, in his presentation of the model (see Appendix C) refers to the following types of

subjects: DAP treatment subjects (those who volunteered for and enrolled in treatment); DAP

comparison subjects (those who were offered treatment but declined); and non-DAP control

subjects (those whom were never incarcerated in a facility that sponsored treatment programs).

Throughout our report, we refer to these groups as DAP treatment groups, DAP comparisons,

and non-DAP controls, respectively. Dr. Rhodes used information we provided about the

probability that an inmate will receive treatment to estimate a latent variable representing the

factors that determine whether an inmate will volunteer for treatment. He then incorporated this

information into a model that measures treatment effects in the presence of selection bias

pressures. Thus, he was able to use information simultaneously from the DAP treatment groups,

DAP comparison group, and non-DAP control group in measuring treatment effects.

Our first approach to control for selection bias — the Bloom model —did not test for selection

bias explicitly. In using this approach, for which we combined all treatment groups, a question

arose as to how to treat the DAP comparison subjects when we contrasted the combined

treatment outcomes with the non-DAP control subjects. As we argued in the section on

“Selection Bias and the Evaluation of Prison Drug Treatment Programs” in Chapter 2, DAP

comparison subjects should have been combined with DAP treatment subjects and this

combination should have been contrasted with the non-DAP control group. Our reasoning was

that the DAP comparison group was composed of inmates who — theoretically — would have

declined treatment if it were offered and that these inmates should have been represented

proportionally in our non-DAP control group. 

Because our samples were convenience samples and were not drawn with an explicit plan to

reproduce proportionality, we estimated the extent to which DAP comparisons were under- or
 For demonstration purposes, this paper also will present analyses that depict group 32

outcomes for those who completed programs separately from those who withdrew or were

terminated. This will allow us to compare our results to those of past studies.

 There are approximately 1,000 additional research subjects for whom we have 33

comprehensive data but who are not included in this preliminary report because their release dates

are after December 31, 1995. These subjects will be included in future reports. Please note,

however, that some of these subjects will not be available for follow-up because they have INS or

State detainers.

This interview — the Intake 1 interview — collects a wide range of background 34

information on the subjects.

55

over-represented relative to their hypothetical proportions in the non-DAP control group. Our

best estimate suggested that we over-sampled DAP comparisons. Furthermore, other information

indicated that the probability of volunteering varied over time and by site. Thus, in our analysis,

we weighted the DAP comparison sample to approximate proportionality with the volunteering

rate for both the period during which the subject was selected and the site at which the subject

was housed.

If treatment was having an effect, one would expect that the combined DAP group average

outcomes would have been significantly better than the non-DAP control group average

outcomes. Most studies in this domain have looked at the average outcomes of inmates who have

completed drug treatment. The authors of this paper have argued that this approach does not

allow us to disentangle the effects of a selection process from the effects of treatment.32

Research Subjects

This report on 6-month post-release outcomes describes results concerning only those subjects

who were released from BOP custody as of December 31, 1995. This includes approximately

two-thirds of the total number of research subjects in the overall study, as the remaining one-third

had release dates after December 31, 1995.33

The report is based on outcomes for 1,866 individuals (899 treatment subjects, plus 530

comparison subjects at DAP sites and 437 control subjects at non-DAP sites) to whom, at the

very least, one of two interviews was administered. Results concerning research subjects, both 34

treatment as well as DAP comparison and non-DAP control, for whom these interview data were

not collected are not included in this report. The background information from these interviews

was crucial to the analysis of outcomes. A detailed assessment of whether the individuals included

in the report are different from those not included in the report, as well as an assessment of other

possible biases resulting from subject attrition, is contained in a subsequent chapter entitled

“Subject Attrition” (see Chapter 5).
56

Individuals who had Immigration and Naturalization Service (INS) detainers or State detainers

who had not been released from custody are excluded from this report. There were 209 such

subjects for whom interview data were collected. Of these, 110 subjects were INS detainees (89

men and 21 women). Although some INS detainees are released to the streets, it is difficult to

assess consistently whether these subjects were deported or not. Most of these 110 INS-related

subjects will be followed in the future, as they are expected to be released before the end of the 3-

year follow up period. The other 96 (86 men and 10 women) went directly from BOP custody to

another form of incarceration. It must be noted that admission criteria had specified that INS

detainees and State detainees were not to be admitted to DAP’s.

The following section describes the history of site selection and the logistical problems

encountered in the study.

A Chronological History of the TRIAD Subject Selection Process

Treatment Subjects

Eight sites were originally selected for the study — three 12-month programs (at FCI’s Butner

and Tallahassee and FMC Lexington) and five 9-month programs (at FCI’s Fairton, Oxford,

Seagoville, and Sheridan, plus FMC Rochester). Data collection for the three 12-month programs

began with cohorts admitted after August 1, 1991, and for four 9-month programs with cohorts

admitted after October 1, 1991. 

FMC Rochester was dropped as a research site in the summer of 1991 even before data collection

started, and this was done because the program model least resembled the others. After a site visit

to FCI Seagoville in November 1991, the decision was made to drop this site as well. That

program had accepted many non-English speaking inmates and had developed two separate

programs, one in English and one in Spanish, and many of the Spanish-speaking inmates had

detainers. Because other programs were scheduled to be implemented in early 1992, another

program was to be selected as a replacement.

FCI Marianna was chosen in February 1992 because its first admission cohort, compared to the

cohorts at the other new sites, had a greater percentage of inmates who were within several years

of release. Programs admitting inmates with higher averages of time before release would only

serve to delay follow-up data collection.

By the early fall of 1992, preliminary estimates of the numbers of research subjects to be available

for follow-up within several years fell short of expectations. This paucity resulted from a

decreasing percentage of new admissions who were within 2 to 3 years of release dates and to the

fact that two of the research sites had temporary delays in new admissions resulting from an

insufficient number of available drug treatment staff. Approximately half of the admissions

between September 1991 and March 1992 at the seven research sites were within 3 years of their
Although priority was given to individuals close to release, the number of treatment 35

volunteers close to release was initially too small to fill all available treatment slots.

 Please note that FCI Seagoville had a sufficient number of English-speaking inmates, 36

unlike the circumstances at the time this site was dropped as a research site in 1991.

57

release dates at time of admission. This percentage decreased to approximately 27 percent for

admissions between October 1992 and March 1993. At this time NIDA requested a revised

research plan to accommodate this unexpected development.35

The revised plan increased the number of treatment subjects near release by calling for the

selection of additional research sites. As of March 1993, there were 30 BOP residential drug

treatment programs nationwide, including the 7 original research sites. Residential Drug Abuse

Treatment Programs at 11 sites were eliminated from consideration because they (1) were at

maximum-security institutions with very low percentages of inmates near release, or (2) served

Cuban inmates who were INS detainees, (3) were not fully operational, or (4) were of a 4-month

duration. The remaining 12 programs not already in the study were selected as research sites

(these included the programs at FCI’s Danbury, LaTuna, Lompoc, McKean, Morgantown,

Phoenix, Seagoville, Terminal Island, and Three Rivers; FPC’s Yankton and Alderson; and FMC 36

Rochester). Three of these sites were minimum-security sites (FCI Morganton and FPC’s

Alderson and Morgantown), unlike the original study sites. Two of the 12 newly added research

sites housed female inmates (FPC Alderson and FCI Danbury), while only one of the original

seven study sites housed women.

To ensure a sufficiently large sample available for follow-up in the not-too-distant future, data

collection was limited to those individuals expected to be released from BOP custody by the end

of FY 1996. Data collection at 11 of the 12 additional sites began in April 1993. The twelfth site

(FCI Danbury) was not expected to be operational until January 1994, due to its transition from a

male-only to a female-only facility. Following a prison disturbance in the summer of 1993 at FCI

Phoenix — a site housing male inmates — this site was dropped and replaced with FCI Dublin (a

female institution in California). Previously, FCI Dublin had not been selected because it was not

fully operational. 

After a review of notes about trips to various sites and of quality control reports in February

1993, the decision was made to drop FCI La Tuna as a treatment research site. Much of the DAP

program at FCI LaTuna was conducted in Spanish (meaning that a high percentage of program

participants were not English-speaking) and many of the participants had INS detainers. In

February 1994, FMC Lexington was dropped as a research site because it was beginning the

process of converting to a male-only facility. 

Residential drug treatment subjects were followed after release from custody irrespective of

program status upon discharge. Individuals not completing the program received the following

discharge classifications: disciplinary discharges, dropouts, and incompletes (due to transfers,
58

releases to halfway houses, or releases from BOP custody). Identification of treatment subjects

from program admissions ended in the summer of 1995.

Non-Treatment Subjects

As noted above, the research design was intended to be experimental in nature. Inmates who had

volunteered for treatment would be randomly assigned by research staff to either the 12-month

intensive residential program or to the “control group.” This control group would be composed of

various comparison groups, and these subjects could choose to volunteer for a 9-month,

moderate-intensity residential program, making them essentially comparison subjects exposed to

lower levels of treatment. Inmates who did not opt for the 9-month program would compose a

second comparison group that received no residential treatment but could have received treatment

of a very low intensity. The low-intensity treatment consisted of in-prison outpatient counseling

services or treatment services while in a CCC placement. All these groups (i.e., the two control

groups and the above-mentioned treatment group) together would provide one primary set of

comparisons, that between subjects randomly assigned to a 12-month residential program and

those who volunteered for this treatment but instead received lower-intensity or no treatment. 

Inaccurate case flow estimates proved to be the most important reason for not implementing a

randomized design within the BOP. The status of the BOP’s drug abuse treatment programs in

the summer of 1991 indicated that we would not have an excess of volunteers. This was due both

to the BOP’s rapid expansion of drug treatment programs and to the fact that program expansion

was not limited to one geographical region. This program expansion thus was able to provide

treatment to most individuals who desired it, which prevented the creation of waiting lists to be

used for random assignment procedures. In fact, keeping bed capacity filled required the

admission of inmates who, contrary to the initial admission criteria, had more than 3 years left to

serve before being released. In addition, research sites scattered nationwide created significant

logistical problems for implementing a randomized design.

Therefore, two non-treated groups were selected. The first group consisted of individuals at a

DAP site who did not volunteer for treatment (i.e., DAP comparison subjects), and the second

group consisted of individuals who did not have the opportunity to volunteer for DAP because

they were housed in institutions that did not offer DAP (i.e., non-DAP control subjects).

We recognized that the simple fact of being housed at an institution without a DAP did not

provide sufficient rationale to conclude that such individuals did not have the opportunity to

volunteer for treatment, because BOP policy did not preclude anyone from transferring to a DAP

site and then volunteering for treatment. Therefore, we could not yet establish definitively that

treatment was not available to individuals housed at non-DAP institutions. However, an

assessment of transfer rates provided evidence that treatment rarely occurred for individuals from

the non-DAP institutions. In December 1994, an analysis was undertaken to assess the extent to

which those receiving DAP residential treatment had been transferred from an institution without

a DAP. Of all the DAP participants to that date — not just the research subjects — only 4.6
 This will be noted later in discussing the subjects not available for follow-up data 37

collection.

 This serves as a proxy for criminal justice history because security level is determined by 38

information about the current offense(s) and the history of previous offenses.

59

percent had transferred within 90 days prior to being admitted to a DAP. Anecdotal information

showed that at a few select DAP sites there was some effort to recruit individuals from other

institutions. However, the extent of this recruitment was minimal, as substantiated by this analysis

of transfers. We thus felt satisfied in concluding that individuals from non-DAP sites did not have

treatment available. 

Toward the end of the process of identifying the non-DAP controls, passage of the 1994 Violent

Crime Control and Law Enforcement Act (VCCLEA) created an opportunity for inmates to

receive a one-year sentence reduction with successful completion of a drug treatment program.

Thus, VCCLEA increased the likelihood that inmates from non-DAP sites would request transfers

in order to participate in treatment. Although non-DAP controls were selected after passage of

VCCLEA, those selected were too near to release to qualify for its early release provision. For

women, it was very difficult to identify non-DAP control subjects, as there were relatively few

female-only prisons, and most of them, over time, had implemented a residential DAP.

The first group of non-treated subjects comprised we selected was composed of DAP

comparisons. All non-treated subjects — both DAP comparison subjects and non-DAP control

subjects — were individuals who, according to their self-reporting, were regular users of drugs.

Regular users were defined as those subjects ever having used an illicit drug at least once per

week for at least one month or ever having used alcohol daily for at least one month. This would

approximate meeting the minimal criteria for admission to a DAP. Other admission criteria could

not easily be assessed through readily available data sources. In addition, some of these other

admission criteria — such as not having a detainer — were not followed consistently. The 37

screening for drug use was accomplished through the administration of a questionnaire, the

History of Drug Use (HDU) survey, developed for this purpose.

The first attempt to identify non-treated individuals who would have been eligible for treatment

focused upon the DAP comparisons and used matching procedures. After identifying individuals

eligible for drug treatment according to the HDU survey, a sample was to be selected through

prospective matching to the cohort of residential drug treatment research subjects. The matching

criteria were to include sentence length, age, race, individual security level, and the severity of 38

drug use. This matching process proved ineffective in identifying subjects in time to plan a data

collection trip before individuals were released to halfway houses or released from BOP custody.

Mainly for this reason, the matching procedure was abandoned after only one set of selections,

which consisted of 124 subjects.
 Details concerning subject attrition are presented in Chapter 5. 39

60

Subsequent selection of non-treated subjects, both DAP comparison and non-DAP controls,

followed the same procedure. We identified individuals within 6 to 15 months of release who had

not volunteered for DAP and for whom it was too late to volunteer. We attempted to administer

the HDU to all of these individuals. Any individual who self-reported regular drug use became a

potential non-treated comparison subject or a non-DAP control subject and was approached to

participate in research. 39

The institution from which an individual was selected did not serve as the sole determining factor

in whether the subject was classified as a DAP comparison or non-DAP control subject.

Individuals identified at DAP sites might have arrived just prior to release (i.e., they were

transferred to these sites because they were the institutions closest to their release destinations)

and thus did not have time to volunteer. On the other hand, individuals selected at non-DAP

institutions might have been there only a few months prior to release but had spent most of their

previous few years at DAP institutions. Thus, classifying the type of comparison subject was

accomplished through looking at each subject’s admission and release history and determining

whether the individual had been at a DAP institution at a time when a program was available and

with sufficient time left to serve to volunteer and complete the DAP.

Data Collection Instruments

The data collection instruments were selected by replicating measures used in previous and

current drug treatment evaluations — choosing measures that in previous recidivism and

treatment evaluation research had been shown to be related to either treatment outcomes or

recidivism, and selecting measures that test some of the theoretical assumptions underlying the

drug treatment programs. Many of these measures were used as statistical controls to ensure that

possible differences in group composition did not account for differences in the outcome

variables.

The measures collected for the study can be summarized as follows:

 Pre-incarceration background data — family background, employment and educational

history, drug and alcohol use and treatment history, mental health treatment

history, illegal activities, and incarceration and arrest histories.

 Psychological/cognitive measures — motivation and expectations about treatment,

Change Assessment Scale (Prochaska and DiClemente, 1986) (a survey of

motivation for change), DSM-III-R diagnoses of depression and antisocial

personality, Attributional Style Questionnaire (ASQ), Drug-Taking Confidence

Questionnaire (DTCQ) (Annis and Martin, 1985a), Inventory of Drug-Taking

Situations IDTS) (Annis and Martin, 1985b), Ways of Coping Checklist (Lazarus

and Folkman, 1984), and Hope Scale (Snyder, et al., 1991).
 Refusal rates are reported in Chapter 5. 40

 Performance pay refers to the minimal salaries inmates receive for work performed on 41

assigned work details. UNICOR refers to Federal Prison Industries which provides work details at

Federal prisons. . 

61

 Treatment structure and process — Drug Program Description Checklist (a staff

survey), observations of group sessions and staff meetings (at a limited number of

research sites), inmate perceptions of staff empathy and program environment, and

length and type of services received.

 Proximal outcomes — institutional adjustment using indicators such as disciplinary

actions and positive urine results, changes in pre- and post-treatment measures on

Change Assessment Scale, Ways of Coping Checklist, and Drug-Taking

Confidence Questionnaire.

 Post-release environment — indicators of poverty and employment rates from census

data.

Data Collection Procedures

In-Prison Data

Inmates participating in DAP’s were approached by researchers, who explained the project and

administered surveys and interviews to those inmates who signed the requisite informed consent

statement. The set of pre-treatment surveys was administered within 6 weeks before or after 40

admission to the DAP. The post-treatment surveys were administered within 4 weeks before or

after program completion or termination. In addition to the surveys, two personal interviews were

administered. While the two interviews — Intake1, with background information, and Intake2,

with diagnoses of antisocial personality and depression — had no specified time frame for

administration, they generally were administered within several weeks of the pre-treatment

surveys. The surveys and interviews generally were administered within the same week for nontreatment subjects, with administration occurring as soon as possible after identification of the

subject in order to ensure that the subject would still be in prison (these subjects were selected

close to their release dates). At times, this was infeasible due to the large number of research sites

and the limited number of researchers.

To encourage inmate participation in the evaluation project, the BOP Executive Staff issued a

memo in March 1992 informing wardens that inmates participating in the TRIAD evaluation

project were not to lose their performance pay or UNICOR pay while participating in surveys 41

and interviews.

Data on services received were obtained from both treatment staff and automated databases. Staff

perceptions about the programs were obtained from three annual staff surveys — 1993 through
 SENTRY, the BOP’s automated database, provides comprehensive information on 42

currently and formerly incarcerated inmates. 

 The subset of items included in this report exclude the use of several surveys. These 43

surveys will be used in the future to address additional research questions. These research

questions are discussed at the conclusion of this report.

62

1995 — administered to all DAP staff at the research sites. Supplementary background data and

information on the subjects’ current incarcerations were extracted from the automated SENTRY

database.42

Community Corrections Center (CCC) Data

Information on employment and educational activities, urinalysis testing and results, and

participation in self-help groups during CCC placement was obtained from surveys mailed to the

contract CCC staff. The information on transitional services received was obtained from the

transitional services managers and automated databases. Other information about the length of the

CCC placement, disciplinary infractions, and successful completion was obtained from the BOP’s

automated SENTRY database.

Post-Release Data

For those subjects released to supervision, information was obtained through phone calls with

Probation officers at three points in time after release: 6 months, 18 months, and 3 years (or

completion of supervision at any point). The Probation officers provided information on

employment, educational activities, violations of conditions of supervision, urine testing

frequencies and results, the numbers and types of supervisory contacts, arrests and incarceration,

treatments received, and living situations.

Arrest data were obtained from National Crime Information Center (NCIC) databases for nonsupervised subjects. NCIC is the FBI’s computerized record system that holds arrest and

conviction information about Federal — and most State — crimes. These data were obtained also

for the time between end-of-supervision and 6 months after release for those subjects who

completed supervision in advance of their 6-month follow-up dates.

Description of Measures

Measurement indicators in this report reflect those items known to be associated with treatment

outcome or recidivism and items we consider to be important control variables that have not been

examined in previous studies. A subset of background and treatment measures from among those

collected are included in this report. The following identifies and defines, where necessary, the 43

measures selected for use in our analyses.
 Logistic regression is an appropriate analytic method when the dependent variable is 44

binary (Menard, 1995).

63

Background Characteristics

The BOP’s automated SENTRY database provides information on several background

characteristics, including sex, race, ethnicity, prior commitments, criminal justice status at time of

incarceration, history of violence, sentence length, and age upon release from incarceration. 

The self-report data obtained from two interviews administered to research subjects provide other

pre-incarceration information. This information includes employment status during the month

before incarceration, educational level, drug use, drug and alcohol treatment history, and mental

health treatment history. 

Psychological/Attitudinal Measures

The Diagnostic Interview Schedule (DIS) interview using the DSM-III-R criteria (American

Psychiatric Association, 1987) provides the diagnoses of depression and antisocial personality. 

Although the DIS interview for obtaining measures of drug and alcohol dependence was

administered to 706 treatment subjects, these measures had to be imputed for the remaining

subjects, and this was done in several steps. 

First, using the subsample of 706 inmates interviewed with the DIS interview schedule, other

variables from the drug and alcohol sections of the Intake1 interview known to be correlated with

dependence were selected to serve as proxies of drug and alcohol dependence. Second, logistic

regression was used to regress the log odds of dependence on these other drug-related variables

for the drug dependence measure and on a set of alcohol-related variables for the alcoholdependence measure. As a result of these logistic regressions, two equations for the estimated 44

log odds of drug and alcohol dependence were generated. These equations were applied to the

larger data set, which included data on inmates for whom the original DSM-III-R diagnosis was

unavailable. This allowed an estimated log odds of drug and alcohol dependence to be generated

for all subjects in the data set, with a few exceptions for cases with missing data.

Because our interest was more in the accuracy of prediction and less in theory, the predictive

efficacy of the models was relatively more important than was the fit of the models. Nonetheless,

the fit of both models was quite acceptable. For both alcohol and drug dependence, the HosmerLemeshow measure of goodness of fit did not suggest any problems with the models. Looking at

indicators of predictive accuracy, the concordance value was 93.6 percent for alcohol dependence

and 92.2 percent for drug dependence. Both levels of concordance were very high. 

The attitudinal measure — the Change Assessment Scale — replicates Prochaska’s 32-item

survey (Prochaska and DiClemente, 1986). This scale was used to measure the individual’s level

of recognition of a problem and motivation to do something to change the problem. It was
 While the cluster titles resemble the titles of the factors, they are not synonymous. Each 45

cluster is represented by a unique profile of scores across the four factors.

64

selected for this analysis due to its possible association with volunteerism and its previously

demonstrated relationship to treatment retention. Furthermore, it represented the dynamic factors

investigators are beginning to use to understand better the treatment process.

Confirmatory factor analyses were done to verify the four factors identified by Prochaska. These

four factors — each composed of eight items — include:

 Precontemplation — when the individual is unaware of his or her problem.

 Contemplation — when the individual is aware that a problem exists and is

contemplating taking some action.

 Action — when the individual has not only considered taking action, but is taking steps

to remedy the problem.

 Maintenance — when an individual who has taken action works to maintain the gains

attained during the action phase and thus prevent relapse.

These factors were verified through confirmatory factor analytic procedures conducted both with

the exclusion of cases with missing items and with the inclusion of all cases, using mean score

substitution for missing items. Values on all four factor scales are needed to obtain accurately the

“stage of change” for an individual. Cluster analytic procedures were used to classify individuals

into their appropriate stages of change based upon their profiles of scores across all four factors.

Standardized scores with a mean of 50 and standard deviation of 10 for the four scales (with

missing items estimated using mean scores) were cluster-analyzed using Ward’s minimumvariance method (Ward, 1963). The six-cluster solution that was most interpretable closely

resembled the six clusters identified by Tsoh (1995). 

The six clusters, which bear some resemblance to the four factors listed above, can be briefly

characterized as follows: 45

 Uninvolved — the individual does not endorse any of the four scales and can best be

described as both denying having a problem and not attempting to change his or

her behavior to address the problem. 

 Precontemplation — the individual does not recognize the existence of a problem.

 Reluctant — the individual recognizes the problem and is considering taking some

action but does not take any action.

 Contemplation — the individual is considering changing and recognizes a problem but

has not yet actively addressed the problem.

 Preparation — the individual has made a decision to start changing and has actively

started to make changes but has not yet recognized the possibility of relapsing.

 Action — the individual is actively engaged in changing his or her behavior and has

started working toward maintaining the change and avoiding relapse.
 Individuals who dropped out of a program or were disciplinarily discharged could later 46

reapply for admission to DAP. In addition, some individuals who successfully completed DAP

applied for readmission at the same site or another site to which they were transferred and thus

completed DAP twice. Almost five percent of the DAP treatment subjects had more than one

episode of treatment.

65

Treatment Received and Post-Release Supervision

For those receiving in-prison DAP treatment, the time in treatment is recorded as the combined

amount of time across all episodes of DAP enrollment, and the type of discharge is recorded as 

discharge from the last episode. Very few individuals had enrolled in DAP more than once.

Additional in-prison treatment, such as enrollment in an outpatient treatment program or in a selfhelp group, also was recorded. 46

In general, in-prison drug treatment is offered only to individuals nearing release; treatment

providers feel that treatment effects will be eroded by a lengthy exposure to prison culture after

treatment. Because a significant number of individuals were not released from prison immediately

following treatment, a measure of the time between program completion and release was

calculated. This measure assesses whether the effects of treatment diminish with a lengthier stay in

prison after treatment. The time between a program’s initial startup and the admission of an

individual to that program provides an indicator of program stability. Many research subjects were

in the first or second cohort of admissions to their programs.

Involvement in transitional services during halfway house placement was recorded for all research

subjects — both those who received DAP treatment and those who did not. “Post-release

treatment” status depended on whether an individual received treatment required by the Probation

officer (contract services) or sought treatment at his or her own initiative. Information on selfhelp group involvement was recorded as well.

Differing levels of supervision affected outcomes, as some supervised individuals were monitored

more closely than were others. The differing types of supervision mentioned in this report break

down into the following categories: those who received a halfway house placement, those who

were supervised by a Probation officer after release, those who received urinalysis testing while

under supervision, and those who were placed in a halfway house by a Probation officer during

supervised release. 

Post-Release Behaviors

The post-release behaviors and conditions of living included in our analyses consisted of (1) living

situation (e.g., was individual living with a spouse), (2) employment status, and (3) adherence to

conditions of supervision. Those who violated supervision conditions were divided into two

categories: those whose violations related to drug or alcohol use, and those committing other

types of violations. The use of some of these behaviors in outcome analyses varied with the type
 The discussion of the Heckman and Maddala approach to modeling outcomes in 47

Appendix C explains the role of this variable in controlling for bias.

This represents the average difference in percent eligible for treatment between 1991 and 48

1996 as indicated by the results of surveys administered at two sites: FCI’s Marianna and Fairton.

66

of outcome being examined. For example, employment was one of our outcome measures but it

was used as an explanatory variable when examining drug use or arrests as an outcome measure. 

Probability-of-Volunteering Coefficient

A probability-of-volunteering coefficient was developed to assist in controlling for selection bias.47

This coefficient was calculated for each research subject who was at a DAP institution, regardless

of whether he or she actually entered treatment. 

The first step in the process was to calculate a probability-of-volunteering coefficient for each

institution. Because of the differing security levels of the institutions in which our research

subjects were housed, we presumed that the rate of volunteering would differ among institutions.

Creation of this institution coefficient required obtaining estimates of the percentage of the

population eligible for treatment and the percentage actually volunteering. The percent of the

population eligible serves as the denominator of the coefficient, and the percentage actually

volunteering serves as the numerator. 

The percentage eligible was estimated using the 1991 Inmate Survey Data. This survey contained

questions on drug use analogous to those contained in the History of Drug Use (HDU)

questionnaire used to screen DAP comparison and non-DAP control subjects. Some of the

research DAP sites were not included in the survey (or had changed security level or gender of

population housed), so for some of these sites we used data from the 1996 administrations of the

History of Drug Use (HDU) survey, with a downward adjustment of 12.5 percent. For several 48

additional sites where neither the 1991 Inmate Survey Data nor 1996 HDU data were available,

the percent eligible was estimated using the average for the other sites of a similar security level.

When the 1997 Inmate Survey data become available, the calculation of this coefficient will be

reviewed by comparing these results to the 1991 results used in calculations for this report.

Given the expectation that the probability of volunteering had changed over time with the passage

of the 1994 Violent Crime Control and Law Enforcement Act, which allows for early release

upon successful completion of a residential DAP, estimates were created for eight points in time.

The estimates were made for the beginning of each quarter for FY’s 1994 and 1995. Calculations

for earlier times were not made, due to incomplete and unreliable information concerning

volunteering rates. 
 For stays at an institution prior to January 1994, the coefficient value of January 1994 49

was assigned. It is presumed that the coefficients are most likely to have been stable prior to that

point in time — that is, before rumors of the possibility of the incarceration-reducing provisions of

the Violent Crime Control and Law Enforcement Act of 1994. The waiting lists for the DAP’s

provide good indicators of when these rumors began to affect participation rates. The size of the

waiting lists began to see a dramatic rise in November and December 1994. 

67

Because all individuals identified as DAP comparison or non-DAP control subjects were within 15

months of release (including halfway house placement time), both the denominator and numerator

of each institution-level coefficient were limited to estimates based on individuals within 15

months of release. The percent eligible for treatment described above was thus multiplied by the

population within 15 months of release housed at the particular institution. The numerator — the

number actually volunteering — for each site was obtained from automated databases and

included all individuals who (1) were housed at that site, (2) were within 15 months of release,

and (3) were either on the DAP waiting list, actively participating in the DAP, or DAP discharges. 

The institution coefficients were used to calculate individual-level coefficients in the following

manner: the history of an individual’s institutional transfers was examined to flag those inmates

who spent time at a DAP site and who had enough time to participate in the DAP at such a site.

To be more specific, an individual’s stay at a DAP institution had to have been for at least 30 days

and had to have occurred at a time in his or her incarceration for which there was sufficient time

before release to volunteer and complete treatment.

The coefficient for each individual — p — was the weighted average of the probability-ofvolunteering coefficient for each stay that the inmate had at a DAP site where he or she could

have entered DAP treatment (i.e., meeting the specifications just mentioned). For example, the

coefficient for each institution at the particular time the individual was housed there was assigned

for each month, and then divided by the total number of months spent at any DAP site. 49

Summary

The original experimental research design for the TRIAD drug treatment evaluation project was

modified several times to accommodate the initial absence of waiting lists for admission to a DAP

and to accommodate changes in time-left-to-serve for the drug treatment population. The lack of

waiting lists during initial program implementation made the original plans for random assignment

infeasible. Therefore, we resorted to a research design that was quasi-experimental. In addition,

the admission of inmates to treatment who were not near release required the expansion of

treatment research sites from 8 to 20. This change was needed to obtain sufficiently large sample

sizes. 

Because drug treatment was not available at all sites from which we selected research subjects, we

were able to identify two types of comparison groups: one having had DAP treatment available —
68

the DAP comparisons — and the other not having this treatment available — the non-DAP

controls. As discussed in detail in Chapter 2 and in this chapter, these two different non-treated

subjects groups allowed us to address selection bias issues using two different analytic methods.
For the sake of simplicity, in this Chapter we use the term “comparison subject” to refer 50

to the DAP comparison subjects and the term “control subject” to refer to non-DAP control

subjects.

 Refusals for Intake1 were examined because this was the interview essential for the 51

analyses, as discussed in “Research Subjects” (see Chapter 4). Intake1 contained most of the

background information used in the analyses. In contrast, the Intake2 interview contained only

two variables— the DSM-III-R diagnoses of antisocial personality and depression — and was

administered after the research subject had agreed to the Intake1 interview.

69

CHAPTER 5: SUBJECT ATTRITION

Introduction

In this chapter, we examine the potential bias that could occur as a result of either an inmate’s

omission from the data collection process (those we “missed”) or an inmate’s refusal to

participate in the research (called “refusals”). Inmates were missed — i.e., omitted from the data

collection process — due to logistical issues related to institutional transfers and releases. 

By contrasting those inmates who did participate with those who did not (the missed and the

refusals), we hope to understand the nature of any bias that may result from non-participation.

This chapter examines three aspects of research participation that could result in a biased inmate

sample:

 Comparison/control subjects’ willingness to complete the History of Drug Use

Questionnaire.50

 Treatment and comparison/control subjects’ willingness to be research subjects.

 Treatment and comparison/control subjects’ willingness to complete an Intake151

interview.

We did not expect bias resulting from an inmate’s omission from the data collection process,

because we believed that any such omission resulted from project data collection logistics rather

than some systematic mechanism. Nonetheless, we felt that an understanding of this process, as

well as of the refusal process, would increase our knowledge about the evaluation process and

about impediments to implementing multi-site evaluations.

Research Variables

The contrasts to be examined in assessing bias resulting from subject attrition were quantified in a

series of dichotomous variables. Therefore, logistic regression procedures, traditionally used for

analyzing binary dependent variables, were used for these analyses (Menard, 1995). Prior to
 This is not to be confused with the DAP comparison/non-DAP control distinction used 52

to differentiate the non-treatment subjects into those who had treatment available and those who

did not. For comparison and control subject groupings in our analyses of outcomes, individuals

were classified according to whether or not they had ever been housed at a DAP site when a

treatment program was operational. In assessing subject attrition, we sought to understand the

effects of an inmate’s current institution (i.e., the institution where the inmate was housed when

approached for participation in the research project) upon his or her likelihood of participation in

the research. 

 Prior to 1994, the generation of the list of individuals selected for the History of Drug 53

Use questionnaire administration was not automated.

70

conducting logistic regression analyses, Chi-square tests were performed on a group of variables

(drawn from the automated SENTRY database) that may have influenced an inmate’s likelihood

of participating in the project. For example, did an inmate’s race, age, or ethnicity influence his or

her likelihood of participating in the research project? If any of these variables (described below)

were found to be significant in the Chi-square test (p <=.25), they were included in the logistic

regression equations. A coefficient in the logistic regression equation was considered to be

significant if the probability for that coefficient was less than or equal to .05. Results for the

regression are contained in Appendix A, and a codebook of the variables used in the analyses is

contained in Appendix B.

Comparison/Control Subjects — History of Drug Use (HDU) Administration

Inmates who had not participated in residential treatment while incarcerated and were between 8

and 11 months from release (in 1996, the criterion for inclusion was changed to inmates between

7 and 13 months from release) were identified as potential comparison or control subjects.

Comparison/control subjects were drawn from institutions at which treatment was available (DAP

sites) and from institutions that did not offer treatment (non-DAP sites). An inmate was identified

as a DAP subject in this analysis if he or she was housed at a DAP site at the time he or she

became a research subject. This analysis was conducted on only those persons identified as 52

potential comparison/control subjects from 1994 through 1996. In addition, for a short period of 53

time, the process of selecting comparison/control subjects involved a matching process (see “A

Chronological History of the TRIAD Subject Selection Process,” Chapter 4).

HDU questionnaires were given to potential comparison/control subjects only to determine their

eligibility for inclusion in the research effort. (Note that potential treatment subjects were

identified as such by virtue of their participation in the DAP and, therefore, did not complete the

HDU questionnaire.) By refusing to complete the HDU questionnaire, comparison/control

subjects essentially refused participation in the research project.
 Most of these individuals were of Hispanic origin and could not read or write English, 54

although some could speak English. 

71

A total of 4,121 male and 1,283 female inmates were identified as potential comparison/control

subjects to be screened using the HDU questionnaire. Of these inmates, 3,727 men and 1,113

women were approached to complete the HDU. The remainder either did not appear at their

appointments (n=90); were not in the institution (e.g., were on writ) (n=9); were in special

housing (n=15); were not fluent in English (n=438); or were not available (n=12) to complete 54

the survey.

HDU Refusals

When approached to complete an HDU, the inmate was notified that participation was voluntary

and confidential. Of the 3,727 men identified as potential research subjects, 926 (25 percent)

refused to complete an HDU. Significant differences (Chi-square) between inmates willing to take

the HDU and those refusing it were found for the following variables: institution, race, ethnicity,

institution security level, offense severity, prior commitments, and history of violence. These

variables, along with age, were included in the logistic regression models.

Because security level and institution were linear combinations of each other and could not be

entered simultaneously into a logistic regression, two models were run. The first model contained

the significant variables, with the exception of institution. The second model contained institution

variables, and the security-level variable was dropped.

The first model (security level) showed a better fit (Hosmer-Lemeshow Goodness of Fit = 7.65,

p=0.4687) when compared with the second model (institution) (Hosmer-Lemeshow Goodness of

Fit = 10.03, p=0.2632). Therefore, the results from the first model are reported below.

The logistic regression model (see Table A1) shows that six variables had statistically significant

effects on refusal rates for male inmates: age, ethnicity, institution security level, offense severity,

race, and history of violence. Inmates housed at low-security facilities were 20 percent less likely

to refuse an HDU than were inmates housed in minimum-security facilities. Those inmates who

had histories of serious violence were 35 percent more likely to refuse than were inmates who did

not have histories of violence. Additionally, persons whose offenses were moderate or great were

more likely to refuse than were those whose offenses were low/moderate (however, no effect was

seen for high-severity offenses). Race and ethnicity, too, played roles in the likelihood of refusing.

Black male inmates were 23 percent less likely to refuse than were white male inmates, and

Hispanic male inmates were 21 percent less likely to refuse than were non-Hispanics. As the age

of the inmate increased, so did the likelihood of refusing.

Of the 1,113 women approached to complete an HDU, 137 (12 percent) refused. Significant

differences (as measured by Chi-square) between women who agreed and women who refused to
72

complete the HDU were found for the following variables: institution, race, institution security

level, offense severity, and history of violence. These variables, as well as age, were included in

the logistic regression equation. 

As with the model for male HDU refusals, two logistic regression models were run. The first

model contained security level, with institution dropped. The second model dropped security level

and added institution.

The first model (security level) showed a better fit (Hosmer-Lemeshow Goodness of Fit = 4.96,

p=0.7615) than did the second model (Hosmer-Lemeshow Goodness of Fit = 10.91, p=0.2068).

Therefore, the results from the first model are reported below.

Four variables have statistically significant effects on HDU refusal rates among women: race, 

offense severity, history of violence, and age. Black female inmates were 64 percent more likely to

refuse an HDU than were white female inmates. Inmates with histories of minor violence —

compared to those with no such histories — and those who committed an offense of moderate or

high severity — compared with low/moderate offense severity — were almost twice as likely to

refuse to complete the questionnaire. No effect was found for offenses of great severity or for

histories of serious violence. The older the inmate, the more likely she was to refuse (see Table

A2).

Comparing the results between men and women, it is evident that although many of the same

variables were significant, the “direction” of the relationship was not always the same. Where

black men were less likely than white men to refuse completing the HDU survey, black women

were more likely than white women to refuse completing this survey. Furthermore, male inmates

with a history of serious violence were more likely to refuse, whereas female inmates with a

history of minor violence were more likely to refuse. 

Attrition of Identified Research Subjects 

Once the comparison subjects who had self-reported drug use histories and treatment subjects

who entered a DAP were identified for data collection, subject attrition resulted either from

subjects not being approached for data collection or from subjects refusing to participate. Table 1

summarizes this attrition process, and the process is examined in detail at the conclusion of this

chapter.

Once both treatment and comparison subjects had been identified, researchers visited their sites to

conduct surveys; however, not all subjects identified initially were able to participate in the

research effort. Between the time an individual was identified as a research subject and the time a

researcher was scheduled for the return trip to that institution to collect data, some research

subjects were no longer housed at the institution due to such events as transfers to other

institutions, absences due to writs, and releases to CCC’s or from BOP custody. Other inmates
 Detention in a special housing unit segregated from the general population occurs for 55

administrative reasons and as a sanction for disciplinary infractions.

73

had to be excluded from the research pool because they were under special housing restrictions;55

others were excluded because they had illnesses. A logistic regression was performed to analyze

possible differences between those persons who were not available for research participation (i.e.,

“missed”) and those who were.

Subjects Who Were Missed as Research Subjects

A total of 2,459 male inmates were identified as research subjects. Of those, 378 (15 percent)

were missed. The following variables had significant differences for those inmates who were

missed as research subjects compared with those subjects who were included in the subject pool

(Chi-square): status as comparison vs. treatment subject, being housed at a DAP vs. non-DAP

site, ethnicity, institution, and race.

These variables (excluding specific institution) along with age were included in the regression

analysis. Site was not included in the regression model because of zero cells. The proportion of

male inmates across the 34 sites who were missed ranges from 0 to 52 percent.

The logistic regression (see Table A3) showed significant effects for status as comparison vs.

treatment subject, and for DAP vs. non-DAP site. Male subjects identified at non-DAP sites were

nine percent less likely to be missed than were inmates housed at DAP sites. Comparison subjects

were 344 percent more likely to be missed than were treatment subjects. 

Of the 571 female inmates who were approached to participate in the research project, 22 percent

(n=127) were missed. Chi-square tests showed significant differences on the following variables

between those persons approached for research and those who were missed: status as comparison

vs. treatment subject, institution, institution security level, being housed at a DAP vs. non-DAP

site, offense severity, and prior commitments. These variables (along with age), excluding the

variables DAP vs. non-DAP site and institution, were included in the logistic regression equation.

The variable DAP vs. non-DAP site could not be included because no one at a non-DAP

institution was missed (23 percent [n=127] of the women at DAP sites were missed). Institution

could not be included in the regression because of zero cells. The proportion of female inmates

missed ranged from 0 to 31 percent, across seven sites.

Two variables were significant in the regression (see Table A4): status as comparison vs.

treatment subject, and security level. Comparison subjects were 214 percent more likely to be

missed for research than were treatment subjects, and inmates housed at low-security institutions

were 67 percent more likely to be missed than were those housed at minimum-security

institutions.
74

Comparison subjects were more likely to be missed, for both men and women. This can be

attributed primarily to the logistics of planning data collection trips. The first priority during data

collection trips was placed upon cohorts newly admitted to DAP’s and upon graduating cohorts.

DAP participant data collection required adherence to specified time frames for administering the

surveys and interviews at the beginning and end of treatment for the data serving as pre- and posttreatment measures. Identification of comparison subjects occurred close to those subjects’

release dates (approximately one year from release), and at the time of selection it was unknown

whether these individuals would receive CCC placements. Therefore, it was more likely that these

subjects would be missed; by the time a trip to that site occurred for DAP subject data collection,

some of the individuals selected as comparison subjects would likely already have been released to

CCC’s.

Research Refusals

Once inmates were identified as research subjects — either as DAP participants or as comparison

subjects through the HDU — and were available to participate, they were asked to complete two

interviews and various surveys. Inmates were reminded that participation in the research was

voluntary, and they signed informed consent forms.

Of the 2,081 male inmates who were approached to participate in the research, 223 (11 percent)

refused. A logistic regression was run to examine differences between those subjects who

participated and those who refused.

The following variables were significant by Chi-square and were included in the regression model:

status as comparison vs. treatment subject, being housed at a DAP vs. non-DAP site, ethnicity,

prior commitments, race, and history of violence. Institution also was significant, but was not

included because of too many zero cells. Refusals for research ranged from 0 to 44 percent across

the 34 sites.

The only significant variable in the regression equation (see Table A5) for men refusing research

was the variable denoting type of research subject. Comparison subjects were 160 percent more

likely to refuse to complete the research surveys as were treatment subjects.

A total of 444 female inmates were approached to participate in the research. Of those, only 27 (6

percent) refused to complete any of the research forms. Because the number of women who

refused was small, no regression equation was performed.

Intake1 Missing and Refusals

Inmates who agreed to participate in research were asked to complete two interviews. As

mentioned in the previous chapter, “Research Subjects” (see Chapter 4), only those individuals
 Please note that although some subjects initially had agreed to participate in the 56

evaluation project, some later refused participation in one or more survey or interview.

 The specific sites varied at different times of the project. 57

75

who had completed the Intake1 interview were included in the analyses. Therefore, analyses were

conducted to examine characteristics of those persons who missed the Intake1 interview and

those who refused to complete it.56

A segment of comparison inmates (75 men and 7 women) was interviewed at halfway houses

rather than at institutions. They have been added to the comparison sample for the Intake1

interview analyses. A halfway house category was added to the following variables: being housed

at a DAP vs. non-DAP site, institution, and security level.

Out of the 1,933 men to be interviewed, 149 (8 percent) were missed. Chi-square significance was

found for the following variables: being housed at a DAP vs. non-DAP site, status as a

comparison vs. treatment subject, institution, and security level. Institution could not be included

in the regression because of too many zero cells. The range of those who missed the Intake1

interview was 0 to 29 percent, across 35 sites. 

A regression equation was attempted with the remaining significant variables. However, due to

quasi-complete separation in the sample points and linear combinations of variables, a regression

equation could not be estimated. Because no halfway house subjects were missed, deleting them

from the regression equation allowed for an analysis of persons missed for the remaining sites. 

Significant values were found for the DAP vs. non-DAP site and comparison vs. treatment subject

variables. These variables, along with age, were included in the regression equation. Age was not

related significantly to the likelihood of being missed. Subjects at non-DAP sites were 84 percent

less likely to be missed than were subjects at DAP sites. Additionally, comparison subjects were

50 percent less likely to be missed than were treatment subjects (see Table A6). 

We can conjecture that the lower rate of missed Intake1 interviews among comparison subjects

— in particular those from non-DAP sites — can be attributed to the logistical procedures

involved in data collection. During the in-prison data collection phase of the evaluation project,

research staff were located at as many as six different DAP research sites. At these sites, the 57

logistics of data collection did not require all data to be collected during a single week. However,

at many DAP sites and all non-DAP sites, data collection required a special trip by a field

researcher. Therefore, comparison subject data, most notably at non-DAP sites, tended to be

collected during a week-long data collection trip to the site. This resulted in a decreased

likelihood of missing the Intake1 interview in the case that the individual had been transferred or

released. 
 We are collecting arrest outcome information for subjects who refused to be interviewed 58

and for subjects who were “missed” due to administrative reasons. Future analyses will examine

whether the arrest rate of these individuals differ from those included in our analyses, controlling

for the background characteristics available from automated data files. 

76

Seven percent (n=29) of 424 female inmates were missed when the Intake1 interviews were being

administered. Due to the small number missed, regression equations were not performed.

Refusal Rates for Intake1 Interview

Refusal rates for the Intake1 interview were low for both men (2 percent, n=40) and women (less

than 1 percent, n=3), so regression equations were not performed.

Summary of Results for Subject Attrition

It is apparent from Table 1 that the subject attrition problem was most pronounced at the point

when the pool of potential comparison subjects was being identified. Not only was the refusal rate

highest at this point, but this was the only point at which characteristics of individuals were

predictive of refusal. Among men, there were significant effects for race, ethnicity, offense

severity, age, and history of violence. Among women, there were significant effects for the same

variables except ethnicity, although the “direction” of the relationship was not always the same. It

must be noted that all the factors found to be predictive of HDU refusal rates were used as

control variables in the analyses of results. 

At all other times during the process of data collection, it is clear that subject attrition was

attributable solely to administrative causes. For example, the greater rate of missing data

collection for comparison subjects was due to the fact that it was logistically more difficult to

approach all subjects identified because there was a much shorter time frame within which to

coordinate data collection trips for comparison subjects.58
 Separate multivariate outcome analyses for men and women were precluded due to 59

small sample sizes for women. However, as we mention in Chapter 8, we plan to conduct separate

analyses for all outcomes when data become available for the entire subject sample. 

77

CHAPTER 6: DESCRIPTION OF SAMPLE

This chapter profiles the research subject sample, the various DAP treatment subject groups, the

DAP comparison group, and the non-DAP control group, for whom — at the very least — the

Intake1 interview containing extensive background information was completed. Taken together,

these subjects were included in the analysis of the various outcome measures, and we provide a

profile of the subject sample using the variables included in the outcome analyses. This description

provides a simple understanding of how the in-prison and post-release services received differ

among the various subject groups, and it serves as a basic description of both a male and female

incarcerated population with a history of drug use. We provide separate tables of descriptive

statistics for men and women because, as mentioned in Chapter 7, men and women were in

separate treatment programs and, when possible, we analyzed men and women separately.59

DAP Treatment Groups

This section briefly describes the four types of residential Drug Abuse Treatment Program (DAP)

participants as they were categorized in the analyses. The four groups include: (1) inmates in

residential drug treatment who completed that treatment (DAP-complete subjects), (2) inmates

who dropped out (DAP-dropout subjects), (3) inmates discharged for disciplinary reasons (DAPdischarge subjects), and (4) inmates who, for a variety of other reasons, did not complete the

program (DAP-incomplete subjects). This “incomplete” category, in general, comprises inmates

who were transferred to another institution or to a Community Corrections Center (CCC) before

they could complete the full 9 or 12 months of treatment, those who had their sentences shortened

toward the end of their incarceration and were released from BOP custody before they were able

to complete the treatment program, and those who spent an extended amount of time on writ or

medical furlough and thus were unable to complete treatment before release.

Of the 1,524 male subjects in this analysis, 719 (47 percent) entered residential treatment. The

other 53 percent were comparison subjects who never entered residential treatment. Of the 719

who entered treatment, 73 percent completed the treatment program, 5 percent voluntarily

dropped out of the program, 8 percent were removed for disciplinary reasons, and 14 percent

constituted the “incomplete” subject category.

Of the 342 female subjects in this analysis, 180 (53 percent) entered residential treatment. The

other 47 percent were comparison subjects who never entered residential treatment. Of the 180

who entered treatment, 54 percent completed the treatment program, 9 percent voluntarily

dropped out of the program, 13 percent were removed for disciplinary reasons, and 24 percent

fell into the incomplete category. The fact that there is a lower percentage of treatment
We note that the information on women, in this and subsequent tables, must be 60

interpreted with caution due to small sizes for several of the subject groups.

78

“completers” for women than for men may be related to policy differences between treatment

sites and differential enforcement of program rules.

Sample Demographics

This section describes the 1,524 male inmates and 342 female subjects who were interviewed as

part of the drug treatment evaluation project and were included in the analysis. The samples were

divided into those inmates who received treatment in the DAP and those who did not receive

treatment while incarcerated (i.e., comparison subjects). Further divisions were made among both

the treatment and comparison groups. The comparison sample was divided into those inmates

who were ever housed at a site that offered treatment and who were there long enough to

participate in the program (DAP comparison subjects) and those who did not have the

opportunity to participate in the program (non-DAP control subjects).The treatment group was

divided into the four categories mentioned in the preceding section: those who completed the

treatment program (DAP-complete subjects), those who dropped out (DAP-dropout subjects),

those who were discharged for disciplinary reasons (DAP-discharge subjects), and those who did

not finish the program for other reasons (DAP-incomplete subjects).

Race/Ethnicity

The male sample’s racial composition was 59 percent white, 38 percent black, and 3 percent other

races (see Table 2). Blacks composed more of the non-DAP control group (46 percent) than they

did the other groups. There was less difference in the racial makeup among the female subject

groups: 52 percent white and 47 percent black (see Table 3). There were only five female inmates

of other races in the sample. Treatment dropouts — DAP-dropout subjects — had the lowest 60

number of whites (38 percent) and DAP comparisons had the highest (56 percent).

The majority of subjects in both the male and female samples were United States citizens — 94

percent and 93 percent, respectively. Nine percent of the men and eight percent of the women

were of Hispanic origin.

Age 

Tables 2 and 3 show the ages for inmates at the time of their release from BOP custody. Among

women, more than half of the sample were 34 years old or younger at the time of their release.

Men, on the other hand, tended to be older, with 57 percent being at least 35 years old at the time

of their release. Differences within sample categories also can be seen. For example, among both

the male and female treatment subjects, those who were discharged for disciplinary reasons

(DAP-discharges) or had withdrawn (DAP-incomplete subjects) tended to be younger than were

the subjects in the remaining categories.
79

Education

Sixty-nine percent of the male sample and 78 percent of the female sample had completed at most

12 years of education, or had achieved a General Education Degree (GED). The remainder of the

sample (31 percent of the men and 22 percent of the women) reported completing more than 12

years of education, with the years of education for these subjects ranging from 13 to 20 years

(data were missing for 10 men and 5 women). Female subjects in the non-DAP control group

were much more likely to have reported having completed more than 12 years of education (41

percent) than were those in the other sample groups (see Tables 2 and 3). 

Frequency of Drug Use During Heaviest Use Period

Tables 4 and 5 present information on pre-incarceration self-report drug use patterns for the

1,524 male and 342 female research subjects. Subjects reported the frequency of drug use during

their heaviest use period prior to the incarceration during which they were interviewed. The

following “screening” question was asked for each of a number of drugs:

Have you ever taken one of these drugs more than five times in your life (to feel

good, to get high, for other mental effects, or longer than was prescribed)?

When inmates responded “yes” to this screening question, they were then asked a series of other

questions regarding drug use.

Think about the period of time when you were using each drug the most

(heaviest). At that time, about how often did you use each drug? Would you say

that it was...

 daily or almost every day.

 3 or 4 days a week.

 1 or 2 days a week.

 1 to 3 days a month.

 less than one day a month.

When inmates responded “no” to the screening question for a particular drug, no further questions

were asked about that drug.

In order to make the tables more concise, response categories were condensed into “Not used,”

“Used less than daily,” and “Used daily.” The category “Not used” is presented for individuals

who were not asked this frequency question because they had used the drug five times or fewer in

their lives or had never used the drug. The “Missing” values are the sums of those subjects for
 The imputation procedure is discussed in Chapter 7. 61

 Examples of opiates other than heroin include opium, morphine, demerol, dilaudid, 62

talwin, percodan, codeine, and non-prescribed methadone.

 A description of the logistic regression procedure used to impute these values is 63

contained in Chapter 4 under “Psychological/Attitudinal Measures.”

80

whom we have no data for that particular drug. For analysis purposes, all values of the response

set were used in the models, and missing data were imputed. 61

Tables 4 and 5 indicate that marijuana was the drug used most frequently by research subjects.

Among men, 18 percent reported that they did not use marijuana (i.e., used it five times or fewer,

or never, during their lives), 25 percent reported use that was less than daily, and 57 percent

reported daily use during their heaviest use periods. Among women, 27 percent reported that they

did not use marijuana, 26 percent reported less than daily use, and 46 percent reported daily use.

Cocaine (excluding crack) was the next most frequently used drug, with 36 percent of men

reporting that they did not use cocaine, 31 percent reporting less than daily use, and 33 percent

reporting daily use. Among women, 34 percent reported no use, 24 percent reported less than

daily use, and 42 percent reported daily use during their heaviest use periods.

The drug with the lowest use level was opiates, excluding heroin. Among men, 81 percent 62

reported no use, 11 percent reported less than daily use, and 8 percent reported daily use. Among

women, 82 percent reported no use, 8 percent reported less than daily use, and 10 percent

reported that they used opiates daily during their heaviest use period.

Table 4 indicates that 77 percent of male subjects reported that they did not use crack cocaine, 9

percent used it less frequently than daily, and 14 percent used it daily at some point. Among

women, 61 percent reported no use of crack cocaine, 10 percent reported less than daily use, and

29 percent reported daily use. These seemingly low percentages may be partly due to the fact that

crack cocaine made its way into the drug market in the 1980's, and thus some of our subjects

were already incarcerated when this drug came into wider use. 

Drug and Alcohol Dependency

Table 6 depicts the means and standard deviations for imputed values of drug and alcohol

dependency for male inmates. Table 7 depicts the same for female subjects. For these variables, 63

we also depict the odds ratios. The odds ratios are computed by raising the natural log base e to

the log odds values represented in the two tables. For men, the odds of being drug dependent

were highest for DAP-discharges. The odds were 8.47, compared to an average odds ratio of

3.69. Also among the men, those least likely to be drug dependent were inmates who dropped out

of treatment (odds ratio of 0.98). This pattern was similar for the women, who were much more
The percentage of individuals receiving outpatient services may be under-reported due to 64

inconsistencies in recording this information on SENTRY.

81

likely to be drug dependent if they were DAP-discharge subjects (odds ratio of 20.04) but least

likely to be drug dependent if they dropped out of the treatment program (odds ratio of 2.71). In

addition, women were most likely to be drug dependent if they completed the drug program (odds

ratio of 19.76).

Compared to that of drug dependence, there was less variability among the group means for

alcohol dependence. Among the men, non-DAP subjects were most likely to be alcohol

dependent, with an odds ratio of 1.5. Among the women, DAP-discharges were the most likely to

be alcohol dependent. Their odds ratio was 3.41, compared to an average odds ratio of 0.56.

The most consistent pattern emerging from Tables 6 and 7 is that both men and women were

most likely to be drug dependent if they were DAP-discharges. Women discharged for disciplinary

reasons were more likely to be alcohol dependent, but this relationship is not demonstrated for the

men.

In-Prison Outpatient Treatment and Self-Help Group Participation

In the analysis, several variables were used to represent subjects’ participation in outpatient drug

and alcohol treatment or self-help groups while in prison. The focus of our evaluation is on the

effect of residential 9- and 12-month drug treatment programs. However, some inmates

participated in “outpatient,” or non-residential, drug treatment consisting of several hours of

group sessions per week. Also available to most inmates were self-help groups similar to

Alcoholics, Cocaine, and Narcotics Anonymous, often led by community members or other

inmates. Inmates participating in outpatient or self-help groups may or may not have also

participated in residential drug treatment. All drug treatment participation was voluntary.

Tables 8 and 9 present the percentages of both male and female subjects, respectively, who

received in-prison, outpatient drug and alcohol treatment and the percentage of subjects who

participated in the in-prison self-help programs. 64

For men, the “DAP completion” group had the greatest proportion of subjects involved in

outpatient treatment (10 percent). The same group had the highest percent of participation in selfhelp programs (6 percent). Women in the non-DAP control subject category had the highest

participation rate in outpatient programs (14 percent), while the DAP comparison subjects had the

highest participation rate in self-help programs (9 percent). 
82

Alcohol and Drug Treatment History

Tables 10 and 11 present self-report data on the percent of male and female subjects who had

previously received treatment for drug or alcohol use. Subjects were asked, “Excluding now, did

you ever get any treatment that was primarily for the use of or addiction to drugs/alcohol?” Selfhelp groups such as Alcoholics Anonymous and Narcotics Anonymous were not considered to be

treatment.

The tables show that men and women were equally likely to have received drug treatment, at

proportions of 31 percent and 35 percent, respectively. Men and women also were equally likely

to have received alcohol treatment — at 5 percent and 4 percent, respectively. Women in the nonDAP control and treatment dropout (DAP-dropout) groups were less likely than were other

women to have received previous drug treatment. None of the treatment subjects who did not

complete a DAP reported having past alcohol treatment. Other than these differences, subjects in

the various treatment groups and DAP comparison and non-DAP control subject groups were

equally likely to have received previous drug and alcohol treatment.

Criminal/Incarceration History

Tables 12 and 13 present four variables detailing subjects’ criminal and incarceration histories:

whether they had any prior commitments, how recently they had committed an act of violence,

their lengths of incarceration, and whether they were in the criminal justice system (e.g., on

probation or on parole) when arrested. Only the last variable — referring to their being in criminal

justice system when arrested — is based on self-report data; the other three variables listed in

Tables 12 and 13 come from official records.

A subject is considered to have had a prior commitment if he or she previously had been

sentenced to confinement in any type of criminal justice facility for any period of time. Men were

much more likely (at a proportion of 71 percent) than were women (45 percent) to have had prior

commitments. The percent of subjects who had prior commitments varied across treatment and

DAP comparison/non-DAP control groups. Among men, DAP-discharges were the most likely to

have had prior commitments (at 80 percent), and those who were unable to complete the DAP

were least likely to have had prior commitments (at 65 percent). Among women, DAP

comparisons were the least likely to have had prior commitments (at 37 percent), which is below

the average for women (45 percent). Women who were unable to complete DAP or were

discharged for disciplinary reasons from a DAP were more likely than were other women to have

had prior commitments (58 percent and 62 percent, respectively).

The “recency of violence” variable denoted whether the subject had a history of committing

violent acts and, if so, whether the most recent violent act had been committed less than or more

than 5 years before the subject’s initial designation to a BOP institution for the current offense.

This measure included both minor and serious acts of violence, but excluded violence associated

with the current offense.
83

More men had histories of violence than did women. Eighty-six percent of the women had no

history of violence, compared with 54 percent of the men. Violence among men was more often

committed 5 or more years prior to the current incarceration (at 32 percent), as opposed to such

an act being committed more recently (14 percent). Acts of violence committed by women were

equally likely to have been committed more than 5 years before the current incarceration as they

were to have been committed less than 5 years before the current incarceration (7 percent for

each category).

Men who dropped out or were discharged from DAP for disciplinary reasons were more likely to

have had histories of violence than were men in the other DAP treatment groups. Male subjects

who did not complete a DAP were the least likely to have had histories of violence. Women in the

DAP treatment groups were slightly more likely than were those in either the DAP comparison

and non-DAP control groups to have had no history of violence, with the exception of women

who received disciplinary discharges from a DAP. 

Tables 12 and 13 also show the length of subjects’ incarceration for the current Federal offense.

This variable was computed by “subtracting” the date of admission to a Federal prison from the

date of release from Federal custody. Any time subjects spent in custody prior to sentencing was

excluded.

Most subjects spent between 1 and 5 years in custody for their current offenses. Women spent

less time in custody than did men; 34 percent of women and 19 percent of men spent less than one

year in custody. Length of incarceration varied across the subject groupings, and this pattern of

variation was the same for men and women. Both male and female non-DAP control and DAP

incomplete subjects spent less time in custody than was the overall average by sex, while DAP

graduates spent more time in custody than was the average. Only 9 percent of DAP graduates

spent less than one year in custody.

The last variable displayed in Tables 12 and 13 reveals whether the subject said he or she was in

the criminal justice system when arrested for the current offense. Inmates were asked, “At the

time of your arrest, what was your legal status?” Subjects were considered to be in the criminal

justice system at the time of their arrests if they were on any type of judicially imposed

confinement or supervision for a previous offense. Escapees and absconders were counted as

being in the criminal justice system.

The percentage of men in the criminal justice system at the time of their arrests was similar to the

percentage of women, at 42 percent and 37 percent, respectively. Among men, the likelihood of

having been in the system when arrested was greatest for DAP-discharges (at 53 percent) and

non-DAP controls (at 48 percent). Men who completed or dropped out of a DAP were least

likely to have been in the system when arrested (at 36 percent each). Among women, non-DAP

controls and those failing to complete a DAP were most likely to have been in the system when

arrested (at 52 percent and 45 percent, respectively). Female DAP comparisons were the least

likely to have been in the system when arrested (at 29 percent).
84

Past Mental Health Treatment/Psychiatric Diagnoses

Tables 14 and 15 contain information relating to the mental health of male and female subjects.

Two sets of variables are displayed in these tables: psychiatric diagnoses and histories of previous

mental health treatment. 

With respect to the psychiatric diagnoses of antisocial personality and depression, men and

women had similar percentages of subjects with no such diagnosis (at 54 percent and 49 percent,

respectively). However, of those subjects with psychiatric diagnoses, men were more often

diagnosed as having antisocial personality (37 percent of the men and 30 percent of the women),

whereas women were more often diagnosed with depression (34 percent of the women and 15

percent of the men). Among both men and women, the DAP-discharges were the most commonly

diagnosed with antisocial personality (at 45 percent and 47 percent for men and women,

respectively) and were the least likely of any group to have no psychiatric diagnosis (40 percent

and 24 percent, respectively). Women who were disciplinarily discharged were the most likely to

be diagnosed with both antisocial personality and depression (29 percent). Overall, a greater

percentage of women (13 percent) were diagnosed with both antisocial personality and depression

than were men (7 percent).

Subjects were asked, “Did you ever get treatment for your emotions, nerves, or your mental

health...?” Any mental health treatment or counseling received prior to the current incarceration

was included. 

Men were less likely than were women to report that they had received treatment for their

emotions, nerves, or mental health (at 18 percent vs. 40 percent). Male DAP treatment subjects,

as well as DAP comparison and non-DAP control subjects, were equally likely to have received

mental health treatment. Among women, DAP graduates were the least likely group to have

received mental health counseling in the past (at 31 percent), while those failing to complete a

DAP were the most likely to have received such services (at 53 percent). Excluding DAP

graduates, the other female subject groups were equally likely as each other to have received

mental health treatment.

Employment

Tables 16 and 17 present self-report data on male and female subject employment histories.

Subjects were asked, “Did you ever support yourself mainly from illegal activity for at least one

year?” Men and women were equally likely to have supported themselves mainly with illegal

activity, at proportions of 39 and 41 percent, respectively. Male and female subjects who were

unable to complete the DAP were among the least likely to have supported themselves illegally (at

24 percent and 31 percent, respectively). In addition, women who graduated from the DAP and

men who were discharged from the DAP for disciplinary reasons were also among the least likely

to have supported themselves illegally (at 26 percent and 27 percent, respectively).
85

Tables 16 and 17 also present data on subjects’ employment status in the month prior to their

current incarcerations. Subjects were asked a series of questions designed to ascertain how they

were supporting themselves during this month. Subjects who had worked at all during the month

were coded as “full- or part-time.” Subjects who were unemployed for legitimate reasons such as

retirement or disability were coded as “not in labor force.” Homemakers also were coded as “not

in labor force.” Subjects who were unemployed and seeking work during this month were coded

as “looking for work.” The remaining subjects were given a code denoting “miscellaneous” for

their employment statuses. This last category included subjects who were unemployed because

their income was derived from illegal activities, subjects who had never worked, and subjects who

were unemployed for other reasons.

Male subjects were more likely than were female subjects to have been employed during the

month before their incarcerations, at proportions of 55 percent and 39 percent, respectively.

Among men, those discharged from the DAP for disciplinary reasons were least likely to have

been employed (at 46 percent) and those who were unable to complete the DAP were most likely

to have been employed (at 67 percent). The percent of men who were not in the labor force was

similar for treatment and DAP comparison/non-DAP control subjects (at about 5 percent).

Likewise, the percent of male subjects who were actively looking for work in the month before

their incarceration was similar across treatment and DAP comparison/non-DAP control categories

(at about 9 percent).

Among women, non-DAP controls were most likely to have been employed (at 49 percent) and

those unable to complete the DAP were least likely to have been employed (at 31 percent). The

percent of women who were not in the labor force was approximately equal between the DAP

treatment and the DAP comparison and non-DAP control groups (at roughly 7 percent), as was

the percent of all women who were looking for work (at about 10 percent).

Motivation for Change

Tables 18 and 19 present information on the distribution of the research subject groups among the

six “motivation for change” clusters. The greatest percentage of male subjects, 28 percent, were

categorized into the contemplation cluster, representing individuals who recognized that they had

a problem and were considering changing but had not yet actively started to do so. The least

populated category for men was the “uninvolved” category — that is, those who did not endorse

any of the four change assessment scales. The “reluctant,” “precontemplation,” and uninvolved

clusters — representing those least motivated for change — were mostly populated from the DAP

comparison and non-DAP control groups. With the exception of the DAP disciplinary discharge

group, the DAP treatment groups had higher percentages of subjects populating the “preparation”

cluster (at approximately 25 percent) than did the DAP comparison and non-DAP control groups

(at 14 percent and 16 percent, respectively).

Table 19 shows that, among women, the greatest percentage of the subjects fell within the action

cluster (31 percent). Similar to men, the least populated cluster was the uninvolved cluster. Unlike
86

the men, women non-DAP control subjects were more likely to fall in the preparation cluster than

were treatment subjects, at 32 percent vs. 10 to 24 percent. 

Other Variables

A variable, COHTIME, was constructed to measure the amount of time (in months) that had

elapsed between when a program started and when an inmate participated in the program. This

variable was intended to be a proxy for the maturity of a program at the time an inmate

participated in that program. 

For men, the average time elapsed for treatment participants was 23.5 months. Among men who

completed treatment, the mean value of COHTIME was 22.9 months. For men who withdrew

from treatment, the mean was 22.8 months. The average for disciplinary terminations among men

was 24.6 months, and the average time elapsed for men who did not complete treatment was 26.1

months.

For women, the average for COHTIME was 24.9 months. Among women completing treatment,

the average time elapsed was 21.7 months. For women who withdrew from DAP, the average

was 27.4 months. For disciplinary terminations, the average was 36.2 months, and the average

time elapsed for women of the DAP-incomplete subject type was 25.1 months. 

Another variable was computed to measured the number of months between discharge or termination from a DAP and release from custody (TIMETORL). The idea behind measuring this time

span was to capture whether inmates would forget what they had learned in treatment or whether

they would lose some of their motivation to stay drug-free with increases in the time between

program completion and release from custody. 

For men, the overall average for everyone who entered treatment was 9.8 months between

treatment discharge (be it completion or termination) and release. This variable was skewed to the

right, as one might expect, with the longest period between completion of treatment and release

from custody being 38.5 months. Among male treatment completers, the average was 10.4

months. For men who withdrew, the average was 13.4 months. For men who were terminated for

disciplinary reasons, the average was 11.1 months. The average for male DAP-incomplete

subjects was 5 months.

For women, the average for TIMETORL was 8.2 months. Their distribution was also rightskewed. For women who completed treatment, the average time between completion and release

was 9.2 months. For withdrawals, the average time was 9.9 months. For women who were

disciplinary terminations, the average time was 11.0 months. Finally, for female DAP-incomplete

subjects, the average TIMETORL was 3.9 months. 

The shortest average periods between program termination and release were found among the

male and female DAP-incomplete subjects. This probably is due to the definition of DAP-
 A description of CCC placements and transitional services is found in Chapter 3. 65

 This represents the sum of the first four reasons listed as reasons for failing CCC 66

placement.

87

incomplete subjects: subjects who could not complete their programs because they were released

to a halfway house or from BOP custody, or they had to be transferred to another facility sooner

than expected.

CCC Placements

This section presents information regarding subjects who received Community Corrections Center

(CCC) placements and who received transitional services during these placements. Data 65

generally were obtained through SENTRY, but other elements were obtained from a questionnaire completed by CCC staff upon each subject’s release from the facility.

The CCC questionnaire posed questions regarding the subject’s CCC completion status, employment during placement, participation in any educational programs, participation in transitional

services or other drug treatment, the number of urinalysis tests administered, the number of

positive urinalysis tests, and — for positive tests — the dates of the tests and the types of drugs

for which the tests were positive.

Tables 20 and 21 demonstrate that of the 1,866 subjects in this analysis (1,524 men and 342

women), 68 percent of the men and 67 percent of the women received CCC placements. The

breakdown by subject type shows that, for both men and women, those who ended their DAP

participation because they dropped out of the program (DAP-dropout subjects) or received a

disciplinary discharge (DAP-discharge subjects) were less likely to have received a CCC

placement. Twenty-three percent of the men and 15 percent of the women failed to complete their

CCC placements successfully. For the men, DAP-discharges showed the greatest likelihood of

failure, while for the women, those whose treatment was incomplete were most likely to fail at the

CCC. The most common reason for failing a CCC placement for both men and women was drug

or alcohol use or possession (71 percent of the men who failed did so for this reason, as did 68

percent of the women).66

It should be noted that in Tables 20 and 21, one reason for CCC failure is listed as “Accountability,” and subjects who failed for this reason usually were cited as having unexcused absences from

the CCC. Another category, “Violation of CCC rules,” comprises subjects who were cited for

committing any of a variety of transgressions. These transgressions include gambling, acting

disruptively, refusing an order, failing to find a job, engaging in sexual acts, being in an unauthorized area, driving without permission, and leaving work without authorization. Also, in Tables 20

and 21, the category, “Other” encompasses the following reasons for failure: possession of a

weapon in the treatment program, commission of new criminal activity or arrest, violation of the

transitional services program rules, and commission of one or more other objectionable acts.
88

Table 22 shows that of the men who received CCC placements, 61 percent participated in transitional services. As expected, subjects who completed the DAP and those who terminated the

program as “incomplete” (usually meaning they were transferred or released before they had time

to finish the program) were more likely to receive transitional services. Sixty-six percent of the

men who participated in transitional services successfully completed the program. DAPdisciplinary discharges showed the highest percentage of members having failed transitional

services (at 57 percent). The reasons for failing transitional services were very similar to the

reasons for failing CCC placements (see discussion above). The reason for this similarity is that,

according to the rules of the transitional services program, a failure of transitional services results

in a failure of the CCC placement. Similarly, whenever one failed the CCC placement, one was

removed from transitional services.

Table 23 shows that of the women who received CCC placements, 60 percent participated in

transitional services. Again, those of the “DAP complete” and “DAP incomplete” groups showed

the highest percentages of having received transitional services. Seventy-two percent of the

female subjects who received transitional services successfully completed the transitional services

program. As with the men, the reasons for failing transitional services closely mirrored the reasons

for failing CCC placements.

Note that in Tables 22 and 23, which deal with transitional services release status, there is a

category called “Admin/Neutral.” This code was used somewhat differently from region to region.

Sometimes it was used simply to reflect that subjects were continuing transitional services upon

release to supervision. Other cases reflect that BOP custody ended before sufficient services were

rendered to consider treatment as “completed.” In general, this category seems to have been used

when a subject was participating in, but did not complete, transitional services treatment due to

some circumstance beyond his or her control.

Tables 24 and 25 demonstrate that most of the subjects in a CCC (95 percent of men and 93

percent of women) were tested for drug or alcohol use during their CCC placements. Sixteen

percent of the men who were tested and nine percent of the women who were tested had a

positive test during their stays at CCC’s. The tables also show the drugs for which subjects tested

positive.

Information regarding the subjects’ placements into home confinement was collected from

SENTRY. Thirty-five percent of the men and 50 percent of the women received periods of home

confinement. The average length of time in home confinement for both men and women was 8

weeks.

Post-Release Behavior

Several items included in the analysis pertain to subject behavior after release from the

incarceration during which they were identified as research subjects. This section presents

descriptive statistics on the following post-release variables: supervision, new offenses and
89

revocations, violations of conditions of supervision, CCC placements while under supervision,

drug and alcohol treatment, self-help group participation, employment status, cohabitation status,

and drug use while under supervision.

Most of the data for this section were gathered from interviews with U.S. Probation officers for

those subjects who were released to supervision (85 percent of the male subjects and 82 percent

of the female subjects — see Tables 26 and 27). For subjects who were not released to

supervision, new offense data were gathered from the National Crime Information Center (NCIC)

database.

Post-Release Offenses

When both supervised and unsupervised subjects are viewed together, we see that 14 percent of

the men were arrested for a new offense in the first 6 months after release from Federal prison

(see Table 26). The subject types showing the highest percentages of new offenses were DAPdropouts (at 19 percent) and DAP-discharges (at 18 percent). The offenses for which the male

subjects were arrested were categorized into drug-related, violent, robbery, property, forgery and

fraud, traffic, and other. “Other” offenses included arson, kidnaping, property damage, family

offenses, gambling offenses, liquor offenses, trespassing, obstruction of police, public peace

offenses, weapon offenses, and extortion. Of the men who were arrested, more were arrested for

“other” offenses (at 21 percent) than for any other category of offense, followed by property

offenses (at 18 percent) and drug-related and traffic offenses (at 17 percent each).

Among supervised and unsupervised women, 6 percent were arrested for a new offense in the first

6 months after release (see Table 27). Of the women who were arrested, more were arrested for

property offenses (29 percent) than for any other offense category. There were insufficient

numbers of women with new offenses to discuss meaningfully the differences among those

offenses across subject types.

Post-release arrests were examined for subjects released to supervision. Arrests for these

individuals were categorized into arrests for a new offense and arrests for revocation (i.e., for

violations of conditions of supervision). Eighty-five percent of male subjects were released to

supervision, and, of those, 14 percent were arrested for a new offense during the first 6 months of

their releases, 7 percent were revoked from supervision, and 78 percent had neither a new arrest

nor a revocation (see Table 28). Men who completed the treatment program showed the highest

likelihood of having no new offense or revocation. The breakdown of offense categories for those

supervised male subjects who had either a new offense or a revocation showed that the most

commonly committed offenses were revocations (35 percent), “other” offenses (16 percent), and

traffic offenses (13 percent).

Eighty-two percent of female subjects were released to supervision. Of those, 6 percent were

arrested for a new offense, 5 percent had their supervision revoked, and 89 percent had neither an

arrest for a new offense nor a revocation (see Table 29). Of those who had any arrest or

revocation, more committed offenses for which they were revoked (47 percent) than committed a
90

new offense. There were insufficient numbers of supervised women with new offenses to discuss

meaningfully the differences among subject types in this regard.

Post-Release Drug Use

Tables 30 and 31 present information on urinalysis (UA) testing and drug use for subjects on

post-release supervision. The tables show the monthly rate of urinalysis testing, the percent of

subjects who used drugs, the breakdown of how such use was detected, and the drugs for which

urinalysis tests were positive. The data for these tables come from information supplied by

Probation officers. Additionally, Tables 30 and 31 show that the mean monthly number of drug

tests was between two and three across all subject types and across gender.

The “Drug Use” sections of the tables derive their percentages from the number of subjects who

had a positive urinalysis, refused a urinalysis test or missed such a test, admitted using either illicit

drugs or alcohol to their Probation officers, or had a positive alcohol breathalyser reading. The

male and female subjects differed quite a bit in their overall likelihood of using drugs while on

supervision, but for both sexes the most common method of detection was the urinalysis test. The

drug detected most often for both men and women was cocaine.

As seen in Table 30, 31 percent of the men used drugs while under supervision, and the male

DAP-discharges were most likely to have used drugs (at 36 percent). The percent of male DAPdropouts who used drugs was lower than that of the other subject types, and by a fairly wide

margin (16 percent for the DAP-dropouts, as opposed to the next-lowest group — DAPcompleters — whose proportion of drug users was 28 percent).

Table 31 shows that 20 percent of the women used drugs while on supervision. Those of the

DAP-incomplete subject type were the most likely to have used drugs (at 23 percent), while

DAP-dropouts were least likely to have done so (at 17 percent).

For the men, cocaine and marijuana were the drugs most commonly detected by urinalysis — at

48 and 24 percent respectively — with opiates ranking third (at 10 percent). For the women, the

drugs most commonly detected by urinalysis were, as was the case for men, cocaine, marijuana,

and opiates — in that order. Sixty-nine percent of the tests were positive for cocaine. Barbiturates

and benzodiazepine were the only other drugs for which women had a positive urinalysis result (3

percent each).

Violations of Supervision and Post-Release CCC Placements

Tables 32 and 33 show the percentages of supervised subjects who committed violations of

supervision — excluding drug use violations — as well as the percentages of supervised subjects

who received CCC placements during their supervision periods. (Note that these CCC placements

are not to be confused with BOP CCC placements that occur before the inmate is released from

BOP custody.)
91

In Tables 32 and 33, violations of supervision include failure to report to a Probation officer,

failure to report for treatment services, association with anyone involved in criminal activity,

abscondence, failure to turn in or falsification of monthly reports, failure to inform Probation

officer of police contact or arrest, and involvement in new criminal activity. The male subjects

who dropped out of treatment were the least likely to have violated a condition of supervision (at

10 percent), and those who did not complete treatment the most likely (at 22 percent). The nonDAP control subjects were those most likely among female subjects to have committed a violation

of supervision (at 15 percent).

Supervised subjects were placed in CCC’s — by Probation officers or the court — for three

reasons: to undergo more intensive supervision, to spend time there as a consequence of drug use

violations, or to serve the placement as an alternative to a new prison term. Looking at the postrelease CCC information in Table 32, it is clear that the highest percentage of the men to have

received post-release CCC placements (at 7 percent) were the DAP-discharges. There are

insufficient numbers of women with post-release CCC placements to discuss meaningfully the

differences among subject types.

Drug and Alcohol Treatment and Self-Help Participation

Tables 34 and 35 present information on supervised subjects’ participation in post-release drug

and alcohol treatment and participation in self-help groups. Because this information was drawn

from interviews with U.S. Probation officers, data are available for the 85 percent of male subjects

and the 82 percent of female subjects who were under supervision at the time of their release from

BOP custody. 

A distinction is drawn between contract and non-contract services. The basic distinction between

these services is that contract services are provided by an agency that has a contract with the

judicial district, whereas non-contract services are provided either by an agency without such a

contract or by the Probation officer. In addition, some contract services may be mandated as a

condition of post-release supervision by the sentencing court and some are funded through

Federal judicial districts. Non-contract services also may be mandated and funded through the

Federal judicial districts; however, non-contract services can be voluntary and can be funded by

outside sources such as the Veterans Association, American Indian Reservations, State or local

government, and so on. In some cases, the releasee pays for services if he or she is able. Some

subjects received both contract and non-contract services.

Forty-five percent of the male subjects participated in contract drug or alcohol treatment after

release from prison. Three percent of the men participated in non-contract services, and another 3

percent participated in both. The other 49 percent of the male subjects did not participate in postrelease drug or alcohol treatment. Looking across subject types, those subjects who terminated

their DAP participation by dropping out of the program showed the highest percentage not

receiving post-release treatment (at 65 percent). Those who completed treatment and those who

did not complete treatment for reasons beyond their controlshowed the lowest proportions of

subjects not receiving post-release treatment (at 41 percent).
 The period of time was less than 6 months if the subject’s supervision was terminated, if 67

the subject was arrested, or if the subject spent any time incarcerated on a detainer for a previous

offense.

92

Twenty-two percent of the male subjects participated in self-help groups such as Alcoholics

Anonymous, Narcotics Anonymous, and the like. Participation in these groups can be mandated

or voluntary. Again, DAP-dropouts showed the lowest percentage of participation in self-help

groups (at 16 percent), while the treatment completers showed the highest percentage (at 26

percent). However, unlike the case for post-release drug or alcohol treatment, with self-help

groups for men, the DAP-incomplete subjects did not show as high a percentage of participation

as did the treatment group.

Fifty-one percent of the female subjects participated in contract drug or alcohol treatment after

their releases from prison. Three percent of the women participated in non-contract services, and

another 3 percent participated in both. Forty-three percent of the female subjects did not

participate in post-release drug or alcohol treatment. Looking across subject types, non-DAP

control subjects and subjects who terminated their DAP participation by dropping out (at 52

percent and 50 percent, respectively) showed the highest percentage of members not receiving

post-release treatment. Those who completed treatment showed the highest percentage of

members receiving any post-release treatment (at 64 percent). Twenty-nine percent of the female

subjects participated in self-help groups such as Alcoholics Anonymous, Narcotics Anonymous,

and the like. The comparison DAP and control non-DAP subjects showed the lowest percentages

of participation in self-help groups (at approximately 20 percent each), while the DAP-incomplete

subjects and those subjects terminated from the program for disciplinary reasons show the highest

percentages (at 46 percent and 43 percent, respectively).

Employment and Cohabitation Status for Supervised Subjects

Tables 36 and 37 present information on supervised subjects’ employment and cohabitation

statuses. Because this information was drawn from interviews with Probation officers, data are

available only for the 85 percent of male subjects and 82 percent of female subjects who were

under supervision at the time of their releases from Federal prison. 

Forty-four percent of the male subjects who were being supervised were employed full-time for

the entire supervision time period (usually 6 months), and another 29 percent were employed fulltime for a portion of that time period. Nine percent of supervised male subjects were employed 67

part-time, either for the entire time period or for at least some portion of that time. The 5 percent

of subjects who fell into the “ineligible” category were either involved in a school program, retired

or disabled, or detained in custody and had very little time available “on the streets” to be

employed. The 13 percent of individuals who were unemployed fell into one or more of the

following categories: looking for but not finding work, not working due to domestic or child care

responsibilities, not looking for work, not working because involved in drug treatment,

temporarily unemployed for medical reasons, or on abscond status (with the Probation officer

unable to verify employment).
93

The male subjects with the highest employment rates were the DAP-complete subjects (49

percent were employed full time for the entire time period) and DAP-incomplete subjects (47

percent full time for the entire time period). The highest unemployment percentages were 26

percent (for DAP-dropouts) and 23 percent (for DAP-disciplinary discharge subjects).

Twenty-eight percent of the female subjects who were being supervised were employed full-time

for the entire supervision time period (usually 6 months), and another 35 percent were employed

full time for a portion of that time period. Sixteen percent of supervised female subjects were

employed part time, either for the entire time period or for at least some portion of that time. The

8 percent of subjects who fell into the “ineligible” category were either involved in a school

program, retired or disabled, or detained in custody and had very little time available on the

streets to be employed. The 13 percent of individuals who were unemployed fell into one or more

of the same categories listed above for male unemployed subjects.

The female subjects with the highest employment rates were those who completed treatment (34

percent were employed full time for the entire supervision time period). The highest

unemployment percentages were for non-DAP control subjects (at 23 percent) and for those

subjects who were terminated from the program for disciplinary reasons (at 21 percent).

Twenty percent of the male subjects who were supervised lived with a spouse, 15 percent with a

common-law spouse, and 65 percent with neither. Those subjects who fell into the “neither”

category were either living alone or with one or more of the following: children, parents or

guardians, relatives, friends (non-relatives), or unspecified others. DAP-discharges were the least

likely to be living with a spouse or common-law spouse while under supervision (at 76 percent).

Twelve percent of the female subjects who were supervised lived with a spouse, 9 percent with a

common-law spouse, and 80 percent with neither. DAP-discharges and DAP-completers were the

least likely to be living with a spouse or common-law spouse while under supervision (at 87

percent and 86 percent, respectively).

Summary

The description of our research subject characteristics indicates that there were both similarities

and differences between male and female subjects. The differences within each gender were most

pronounced between the combination of (1) DAP-discharges and (2) DAP-dropouts and DAP

graduates. In general, there were few differences between the DAP comparison group and the

non-DAP control group. 
 Post-release data from a Probation officer were not obtained for 18 individuals released 68

to supervision. For these subjects, arrest data were obtained from the NCIC database. 

94

CHAPTER 7: ANALYSIS AND RESULTS

This chapter describes the approaches taken to model the five selected outcomes and presents the

results of these models. The five outcome measures are arrests, arrests or revocations, drug use,

Community Corrections Center (CCC) failure, and post-release employment. The chapter begins

with a description of the five outcome measures. The next section provides a description of our

three different analytic strategies and our method of hierarchically testing the effects of adding

blocks of variables. This is followed by a section presenting the procedures we used to handle

missing data. Appendix B contains a comprehensive codebook of the variables used in the

different analyses.

Subsequent sections present what we believe to be the most significant results of our study, i.e., 

results for three of the outcome measures: arrests only, arrests or revocations, and drug use.

Following these results, we present results for the two other outcomes. The final section focuses

on possible institutional differences in program effectiveness. These results are based on analyses

exclusively of inmates who entered treatment. 

Outcome Measures

Our outcome measures in this report are: recidivism (measured two ways), drug use, Community

Corrections Center placement failure, and employment. In order to have one outcome measure for

all research subjects, the measure of recidivism is divided into two categories — arrest for a new

offense and arrest for a new offense or revocation. The primary source of information for postrelease outcomes was telephone interviews with Probation officers. However, almost 20 percent

of the research subjects were not released to supervision. We did not want to exclude these

individuals from our analyses because we could still gather their arrest information from the NCIC

database and — for some — from our BOP databases. However, NCIC data do not 68

consistently identify revocations and do not record information on minor misdemeanors, while this

information is consistently recorded by Probation officers. Therefore, our measure of recidivism

for all subjects was an arrest for a new offense and our measure of recidivism for supervised

subjects was either an arrest for a new offense or a revocation.

Telephone interviews with Probation officers provided outcome information for supervised

subjects for the following outcomes measures: arrest for a new offense, arrest for a revocation,

post-release employment, and drug use. The outcome information for new arrests was obtained

from the automated NCIC database for subjects not released to supervision, for subjects ending

supervision before 6 months, and for subjects released to supervision without information from a

Probation officer. The third data source — the automated BOP databases — was used to obtain

the outcome information on CCC placements.
 NCIC codes are ordered by severity of offense, so we used their hierarchy when 69

determining which offense was to be considered the most severe.

 The Probation officer was recontacted to verify that we had correctly recorded the 70

arrest information.

 Four of the individuals who did not receive urinanalysis testing during supervision were 71

detected as having used alcohol (by means of a positive breathalyzer or admission of alcohol use

to the Probation officer). However, information on alcohol-only users is insufficient for inclusion

in drug use models because the majority of detected drug use is for that of an illicit drug (i.e.,

there is no regular screening for use of alcohol). Future analyses will assess why some individuals

did not receive urinalysis testing. 

95

Excluding the outcome measure of CCC placement failure, data collection for post-release

outcomes were scheduled for three points in time following a subject’s release: at 6 months, 18

months, and 3 years. If an individual terminated supervision within 3 years of release, the

information from Probation officers was collected through the end of supervision. This report

contains information through the first data collection point, i.e., through 6 months after release.

Arrest data obtained from Probation officers contained all arrests during supervision, regardless of

whether the individual was convicted or incarcerated. The measure of arrest for this report was

defined as the first occurrence of an arrest during the first 6 months of release from custody. We

analyzed the single most serious offense for individuals with multiple charges at time of first such

arrest. In cases where individuals had multiple offenses they were classified as being revoked 69

only when there were no arrests for a new offense.

We verified the consistency of information on arrests for a new offense by comparing Probation

officer arrest information to NCIC arrest information. This comparison was done for 50 randomly

selected subjects with arrest information obtained from a Probation officer. We found only one

subject for whom NCIC data showed an arrest not reported by a Probation officer, which gives 70

us reason to believe in the comparability of data from the two sources (i.e., Probation officers and

NCIC).

Drug use information was obtained from the Probation officers’ records of violations of

conditions of supervision for our research subjects. We limit this outcome measure to individuals

who were tested for drug use. There were 142 individuals released to supervision who were not

included in the analyses of drug use — 134 individuals who had no urinalysis testing and 8 who

had missing information on urinalysis testing. Four different violation categories were used as 71

indicators of drug use: a positive urinalysis test for any illegal drug, a refusal to submit to a urine

test, a positive breathalyser test, and an admission of drug use to the Probation officer. Although

we would ideally like to model the number of drug use occurrences, we limit our analyses to the

first occurrence. This was necessary because in some districts an individual is revoked after the

first positive urinalysis while in other districts individuals are revoked only after repeated
We considered arrest to be our primary indicator of failure and thus censored all other 72

outcome events after the first arrest. 

We also excluded time spent in jail due to a detainer from consideration as time available 73

for employment.

As mentioned in Chapter 4, we weighted the DAP comparison sample to make its size 74

proportional to the volunteering rate both for the period during which the subject was selected

and for the site at which the subject was housed.

96

positives. When an individual was arrested during the 6-month post-release period, we counted

the first evidence of drug use only if the detection occurred before the arrest.72

Post-release employment was measured as the percentage of time an individual was employed full

time during supervision. When an individual was arrested before the end of the 6-month period,

the time period considered was limited to the time between the date of release from prison or

CCC (i.e., the date of release from BOP custody) and the date of arrest. Probation officers 73

provided information about the starting and ending dates — as well as the number of hours

worked per week — for each job the subject held during supervision. This information was used

to calculate the total number of days employed full time.

Analytic Strategies

For each outcome variable, we first analyzed the data in a fashion similar to that of past practices.

We refer to this as the traditional approach. In the traditional approach, we decompose treatment

groups into the following subgroups: inmates who completed a 12-month drug abuse program

(GRAD12MO), those who completed a 9-month program (GRAD9MO), inmates who dropped

out of a program (WITHDRTX), inmates discharged for a disciplinary reason (DISCIPTX),

inmates who — through no fault of their own — did not complete a program (INCOMPTX), and

inmates from drug treatment sites who did not volunteer for treatment (COMPDAP). These

groups were compared to inmates who were sampled from institutions at which there was no drug

abuse program offered — i.e., our non-DAP control subjects.

A second analytic approach was to collapse treatment groups (including inmates who completed

treatment and those who did not), combine them with the DAP comparison group, and compare

this combined group’s outcomes to those of non-DAP control subjects. As mentioned earlier, we

refer to this as a Bloom model. For reasons discussed in our conceptualization of the selection

bias problem, we believe that this gives a fairer test of the effect of drug treatment than does our

traditional model. The Bloom approach compares the weighted combined outcomes for the DAP

treatment and comparison groups to the outcomes for the non-DAP control group. The weights

were the inverse of the probability of selection for the DAP comparison subjects, one for the DAP

treatment subjects, and zero for the non-DAP control subjects.74
97

Finally, we report on an analysis that explicitly models the effects of selection bias and tests for

the impact of treatment under conditions of selection bias. As mentioned earlier, we refer to this

model as the Heckman model. The statistical models we used varied with the specific outcome

being measured and the approach we took (see Appendix C).

Following these analyses, we present inter-institutional comparisons of the outcome measures for

the treatment subjects. Because one of this study’s strengths is that it is a multi-site evaluation, it

is important to analyze any potential differences in the drug treatment programs. Such differences

are relevant for two reasons. First, if there are program differences, we could use this information

as a first step in trying to understand what it is about drug treatment programs that makes them

effective. Second, program differences might mask treatment outcomes; for example, if some

programs were very effective and others extremely ineffective, we might find no overall treatment

effect. 

In the inter-site analyses, we looked at two variables that helped us understand the nature of the

institutions’ varying degrees of “success.” One variable measured the amount of time between

discharge from a DAP and release from prison. If there was treatment decay for inmates who

spent a lot of time in prison after completing a DAP, this variable would measure such a

phenomenon. A second variable measured the amount of time between the start of a program and

the time an individual entered the program; the longer a program had been established, the larger

the value of this variable. This variable helped us find the correlation between the “maturity” of a

program and its “success.”

Because there were a large number of variables that we could have incorporated into our models,

we adopted a hierarchical procedure for adding “blocks” of variables. We began with a simple

“base” model that included variables representing treatment effects, as well as such demographic

characteristics as age, sex, race, and “background” variables (which we knew from previous

research to have been related to criminal recidivism). The background variables included measures

of criminal history and employment prior to incarceration. In addition, the base model contained

information on drug use and drug dependency. The drug measures were important in

understanding the extent to which treatment was related to extent of previous drug use.

All other measures were organized into blocks of related variables. The variables included in each

block are listed below. Note that the variables varied depending on the outcome measures and

that the last two blocks were not applicable to the models of CCC failure.

 Additional background variables — previous drug treatment, previous alcohol

treatment, previous mental health treatment, and diagnoses of antisocial

personality and depression.

 Change Assessment variables — six clusters, representing the various profiles on the

four change assessment subscales (see Chapter 4).
 Differences between subject groups in monthly rate of urinalysis testing do not 75

necessarily indicate that some groups were “watched” more than others. This is only one indicator

of post-release level of supervision. Future analyses will include measures of the frequency of

contact with Probation officers.

98

 Additional treatment variables — participation in in-prison self-help groups, in-prison

outpatient treatment, CCC transitional services, post-release contract and other

treatment services, and post-release self-help groups.

 Supervision variables — CCC placement (including successful and unsuccessful

completion), “released to supervision,” monthly rate of urinalysis testing during

supervision, and CCC placement during supervision. 75

 Post-release behaviors — cohabitation with a spouse, violation of a condition of

supervision (for infraction other than drug use), drug use after release, and hours

worked per week during post-release period.

The hierarchical procedure first tested the difference between the base model and each of several

other models (which were built by adding one block of variables at a time to the base model).

Because the base model was nested within the “built” models when adding blocks of variables, we

were able to analyze the effects of the blocks. For those analyses in which we used logistic

regression (including the discrete-time hazard models), we compared the differences in minus

twice the log of the likelihood ratio test statistic. This statistic is distributed as a Chi-square

variable with degrees of freedom equivalent to the additional number of parameters being

estimated. 

In the case of the ordinary least squares regressions, the difference in R-square was tested with an

F ratio. After all of the blocks were tested separately, we added all blocks of variables that passed

the first significance test (p<.05). We then computed a Chi-square or F-test, comparing these

additional blocks’ combinations to the base model. If the combined set was significantly different

from the base model, this became our final model. We also included effects vectors representing

the time periods during which the inmates were released. This release cohort variable was

intended to proxy any effects on outcomes that may have been attributable to changes in the

release environment over time. For example, economic conditions can vary over time and this can

have an effect on criminal recidivism and employment.

The hierarchical testing of blocks of variables was completed using the traditional approach. The

blocks of variables identified as significant were then used in the other two analytic strategies —

the Bloom and Heckman approaches — that address selection bias issues. We recognized that

different blocks of variables may have been significant when using other approaches, but because

the focus was upon treatment effects, we opted to present models controlling for the same sets of

variables. In the models examining possible site differences in treatment effectiveness the model

specification was the same as that used in the traditional models. 
 Tables for male subjects only will be made available upon request. We do not report 76

differences in effects when variables are near significance in one model (p<.10) but significant in

the other model (p<.05). Such differences do not reflect substantial changes in the size of

coefficients and their standard errors. 

99

In discussing the results, we reported coefficients significant at .05 or less. We used a two-tailed

test for all coefficients except those representing effects for DAP in-prison treatment. For these

treatment effects we used a one-tailed test because we hypothesized that individuals who received

in-prison drug treatment would have more favorable outcomes than would those who did not

receive drug treatment. Furthermore, we hypothesized that those who completed in-prison

treatment would have more favorable outcomes than those who received but did not complete inprison treatment.

Ideally, we would have modeled male and female outcomes separately because men and women

were in separate treatment programs. However, a thorough representation of male and female

differences would have required the inclusion of a large number of interaction terms in the

analyses, and the smaller female sample size and the lower female failure rate reduced the power

for all analyses of outcome measures excepting employment. Unlike our other outcome measures,

employment outcomes did not consist of dichotomies of success and failure, but rather were

represented by continuous variables. We therefore were able to model employment separately for

males and females. For all other outcome measures we presented results for the combined male

and female sample, testing for the significance of gender. However, we conducted additional

analyses with male subjects only and report the differences in significant coefficients when

comparing the results for the combined model of both men and women to the model with men

only. 76

For the event history and logistic regression analyses we report fit statistics: the concordance rate,

the Hosmer-Lemeshow goodness-of-fit index, and Somer’s D.

Although we report the results for all significant coefficients, we focus this chapter, as well as the

conclusions that follow, on the effects of the in-prison DAP. This is true especially for

comparisons among the three modeling approaches. Our comparisons focus upon identifying the

consistency of the effects of DAP treatment across the models. 

Missing Data

We eliminated six subjects from our analyses because these six answered only a few questions

during the Intake1 interview. Because a large number of our variables are based upon Intake1,

these subjects would have had considerable missing data, and it would have been impractical to

estimate the missing values. Except for these six individuals, most cases had very few missing

values. 
 Tables of results where cases with missing data were deleted are available upon request. 77

As with our gender comparisons, we did not report differences in effects when variables were

near significance in one model (p<.10) but significant in another model (p<.05). 

100

We decided to handle missing data by either adding a category for “missingness” or imputing

values, rather than accepting the default method of listwise deletion for the analyses. Without

using these methods, listwise deletion would have resulted in too many cases being omitted from

the analyses. We felt that the biases resulting from a listwise deletion of a large number of cases

would be greater than those resulting from imputation. This is true especially given the much

smaller number of cases with imputed values than those with a “missingness” code. In addition,

we did verify whether the use of imputed values modified the results. Base and final models with

imputed values were compared to analogous models deleting those cases with imputed values,

and this was done for both the traditional and Bloom modeling approaches for all outcomes. The

treatment effects were not significantly different and thus were not reported in tables. However,

we reported the differences in variables significant in the model with imputed values and those

significant in the model where cases with missing data were omitted from the analysis. 77

For categorical variables in which there was a substantial amount of missing data, we treated

“missingness” as another category. This allowed us to test explicitly the relationship among

missingness, other categories of the variable, and the outcome variables. The most notable

variables with large numbers of missing values were the diagnoses of depression and antisocial

personality and the motivation for change cluster scores. We collected this information through an

interview and survey that was separate from Intake1 (our primary interview) and, due to logistical

problems, it was not always possible to administer this additional interview and survey. Other

variables with a code of “missingness” in the effects vectors for categorical variables were

employment status in the month before incarceration, criminal justice status at time of

incarceration, type of post-release treatment, and participation in self-help group after release.

For the variables with a small amount of missing data, we estimated categorical and ordinal values

with a hotdeck procedure and continuous values with BMDP’s maximum-likelihood missing value

procedure. The following were the categorical and ordinal variables and the number of cases for

which values were estimated:

 Frequency of drug use for various drugs — cocaine (n=32), crack (n=4), hallucinogens 

(n=5), heroin (n=12), marijuana (n=5), opiates (n=4), barbiturates (n=5), and

stimulants (n=4);

 Previously supported self mainly through illegal sources (n=16);

 Level of education (n=14);

 Previous mental health treatment (n=10);

 Previous drug treatment (n=10);

 Received UA testing during post-release supervision (n=10);

 Received transitional services in a halfway house (n=11);

 With whom living after release (n=10); and

 Number of hours worked in post-release job (applies only to those employed) (n=22).
Although substitution for missing values tends to bias parameter estimates toward zero, 78

we did not consider this problematic. Such substitution did not occur frequently.

101

Three continuous variables were estimated using the maximum-likelihood regression procedure in

BMDP. These variables were the estimated log odds of drug dependency (n=17), estimated log

odds of alcohol dependency (n=52), and monthly rate of urinalysis testing (n=32). 78

Traditional Models of Arrest and Drug Use

Event history techniques were used to perform the analyses of arrests, arrests and revocations,

and drug use. In an event history analysis, the hazard rate is modeled. The hazard rate is the risk

of having an event at a given point in time, t, given that the event did not occur before that point.

Databases appropriate for use in discrete hazard rate logistic regression models were constructed

following the principles described by Allison (1984) and Yamaguchi (1991). In our presentation

of results, we discussed the odds ratio because this was the easiest parameter to interpret in

logistic regression. The odds ratio is simply the percentage of difference between the odds of

failure to non-failure, given a one-unit increase in the independent variable. An odds ratio of less

than one indicates that the odds of failure decrease when the value of the independent variable

increases. On the other hand, an odds ratio greater than one indicates that the odds of failure

increase when the value of the independent variable increases. 

The time unit used in the discrete event history models is “month.” Actually, each month is set

artificially in the analyses as 30 days. The variables D_T2, D_T3, D_T4, D_T5, and D_T6

represented the second through sixth months of release, and the coefficients for these variables

represented the likelihood of failure in each month as compared to the first month of release.

Most of the independent variables used in the event history models were time-independent. That

is, the values for the variables were fixed at the beginning of the observation period and did not

change over the post-release observation period. However, several of the independent variables

were time-dependent. That is, the values for these variables could have changed from one month

to the next during the period of post-release observation. 

“Average number of hours employed per week” is an example of a time-dependent independent

variable that is used in this analysis. For each month that a research subject remained in the risk

set (at risk of failure, defined as arrest, arrest or revocation, or detected drug use), the average

number of hours employed per week during that month (EMP_HRS) was computed. The other

independent variables whose values could have changed on a monthly basis were (1) a dummy

variable (HHSE_STR) indicating whether the research subject had been placed in a halfway house

during the month in question, (2) a dummy variable (SUP_REL) indicating whether the research

subject was under supervised release during the month in question, and (3) a dummy variable

(DIRTY) indicating whether the research subject tested positive for or admitted to drug or

alcohol use (or had refused to submit to the test) during the month in question.
 We found no additional block of variables significant in the model for men only. We 79

note, however, that our model of arrests for all subjects has fewer variables in this block than our

model for supervised subjects. This was because most of the post-release supervision variable

information is available only for supervised subjects. 

102

For arrests, we presented two sets of models. The first contained results for both supervised and

unsupervised subjects. The second contained results for supervised subjects only. In the models

for supervised and unsupervised subjects together, we were obliged to omit all of the post-release

supervision and behavior variables because this information was not available for unsupervised

subjects. However, we included a variable indicating whether or not the individual was

supervised.

The results of the likelihood-ratio tests are presented in Tables 38, 39, 40, and 41 for arrest as

failure for all subjects, arrest as failure for supervised subjects, arrest or revocation as failure, and

drug use as failure, respectively. As can be seen in Table 38, adding blocks of variables to control

for additional background factors, change assessment, and additional treatment did not

significantly improve the base model for arrests when all subjects were considered. On the other

hand, adding variables for level of supervision did significantly improve the fit of the model for all

subjects. In the model of arrests, when supervised subjects only were considered, we also found 79

that the blocks for supervision and post-release variables were significant (see Table 39).

The results in Table 40 demonstrate a similar pattern for models of failure defined as either an

arrest or a revocation. For this type of failure, the additional treatment variables significantly

improved the fit of the base model. The pattern for male subjects was the same as the pattern

noted for all subjects.

The results presented in Table 41 show that the additional background variables and the change

assessment variables once again had no effect upon post-release failure, with failure defined in this

case as drug use. Again, we see that adding separately the additional treatment variables, the

supervision variables, and the post-release variables increased the fit of the model. Consistent with

the results presented in Tables 39 and 40, the models for male subjects behaved in a similar

fashion to the models for all subjects.

For all three measures of failure for supervised subjects, we see that for men the same set of

additional blocks of variables significantly improved the fit of the model, as was the case for

models of all subjects. Below are the results for the final models with comparisons to the results

for the base models for the three types of failures, using the traditional approach.

Arrest as Failure

Arrest for a new offense was our only outcome measure that included all the research subjects.

The information for the other outcome variables was available only from Probation officers and

was thus obtained only for the subjects released to supervision (approximately 80 percent of the

subjects). While arrest data were obtained from Probation officers for those released to
 Details for this and all other t-test comparisons between 9-month and 12-month 80

program graduates are available upon request. 

 Violence has two dimensions: severity and recency. Previous research by Harer (1994) 81

and others has found that recency of violence is a more important predictor of recidivism than is

103

supervision, such data were obtained from the National Crime Information Center automated

databases for individuals who were not supervised by a Probation officer and for those who ended

supervision in less than 6 months.

The results for the base and final models of a new arrest as an indication of release failure are

presented in Tables 42 and 43. Table 42 presents the results for all subjects and Table 43 the

results for supervised subjects only. 

As can be seen in both tables, the only group that differs from the referent group — that is,

subjects who were never at a DAP site and never had the opportunity to receive DAP treatment

— was the 9-month program treatment completers (GRAD9MO). This was true in both the base

and final models. Completing the 9-month program significantly reduced the odds of being

arrested during the first 6 months following release from prison. In the base models of all subjects

and of supervised subjects only, 9-month program completers had odds of arrest that were around

40 percent lower than were the odds of arrest for non-DAP control subjects. In the final model

with all subjects, the 9-month program completers had odds of arrest that were 41 percent lower

than those for non-DAP control subjects, as compared with odds that were 34 percent lower in

the model for supervised subjects only. 

Even though 12-month completers (GRAD12MO) had lower odds of being arrested than did nonDAP control group members, the difference was not statistically significant in either model. This

nonsignificant result was partly the product of a smaller effect than is noted for 9-month

completers, and it was affected by the larger standard error associated with the estimate of the 12-

month effect.

Although the comparisons are not reported in Table 42 or 43, it is possible to compare the

coefficients for the 9-month and 12-month subjects. This is done with a standard t-test. We

performed appropriate t-tests to compare the coefficients for both the base and the final models.

In neither case were the differences between the 9-month and 12-month subjects significantly

different.80

The arrest model for all subjects shows that several other base model variables were significantly

related to arrest, although none of the drug use variables were related to arrest (see Table 42).

Age at release (AGERLSE) had the expected effect: older inmates were less likely to be arrested

than were all inmates on average. The odds of women (EFEM) being arrested were 28 percent

lower than those for all subjects on average. Subjects with prior commitments (EPRIORCM)

were more likely to be arrested than were subjects with a history of recent violence

(ERECVIOL).81
seriousness. As such, we chose our violence measure in the base model to reflect recency rather

than severity. We also re-estimated the model with seriousness of violence included instead of

recency and found that severity of violence is not significantly related to arrest. Because we

cannot include recency and seriousness of violence simultaneously due to collinearity between the

measures created by those with no history of violence, we include recency of violence in our

models for both theoretical and empirical reasons.

 Although most individuals who failed their CCC placements did so due to drug use, a 82

few individuals failed their CCC placements because of arrests for new offenses. This arrest is not

to be confused with a post-release arrest occurring after release from BOP custody.

 The coefficient indicating whether an individual released to supervision (SUP_REL) was 83

not significant. Nonetheless, we ran the same model with supervised subjects only and we found

no difference in significant coefficients. 

104

There was a significant effect for one of the supervision variables in the final model. Subjects who

failed their CCC placements (ECCCFAIL) had 39 percent higher odds of being arrested.82

The time coefficients showed a pattern that is not surprising. In comparison to month one, the

odds of arrest increased significantly each month beginning in the fourth month (D_T4).

However, it appears that the odds of arrest start to decline in month six (D_T6). 83

Most of the same base model variables significant in the model with all subjects were significant in

the model for supervised subjects only (see Table 43). Two variables, significant in the model

with all subjects, were not significant in the model with supervised subjects only: recent history of

violence (ERECVIOL) and CCC placement failure (ECCCFAIL). On the other hand, we found

two variables significant only in the model with supervised subjects. Individuals who served

longer amounts of time (TIMESRVD) and individuals with higher log odds of alcohol

dependency (DEPLOGTA) had higher odds of arrest than subjects on average.

In the model with supervised subjects only, several supervision and post-release behavior variables

were significant. Subjects with higher rates of urinalysis tests (UARATE) had lower odds of being

arrested. Of the post-release variables, living with a spouse (ESPOUSE) and having more hours

of employment during the month of observation (EMP_HRS) both lowered the odds of being

arrested. Having evidence of drug use during a month (DIRTY) significantly increased the odds

of being arrested.

The various fit statistics at the bottom of Table 42 indicate that in the models for all subjects, both

the base and final models had adequate fit statistics. However, we did not find that the final model

had a higher concordance rate, Somer’s D, or goodness-of-fit index. This is not surprising for the

model with all subjects because the final model includes few variables in addition to the base

model variables. On the other hand, the fit statistics for supervised subjects only (see Table 43)

showed the final model to have a higher concordance rate and a higher Somer’s D than did the
We note that, as will be discussed in Chapter 8, arrests and revocations combined may be 84

considered to be very different phenomena from that of arrests.

105

base model. However, the goodness-of-fit index indicates that the final model had a poor fit,

whereas the base model had a good fit.

Comparisons between both models, for all subjects and for supervised subjects only, for males and

females combined vs. males only, showed very few differences. In both models for men — unlike

the models for men and women combined — those with more frequent past use of crack

(CRK_FRQ) had decreased odds of failure. Among supervised and unsupervised subjects, the

coefficient for failing a CCC placement (ECCCFAIL) was significant in the arrest model for men

and women combined but not in the arrest model for men only.

The model for supervised and unsupervised subjects where we deleted cases with missing data

was very similar to the model where we did not delete these cases. There were no differences in

significant coefficients. A similar comparison for the supervised subjects indicated that, with one

exception, there were no differences in significant coefficients. Not being in the labor force just

before the most recent incarceration (ELEGITUN) was significant and associated with lower odds

of rearrest in the model for supervised subjects where missing data cases were deleted but not in

the model with estimated values for missing data.

In summary, the traditional model of failure defined as a new arrest showed that individuals who

completed the 9-month DAP had lower odds of failure. The lower odds of failure for 12-month

DAP completers was not statistically significant. The likelihood of being arrested was significantly

higher in months four through six after release, although the odds ratio decreased between the

fifth and sixth months.

Arrest or Revocation as Failure

Unlike the models for failure defined as a new arrest, those for failure defined as either a new

arrest or revocation were limited to those subjects under supervision, which constituted more than

80 percent of the subjects.84

 

The results for either a revocation or an arrest counting as a failure are presented in Table 44. In

the base model of this type of failure, we see that 9-month DAP-complete subjects (GRAD9MO)

had significantly lower odds of failure than did the non-DAP control subjects. However, when the

effects of the additional variables considered in the final model were included, the effect of 9-

month treatment was no longer significant. None of the other coefficients for a DAP subject

group were statistically significant in either the base or final models.

Even though neither treatment group was significantly different from the non-DAP controls, it is

possible that the two treatment groups were different from each other. We tested whether this

was the case with the t-test comparison method. In neither the full nor the reduced model were

the differences in coefficients between the 9- and 12-month groups statistically significant.
106

For the final model, with failure as measured by an arrest or revocation, many of the same base

variables significant in the model of arrests considered alone were significant here as well. Older

subjects (AGERLSE) and female subjects (EFEM) had significantly lower odds of experiencing a

failure. Having a prior commitment (EPRIORCM) or a longer period of time served

(TIMESRVD) increased the odds of failure. A base variable not significant in the model of arrest

but significant in the model of arrest or revocation was the effect of having a history of working

before coming to prison (EWORKJOB). The effect, though, was not what one would expect.

Having a history of work increased the odds of failing upon release. In addition, both those who

were not in the work force (ELEGITUN) and those who were unemployed (EUNEMP) before

their most recent incarceration periods had lower odds of failure upon release. 

Compared to all respondents on average, those subjects who received both contract and noncontract treatment (EBOTH) had lower odds of failure. On the other hand, those subjects about

whom the information on whether they received post-release treatment was missing (ETXMISS)

had significantly higher odds of failure. Subjects with higher rates of monthly urinalysis testing

(UARATE) had lower odds of failure, whereas those who had failed their CCC placements

(ECCCFAIL) had higher odds of failure. 

For arrests or revocations, as with new arrests only, both living with one’s spouse (ESPOUSE)

and having a higher number of hours worked full time per month (EMP_HRS) were associated

with lower odds of failure. The variable DIRTY — denoting evidence of drug use — was

significant and indicated that individuals with one or more positive tests were 225 percent more

likely to fail. A violation of a condition of supervision for reasons other than drug use

(SUPVVIOL), not significant in the arrests model, was significant in the model of arrests and

revocations. Having violated a condition of supervision (SUPVVIOL) was associated with 

increased odds of failure.

Release cohort10 (ECOHO10) members were more likely to fail when failure was defined as

having a new arrest or revocation. The time coefficients (D_T3 ... D_T6) showed a pattern that is

not surprising. In comparison to month one, the odds of failure increased each month beginning in

the third month.

The fit statistics suggest that there was an adequate fit of the models to the data, and this was

especially true for the final model. The improvement in concordance and Somer’s D between the

base model and final model was fairly large. The concordance value increased from 67.7 percent

in the base model to 85.6 percent in the final model and Somer’s D increased from .378 to .723.

We compared the results for men only to those of men and women combined. Unlike the results

for arrests, we found several differences in the results when failure was defined as arrests and

revocations. A number of coefficients significant in the models for men and women combined

were no longer significant in the models for men only: working before incarceration

(EWORKJOB), not being in the labor force before incarceration (ELEGITUN), two post-release

treatment variables — having had both contract and non-contract services, and having no postrelease treatment information (EBOTH and ETXMISS), failing a CCC placement (ECCCFAIL),
107

and violating a condition of supervision for reasons other than drug use (SUPVVIOL). Several

variables not significant in the model with both genders were significant in the male-only model.

Individuals with more frequent past crack use (CRK_FRQ) had lower odds of failure, and those

with higher odds of alcohol dependency (DEPLOGTA) had higher odds of failure in the male

model.

The results for the final model where we delete cases with missing data were very similar to the

analogous model with imputed values for missing data. Only two variables not significant in the

model with imputed values were significant in the model with cases deleted. More frequent past

use of crack (CRK_FRQ), as well as having received contract services only while under

supervision (ECTRONLY), were associated with lower odds of failure in the missing data model.

In summary, unlike the results for failure defined as a new arrest only, the results for failure

defined as either a new arrest or revocation showed no direct effects for treatment. The pattern of

increasing failure rates throughout the 6-month period was similar to that found for new arrests

only, with the exception that we did not see a significant decrease in the failure rate in the 6-

month period. 

First Detected Drug Use as Failure

The results for failure defined as drug use, like those for arrests and revocation, were limited to

those subjects who were released to supervision and for whom data were obtained from a

Probation officer (n=1,436). This represents approximately 80 percent of the subjects. While we

use the term drug use to refer to this outcome, note that drug use includes a positive urinalysis

test for any illegal drug, refusing to submit to a test, a positive breathalyser, or admitting drug use

(including alcohol use) to the Probation officer. 

The results for first drug use are presented in Table 45. As can be seen in the results, only the 12-

month DAP completion subjects (GRAD12MO) differed significantly from the non-DAP control

group in the base model. In the final model, though, the comparisons of both 9-month

(GRAD9MO) and 12-month DAP completers (GRAD12MO) to non-DAP control subjects were

significant. Twelve-month DAP completers had odds of drug use that were 49.7 percent lower

than were the odds for non-DAP control subjects. The odds of drug use for the 9-month group

were 32.0 percent lower than were those for the non-DAP controls. Surprisingly, we found that

the comparison subjects from DAP sites (COMPDAP) had odds of failure that were 26.4 percent

lower than were the odds of failure for the non-DAP controls. In addition, individuals who did not

complete treatment through no fault of their own (INCOMPTX) had odds of failure that were

43.3 percent lower than were the odds of failure for the non-DAP controls.

Once again, we tested whether there were significant differences between the two treatment

completer groups themselves. There were no significant differences in either the base or final

model. 
 The variable POT_FRQ was significant at the .10 level in the base model. 85

 Anecdotal information from the research staff interviewing Probation officers indicated 86

that some individuals receiving both services received the non-contract services only after having

had a positive urinalysis. Thus, such individuals began supervision receiving contract services only

but were referred to receive additional non-contract services after the positive urinalysis. This

means that receipt of non-contract services may have represented an effect rather than a cause of

detected drug use.

108

In the final model we noted that several of the background variables were related to the odds of

drug use. Being female (EFEM) and having a prior commitment (EPRIORCM) had the expected

effects. Women were less likely than were men to have used drugs in the first 6 months, and

subjects with prior commitments had higher odds of drug use. Also, race and ethnicity assumed

importance in these models. Compared to all subjects on average, blacks (EBLACK) had 30

percent higher odds of failure and Hispanics (EHISP) had 33 percent higher odds of failure. The

pattern of effects for the drug use variables showed that although the variable measuring the odds

of drug dependency (DEPLOGTD) was statistically significant in the base model, it was not

significant in the final model. Instead, we found the variable indicating frequency of past marijuana

use (POT_FRQ) to be significant in the final model. More frequent marijuana use in the past

increased the odds of failure.85

Two of the treatment variables had significant effects. The most noteworthy is the effect of

receiving both contract treatment services and other post-release treatment (EBOTH). Compared

to all other subjects, these subjects were more than 100 percent more likely to fail (i.e., use drugs

after release). This is a counterintuitive finding, suggesting that there may have been problems

with the model specification. Potential problems with model specification included the potential

that some of the post-release treatment variables may have measured risk of failure and that we

may not have been capturing the time ordering of treatment. The other treatment variable with a 86

significant effect was ENRGENY, which represented having received outpatient treatment while

in prison. These individuals were 28 percent less likely to fail. 

Similar to our findings for the model of arrests and revocations, subjects who failed their CCC

placements (ECCCFAIL) had increased odds of drug use. The effects of the post-release variables

were generally consistent with patterns noted for the models of arrests and arrests or revocations

(see Table 45). 

The time coefficients showed a different pattern from that found in either the models of arrests or

the models of arrests and revocation. Rather than the likelihood of failing increasing each month

and beginning to decline in the sixth month, we see a pattern of decline each month, with the

coefficients being significant only for months four and six. In month four, individuals were 34

percent less likely to fail, but in month six, they were 64 percent less likely to fail.
109

The fit statistics suggested that there may have been a problem of fit to the data in the final model.

The concordance and Somer’s D measures for the final model were fine, but the HosmerLemeshow statistic suggested that the final model did not fit the data very well.

The final model of drug use for men only was very similar to the model for men and women

combined. The only coefficient that differed was that for release month four (D_T4). This

coefficient was not significant in the male-only model. However, it is noteworthy that in the final

model for men, unlike the model for men and women combined, the Hosmer-Lemeshow good-offit statistic showed that the model fit the data well (p=.7006). 

Comparing the final model with imputed values for missing data to a model with listwise deletion

of cases with missing data showed few differences. Only in the model with deleted cases did we

find that subjects with histories of recent violence (ERECVIOL) had increased odds of failure. On

the other hand, we found that the coefficient representing individuals who entered but did not

complete treatment (INCOMPTX) was significant only in the model with imputed values for

missing data.

To summarize, the results for failure defined as drug use showed that there were treatment

effects. We found positive effects for both the 9-month and 12-month DAP completers, and we

found positive effects for those who had treatment available but chose not to participate in

treatment (COMPDAP). The pattern of failure by month differed from that found in the previous

two models. Rather than seeing the risk of failure increasing after the first month, we saw a

pattern of decreasing failure. The time coefficients were significant only for the fourth and sixth

months. 

Bloom Models of Arrests and Drug Use

This section describes the results for models of failure that compared all research subjects in DAP

sites to all research subjects in non-DAP sites, i.e., we took the Bloom approach. As noted

previously, these comparisons allowed us to avoid the problems of selection bias caused by the

multiple stages in which subjects volunteer themselves into or out of treatment. 

Arrest as Failure

As can be seen in the results presented in Tables 46 and 47, the dummy variables comparing the

different groups to the non-DAP control subjects were replaced with the variable SUBJECT,

which took on a value of one for research subjects who were ever at a DAP site. For the reasons

provided above, this variable did not take into consideration whether the research subject actually

received treatment.

Examination of the results in Tables 46 and 47 shows that there was no effect for treatment

demonstrated in either the base or the final model when considering either all subjects or

supervised subjects only.
110

While the other control variables significant in the traditional model were also significant in the

Bloom model, we found additional variables significant in the Bloom model. When considering

all subjects or supervised subjects only, we found those with higher education levels (GRADEA)

and those who were neither black nor white (RACEOTH) had increased odds of failure, but those

with more frequent previous marijuana use (POT_FRQ) and those not having had a CCC

placement (ECCCNO) had lower odds of failure. 

In addition, in the model with both supervised and unsupervised subjects, more frequent previous

use of hallucinogens (HAL_FRQ) was associated with lower odds of arrest. Individuals who had

been under criminal justice supervision at the time of arrest (ECJSUPVNY), those with more

frequent past use of crack (CRK_FRQ), those with higher log odds of alcohol dependence

(DEPLOGTA), and those released to supervision (SUP_REL), had increased odds of failure.

Also, the odds of failure increased during the second month of release (D_T2).

Several different variables were found significant in the model for supervised subjects only. 

Individuals who were black (BLACK), or were not in the labor force before incarceration

(ELEGITUN), or who had a greater number of non-drug related violations of their conditions of

supervision (SUPVVIOL) had lower odds of failure. 

As with the traditional model, the fit statistics for the final models were somewhat suspect. The

concordance and Somer’s D statistics appeared to be reasonable, but were somewhat inconsistent

with the Hosmer-Lemeshow statistic; this indicates that there may have been a problem of fit of

this model to the data. We found a higher concordance rate and a higher value for the Somer’s D

statistics for the model with supervised subjects only than for the model with supervised and

unsupervised subjects.

Among both supervised and unsupervised subjects, the model for men only — contrasted with the

model for men and women combined — showed differences for two of the drug use variables. In

the model for men, frequent past use of crack (CRK_FRQ) was no longer significant, but frequent

past use of stimulants (STIM_FRQ) was significant. Among men, subjects with more frequent

past use of stimulants (STIM_FRQ) had higher odds of arrest. 

A similar contrast for the supervised subjects also showed differences for two drug use variables.

In the model for men only, subjects with more frequent previous use of crack and stimulants

(CRK_FRQ, STIM_FRQ) had higher odds of failure. Both of these coefficients were not

significant in the model for men and women combined.

Models where cases with missing data were deleted did not differ from the models with imputed

values for missing data.

Arrest or Revocation as Failure

The results presented in Table 48 demonstrate that there was no treatment effect when failure was

defined as either an arrest or a revocation in either the base or final model using the Bloom
111

approach. This fairly closely replicated the finding for treatment discovered in the traditional

model as well, although in the traditional model of treatment, 9-month DAP-complete subjects

were significantly different from non-DAP control subjects in the base model specification. In the

final traditional model, though, there was no treatment effect, as was true for the results reported

in Table 48. 

The effects of the other independent variables again show differences when comparing results

using the Bloom approach to that using the traditional approach. All the variables, except release

cohort 10 (ECOHO10), violations of the conditions of supervision (SUPVVIOL), and the third

release month (D_T3), which were significant in the traditional model, were also significant in the

Bloom approach. However, the Bloom approach resulted in additional significant variables.

Individuals with higher levels of education (GRADEA), more frequent past use of stimulants

(STIM_FRQ), and higher log odds of alcohol dependence (DEPLOGTA) had increased odds of

failure, whereas individuals having received transitional services (ETSYES) and individuals not

having received a CCC placement (ECCCNO) had decreased odds of failure. 

The fit statistics of the Bloom model were similar to those of the traditional model with the

exception of the Hosmer-Lemeshow goodness-of-fit statistics, which did not show a good fit of

the model to the data.

A model for men only did not converge. Nonetheless, we ran several models, deleting one of the

post-release variables at a time, and found that there was no effect for treatment as represented by

the variable SUBJECT. 

When we ran the final model deleting cases with missing values, we found no differences in the

results from those obtained through models with imputed values for missing data.

First Detected Drug Use As Failure 

The results presented in Table 49 demonstrated a statistically significant effect of treatment in

reducing the odds of having a failure (when defined as drug use). This finding did not appear until

the additional blocks of variables were entered into the final model. According to the results of the

final model, the group of individuals who spent time at DAP sites had odds of drug use that were

37 percent lower than did subjects who were never at a DAP site. Given the nature of this test,

this was a conservative estimate of the direct effectiveness of treatment in lowering the odds of

drug use.

Similar to the findings for arrests and arrests or revocation, the independent variables were 

sensitive to the specification of treatment (compare Tables 45 and 49). Being either black

(EBLACK) or Hispanic (EHISP), while not significant in the Bloom model, were significant in the

traditional model of drug use. In contrast, four variables not significant in the traditional model

were found significant in the Bloom model. Individuals of another race (EOTHRACE),

individuals with histories of supporting themselves primarily through illegal means (ESUPILL),

individuals with higher levels of education (GRADEA), and individuals with histories of more
112

frequent crack use (CRK_FRQ) had significantly higher odds of drug use in the Bloom model but

not in the traditional model. 

Several treatment variables and many of the supervision variables not significant in the traditional

model were significant in the Bloom models. Individuals who were involved in self-help groups

after release (EAAYES), those placed in a CCC during supervision (HHSE_STR), and those with

higher rates of urinalysis testing (UARATE) had higher rates of failure in the Bloom model. In

addition, individuals who did not receive CCC placements (ECCCNO) had lower odds of drug

use. 

Living with one’s spouse after release (ESPOUSE) was not significant in the Bloom model, as

were the time variables D_T4 and D_T6. In contrast, several of the release cohort variables were

significant. Those subjects in release cohorts 1 through 6 and release cohort 12 (ECOHO1_6 and

ECOHO12) had increased odds of failure, whereas those of release cohort 13 (ECOHO13) had

lower odds of failure.

As was the case with the traditional model of drug use, the fit statistics for the Bloom model of

drug use suggested that there may have been a problem of fit to the data in the final model. The

concordance and Somer’s D measures for the final model were fine, but the Hosmer-Lemeshow

statistic strongly suggested that the final model did not fit the data very well.

Once again we compared the results for men to those for men and women combined. Base

variables not significant in the traditional model but significant in the Bloom model included

recency of violence (ERECVIOL), being Hispanic (EHISP), and the log odds of alcohol

dependency (DEPLOGTA). These variables were associated with higher odds of failure. On the

other hand, variables not significant in the Bloom model but significant in the traditional model

included past use of crack (CRK_FRQ), having received both contract and non-contract services

(EBOTH), having no information on post-release treatment (ETXMISS), and having been

involved in a self-help group during supervision (EAAYES). 

The results from our models where we deleted cases with missing data differed somewhat from

those where we imputed missing values. Several coefficients of variables significant in the model

with all cases were no longer significant in the model with listwise deletion of those cases with

missing data. These variables included: having had a CCC placement before release (ECCCNO)

or during supervision (HHSE_STR), having been involved in a self-help group during supervision

(EAAYES), and having had both contract and non-contract services during supervision

(EBOTH). The reverse occurred for those living with a common law spouse after release

(ECOM_LAW). In the model with missing data cases deleted, ECOM_LAW was associated with

lower odds of failure. 
113

Models of Arrest and Drug Use Controlling for Selection Bias

To assess the impact that selection processes may have had on the effect of drug treatment, a

survival model was developed for our outcomes of arrest, arrest and revocations, and drug use.

The analysis was developed and conducted by William Rhodes of Abt Associates, and the

explanation of the procedure and the mathematical basis of the Heckman selection bias models

can be found in Appendix C. In this section, we present the results and interpret the significant

coefficients for the Heckman models.

The arrest results from the survival model refer to the effects of variables on the survival time.

Thus, a significant positive coefficient indicated that a variable increased the survival time of an

offender, while a significant negative coefficient implied the opposite. In the previous analyses,

where we reported results based on a discrete-time hazard model using logistic regression, the

hazard was a reciprocal function of survival. Thus, any coefficient that increased survival should

have decreased the hazard of an event. Coefficients that were significant and positive in these

analyses should have been significant and negative in the hazard rate analyses if there had been

symmetry in the results. 

Arrest as Failure

Having settled upon a final model specification in our previous analyses, we used the same final

specification for the analyses presented in this section. Thus, all of the variables we used in

previous results sections (where we depicted the final model) were represented in the analyses we

reported in this section. The only difference in the specifications was the way selection bias was

modeled. In the first set of analyses — where the treatment groups were decomposed into those

who completed treatment, those who did not complete treatment, those who withdrew, and those

who were terminated for disciplinary reasons — we also generated a separate dummy coefficient

for DAP comparison subjects who were sampled from the treatment sites. For all these contrasts,

the control subjects from the non-DAP sites composed the referent group. In the second set of

analyses, we collapsed into one group all subject groups we sampled from DAP sites, namely

everyone who started treatment — whether they finished or not — and everyone who was

sampled as a DAP comparison subject. This combined group was contrasted with control subjects

from the non-DAP sites. Our logic was that this would be a conservative test of treatment’s

effects, presuming that all subgroups at DAP sites should have been represented at approximately

the same proportions as they would among the non-DAP sites.

In this last set of analyses, because we had an explicit test of selection bias, we were able to model

all treated subjects together and use the comparison subjects from the DAP sites, as well as the

non-DAP control subjects. Comparison subjects from the DAP sites had differing degrees of

selection bias affecting them, while the probability of selection bias for control subjects from nonDAP sites should have been zero. 

Table 50 depicts the survival analysis for all subjects, and Table 51 depicts the survival analysis

for supervised subjects only, where the outcome measure is arrest for a new offense. The
'
1	 2

1e Covarian

There was some indication that a “creaming” effect did not occur, most notably among 87

men, from our examination of the motivation to change data. Thirty-eight percent of the men (in

contrast to 24 percent of the women) fell into the three least motivated clusters:

precontemplation, uninvolved, and reluctant. The admission of “unmotivated” individuals into

DAP may have been a function of the perceived and real benefits of the treatment programs.

114

(7)

parameter estimate COVARIAN at the very end of the tables represents the level of selection bias.

That is, 

where ' is the estimated correlation between 1  and 2. The variable COMPLETE represented

the effects of treatment, controlling for selection bias. In our model, the variable COMPLETE

was the conditional probability of completing treatment, given that someone had begun treatment.

Thus, inmates who withdrew or were terminated had lower values for this variable.

The effect of treatment represented by the coefficient COMPLETE was positive and significant in

the model for all subjects, as well as in the model for supervised subjects only. Thus, inmates who

completed treatment had longer survival times until arrest than did untreated comparison subjects.

In other words, the time until their arrests was significantly longer if they had completed drug

treatment. 

One of the last variables in the tables, COVARIAN, represented the level of selection bias. The

coefficient was negative and significant, indicating that inmates who were more likely to fail were

selected into the treatment groups. Rather than “creaming” the best candidates for drug treatment,

the selection processes captured inmates who were less likely to succeed than were the

comparison subjects from the DAP sites. That is, after controlling for other variables that 87

affected recidivism, those who entered treatment seem to have been — based on unmeasured

variables — higher risks than were those who did not enter treatment. Thus, the selection bias

occurring in this study operated to mute the effects of treatment. By measuring and controlling for

this bias, we were able to detect a rather strong treatment effect. 

In the model for supervised and unsupervised subjects, the control variables found to be

significant were the same as those found significant in the traditional model. A comparison of

significant coefficients between the traditional and Heckman models for supervised subjects

showed few differences between analogous coefficients in the two models. In the Heckman

model, the coefficients for the amount of time served (TIMESRVD), the log odds of alcohol
 We note that the differences in the two models for the coefficients representing drug use 88

may have been due to the differences in the variables, where one was a time-dependent covariate

and the other was the number of drug violations occurring during the post-release time period.

 All observations were in the extreme portion — the first 6 months — of the distribution. 89

115

dependency (DEPLOGTA), and for a drug-related violation of supervision conditions

(DRUGVIOL) were no longer significant.88

We could have calculated the average or median changes in survival time that result from

treatment, but because we were using just the left tail of this assumed log-normal distribution to

estimate the mean and median of the entire distribution, such an estimate could have been very

misleading. Instead, we estimated the change in the conditional probability of being arrested 89

within the first 6 months, depending on whether a subject was treated or untreated. We evaluated

these probabilities at the means of all variables other than the treatment variable. This gave us an

estimate of the size of the effect, controlling for all the other variables in the model. When these

probabilities were computed for supervised subjects only, where “treatment” was interpreted as

the conditional probability of completing treatment, untreated subjects had a probability of .127 of

being arrested in the first 6 months, while treated subjects had a probability of .030. The

analogous probabilities for supervised and unsupervised subjects were .121 for untreated subjects

and .033 for treated subjects.

Arrests and Revocations as Failure

The findings for failure, with failure defined as either an arrest for a new offense or a revocation,

using the Heckman approach were similar to those found using the two other approaches (see

Table 52). When controlling for self-selection, we found an effect for DAP treatment. As with

the models for failure defined as a new arrest only, the Heckman model for arrests and

revocations found an effect for the level of selection bias (COVARIAN). The negative coefficient

for COVARIAN once again indicated that inmates who were more likely to fail were selected into

the treatment groups. 

The coefficient for COMPLETE, the conditional probability of entering and completing treatment,

was positive, as was the case for arrests, indicating that individuals who entered and completed

treatment had longer survival times. Most of the coefficients found significant using the Heckman

approach were the same as those found significant in the traditional model.

The only coefficient found to be significant in the Heckman model but not in the traditional model

was the coefficient for individuals receiving contract services only (ECTRONLY), with these

individuals having longer survival times. Several coefficients significant in the traditional model

were not significant in the Heckman model. Three coefficients for employment status before the

most recent incarceration — employed (EWORKJOB), not in the labor force (ELEGITUN), and
 The correlation of the error term (correlation = 1-2/(1+exp(COVARIAN)) was -.261 90

for drug use, as compared to -.331 and -.369 for rearrest and rearrest or revocation, respectively.

Thus, for both arrests and drug use, the correlation indicated that the highest-risk subjects were

selected into treatment.

116

unemployed (EUNEMP) — as well as the coefficient for the amount of time served

(TIMESRVD) were not significant in the Heckman model. 

When we estimated the size of the effect controlling for all the other variables in the model, we

found that untreated subjects had a conditional probability of .174 of being arrested or revoked in

the first 6 months, while treated subjects had a probability of .047. 

First Detected Drug Use as Failure

Table 53 presents the survival model results for first drug use, controlling for selection bias. This

model included only those subjects who had urine testing during their post-release period (1,436

of the 1,860 subjects). Once again, the variable COMPLETE, which represented the conditional

probability of entering and completing treatment, was significant. The positive coefficient for

COMPLETE indicated that individuals who completed treatment had a longer time until first drug

use than did non-treated subjects. 

As in the models using failure defined as arrest for a new offense and arrests or revocations, the

variable COVARIAN — which represents the level of selection bias — was negative. Although

COVARIAN was not statistically significant (p=.0936), our experience with selection bias models

was that the power for tests of selection bias were very low. While the parameter estimate was

not statistically significant, the implied correlation of -.26 seemed substantively significant, and we

chose to reject the null hypotheses of no selection bias. This means that individuals who 90

completed treatment were less likely to have a longer survival time than were individuals who did

not enter treatment or did not complete treatment. Once again, as with the arrest model, without

controlling for selection bias, the effects of treatment would have been attenuated.

The background characteristics found to be significant in the Heckman model differed somewhat

from those found significant in the traditional model. There was a significant effect for blacks

(EBLACK) in the traditional model but not the Heckman model. On the other hand, two variables

not significant in the traditional model were significant in the Heckman model. In the Heckman

model, individuals who were involved in self-help groups after release (EAAYES) had shorter

survival times and individuals who did not receive a CCC placement (ECCCNO) had longer

survival times. This result was unexpected, but it is consistent with the results for the Bloom

model and will require further investigation.

Similar to the process used in the analysis of the probability of arrest within the first 6 months, we

computed the conditional probability of first drug use within the first 6 months. Untreated subjects

had a probability of .367 of using drugs in the first 6 months, while treatment completers had a

probability of .205 of using drugs in the first 6 months.
117

CCC Failure

The effect of in-prison DAP treatment upon Community Corrections Center outcomes was

limited to the research subjects who received CCC placements — approximately two-thirds of the

sample. A logistic regression was run on a variable indicating whether an inmate failed to

successfully complete his or her CCC placement. (During this placement, an individual was

ordinarily housed in a contract halfway house but may also have spent the latter part of this

placement on home confinement.)

Logistic regression was used because this analytic method is appropriate when the dependent

variable is binary (Menard, 1995). We present the results using the odds ratios, as was the case in

our previous logistic regression models of arrest, revocation, and drug use.

Traditional Model

Table 54 presented the results of the Chi-square difference between the base models and the

models with additional blocks of variables for all subjects and for males only. There were two

blocks of variables — the additional background factors and the change assessment variables —

that significantly improved the fit of the model for all subjects and for males only (see Table 54). 

Table 55 showed the analysis for all subjects who had CCC placements. The final model included

only those additional blocks of variables that decreased the log likelihood Chi-square so that the

difference between the base model and the model with additional variables produced a Chi-square

that was statistically significant.

Looking at the variables that represented treatment groups, we saw that in the base model the

groups that differed significantly from the referent group — i.e., non-DAP control subjects —

were the 12-month and 9-month DAP-complete groups (GRAD12MO and GRAD9MO). In the

final model, we saw that both the 12-month and 9-month DAP-complete group (GRAD12MO

and GRAD9MO) coefficients remained significant. In both cases, completion of the program was

associated with a lower probability of failure in the CCC. The odds ratio associated with the 12-

month program was .495, and the odds ratio associated with the 9-month program was .676. This

indicated that inmates who had completed the 12-month program were 50 percent less likely to

fail in the CCC than were control subjects from non-DAP sites, while graduates of the 9-month

program were 32 percent less likely to fail in the CCC. Although not reported in Table 55, we

performed t-tests to compare the two coefficients. This test indicated that the coefficients for the

12-month and 9-month program graduates did not differ significantly from each other.

As can be seen in Table 55, women (EFEM) had lower CCC placement failure rates than did

subjects on average. The odds of failure for women were 24 percent lower than for all subjects on

average. Among the background variables that we expected to be related to post-release

recidivism, prior commitments (EPRIORCM) was significant for this analysis. Inmates who had a

prior commitment were 32 percent more likely to fail in the CCC than all subjects, on average. In
118

addition, inmates who were not in the labor force at the time of incarceration — meaning that the

offender was in school, was a homemaker, or was disabled (ELEGITUN) — and inmates who

used stimulants more frequently in the past (STIM_FRQ) had lower odds of CCC placement

failure than did all subjects, on average. 

Three of the six clusters from the motivation for change scores were significant. (Effects vectors

were used to represent groupings.) ECLUST1 inmates are those in the “contemplation” stage and

can be described as individuals who recognize that they have problems and are considering

changing. These individuals were 35 percent more likely to fail the CCC placement. ECLUST2

inmates were characterized as being in the “preparation” stage of motivation for change. This

stage consists of individuals who had actively started to change their behaviors. These inmates

were almost 39 percent less likely to fail than were inmates on average. Finally, inmates in

ECLUST4 represented those in the action phase of motivation for change. This stage involves not

only changing one’s intemperate behaviors, but also avoiding relapse. These inmates were 51.8

percent more likely to fail in the CCC than were inmates on average. Of the three significant

motivation for change coefficients, this last effect does not make sense. One would expect that of

all the stages of motivation for change, the action stage should have been associated with the

lowest likelihood of failure. We found quite the opposite. 

The fit statistics presented at the bottom of Table 55 indicated that both the base and final models

had adequate fits and that the measures of fit for both models were quite similar. The concordance

measure, which assessed how well the predicted probabilities generated by the model matched the

actual outcomes, was 69.5 percent in the base model and 71.7 percent in the final model. The

Hosmer-Lemeshow statistics indicated a good fit of the model to the data.

The logistic regression results for CCC failure for men differed somewhat from those for male and

female subjects together. The variable GRAD12MO, representing the 12-month DAP-complete

group, was significant in the final model for both men and women, but was not significant in the

final model for men exclusively. The variable DISCIPTX, representing inmates terminated from

treatment for disciplinary reasons, was significant in the final model for men but was not

significant in the final model for men and women combined. The odds ratio showed that inmates

in this group were 199 percent more likely to fail their CCC placement than were inmates in the

referent group (non-DAP controls). Lastly, the coefficient for previous stimulant use

(STIM_FRQ) was no longer significant in the model for men only. 

The results for listwise deletion of cases with missing data differed somewhat from results for

models where we imputed values for missing data. The coefficient for age at release (AGERLSE)

was significant in the model with listwise deletion but not in the model with imputed values for

missing data. On the other hand, the model with missing data cases deleted showed that the

coefficients for two of the motivation for change profiles — ECLUST1 and ECLUST2 — were

no longer significant.
119

Bloom Model

For the next analysis, the Bloom approach was used; that is, all DAP site groups, both treated and

not treated, were collapsed into one category (SUBJECT) and the referent group was composed

of the non-DAP control inmates. Comparing this logistic regression analysis to Table 55 — which

decomposed the treatment effects into program completions, withdrawals, and disciplinary

terminations — we found that the models were very similar in most of the statistically significant

background coefficients (see Table 56). However, after collapsing all of the treatment groups into

one group, there was no longer a treatment effect on the probability of CCC failure. 

While the previous model indicated that inmates completing either the 9-month or 12-month

program were less likely to fail in the CCC, the Bloom model indicated no effect of treatment.

This suggested that either the prior effect may have been attributable to the selection bias that

operated in the treatment groups or that selection bias masked the effects of treatment.

Several variables not significant in the traditional model were significant in the Bloom model for

CCC placement failure. In the Bloom model, subjects who reported ever having supported

themselves illegally for at least one year (ESUPILL) were 28 percent more likely to fail than were

all subjects, on average. In addition, subjects who had higher levels of past cocaine use

(COC_FRQ) had lower odds of failure, whereas those who had higher log odds of illicit drug

dependence (DEPLOGTD) had higher odds of CCC placement failure. The frequency of past

stimulant use (STIM_FRQ), significant in the traditional model, was not significant in the Bloom

model. 

As in some of our previous models, the fit statistics indicated that there may have been a problem

of fit to the data in the final model. The Hosmer-Lemeshow goodness-of-fit statistic suggested

that the final model did not fit the data very well. However, the other fit statistics indicated that

the fit was adequate.

The Bloom model for men only did not differ from the Bloom model for men and women

combined. However, the model with listwise deletion of cases with missing data differed

somewhat from the model with imputed values for missing data. The coefficients for the amount

of time served (TIMESRVD) and past use of opiates (OPIA_FRQ), previously insignificant, were

significant in the model with deleted cases. Subjects with longer amounts of time served

(TIMESRVD) and those with more frequent past use of opiates (OPIA_FRQ) had lower odds of

CCC placement failure only in the Bloom model. 

Heckman Model

The results using the Heckman approach, which controls for selection bias, are presented in Table

57. Similar to the findings using the Bloom method of addressing selection bias, we found no

effect for DAP treatment. All the coefficients significant in the traditional model were also

significant in the Heckman model. There was only one coefficient not significant in either the

traditional or Bloom model but significant in the Heckman model: higher frequency of past heroin
 We note, however, that the coefficients for HER_FRQ was marginally significant 91

(p=.10) in the traditional model.

 Our dependent variable — percent of time employed full time — has a distribution that 92

is both limited on the left and truncated on the right. Although it would have been more

appropriate to have used a truncated Tobit analysis to analyze these data, ordinary least squares

procedures typically were robust enough to use on this type of data. 

120

use (HER_FRQ) was associated with a higher probability of CCC placement failure in the

Heckman model. 91

In summary, the results for CCC placement were not consistent when comparing the traditional

approach to the two approaches addressing selection bias. The traditional approach showed that

both the 9-month and 12-month program graduates were less likely to fail their CCC placements,

while both the Bloom approach and the Heckman approach found no significant effect for DAP

treatment. We must point out, however, that the coefficient for the 12-month program graduates

was not significant in the male-only traditional model.

Percent of Time Employed Full Time

Employment as an outcome variable was measured as percent of time employed full time for the

6-month post-release reporting period or until arrest (for those arrested). The time detainees spent

incarcerated for a previous offense (i.e., an offense committed before release from BOP custody) 

was excluded as time available for employment. These data were available only for inmates who

were released to supervision, as opposed to those released directly to the community without

supervision and those going straight to detainers.

The analysis excluded 19 men and 6 women whose post-release employment statuses were

unknown. In addition, subjects who were unemployed because they were retired, disabled,

homemakers, or in school full time were not included in these models, and this group consisted of

63 men (4.9 percent of the men released to supervision) and 21 women (7.5 percent of the

women). For men, the final analysis sample size was 1,168, and for women it was 254.

Traditional Model

An ordinary least squares regression was used to analyze the percentage of time inmates were

employed full time. Analyses were conducted separately for men and women. The results of F 92

tests of significance for the addition of the five different blocks of variables using the traditional

approach were contained in Table 58. The results indicated that for men, there is only one block

of variables — the additional background variables — that did not produce a statistically

significant increase in R-square. On the other hand, for the women there were two blocks of

variables that did not result in a significant increase in R-square: the change assessment variables

and the post-release supervision variables. 
 The effects vector for cohort was included to control for any differences in the national 93

socioeconomic environments facing subjects at release. Because the coefficients for most of the

different cohorts did not differ significantly from the overall unadjusted mean, we felt fairly

confident that there was very little cohort effect, at least as measured in this indirect manner. The

finding that one cohort, of the many different cohorts, differed significantly from the mean may

have indicated a real effect. Then again, it may have reflected the greater chance of Type I error

(rejecting the null hypothesis with sample data when the null hypothesis was actually true in the

population) when examining the multiple coefficients generated with the effects coding scheme.

Because we did not have any theoretical reason to expect that any cohort differed from the others,

we did not feel comfortable in placing much importance on this coefficient.

There were no significant differences between the models where we deleted cases with missing

data and the models where we used all cases and imputed missing data.

121

Table 59 depicted the result of the analyses for men and Table 60 represented the result of the

analyses for women. The fit of both final models was adequate, and the adjusted R-square for

both the men’s and women’s final models was .19. 

Among men, only one of the treatment groups, DISCIPTX, in the base model was significantly

related to the employment outcome, but the level of significance was marginal (.10 level of

significance). Men discharged for disciplinary reasons (DISCIPTX) spent a smaller percentage of

their post-release periods employed full time. Inmates who served more time (TIMESRVD)

worked less in their first 6 months after release. Inmates who had used illegal activities to make

money prior to their incarceration (ESUPILL) worked less after their release. Inmates who had

jobs prior to their current commitments (EWORKJOB) worked during a higher percentage of

their post-release periods. Also, inmates with higher levels of education (GRADEA) worked a

larger percentage of the time during their post-release period.

Looking at the drug variables, inmates who claimed they had used hallucinogens (HAL_FRQ)

frequently in the past worked less in the post-release period than did inmates who had lower past

frequency of this kind of drug use. Furthermore, inmates who had higher log odds of drug

dependence (DEPLOGTD) worked a greater percentage of time during their post-release periods. 

Inmates who received contract treatment services under supervision (ECTRONLY) were

employed more than were inmates who did not receive these services. Furthermore, inmates who

either failed their Community Corrections Center placement (ECCCFAIL) or who had not been

placed in a CCC (ECCCNO) worked less than did all inmates, on average. 

Finally, those who had a higher number of supervision violations (SUPVVIOL), excluding

violations for drug use, worked less time after release than did inmates on average. Inmates from

cohorts released during the first six quarters of release dates (ECOHO1_6) worked full time less

than did all subjects, on average. 93
122

The data for women were similar in that there were no treatment effects on employment in the

final model (see Table 60). Hispanic women (EHISP) spent a smaller portion of their post-release

time employed full time. Two of the work history variables (EWORKJOB and ELEGITUN) were

significant. Women who had worked prior to their commitments or had not been in the labor

force worked a greater percentage of time after their releases. Women with frequent past use of

cocaine (COC_FRQ) had longer periods of full-time post-release employment. For whatever

reason, women for whom post-release information was unknown (EJOB_UNK) worked a smaller 

percentage of their post-release time. Drug dependence (DEPLOGTD), no previous drug

treatment (EPSTDGTX), and no previous mental health treatment (EPSTMHTX) were all

associated with lower employment levels. Women who received drug treatment transitional

services in a halfway house (ETSYES) worked more after their release. Women who participated

in Alcoholics or Narcotics Anonymous (EAAYES) were more likely to work after release. 

The only coefficient that differed, when comparing models for women with and without deletion

of cases with missing data, was educational level (GRADEA). A higher level of education was

associated with longer periods of employment in the model where we deleted cases with missing

data.

Bloom Model

Using the Bloom approach, we found that for both men and women the results were similar to

those obtained using the traditional model. As was the case in the traditional model, we did not

find treatment effects on post-release employment (see Tables 61 and 62). We did find, however,

differences in the control variables that were significant in the Bloom model.

Among men, six variables not significant in the traditional model were significant in the Bloom

model. Variables that were associated with longer periods of post-release full-time employment

were (1) ECLUST6 (which represented the individual who did not endorse any of the motivation

for change scales), (2) having received transitional services during a CCC placement (ETSYES),

and (3) having received a halfway house placement during supervision (HHSE_STR). The

variables associated with shorter periods of employment were (1) having a history of past violence

(EPASTVIOL) and (2) more frequent use of cocaine and barbiturates in the past (COC_FRQ,

BARB_FRQ). Frequent use of hallucinogens in the past (HAL_FRQ), which was significant in

the traditional model, was not significant in the Bloom model.

Among women, the variables significant in the Bloom model but not significant in the traditional

model were (1) educational level (GRADEA), (2) past use of marijuana (POT_FRQ), (3) having a

diagnosis of antisocial personality (EDIAGASP), and (4) having drug-related violations of

conditions of supervision (DRUGVIOL). All, with the exception of antisocial personality, were

associated with longer periods of post-release employment. Only one variable (EAAYES, i.e.,

post-release involvement in a self-help group) that was significant in the traditional model was not

significant in the Bloom model.
123

Bloom models where we excluded cases with missing data showed few differences from models

where we imputed values for missing data. Among men, the models excluding missing data

showed that frequency of past cocaine use (COC_FRQ) was no longer significant and that release

cohort 9 (ECOHO9) was associated with shorter periods of employment. Among women, we

surprisingly found the coefficient for 12-month DAP graduates (GRAD12MO) to have been

significant and negative when cases with missing data were deleted. Women who graduated from

a 12-month program were shown to have shorter periods of post-release employment.

Heckman Model

Our model of employment for men in which we controlled for selection bias showed that there

was no significant DAP treatment effect and that there was no evidence of selection bias (see

Table 63). We also found differences between the traditional and Heckman approaches in the

control variables that were significant. More frequent past use of hallucinogens (HAL_FRQ),

significant in the traditional model, was not significant in the Heckman model. Four variables not

significant in the traditional model were significant in the Heckman model. Age at release from

prison (AGERLSE), not having been in the labor force just prior to incarceration (ELEGITUN),

and having missing information on post-release treatment (ETXMISS) were associated with

working full time a smaller percentage of the time after release. Having received transitional

services during a halfway house placement (ETSYES, the fourth of the variables mentioned

above) was associated with working full time a greater percentage of the time after release.

The Heckman model for women was similar to the model for men in that it found neither a

treatment effect nor evidence of self-selection (see Table 64). A few of the control variables that

were significant in the Heckman model were not significant in the traditional model. In the

Heckman model, women who were involved in prison-based self-help groups (ENRSUPY) spent

a smaller percentage of time after release in full-time employment. In addition, in the Heckman

model, more frequent past use of hallucinogens and marijuana (HAL_FRQ, POT_FRQ) were

associated with longer amounts of time employed full time. Lastly, involvement in a self-help

group after release (EAAYES), significant in the traditional model, was not significant in the

Heckman model. 

In summary, we found no effects for DAP treatment on full-time employment after release from

BOP custody. This was true for both men and women when using either the traditional, Bloom, or

Heckman approach. We did find some positive effects for other treatment received. Women who

received transitional services and men who received post-release contract services spent a greater

percentage of their post-release periods employed full time. With the exception of the variable

capturing a history of previous employment, which is positively associated with post-release

employment, the variables significantly related to post-release employment differ between men

and women. This suggests that the pathway to post-release employment is different across gender. 
 Comparing outcomes across differing programs is problematic because there were 94

differing criteria for acceptance into the programs. Field notes from researchers suggest that there

was little variation in program acceptance but that there was anecdotal evidence of variation in

program retention. In future analyses we will attempt to incorporate information on program

retention rates.

 In previous analyses, the referent group was created using dummy codes and thus the 95

interpretation of coefficients had to do with a particular group differing from the referent group

rather than from all subjects on average. 

 These models omitted gender as a base variable because it was redundant with the 96

effects vectors for each site.

124

Inter-Institutional Comparisons of Outcome Variables

This last section describes the results for models in which subjects who entered treatment were

considered. Two models were presented for each of the outcome variables. The purpose of the

first model was to compare treatment subject group variation among institutions in terms of their

odds of failure — when defined as arrests, arrests or revocations, drug use, and CCC placement

failure — and the percent of time their subjects spent employed full time. This model also 94

included a measure of the amount of time served after discharge from the program, as well as a

measure of program maturity. The various treatment groups were categorized in a manner similar

to that used in the previous analyses. The groups were categorized into DAP-complete, DAPdropout, DAP-disciplinary discharge, and DAP-incomplete, with the DAP-incomplete group

serving as the referent group. However, because these were effects vectors rather than dummy

codes, the interpretation differs. In this analysis, a significant coefficient for either of the treatment

groups signified that the group differed from all subjects, on average.95

The final model supplemented the first by including two different sets of variables than were used

in the previous analyses. The first set of variables consisted of the individual-level control

variables included in all the base models. The second set of variables consisted of the additional 96

background, change assessment, treatment, supervision, and post-release behavior blocks of

variables found to be needed in the final model in the previous analyses for the particular outcome

variable being considered. Because the block of variables found to improve significantly the fit of

the model as compared to the base model varied by outcome, this variation was incorporated into

the inter-institutional comparisons. 

The final model tested whether any institutional differences discovered in the first models held up

after controlling for the individual characteristics of the subjects in treatment and the block of

variables representing the relevant additional block(s) of variables. It should be noted that

individual selection bias issues were not problematic in the models considered here because the

analyses were limited to those subjects who entered treatment. Selection bias would have been

problematic only in models that compared people who entered treatment to people who did not. 
We note that in the model with both supervised and unsupervised subjects, the sample 97

size for the sites with zero cell counts was small. There were 30 cases from Danbury, 19 from

Dublin, 14 from LaTuna, and 27 from Three Rivers. Also, we note that of the 139 cases from

Alderson (a minimum-security female site), there were only five individuals who were arrested for

new offenses, thus indicating a low failure rate for this site. 

125

Arrests and Drug Use

For each of the three types of failure — new arrests only, arrest or revocation, and drug use —

the final model was a significant improvement in fit over the model with only the site effects

included. This can be easily computed from the -2 Log Likelihood statistics presented in the

tables. For example, in Table 66 we see that -2 Log Likelihood for the sites-only model was

730.505 with 24 degrees of freedom. By comparison, -2 Log Likelihood for the full model was

584.937 with 66 degrees of freedom. Even with a difference of 42 degrees of freedom in

comparing the respective Log Likelihoods, the difference of 145.568 was clearly significant at p <

.05. 

Arrest as Failure

This discussion about the models of failure for all treatment subjects focuses on the effect each 

institution had in raising or lowering the log odds of failure for all subjects who entered treatment

at that institution. As such, we did not focus upon interpreting the effects of the different

treatment groups (COMPLETE, DISCIP, and WITHDRAW), but instead we highlighted the

effects of the institutional effects vectors. The coefficients for the institutional effects vectors

demonstrated the impact that the institutions had in raising (positive coefficient) or lowering

(negative coefficient) the odds ratios for their respective treatment subjects in comparison to all

subjects from DAP sites when taken together. Specifically, the coefficients show how much the

subjects from that institution differed in log odds from the overall, unadjusted log odds for all

treatment subjects.

The sites-only model of arrests omitted cases from five sites. Four of these sites — Danbury and

Dublin (female sites), as well as LaTuna and Three Rivers (male sites) — were deleted because

there were no failures there, thus creating zero cell counts. Cases from Alderson (EALDSITM)

were deleted because of the covariate pattern that led to excessively large parameter estimates.97

Regarding the sites-only model of arrest as failure for both supervised and unsupervised subjects,

the results in Table 65 demonstrated that no institution had a significant coefficient at the .05

level. Individuals who spent more time in prison after leaving the treatment program

(TIMETORL) were less likely to have been arrested, although the odds ratio was only 4 percent

lower than it was for all treatment subjects on average. In the base model of arrest as failure for

supervised subjects only, we found that one site’s subjects — the site of Oxford (EOXFSITM) —

had significantly higher odds of rearrest (see Table 66). We found the same effect for time in

prison after treatment (TIMETORL). 
126

The results (reported in Table 65) for the final model indicated again that there were no significant

coefficients for any sites. In addition, when controlling for the individual characteristics of the

treatment subjects at each site, the coefficient for the amount of time spent in prison after

discharge from DAP (TIMETORL) was not significant. The results for supervised subjects only in

Table 66 once again showed no significant coefficient for any site or for time spent in prison after

discharge from DAP. 

The fit statistics indicated that the final models fit the data in a superior fashion than they did to

the base model, with the exception of the model for supervised subjects, where the HosmerLemeshow goodness-of-fit statistic was better for the base model.

An analogous model for men only showed an identical pattern of site effects for supervised

subjects only.

Arrest or Revocation as Failure

In the sites-only model of failure with failure defined as either an arrest or a revocation, the results

in Table 67 showed that two sites had significant coefficients: EMNASITM (Marianna) and

ESHESITM (Sheridan). Inmates from Marianna had odds of failure that were 102 percent higher

than subjects on average, and inmates from Sheridan had odds of failure that were 128 percent

higher. Once again, a greater amount of time spent in prison after discharge from the DAP

(TIMETORL) was associated with a lower odds of failure. This finding is contrary to what

program providers had anticipated. 

Controlling for the individual-level differences in subjects at the different treatment sites, we once

again saw that the sites-only model results were misleading. The subjects at neither of the two

sites significant in the sites-only model were significant in the final model. On the other hand, the

results for the final model in Table 67 suggested that treatment subjects from Phoenix

(EPHXSITM) and Rochester (ERCHSITM) had higher odds of failure. Once again, TIMETORL,

the time spent in prison after involvement in DAP, was not significant in the final model.

The fit statistics did not indicate any problems of fit to the data for either the base or final models.

Once again, the fit statistics provide further indication that the final model was superior in

performance to the base model.

As was the case with failure defined as a new arrest, the models for men and women were similar

to those for men only. However, in this case the model for men only showed only one of the sites

found significant in the combined model to remain significant in the model for men only (Phoenix

— EPHXSITM).

First Detected Drug Use as Failure

Once again we were faced with limitations in the analysis due to sites having zero cell counts or

excessively large parameter estimates. A total of 14 cases were omitted from the analysis. 
 We note, however, that the coefficients for LaTuna and Marianna were near significance 98

(p<= .10). 

 This analysis excludes four female subjects from Danbury. All four of these subjects 99

completed their CCC placements, thus creating a zero cell.

127

There were no drug use failures at Dublin (EDUBSITM). For Danbury (EDANSITM), with a

sample size of eight, the parameter estimate was excessively large. The results for the sites-only

model in Table 68 suggested that treatment subjects at LaTuna (ELATSITM), Marianna

(EMNASITM), and Oxford (EOXFSITM) had odds ratios for drug use that were higher than was

the norm for all treatment subjects. In addition, we found that a longer amount of time served

after discharge from treatment (TIMETORL) was associated with lower odds of failure. As in the

previous model for arrests or revocations, when we controlled for the individual-level

characteristics and the post-release treatment and supervision received, there was a very different

pattern of effects for the sites. The results for the final model in Table 68, though, showed that the

effects for ELATSITM, EMNASITM, and EOXFSITM were no longer significant. On the other 98

hand, there was now a significant coefficient for EMCKSITM (McKean). Individuals from

EMCKSITM had odds that were 95 percent lower than those for all treatment subjects on

average. We do note, however, that the coefficient for EMCKSITM was near significance in the

sites-only model (p=.07). Once again, the amount of time spent in prison after DAP involvement

was not significant in the final model even though it was significant in the sites-only model.

The concordance rate and Somer’s D measures of fit suggested that the final model provided a

better fit than did the sites-only model. The concordance rate using the sites-only model was 66

percent, whereas it was 83 percent in the final model. The Hosmer-Lemeshow statistic indicated

an adequate fit for both models. 

As was the case with failure defined as a new arrest, the site effects in the drug use models for

male and female treatment subjects combined were similar to those for men only.

CCC Outcomes

This section reports the results for models of CCC failure in which only subjects who entered

treatment at one of the DAP sites and who received a CCC placement were considered. Two

models were presented in Table 69; the first model compared only how treatment subjects varied

across institutions in terms of their odds of failure. The second model supplemented the first by 99

including the sets of variables used in the traditional and Bloom full models — the variables found

to produce a significant difference in the fit of the model over the base model. The final model

was a significant improvement in fit over the model with site effects only. 

In the site model, only two institutions had significant coefficients. Individuals from Lexington

(ELEXSITM) — a female site — had lower odds of failing their CCC placements, but those from

LaTuna (ELATSITM) — a male site — had higher odds of failing. The effects for these sites
 We did not discuss models where we deleted cases with missing data for the other 100

outcome measures presented in this section on inter-institutional comparisons. Earlier in this

chapter, we noted that discussion of such models would occur only when there were differences

between these models and the models with estimated values for missing data. 

128

remained when we controlled for all the variables included in the full model. The log odds ratio

associated with ELEXSITM indicated that women from this site were 93 percent less likely to fail

their CCC placements. The odds ratio for ELATSITM, on the other hand, indicated that these

male inmates had an odds ratio of failing that was 780 percent higher than was the case for all

subjects on average. 

The equivalent model of male treatment subjects indicated that while the coefficient for LaTuna

(ELATSITM) remained significant, there were two additional sites with significant coefficients —

Butner (EBUTSITM) and Fairton (EFAITSITM). Individuals from Butner (EBUTSITM) were

58 percent less likely to fail and individuals from Fairton (EFAISITM) were 50 percent less likely

to fail their CCC placement than were all treatment subjects on average.

Employment Outcomes

The following section presents the inter-institutional results for post-release employment as the

outcome of interest. As with the other employment models, these are presented separately for

men and women. 

An F test for the significance in the R-square difference between the base and final model for both

men and women indicated that the R-square was significantly higher in the final model. The sitesonly model for men in Table 70 showed several sites to be significant at the .05 level. For

EMRGSITM (Morgantown’s) and EYANSITM (Yankton’s) male DAP treatment subjects, there

was a positive relationship with percent of time employed full time. On the other hand, the

relationship was negative for EFAISITM (men at Fairton) and EOXFSITM (men at Oxford).

When controlling for other variables in the full model, none of these sites remained significant.

Rather, we saw a positive coefficient for EMNASITM (males at Marianna). In the sites-only

model, COHTIME — the time served in prison after completion or discharge from the DAP —

was significant at the .01 level. The longer a male inmate stayed in prison after treatment, the less

time he was likely to be employed full time after release. However, COHTIME was not significant

in the final model. In the model excluding cases with missing data, the coefficient for Marianna

was not significant. 100

A similar analysis for women — reported in Table 71 — uncovered no site effects in the sites-only

and full models. This is not surprising given there were no treatment effects on post-release

employment for women in either the traditional or Bloom models. 
 In our summary, we considered a variable consistently significant across all three 101

modeling approaches when a variable was significant at p<.05 in all three analytic strategies or

when a variable was significant at p<.05 in two analytic strategies and marginally significant at p<

.10 in the third analytic strategy. 

 Because the Heckman models are not event history models with time-dependent 102

covariates, the variable representing post-release employment is the percentage of the post-release

period employed full time. 

129

Summary of Results

The following provides a summary of the effects of treatment for each outcome measure, and also

it highlights other factors consistently related to these outcomes. While the background factors 101

related to the outcomes varied across the models, we noted that there was great consistency

across all three models for those factors known to be important predictors of the outcome being

considered. Most of the differences, not surprisingly, occurred in the Bloom model, where we had

to weight comparison cases. There were some differences when we compared models of men and

women combined to those of men only and when we compared models with imputed values for

missing data to models with listwise deletion of cases with one of more missing values. It is

noteworthy, however, that these differences generally did not arise for either the in-prison

treatment indicators or factors known to be related to the outcome measures.

Arrests

The traditional approach showed a positive effect for 9-month program graduates but not for 12-

month program graduates when modeling arrests for either all subjects or supervised subjects

only. We found inconsistent results using the two different approaches that addressed selection

bias issues. We found no DAP treatment effect using the Bloom approach, but we did find an

effect for DAP treatment completers when we used the Heckman approach, which directly

controls for selection bias. In addition, the Heckman approach found evidence for selection bias:

individuals who completed treatment were at a higher risk of arrest. 

Women had lower failure rates across all three modeling approaches. Two background variables

were consistently related to post-release arrests: age at release and prior commitments. Older

individuals had reduced odds of failure, whereas those with prior commitment had higher odds of

failure. Among subjects released to supervision, we found several post-release factors consistently

related to arrest. Higher rates of urinalysis testing, a greater number of hours worked after

release, and living with a spouse were associated with reduced odds of arrest.102

Arrests and Revocations

When we defined failure as an arrest for a new offense or revocation, we found positive effects in

only one of our analytic strategies, the Heckman approach. This approach also found a significant
130

coefficient for the selection bias factor: individuals who entered and completed treatment were at

higher risk for failure. The three background factors consistently related to our outcome measure

of arrest for a new offense — gender, age at release, and prior commitments — were associated

also with our outcome of arrest for a new offense or revocation. Several additional treatment

variables also were found significant across all three analytic strategies. Individuals who received

both contract and non-contract treatment after release had lower odds of failure whereas those

with post-release treatment information missing had higher odds of failure. Concerning the

supervision variables, individuals who failed their CCC placements had higher odds of failure

whereas individuals with higher urinalysis testing rates had lower rates of failure. Not surprisingly,

individuals with drug-related supervision violations had higher odds of failure. Lastly, individuals

who were employed a greater percentage of time after release were less likely to have an arrest or

revocation.

Drug Use

Using all three modeling approaches, we found treatment effects for failure when it was defined as

drug use. Having received DAP treatment lowered the odds of post-release drug use. In the

traditional model we found positive effects of DAP treatment for both the 12-month and 9-month

program graduates. We also found positive effects for DAP treatment in the Bloom and Heckman

models. 

Several background variables were consistently related to post-release drug use: being female, 

having prior commitments, and having higher levels of past use of marijuana. The additional

treatment variables significant across all three models included in-prison outpatient treatment and

a combination of contract and non-contract post-release services. Receiving outpatient treatment

decreased the odds of drug use, whereas receiving both contract and non-contract services

increased the odds of drug use. In all three models, the supervision variable of CCC placement

failure increased the odds of drug use. The significant post-release behaviors included having nondrug related supervision violations and post-release employment. All of these behaviors, except

for supervision violations, were related to decreased odds of failure.

CCC Placement Failure

Our results for CCC placement failure were not consistent across the three modeling approaches.

We found positive effects for DAP treatment when we used the traditional approach, but not

when we used either of the two approaches that addressed selection bias issues. The Heckman

model did not uncover any effects for self-selection. Across all three modeling approaches, several

background variables were consistently significant. Women had lower odds of CCC placement

failure. On the other hand, individuals with prior histories of incarceration had higher odds of

CCC placement failure. We also found several of the motivation for change clusters significant.

Inmates in the “contemplation” and “action” phases of motivation for change were more likely to

fail their CCC placements, whereas those who were in the “preparation” phase of motivation at

the time of admission to treatment were less likely to fail.
131

Employment

Among both men and women, we found no significant effects for DAP on post-release

employment and no evidence of selection bias. The other factors associated with more positive

post-release employment outcomes differed between men and women.

Among men, we found positive effects for receiving post-release contract services. The

background factors consistently related to increased time in full-time employment in all three

modeling approaches included a higher education level, being employed just before the most

recent incarceration, higher log odds of drug dependency, and a longer time served. The

background factor negatively related to post-release employment was having previously

supported oneself mainly through illegal sources. Not having received a CCC placement and

having failed a CCC placement were consistently negatively related to post-release employment,

as was violating a condition of supervision for reasons other than drug use.

In all three models for women, we found that having received transitional services during a

halfway house placement was related to longer periods of post-release employment. The

background factors negatively related to post-release employment across all three modeling

approaches were being Hispanic, having higher log odds of drug dependency, having an unknown

employment status before incarceration, and having a history of previous drug or mental health

treatment. Another factor positively related to post-release employment included being employed

before the most recent incarceration. Unlike our results for men, there were no post-release

behaviors related to increased success in post-release employment for women. 

Inter-Institutional Comparisons

When controlling for background factors, we found little evidence of differences among DAP

treatment subjects in outcomes due to site effects. There were no site effects for arrests for a new

offense or for post-release employment among women. For the other outcome measures, site

effects were limited to one or two sites and were not consistent across the various outcome

measures. We note that our attempt to identify site effects was hindered by small sample size at

several sites. These sites often could not be included in the analyses. In addition, several sites had

no failures and also could not be included in the analyses. 
132

CHAPTER 8: SUMMARY AND CONCLUSIONS

The results of this preliminary analysis compel us to join the growing chorus of researchers who

hold that treatment programs in prison, when properly implemented, can and often do work. Our

findings consistently showed that the residential Drug Abuse Treatment Programs (DAP’s) in the

BOP contributed to a reduced likelihood of post-release failure, when failure was defined as

renewed drug use. In addition, our findings showed positive effects of treatment on arrests for a

new offense in two of our three modeling strategies — both of which controlled for selection bias.

We also found that DAP treatment had a positive effect on arrests and revocations, although this

finding was limited to the results obtained by using the Heckman approach, one of the two

approaches that controlled for selection bias. 

We found marginal support for the proposition that successful completion of a DAP increased the

likelihood of completing a CCC placement successfully; the positive effect was found in only one

of our three modeling strategies, the traditional approach. We had limited confidence in this

finding because it was limited to an analytic strategy that does not address selection bias issues.

Lastly, we do not find support in this analysis for the proposition that DAP treatment had a

significant effect on post-release employment.

We also found that men were more likely to fail than were subjects on average, when failure was

defined as either a new arrest, an arrest or revocation, post-release drug use, or an unsuccessful

completion of a CCC placement.

Selection bias is a pernicious problem in conducting evaluation research, one that is often not

recognized or adequately considered and addressed in research design. We attempted to deal with

selection bias in as many as three different ways. While this has added significantly to the materials

presented in this report, it has provided greater confidence in our results and in the conclusions

we have drawn because of the increased rigor and scrutiny we brought to bear on our empirical

analyses.

The substantive results across the two modeling approaches addressing selection bias issues were

consistent for our outcome measure of drug use but not for our outcome measure of arrest for a

new offense or of arrests and revocations. Thus, it is clear that we cannot assume that two

different methods of addressing selection bias will yield identical results. This finding suggests the

need to use various analytic procedures when conducting treatment evaluations in order to

increase confidence in our findings. Although the findings were similar when comparing a

modeling strategy that does not control for selection bias (e.g., our traditional model) with ones

that do incorporate such controls, for one of our outcome measures — arrests — we point out

that such consistency was fortuitous. In the traditional model of arrests, we found weak evidence

for a treatment effect. It is likely that such an effect was uncovered in this model despite the

direction of selection bias because, as was apparent in the Heckman model, the effect was a

strong effect. Lastly, we note that the Heckman model was able to provide important information

about the nature of self-selection into treatment. We found that our treatment subjects were at a
133

higher risk of experiencing negative outcomes than were individuals not self-selecting into

treatment when our outcome measures were arrest for a new offense, arrest or revocation, and

drug use. We did not, however, find evidence of selection bias for our outcome measures of CCC

placement failure and post-release employment. 

The positive results we found for arrests, arrests and revocations, and drug use using the

Heckman approach — our most efficient method of detecting treatment effects — occurred

within a multi-site context of 20 programs serving both male and females and operating within

different security levels and different geographic regions. Thus, our results have greater

generalizability than would a study with treatment subjects from one or two treatment programs.

We organized this discussion section around the effect that selection bias had on the different

outcomes of interest: a new arrest, a new arrest or a revocation, evidence of drug use, CCC

placement failure, and post-release employment behavior. We also summarized the findings

related to the effectiveness of the DAP’s as implemented at different BOP facilities.

Selection Bias Reviewed

Traditional Approach

Before discussing the results for the different outcomes, we will briefly review our methodological

strategy regarding selection bias. Our first approach related to what we called the traditional

models. In the traditional approach, our outcome models compared treatment subjects — those

identified as having completed treatment, having voluntarily withdrawn from treatment, having

been removed from treatment for disciplinary reasons, or having not completed treatment for

other reasons — to non-DAP control subjects. The logic of these models was that comparisons to

non-DAP control subjects were most defensible, as non-DAP control subjects never had the

opportunity to refuse treatment. DAP comparison subjects, in contrast, had the opportunity to

accept treatment but did not do so. Their lack of participation, by definition, made them different

from DAP treatment subjects in a known way: they did not participate in programming. However,

because we did not know the reasons why DAP comparison subjects did not accept treatment —

and we did not think it reasonable to assume that failure to participate in programming was a

random decision on the part of individuals — we did not know how subjects who refused DAP

treatment differed from DAP treatment subjects. These unknown differences between DAP

treatment subjects and DAP comparison subjects represented selection bias. Were we to have

compared DAP treatment subjects to DAP non-treatment subjects, we knew that our analyses

would have been biased by this unknown process that differentiated those who volunteered for

treatment from those who did not.

In the traditional models, we compared DAP treatment subjects to non-DAP subjects because we

assumed that at least some of the non-DAP subjects would have volunteered for treatment had

they been at a DAP facility and had the opportunity. These non-DAP subjects who would have

volunteered, then, would likely have been similar to our DAP treatment subjects on the dimension
Right censoring occurred for study participants who had not experienced the post- 57

release outcome in question. These subjects remained at risk of experiencing the event, and our

event history models took the censoring of the observations at the 6-month point into account. 

134

of what leads people to volunteer for treatment. However, the problems that remained, and what

made this solution less than ideal, were related to the non-DAP controls also having had an

unknown percentage of subjects who would not have volunteered for treatment (creating the

double quandary of not knowing the numbers and not knowing the selection bias process related

to volunteering) and an unknown percentage of subjects who would have volunteered but would

not have successfully completed the programming. However, we still did not have a pure

comparison of “like” people who did and did not receive treatment. Especially problematic in this

traditional approach was the failure to account adequately for those who failed DAP treatment.

Despite some similarity of our traditional model to models used in previous studies, we tried to

improve upon these previous studies in several ways. First, our traditional approach included a

comprehensive set of control variables. Second, we used event history techniques that most

adequately controlled for the right censoring of data (Allison, 1984; Blossfeld and Rohwer,

1995). Third, our study was multi-site and this increased the generalizability of our findings. 57

Bloom Comparisons

We referred to our second approach for dealing with selection bias issues as the Bloom approach. 

If we assumed that (1) DAP and non-DAP sites had similar types of inmates (we believe a very

reasonable assumption) and (2) DAP and non-DAP sites differed only with respect to whether

DAP treatment was available (on average, a reasonable assumption), and if we randomly sampled

from the population of DAP and non-DAP inmates and measured their post-release behaviors, we

expected that, on average, inmates from DAP sites would have better post-release outcomes

because many of those inmates received DAP treatment. Selection bias was not a problem in these

comparisons because we were not distinguishing between those who volunteered and those not at

the DAP sites. The problems associated with those who finished DAP treatment and those who

did not were not at issue for the same reason. 

However, this approach had its own problems. While we had a “pure” comparison of inmates at

DAP sites to inmates at non-DAP sites, we did not have a “pure” comparison of treated subjects

to non-treated subjects. The reason for this was obvious; less than 100 percent of the inmates at

the DAP sites volunteered for and completed DAP treatment. As such, the comparisons of DAP

inmates to non-DAP inmates were contaminated by the non-volunteers and the non-completers.

In practical terms, this meant that it was harder to uncover the effects in the models using the

Bloom strategy of comparison. On the plus side, however, if we uncovered effects — and we did

for some of the outcomes — we could be fairly certain that the effects were due to the presence

of treatment at the DAP sites, given our two assumptions noted above. In essence, the second

approach, i.e., the Bloom approach, was an unbiased approach (at least in terms of individual

selection bias), but an inefficient method for uncovering treatment effects.
135

Modeling Selection Bias

Our third approach attempted to model the selection bias process based upon the econometrics

approach of Heckman (1979) and Maddala (1983). While this approach required us to make

much stronger assumptions about the nature of the unknown selection bias process, it did provide

us with much more powerful tests of the treatment effects than did the Bloom approach, and it

allowed us to make better use of the DAP comparison groups. Also, it allowed us to have some

understanding of the differences between the treatment and non-treatment groups at the DAP

sites. There was, however, one caveat to using the Heckman approach. Both in this particular

context and in general, we were unable to affirm with complete certainty that all relevant factors

related to selection bias had been identified. In addition, because we were dealing with the

extreme left tail of the log-normal distribution for survival, even the conditional probabilities we

calculated for the various outcome measures must be interpreted with caution. 

The Heckman approach rested upon more statistical assumptions than did our other two

approaches. However, the general congruence between the results produced for outcomes when

modeled by the first two approaches — the traditional and Bloom approaches — and the

Heckman approach, when applied, gave us much more confidence in our results.

Summary Findings

This report focused on assessing whether there was evidence of treatment effectiveness and as

such was concerned primarily with the effects of the control variables in masking or uncovering

the effects of treatment. Therefore, our discussion of the various outcomes was limited to a

discussion of the effects of treatment when other factors were controlled.

New Arrest as Post-Release Outcome

Post-release failure as indicated by a new arrest was modeled using all three approaches.

Consistent findings from all three approaches showed a positive effect of in-prison drug abuse

treatment in lowering the likelihood of post-release failure. In terms of in-prison residential drug

treatment, in the traditional model, 9-month DAP completion subjects were shown to have odds

of arrest that were about 41 percent lower than were those of non-DAP controls for all subjects

and 34 percent lower for supervised subjects only. 

The 12-month completers also had lower odds of arrest than did the non-DAP controls, but not

statistically significant lower odds. It is useful to note that because there were fewer 12-month

completers, an effect for 12-month completers was more difficult to find. Comparing the odds of

arrest for 9- and 12-month DAP completers in the final traditional model revealed no statistically

significant difference between the two groups for both the model of all subjects and the model of

supervised subjects only. 
136

The Bloom comparisons did not show a statistically significant effect for treatment in the final

model. As previously mentioned, this test of a treatment effect was less efficient because we did

not have a direct comparison of treated to non-treated subjects. In addition, as noted in Appendix

C, the Bloom approach can yield relatively poor estimates when the proportion of non-DAP

control cases is small. Our non-DAP control sample was small — it was about 20 percent of the

sample. Lastly, it is not surprising that finding a treatment effect using the Bloom approach would

be even more difficult given the results in the traditional model, where 9-month graduates but not

12-month graduates had significantly lower odds of failure.

The findings from the Heckman approach showed a strong treatment effect for the new arrestsonly outcome. This strong result was due, in part, to the ability of these models to include nontreated subjects at DAP sites as part of the comparison group; the other models could not include

the non-treated DAP site subjects as part of the direct comparison group. The model provided a

measure of the selection bias effects through the COVARIAN parameter. The negative direction

of the coefficient for COVARIAN in the arrest models told us that treatment subjects, in fact,

were at a higher risk for failure than were those who did not select treatment, making our findings

all the more significant. 

New Arrest or Revocation as Post-Release Outcome

Unlike our findings for failure defined as a new arrest only, we found a treatment effect when we

defined failure as either an arrest or revocation when using the Heckman analytic strategy. In the

traditional models of failure, we saw that what appeared to be a treatment effect in the base model

disappeared when we added the further controls in the final model. In addition, the Bloom model

was unable to uncover a treatment effect for the new arrest or revocation outcome. We do note,

however, that we found some evidence of an indirect treatment effect in the traditional model. 

This indirect effect would operate through the intervening post-release behavior variables of

employment and violation of conditions of supervision.

We cannot definitively say what accounted for the lack of an effect for treatment when arrests and

revocations were considered equivalent failures, especially when the models of arrest alone

showed that DAP treatment lowered the odds of failure. Further research is clearly needed to

disentangle this puzzle, especially given parallel findings reported by Saylor and Gaes (1996) in

their analysis of the BOP’s Post-Release Employment Project (PREP). In the PREP study, Saylor

and Gaes found also a greater ability to model arrests alone than arrests and revocations

considered together, suggesting some systematic mechanism at work. 

It may well be that when we considered arrests and revocations as equivalent indicators of postrelease failure, we actually were measuring two very different phenomena. In general, the

behaviors leading to a revocation differed from those leading to arrests, with many revocations

resulting from technical violations. There may have been more discretion in whether to revoke an

individual for a technical violation (in particular for drug use), and, in addition, revocation might

have been associated with greater levels of overall supervision. Revocation may also have

reflected, in part, the policy and philosophy of a particular district. As previously noted, the
137

tolerance for positive urinalysis test results differed by district, and many revocations were a

response to drug use. 

Furthermore, an arrest for a revocation that results in re-incarceration removed an individual from

the risk pool for an arrest for a new offense. It may have been, as we suggested above, that

considering arrest and revocation as equivalent failures was akin to comparing apples and

oranges. In fact, it may have been necessary to model them as competing risks of failure. We plan

to pursue this issue in a future report. We also plan to include level of supervision differences

between districts in future models.

First Detected Drug Test as Post-Release Outcome

Post-release failure, defined as the first detected drug use, was modeled following the same

strategy used for arrest only, i.e., using all three approaches. All three approaches found that DAP

treatment lowered the odds of failure. In fact, the effect of treatment in reducing the odds of being

detected for drug use was the most statistically powerful effect found in these analyses. As seen in

the traditional final model, completing the 12-month DAP treatment program lowered the odds of

detection of drug use by about 49 percent in comparison with detection in non-DAP subjects.

Completing the 9-month program lowered the odds by about 32 percent, again in comparison

with non-DAP subjects. As noted previously, because DAP treatment subjects were at a higher 

risk for post-release failure than were non-treatment subjects, the results for the traditional models

probably understated the effect of treatment in lowering the odds of this type of post-release

failure.

The results for the final drug use model using the Bloom comparison approach were consistent

with the traditional models in pointing to an effect of treatment in lowering the odds of postrelease failure. Subjects from DAP sites were approximately 19 percent less likely to have failed

(when failure was defined as drug use) than were subjects from non-DAP sites. As noted

previously, this approach almost certainly underestimated the treatment effect because treatment

volunteering and completing rates were not 100 percent or close to 100 percent. As such, taken

together with the results from the traditional models, there appeared to be strong reason to claim

that treatment was having an effect in lowering the odds of post-release detection of drug use, at

least during the initial 6-month follow-up period. This was further confirmed by the consistency of

the Heckman models in finding a significant effect for treatment.

CCC Placement Failure

An effect for DAP treatment on CCC placement failure was found only when using the traditional

approach. We did not find any positive treatment effects when using either the Bloom or

Heckman approaches. In the traditional final model, 12-month DAP-completers were 50 percent

less likely to fail in a CCC than were comparison subjects from non-DAP sites, and graduates of

the 9-month program were 32 percent less likely to fail CCC placement. The mixed findings of a

treatment effect in the traditional models, and a non-finding in both the Bloom and Heckman
The DAP’s did incorporate resume writing and job interviewing skills, as well as some 58

discussion of work skills. Nonetheless, this could not be considered to be equivalent to vocational

rehabilitation or habilitation. 

138

models, suggested that there was no treatment effect. The results were consistent for both

approaches, which addressed selection bias issues. 

Post-Release Employment

The percentage of time during the first 6 months following release spent employed full time was

also modeled using the first two approaches, the traditional and the Bloom approaches. While the

models demonstrated that several of the variables included in our models as control variables were

significantly related to employment success, we were unable to uncover any in-prison treatment

effect upon employment. However, we did find that post-release treatment for men and self-help

group participation for women were related to post-release employment success. The absence of

an in-prison treatment effect was not surprising because the DAP programs did not focus upon

vocational rehabilitation. Maintaining a crime-free lifestyle, one of the principal goals of the 58

DAP program, implied being employed in a legitimate occupation. Yet, affecting the ability of an

individual to obtain and keep employment — especially when the individual has limited job

experience and skills — often requires specialized vocational services and the acquisition of job

skills.

Inter-Institution Outcomes

The remaining question considered in this section is whether DAP’s work better at some locations

than at others. The short answer to the question was that it did not appear that our models

adequately discerned institutional differences. The institutional effects were not an adequate proxy

for differences in the quality of treatment program and staff. There were only a couple of sites at

which we saw subjects doing significantly better or worse on average after entering treatment.

We limited our discussion of institutional differences in treatment effectiveness to the two

outcome variables for which we had the most reason to believe that treatment would have an

effect: (1) arrests and (2) detection of drug use as indicators of post-release failure. In the final

models for these outcomes, there were only a couple of significant departures for groups of

subjects treated at a specific institution from the overall odds for all treatment subjects. For the

final model of arrests, no institution was significantly different in its risk of failure from that of all

treatment subjects. For the final model of first detection of drug use, subjects treated at McKean

had lower odds of failure. We note, however, that men-only models show a somewhat different

pattern. As yet, we have not been able to differentiate a real effect from an effect resulting from

the increased likelihood of a Type I error (i.e., a “false positive”).

Unfortunately, the models considered in this analysis did not appear to be very well suited to

capturing the differences among institutions in how DAP’s were operated and how, consequently,
 It must be noted that this statement is true only for individuals who complete the in- 59

prison program component and receive a halfway house placement.

139

this affected the post-release outcomes of DAP treatment subjects. For the most part, we could

not discriminate between more and less successful DAP’s with our current empirical analyses.

One possible explanation for our inability to discriminate appropriately between DAP’s may have

been due to a confounding between site and type of treatment subject categories. Anecdotal

information from program staff indicated that programs differed in their philosophies and in their

implementations of discharge policies. Some program directors were known to have been

reluctant to discharge any participant unless required to do so by non-treatment program policies,

whereas others readily discharged individuals who did not participate or did not cooperate with

the treatment regimen. 

Transitional Services

The primary interest of this preliminary report was in the effectiveness of the DAP’s. The results

discussed indicated that DAP’s, defined as the in-prison residential programs, had positive effects

on several outcomes. Nonetheless, some discussion was needed about the BOP’s transitional

services provided in a halfway house. This was particularly important because the DAP’s, in

general, included transitional services components. The DAP was viewed as consisting of a

continuum of care that included both the in-prison residential component and the transitional

services received in the halfway house. In that respect, one would not have been able to 59

distinguish whether in-prison treatment followed by treatment in a halfway house setting was

better than was in-prison treatment alone, or whether, for that matter, treatment in the halfway

house alone was just as effective in-prison treatment alone. Ideally, we would have liked to assign

relative weights to each component — the in-prison component and the halfway house

component. 

The only effect of transitional services was seen when modeling employment outcomes for

women. Women who received these services spent a greater percentage of their post-release time

employed full-time. This effect occurred even though there was no evidence of any effect of DAP

treatment on employment for either men or women. 

Nonetheless, it would be premature to draw conclusions about the role of transitional services.

The models contained in this report did not account for several possible confounding factors.

First, not all individuals receiving transitional services completed their community corrections

(CCC) placements. Not completing transitional services was possibly confounded with CCC

failure. Comparison subjects and individuals who enrolled in but did not complete a DAP were

coded as having received transitional services regardless of whether they completed their

placements or failed. CCC placement failures may have received very little service because they

failed shortly after release from prison. 
140

This confounding was also the case for DAP graduates, but in a different manner. For DAP

graduates, failure of transitional services was synonymous with CCC placement failure and viceversa, according to policies relating to DAP graduates. The results contained in this report

showed that CCC placement failure was related to the following outcomes: post-release

employment (for males), arrests or revocations, and drug use. Individuals who did not

successfully complete this placement were more likely to fail. It is also noteworthy that, although

not significant in the traditional model for arrests, CCC failure was significant in the Heckman

model, which controlled for self-selection bias.

Conclusions

Positive results reported in this interim report of residential drug abuse treatment join the growing

body of research suggesting that treatment programs for offenders work if properly implemented. 

We found that DAP treatment did seem to “work,” at least in terms of lowering the odds of

experiencing some critical post-release outcomes: new arrests, arrest or revocation, and detection

of drug use. The results presented here, though, did not detect the same impact of DAP treatment

on post-release employment success (defined narrowly as percentage of time employed full time)

or CCC placement failure. Questions remain as to the suitability of these analysis models. 

In summary, it seems that we have made a good case for the proposition that treatment has an

effect on important types of post-release behavior in the first 6 months following release.

However, the effects for men appear to be less favorable than do the effects for women. 

Although an individual’s first 6 months are considered crucial to successful reintegration, this time

frame represents only a portion of the period crucial to reintegration. Recidivism rates generally

are highest within the first year and — while lower after that — are still high for another year or

two. Therefore, these results must be interpreted with some caution. Our future analyses will

evaluate whether these effects and the differences in outcomes between men and women are

sustained over a longer follow-up period.

Future Research Efforts

In the future, we will consider the longitudinal aspects of following subjects for the remainder of

the study’s 3-year period and address newly arising substantive questions. We have attempted to

identify some of the limitations of the conclusions we have drawn, to suggest potential issues to

be addressed when data are available for the entire pool of subjects, and identify general issues

important to future research. The major areas we covered relate to the treatment component, to

measuring program quality, to proximal outcomes, to gender concerns, to disentangling arrest and

revocation as outcome measures, and to a general recognition related to understanding the

treatment process.
 Effects for models of new arrests only were not identified because the additional 60

treatment variables were not included in the final models. This block of variables did not make a

significant difference in the likelihood ratio test statistic.

 It must be noted that the post-release treatment variables were not contained in the 61

Heckman approach models for drug use. This will be considered in future research.

141

Understanding the Treatment Component as a Continuum

While the focus of this report has been upon the effects of DAP treatment, it is important that

treatment be understood as a continuum of programs, not limited solely to services received while

subjects are incarcerated. To understand the treatment services, we must examine more broadly

the role of treatment across its entire spectrum, including post-release treatment. For example, we

know that, overall, almost half of the research subjects under supervision were required by their

Probation officers to seek drug or alcohol treatment services. Our findings suggested that the

post-release services had an effect on outcomes of interest independent of the in-prison DAP

treatment; while there was no evidence of DAP treatment having an effect on post-release

employment, post-release services did seem to have an effect. For men, having received contract

services was related to a greater percentage of post-release time employed full time. For females,

being involved in a self-help group had a positive relationship with full-time employment. These

issues require further examination.

We also found effects for post-release treatment in outcomes affected by DAP treatment. Such 60

effects were found in both the traditional and Bloom models for arrests or revocations and for

drug use. The results for failure when it was defined as an arrest or revocation indicated that

individuals who received both contract and non-contract services were less likely to fail than were

subjects on average. For failure when it was defined as drug use, we found an effect for this same

post-treatment variable, but the direction of the relationship was the opposite. Individuals

receiving both contract and non-contract services were more than 200 percent more likely to fail

when failure was defined as drug use. 

This finding was surprising. However, as noted earlier in our initial discussion of these findings, 61

there were potential problems with model specification. It may have been that some of the postrelease treatment variables were measuring risk of failure. In addition, we recognized that our

models did not capture the time ordering of treatment. While contract services were initiated at

the beginning of supervision, this was not always the case for non-contract services. These

services sometimes began at later points during supervision, most notably after an individual had

received a positive urinalysis test result.

In addition to these issues related to the post-release component of the treatment continuum, we

need to focus on the nature of within-treatment effects, as well as on the relationship between and

among effects. For example, we need to test for interactive effects between treatments. That is,

we need to address such questions as: “are the effects of treatment additive in nature?” and “can

we assess the contribution of the dosage level of the service?” The latter question is applicable
142

particularly to the post-release treatment services that, unlike transitional services in halfway

houses which provide 2 hours of service per week to the vast majority of participants, provide a

wide range in frequency and type of services. The variation might relate to the amount of hours of

individual and group treatment received on a weekly basis, the duration of these services, the

intensity of services received (as reflected by receiving inpatient services or residential treatment),

or a combination of these variations. Evaluation research recently has recognized the importance

of specifying the strength of a treatment needed to produce an effect. Thus, treatments may need

to be provided at some minimal threshold level before any effect will be observed. Conclusions

about treatment in general must be understood in the context of understanding this threshold

level. 

Program Quality

Much remains to be done concerning the assessment of institutional differences. In this report,

institution was used as a proxy for possible differences in program quality. We included a

measure of program maturity to differentiate a program at the beginning stages of operation from

its later, presumably smoother, operation when well established. Since the results reported

included information for only two-thirds of the research subjects, many sites lacked a sufficiently

large sample size to provide the power necessary to detect differences in effects among sites; only

9 of the 20 research sites had more than 50 subjects included in our analyses. Four sites had fewer

than 15 subjects, resulting in very large standard errors for these sites. 

Even with the larger sample sizes we expect to have for our future analyses, sample size will

remain a problem for some sites. However, indicators of program quality other than program

maturity remain to be identified. Whereas the programs had many common components, this was

less the case at the outset of program implementation. Furthermore, even with a common set of

program modules to help establish the content of the materials provided during the programs, the

actual implementation of the programs varied, as did the quality and stability of staffing. Not only

did this vary across sites, but also within sites across time. Such variations could not be accounted

for in our measure of program maturity, which simply measured length of time a program had

been operational. 

Data from several annual staff surveys, staffing data, and other qualitative program information

will be compiled in an attempt to group programs according to one or more dimensions of

“quality.” This could improve our ability to associate program characteristics with outcome not

only because of the statistical power resulting from the grouping of two or more programs

together, but also because there would be some very specific hypotheses to test. For example, we

could test the hypothesis that programs staffed by individuals with previous experience in drug

treatment counseling and corrections are more effective than are programs staffed with individuals

having neither or only one of these experiences.
143

Proximal Outcome

A dimension of outcome not included in this report, but important in and of itself, concerns

proximal outcomes. These proximal outcomes represent the intervening mechanism through

which the treatment program affects ultimate outcome (i.e., “distal outcome”). 

Each program makes assumptions about the cognitive and behavioral deficiencies of the clientele

served, and programs are designed to ameliorate these deficiencies. Without addressing these

deficiencies, the programs cannot be expected to have any effect on the “distal outcomes,” as

these deficiencies contribute to these outcomes. It is likely that these proximal outcomes

contribute to our understanding of inter-site institutions as well.

Although our findings suggested that drug abuse treatment had a positive effect, our study lacked

the programmatic specificity to identify the particular factors that contributed to this successful

outcome, and this would require the identification of intervening mechanisms. An assessment of

the extent to which the population served had the purported deficiencies and the extent to which

these deficiencies were remedied will help us understand how the treatment programs work.

Beyond the theoretical grounding, there is a methodological rationale for examining proximal

outcomes. The causal link between treatment and outcomes is strengthened when a strong

association between treatment and proximal outcomes predicted by theory exists, as well as a

strong association between the proximal outcome and the distal outcome (in this case arrests and

drug use) (Mohr, 1992). This concept of an intervening mechanism based upon theory will be

examined in a future report using pre- and post-treatment measures selected because of their

relationships to relapse prevention and to the theoretical underpinnings of the DAP’s.

Another rationale for the examination of the proximal outcomes arises from the goal of

generalization in any evaluation research. More recent evaluation research recognizes the limited

utility of research that solely addresses the question of whether a program works (Chen, 1990).

When the response is yes, as appears to be the case here, the successful replication of the program

and its improvement depend upon an understanding of the causal mechanisms that lead to this

“success.” 

At the outset of this evaluation project, one of the goals was to assess whether the 1,000-hour,

12-month programs were more effective than were the 500-hour, 9-month programs. The models

included in this report do not lead us to the conclusion that the additional costs of providing 12-

month programs result in a significantly better outcome. (It must be noted that the BOP

eliminated these 12-month programs effective January 1996 for reasons unrelated to the
 The decision to eliminate these 12-month, 1,000-hour programs was made in order to 62

increase program capacity. The 1994 Violent Crime Control and Law Enforcement Act contained

provisions stipulating that, over time, the BOP needed to treat an increasingly higher percentage

of inmates with drug problems. 

144

research. ) Nonetheless, without a more precise method of controlling for the quality of 62

programs, the answer to this question should not be based solely on the results of this report.

In addition, we were not able to model the primary outcomes of concern — arrests and drug use

— separately for men and women. One must entertain the possibility that, because of differences

not only in program implementation but in the population served, the one 12-month program for

women in this study (Lexington) cannot validly be grouped together with the two 12-month

programs for men. In a future report with a larger sample of females, it is hoped that this question

can be addressed more definitively.

Gender

This brings us to the question of gender differences in outcomes. Women had lower failure rates

than did men for all outcomes except employment. With the small sample size of women available

for this report, it was not possible to model women separately from men, with the exception of

our model for employment; therefore, we could not draw specific conclusions about the women.

However, it is expected that models for women could be run in future analyses. Of the anticipated

additional 462 DAP treatment subjects who are not included in this report (excluding some who

have detainers), 20 percent are women (n=125). The anticipated total number of women subjects,

both treatment and comparison, is 610.

New Arrest vs. Revocation

Our results indicated that DAP treatment was found to be related to failure, when failure was

defined as a new arrest, in two of our modeling strategies — in the traditional and Heckman

approaches. However, we found DAP treatment to be related to failure, when failure was defined

as either a new arrest or a revocation, in only one modeling approach — the Heckman approach.

Future event history analyses will be needed to understand how arrests may differ from

revocations; i.e., it will be important to consider the ways in which the behaviors leading to a new

arrest differ from those leading to an arrest for revocation. Further, we will need to analyze the

data using “competing risk” models — i.e., models that acknowledge that different events can

occur within the same risk period and that one event can terminate the risk period for the other

event. For example, an arrest for a revocation, when it results in incarceration, terminates the risk

for a new arrest. It will be essential to treat the two events — a new arrest versus an arrest for a

revocation — differently in the event history analysis.

Future analyses of arrests and revocations will also include information on overall levels of

supervision. Individuals who are more closely supervised are perhaps more likely to be detected

of supervision violations.
145

A Final Note

Sometimes we forget that not only can program evaluation be useful when it examines the

underlying causal mechanism of a program’s effect(s), thus facilitating its replication, it can be

useful also by providing information that may increase the efficiency and effectiveness of existing

program operations. For example, a close look at the descriptive statistics suggests that the

disciplinary discharges represent individuals with a greater number of problems in several

domains. Preliminary analyses not contained in this report indicate that antisocial personality and

pre-incarceration employment status are predictive of program retention. Furthermore, antisocial

personality is associated with a higher rate of polydrug dependency. Such information could be

used by program staff in either improving screening mechanisms or in improving the methods

used to address the issues of these specific subtypes of drug-abusing offenders. Specific issues

such as these will be the focus of specific special issue reports.

Another commentary on future analyses pertains to increasing the understanding of the

relationship among the various outcomes of interest. Models of arrests indicated that having used

drugs during supervision was associated with a higher log odds of being arrested. Both arrests

and drug use were affected by drug treatment. Disentanglement of the causal nature of these

relationships, which were most likely reciprocal in nature, will require utilizing a path analytic

method. Path analytic methods will also be necessary to identify the direct and indirect effects of

treatment. The results of the regression models we presented were limited to identifying the direct

effects of treatment. 

Our last commentary pertains to addressing the question of “what works with whom.” As we

direct our focus to the examination of the effects of factors other than residential treatment on the

various outcomes, we will no longer assume a linear additive model. Interaction effects between

variables such as treatment and level of drug addiction will be considered. Such interaction effects

could identify the variation in treatment effectiveness for individuals with different characteristics.
 These individuals were identified by virtue of being transitional services participants. 63

This was done to increase the pool of subjects who received treatment during their halfway house

placements but not while in prison. 

146

TABLES

Table 1

Subject Attrition

Male Female

Potential Research Subjects 2,459 571

 Missed Research (378) (127)

 Refused Research (223) (27)

Total 1,858 417

 Number Approached in Prison for Intake 1,858 417

 Number Approached in CCC for Intake 75 7 63

Total Approached for Intake 1,933 424

 Missed Intake (149) (29)

 Refused Intake (40) (3)

Total Sample Who Agreed to Intake 1,744 392

Note: A total of 2,136 intake interviews were completed. However, 10 interviews were lost in the mail, 47 interviews

were not usable, and 213 detainees were dropped from the subject pool, resulting in 1,866 complete intake

interviews.
147

Table 2

Demographics

Male Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=419) (n=386) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InRace

 White 59 % 52 % 64 % 57 % 55 % 59 % 59 %

 Black 40 % 46 % 32 % 32 % 38 % 38 % 38 %

 Other 2 % 2 % 4 % 11 % 7 % 3 % 3 %

Age at time of release

 19-29 years 23 % 24 % 17 % 19 % 35 % 36 % 23 %

 30-34 years 19 % 25 % 20 % 16 % 20 % 20 % 21 %

 35-39 years 18 % 19 % 27 % 27 % 20 % 18 % 22 %

 40 + years 40 % 32 % 35 % 38 % 25 % 25 % 35 %

(n=415) (n=380) (n=523) (n=37) (n=55) (n=104) (n=1,514)

Education

 12 years or less 67 % 71 % 67 % 73 % 75 % 74 % 69 % 1

 Greater than 12 years 33 % 29 % 33 % 27 % 25 % 26 % 31 %

Missing=10

Note: Due to rounding, columns may not sum to 100 percent.

 Includes those earning a General Education Degree (GED). 1
148

Table 3

Demographics

Female Subjects

Comparison Treatment Total

DAP (n=51) Complete Dropout Discharge complete

(n=111) (n=97) (n=16) (n=24) (n=43) (n=342)

Non- DAP Discip. InRace1

 White 56 % 47 % 54 % 38 % 54 % 44 % 52 %

 Black 43 % 51 % 44 % 56 % 46 % 56 % 47 %

Age at time of release

 19-29 years 29 % 27 % 16 % 12 % 42 % 37 % 26 %

 30-34 years 29 % 39 % 33 % 31 % 21 % 21 % 30 %

 35-39 years 21 % 14 % 26 % 25 % 29 % 26 % 22 %

 40+ years 22 % 20 % 25 % 31 % 8 % 16 % 21 %

(n=110) (n=51) (n=96) (n=16) (n=23) (n=42) (n=338)

Education

 12 years or less 79 % 59 % 88 % 81 % 87 % 71 % 78 % 2

 Greater than 12 years 27 % 41 % 12 % 19 % 13 % 29 % 22 %

Missing=4

Note: Due to rounding, columns may not sum to 100 percent.

There were 5 women of other races. 1 

Includes those earning a General Education Degree (GED). 2 
149

Table 4

Frequency of Drug Use During Heaviest Use Period

Male Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InBarbiturates

 Not used 74 % 78 % 75 % 81 % 71 % 75 % 76 % 1

 Used less than daily 18 % 14 % 15 % 14 % 16 % 15 % 15 %

 Used daily 8 % 8 % 10 % 5 % 13 % 11 % 9 %

Missing=4

Powder Cocaine

 Not used 35 % 37 % 35 % 53 % 28 % 39 % 36 %

 Used less than daily 34 % 31 % 29 % 17 % 30 % 34 % 31 %

 Used daily 31 % 32 % 35 % 31 % 43 % 28 % 33 %

Missing=27 

Crack Cocaine

 Not used 80 % 75 % 77 % 84 % 75 % 75 % 77 %

 Used less than daily 10 % 11 % 7 % 5 % 11 % 8 % 9 %

 Used daily 10 % 14 % 16 % 11 % 15 % 18 % 14 %

Missing=4 

Hallucinogens

 Not used 73 % 74 % 69 % 68 % 62 % 78 % 72 %

 Used less than daily 21 % 22 % 24 % 24 % 31 % 15 % 22 %

 Used daily 5 % 5 % 7 % 8 % 7 % 7 % 6 %

Missing=5 

Heroin

 Not used 76 % 78 % 79 % 76 % 67 % 78 % 77 %

 Used less than daily 5 % 7 % 7 % 5 % 9 % 3 % 6 %

 Used daily 20 % 16 % 14 % 19 % 24 % 19 % 17 %

Missing=9

Marijuana

 Not used 15 % 13 % 22 % 32 % 15 % 32 % 18 %

 Used less than daily 26 % 26 % 23 % 19 % 24 % 23 % 24 %

 Used daily 59 % 61 % 56 % 49 % 62 % 45 % 57 %

Missing=5 

Opiates

 Not used 80 % 84 % 80 % 92 % 78 % 79 % 81 %

 Used less than daily 12 % 10 % 11 % 3 % 13 % 8 % 11 %

 Used daily 8 % 6 % 9 % 5 % 9 % 13 % 8 %

Missing=4 

Stimulants

 Not used 65 % 69 % 64 % 73 % 65 % 69 % 66 %

 Used less than daily 19 % 18 % 20 % 8 % 18 % 13 % 18 %

 Used daily 16 % 13 % 17 % 19 % 16 % 19 % 16 %

Missing=4 

Due to rounding, columns may not sum to 100 percent.

“Not used” is defined as using the drug fewer than five times during one's life. 1 
150

Table 5

Frequency of Drug Use During Heaviest Use Period 

Female Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Discip. InBarbiturates

 Not used 83 % 84 % 65 % 81 % 54 % 76 % 75 % 1

 Used less than daily 9 % 4 % 23 % 0 % 24 % 14 % 14 %

 Used daily 8 % 12 % 11 % 19 % 21 % 10 % 11 %

Missing=1 

Powder Cocaine

 Not used 35 % 34 % 29 % 56 % 25 % 37 % 34 %

 Used less than daily 23 % 34 % 26 % 12 % 25 % 18 % 24 %

 Used daily 43 % 32 % 43 % 31 % 50 % 44 % 42 %

Missing=5 

Crack Cocaine

 Not used 65 % 53 % 57 % 69 % 54 % 67 % 61 %

 Used less than daily 10 % 18 % 13 % 0 % 4 % 4 % 10 %

 Used daily 26 % 29 % 30 % 31 % 42 % 28 % 29 %

Hallucinogens

 Not used 81 % 84 % 75 % 100 % 67 % 86 % 80 %

 Used less than daily 15 % 16 % 22 % 0 % 30 % 12 % 18 %

 Used daily 5 % 0 % 2 % 0 % 4 % 2 % 3 %

Heroin

 Not used 79 % 84 % 82 % 88 % 71 % 72 % 80 %

 Used less than daily 3 % 6 % 2 % 0 % 0 % 0 % 3 %

 Used daily 18 % 10 % 16 % 13 % 29 % 28 % 18 %

Missing=3

Marijuana

 Not used 23 % 24 % 28 % 38 % 21 % 35 % 27 %

 Used less than daily 28 % 30 % 22 % 31 % 21 % 33 % 26 %

 Used daily 50 % 45 % 49 % 31 % 58 % 33 % 46 %

Opiates

 Not used 83 % 94 % 76 % 88 % 71 % 81 % 82 %

 Used less than daily 8 % 4 % 14 % 0 % 4 % 6 % 8 %

 Used daily 10 % 2 % 9 % 13 % 25 % 12 % 10 %

Stimulants

 Not used 77 % 69 % 56 % 81 % 63 % 74 % 69 %

 Used less than daily 7 % 6 % 16 % 6 % 8 % 13 % 10 %

 Used daily 17 % 24 % 28 % 13 % 29 % 14 % 21 %

Note: Due to rounding, columns may not sum to exactly 100 percent.

“Not used” is defined as using the drug fewer than five times during one's life. 1 .
151

Table 6

Estimated Log Odds of Drug and Alcohol Dependence

Male Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InDrug Dependence

 Mean Log Odds 1.29 1.13 1.46 -0.02 2.14 1.26 1.31

 Standard Dev. 2.92 2.87 3.27 3.85 2.77 3.58 3.11

 Odds Ratio 3.63 3.10 4.30 0.98 8.47 3.54 3.69

Missing=17

Alc. Dependence

 Mean Log Odds 0.09 0.41 0.21 -0.37 -0.01 -0.80 0.14

 Standard Dev. 3.25 3.53 3.70 3.87 3.90 3.88 3.57

 Odds Ratio 1.09 1.50 1.23 0.69 0.99 0.45 1.15

Missing=54

Table 7

Estimated Log Odds of Drug and Alcohol Dependence

Female Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43 (n=342)

Discip. InDrug Dependence

 Mean Log Odds 1.80 1.74 2.98 1.00 3.00 2.58 2.27

 Standard Dev. 2.73 2.64 2.57 3.64 3.51 3.34 2.91

 Odds Ratio 6.06 5.70 19.76 2.71 20.04 13.19 9.70

Alc. Dependence

 Mean Log Odds -1.07 -0.63 -0.17 -2.61 1.23 -0.42 -0.57

 Standard Dev. 3.32 3.40 3.79 2.51 3.16 3.92 3.56

 Odds Ratio 0.34 0.53 0.84 0.07 3.41 0.66 0.56

Missing=8
152

Table 8

Participation in In-Prison Outpatient Drug and Alcohol Treatment and 

Self-Help Groups

Male Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InOutpatient 5 % 4 % 10 % 3 % 5 % 5 % 6 %

Self-Help Groups 0 % 1 % 6 % 0 % 5 % 5 % 3 %

Table 9

Participation in In-Prison Outpatient Drug and Alcohol Treatment and 

Self-Help Groups

Female Subjects

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Discip. InOutpatient 7 % 14 % 0 % 0 % 4 % 5 % 5 %

Self-Help Groups 9 % 4 % 0 % 0 % 0 % 0 % 4 %

Note: The percentage of inmates participating in outpatient treatment may be underreported due to inconsistencies in recording

this information in SENTRY.
153

Table 10

Drug and Alcohol Treatment History

 Male Subjects

Comparison Treatment Total

DAP Non-DAP Completed (n=36) Discharge complete

(n=426) (n=374) (n=521) (n=55) (n=103) (n=1,515)

Drop-Out Discip. InPast Drug Treatment 30 % 32 % 31 % 36 % 31 % 36 % 31 %

Past Alcohol 4 % 4 % 7 % 3 % 4 % 4 % 5 %

 Treatment 

Missing = 9

Note: Self-help groups, such as AA, were not included in treatment history.

Table 11

Drug and Alcohol Treatment History

 Female Subjects

Comparison Treatment Total

DAP Non-DAP Completed (n=16) Discharge complete

(n=113) (n=49) (n=97) (n=24) (n=43) (n=342)

Drop-Out Discip. InPast Drug 37 % 27 % 37 % 19 % 42 % 37 % 35 %

 Treatment* 

Past Alcohol 3 % 6 % 3 % 6 % 8 % 0 % 4 %

 Treatment 

*Missing = 1

Note: Self-help groups, such as AA, were not included in treatment history.
154

Table 12

Criminal and Incarceration History

 Male Subjects

Comparison Treatment Total

Any Prior DAP Non-DAP Completed (n=37) Discharge complete

Commitments (n=430) (n=375) (n=523) (n=55) (n=104) (n=1,524)

Drop-Out Discip. InYes 74 % 71 % 70 % 73 % 80 % 65 % 71 %

Recency of Violence

None 54 % 53 % 55 % 41 % 40 % 62 % 54 %

<5 years ago 14 % 13 % 12 % 16 % 27 % 16 % 14 %

5+ years ago 32 % 34 % 33 % 43 % 33 % 22 % 32 %

Length of Current

Incarceration

0-12 Months 13 % 36 % 9 % 11 % 15 % 34 % 19 %

13-59 Months 74 % 57 % 80 % 76 % 75 % 58 % 71 %

60-83 Months 8 % 6 % 8 % 5 % 7 % 7 % 7 %

84 + Months 5 % 1 % 4 % 8 % 4 % 2 % 3 %

In CJ System at

Arrest1

Yes 44 % 48 % 36 % 36 % 53 % 40 % 42 %

 Subjects were defined as being in the criminal justice system at the time of arrest if they were on judicially imposed 1

confinement or supervision.
155

Table 13

Criminal and Incarceration History

Female Subjects

Comparison Treatment Total

Any Prior DAP Non-DAP Completed Out Discharge complete

Commitments (n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Drop- Discip. InYes 37 % 51 % 41 % 50 % 62 % 58 % 45 %

Recency of 

 Violence

None 86 % 84 % 87 % 100 % 75 % 91 % 86 %

<5 years ago 10 % 10 % 4 % 0 % 8 % 5 % 7 %

5+ years ago 4 % 6 % 9 % 0 % 17 % 5 % 7 %

Length of Current

 Incarceration

0-12 Months 34 % 84 % 9 % 6 % 25 % 46 % 34 %

13-59 Months 61 % 16 % 87 % 88 % 75 % 54 % 63 %

60-83 Months 4 % 0 % 3 % 6 % 0 % 0 % 3 %

84+ Months 1 % 0 % 1 % 0 % 0 % 0 % 1 %

In CJ System at 

 Arrest1

Yes 29 % 52 % 36 % 33 % 41 % 45 % 37 %

 Subjects were defined as being in the criminal justice system at the time of arrest if they were on judicially imposed 1

confinement or supervision.
156

Table 14

Psychiatric Diagnoses

Male Subjects

Comparison Treatment Total

Psychiatric Diagnosis (n=398) (n=333) (n=497) (n=32) (n=47) (n=87) (n=1,394) 1

DAP Non-DAP Completed Out Discharge complete

Drop- Discip. InNone 52 % 51 % 58 % 59 % 40 % 60 % 54 %

Depression 6 % 8 % 10 % 9 % 8 % 6 % 8 %

Antisocial Personality 32 % 33 % 28 % 22 % 45 % 26 % 30 %

Depression and 10 % 8 % 5 % 9 % 6 % 8 % 7 %

 Antisocial Personality

Missing = 130

(n=429) (n=374) (n=520) (n=36) (n=54) (n=102) (n=1,515)

Had Past Mental 20 % 18 % 17 % 22 % 20 % 18 % 18 %

 Health Treatment

Missing = 9

 Diagnoses were based on the Diagnostic Interview Schedule (DIS) using DSM-III-R criteria. 1
157

Table 15

Psychiatric Diagnoses

Female Subjects

Comparison Treatment Total

Psychiatric Diagnosis (n=106) (n=45) (n=76) (n=13) (n=17) (n=33) (n=290) 1

DAP Non-DAP Completed Out Discharge complete

Drop- Discip. InNone 51 % 47 % 54 % 54 % 24 % 45 % 49 %

Depression 23 % 29 % 16 % 31 % 0 % 21 % 21 %

Antisocial Personality 12 % 16 % 18 % 15 % 47 % 18 % 17 %

Depression and 14 % 9 % 12 % 0 % 29 % 15 % 13 %

 Antisocial Personality

Missing = 52

(n=112) (n=49) (n=97) (n=16) (n=24) (n=43) (n=341)

Had Past Mental 41 % 41 % 31 % 44 % 46 % 53 % 40 %

 Health Treatment

Missing = 1

 Diagnoses were based on the Diagnostic Interview Schedule (DIS) using DSM-III-R criteria. 1
158

Table 16

Employment History

 Male Subjects

Comparison Treatment Total

Support Self One Year DAP Non-DAP Completed Dropout Discharge complete

 With Illegal Income (n=424) (n=373) (n=514) (n=36) (n=52) (n=102) (n=1,501)

Discip. InYes 36 % 42 % 43 % 49 % 27 % 24 % 39 %

Employment in Month

 Before Incarceration

Full or Part Time 50 % 60 % 56 % 50 % 46 % 67 % 55 %

Not in Labor Force 5 % 6 % 4 % 3 % 6 % 4 % 4 %

Looking for Work 11 % 6 % 9 % 8 % 13 % 5 % 9 %

Illegal Income, Other 35 % 28 % 31 % 39 % 35 % 25 % 31 %

 Reason Unemployed,

 or Never Worked

Missing = 23

Table 17

Employment History

 Female Subjects

Comparison Treatment Total

Support Self One Year DAP Non-DAP Completed Dropout Discharge complete

 With Illegal Income (n=110) (n=49) (n=95) (n=16) (n=22) (n=42) (n=334)

Discip. InYes 43 % 43 % 26 % 48 % 49 % 31 % 41 %

Employment in Month

 Before Incarceration

Full or Part Time 40 % 49 % 40 % 31 % 32 % 31 % 39 %

Not in Labor Force 5 % 12 % 5 % 13 % 5 % 12 % 7 %

Looking for Work 12 % 10 % 6 % 13 % 9 % 12 % 10 %

Illegal Income, Other 43 % 29 % 48 % 44 % 55 % 45 % 43 %

 Reason Unemployed,

 or Never Worked

Missing = 8
159

Table 18

Motivation for Change

 Male Subjects

Comparison Treatment Total

Motivation for DAP Non-DAP Completed (n=31) Discharge complete

 Change (Prochaska) (n=385) (n=312) (n=460) (n=47) (n=82) (n=1,317)

Drop-Out Discip. InContemplation 25 % 24 % 32 % 29 % 36 % 29 % 28 %

Preparation 14 % 16 % 29 % 26 % 19 % 24 % 21 %

Reluctant 31 % 28 % 12 % 16 % 9 % 13 % 21 %

Action 7 % 10 % 18 % 10 % 19 % 21 % 13 %

Precontemplation 13 % 14 % 8 % 10 % 15 % 7 % 11 %

Uninvolved 10 % 7 % 3 % 10 % 2 % 5 % 6 %

Missing = 207

Table 19

Motivation for Change

 Female Subjects

Comparison Treatment Total

Motivation for Drop-Out Discip. InChange (Prochaska) DAP Non-DAP Completed (n=14) Discharge complete

(n=95) (n=44) (n=67) (n=21) (n=40) (n=281)

Contemplation 24 % 20 % 24 % 21 % 5 % 25 % 22 %

Preparation 21 % 32 % 24 % 14 % 10 % 23 % 22 %

Reluctant 20 % 7 % 7 % 0 % 19 % 3 % 11 %

Action 14 % 23 % 40 % 57 % 62 % 40 % 31 %

Precontemplation 16 % 14 % 4 % 7 % 0 % 8 % 10 %

Uninvolved 5 % 5 % 0 % 0 % 5 % 3 % 3 %

Missing = 61
160

Table 20

CCC Placements, Failures, and Reasons for Failing

Male Subjects

Subjects Who Received CCC Placements

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Drop- Discip. InReceived Placement 61 % 67 % 78 % 51 % 38 % 64 % 68 %

Subjects Who Failed CCC Placement

(n=264) (n=253) (n=407) (n=19) (n=21) (n=67) (n=1,031)

Failed 22 % 26 % 19 % 21 % 48 % 24 % 23 %

Reasons for Failing CCC Placement

(n=48) (n=57) (n=71) (n=4) (n=10) (n=14) (n=204)

Positive drug UA 58 % 53 % 52 % 25 % 60 % 29 % 52 %

Positive alcohol 4 % 7 % 8 % 0 % 20 % 14 % 8 % 1

Possession of drugs 2 % 11 % 3 % 0 % 10 % 0 % 5 %

Possession of alcohol 4 % 9 % 8 % 0 % 0 % 0 % 6 %

Act of violence 0 % 2 % 4 % 0 % 0 % 0 % 2 %

Accountability 2 % 2 % 3 % 25 % 0 % 7 % 3 % 2

Violation of CCC 15 % 7 % 7 % 0 % 10 % 7 % 9 %

 rules3

Escape 8 % 5 % 7 % 50 % 0 % 21 % 8 %

Other 6 % 6 % 7 % 0 % 0 % 21 % 7 %

Missing = 28

 As determined by breathalyzer or urinalysis. 1

 Accountability transgressions consist almost solely of unexcused absences from the CCC. 2

 “Violation of CCC rules” comprises subjects who were cited for committing any of a variety of transgressions, 3

including but not limited to gambling, acting disruptively, and refusing an order.
161

Table 21

CCC Placements, Failures, and Reasons for Failing

Female Subjects

Subjects Who Received CCC Placements

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Drop- Discip. InReceived Placement 62 % 61 % 80 % 44 % 50 % 77 % 67 %

Subjects Who Failed CCC Placement

(n=70) (n=30) (n=8) (n=7) (n=12) (n=33) (n=230)

Failed 14 % 13 % 12 % 14 % 8 % 27 % 15 %

Reasons for Failing CCC Placement

(n=9) (n=3) (n=8) (n=0) (n=1) (n=7) (n=28)

Positive drug UA 44 % 67 % 38 % -- % 0 % 43 % 43 %

Positive alcohol 22 % 0 % 12 % -- % 100 % 0 % 14 % 1

Possession of drugs 11 % 0 % 0 % -- % 0 % 0 % 4 %

Possession of alcohol 0 % 0 % 12 % -- % 0 % 14 % 7 %

Act of violence 11 % 0 % 12 % -- % 0 % 14 % 11 %

Accountability 0 % 0 % 0 % -- % 0 % 0 % 0 % 2

Violation of CCC 11 % 33 % 0 % -- % 0 % 0 % 7 %

 rules3

Escape 0 % 0 % 12 % -- % 0 % 14 % 7 %

Other 1 % 0 % 12 % -- % 0 % 14 % 8 %

Missing = 6

Note: Due to rounding, columns may not sum to 100 percent.

 As determined by breathalyzer or urinalysis. 1

 Accountability transgressions consist almost solely of unexcused absences from the CCC. 2

 “Violation of CCC rules” comprises subjects who were cited for committing any of a variety of transgressions, 3

including but not limited to gambling, acting disruptively, and refusing an order.
162

Table 22

Transitional Services Received and Release Status

Male Subjects

Subjects Who Received Transitional Services

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=264) (n=253) (n=405) (n=16) (n=20) (n=65) (n=1,023)

Drop- Discip. InReceived TS 33 % 40 % 92 % 50 % 35 % 74 % 61 %

Missing = 8

Transitional Services Release Status

(n=87) (n=99) (n=349) (n=6) (n=7) (n=47) (n=595)

Completed 58 % 76 % 66 % 67 % 43 % 62 % 66 %

Failed 24 % 15 % 18 % 0 % 57 % 26 % 19 %

Admin/Neutral 18 % 9 % 16 % 33 % 0 % 13 % 15 % 1

Missing = 34

Note: 493 male subjects did not receive a CCC placement, and 402 did not participate in transitional services.

 “Admin/Neutral” was used somewhat differently from region to region but, in general, this category seems to have 1

been used when a subject was participating in but did not complete transitional services treatment due to some

circumstance beyond his control.
163

Table 23

Transitional Services Received and Release Status

Female Subjects

Subjects Who Received Transitional Services

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=70) (n=29) (n=78) (n=7) (n=11) (n=32) (n=227)

Drop- Discip. InReceived TS 27 % 28 % 96 % 43 % 45 % 84 % 60 %

Missing = 3

Transitional Services Release Status

(n=18) (n=8) (n=72) (n=2) (n=4) (n=25) (n=129)

Completed 67 % 62 % 80 % 100 % 100 % 52 % 72 %

Failed 28 % 25 % 10 % 0 % 0 % 32 % 17 %

Admin/Neutral 6 % 12 % 11 % 0 % 0 % 16 % 11 % 1

Missing = 11

Note: 112 female subjects did not receive a CCC placement, and 90 did not participate in transitional services.

 “Admin/Neutral” was used somewhat differently from region to region but, in general, this category seems to have 1

been used when a subject was participating in but did not complete transitional services treatment due to some

circumstance beyond her control.
164

Table 24

Drug and Alcohol Urinalysis During CCC Placement 

Male Subjects

Percent Tested for Drugs and Alcohol

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=241) (n=229) (n=377) (n=13) (n=16) (n=62) (n=938)

Drop- Discip. InPercent tested 98 % 99 % 93 % 77 % 100 % 87 % 95 %

Missing = 93

Percent of Subjects Testing Positive for Drugs or Alcohol

(n=235) (n=226) (n=349) (n=10) (n=16) (n=54) (n=890)

Percent testing positive 17 % 14 % 15 % 10 % 50 % 17 % 16 %

Missing = 1

Drugs Positive By Urinalysis or Breathalyzer

Drug Type (n=31) (n=39) (n=50) (n=1) (n=8) (n=8) (n=137)

Alcohol 13 % 26 % 20 % 0 % 25 % 38 % 21 %

Amphetamines 3 % 3 % 4 % 0 % 12 % 0 % 4 %

Barbiturates 0 % 3 % 0 % 0 % 0 % 0 % 1 %

Benzodiazepines 0 % 3 % 0 % 0 % 25 % 0 % 2 %

Cocaine 55 % 33 % 40 % 0 % 12 % 50 % 40 %

Heroin 0 % 0 % 0 % 0 % 0 % 0 % 0 %

LSD 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Marijuana 23 % 23 % 24 % 0 % 25 % 12 % 23 %

Methadone 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Opiates 6 % 10 % 12 % 100 % 0 % 0 % 9 %

PCP 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Missing = 4

Note: 493 male subjects did not receive a CCC placement. More than one positive test was reported for 20 male

subjects. The results shown reflect the first positive UA. Due to rounding, columns may not sum to 100 percent.
165

Table 25

Drug and Alcohol Urinalysis During CCC Placement 

Female Subjects

Percent Tested for Drugs and Alcohol

Comparison Treatment Total

DAP Non-DAP Completed Out Discharge complete

(n=62) (n=27) (n=75) (n=7) (n=8) (n=30) (n=209)

Drop- Discip. InPercent tested 98 % 93 % 92 % 86 % 88 % 90 % 93 %

Missing = 21

Percent of Subjects Testing Positive for Drugs or Alcohol

(n=61) (n=25) (n=69) (n=6) (n=7) (n=27) (n=195)

Percent testing positive 11 % 12 % 6 % 0 % 0 % 11 % 9 %

Drugs Positive By Urinalysis or Breathalyzer

Drug Type (n=7) (n=3) (n=4) (n=0) (n=0) (n=3) (n=17)

Alcohol 14 % 33 % 25 % 0 % 0 % 0 % 18 %

Amphetamines 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Barbiturates 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Benzodiazepines 0 % 0 % 25 % 0 % 0 % 33 % 12 %

Cocaine 57 % 0 % 50 % 0 % 0 % 67 % 47 %

Heroin 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Marijuana 14 % 33 % 0 % 0 % 0 % 0 % 12 %

Methadone 0 % 0 % 0 % 0 % 0 % 0 % 0 %

LSD 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Opiates 14 % 0 % 0 % 0 % 0 % 0 % 6 %

PCP 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Prescription 0 % 33 % 0 % 0 % 0 % 0 % 6 % 1

Note: 112 female subjects did not receive CCC placements. More than one positive test was reported for four female subjects. The results shown reflect the first positive UA. Due to rounding, columns may not sum to 100 percent.

 The prescription drug may or may not have been prescribed to the inmate; therefore, we do not know whether the 1

positive test was a transgression of the rules.
166

Table 26

Post-Release Offenses

Male Subjects

 Arrested for New Offense

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InNew offense 14 % 16 % 11 % 19 % 18 % 15 % 14 %

Offense Type

(n=61) (n=60) (n=56) (n=7) (n=10) (n=16) (n=210)

Drug-related 10 % 20 % 16 % 29 % 20 % 25 % 17 %

Violent 16 % 15 % 13 % 14 % 40 % 0 % 15 %

Robbery 5 % 7 % 9 % 0 % 0 % 6 % 6 %

Property 15 % 22 % 20 % 14 % 10 % 13 % 18 %

Forgery, fraud 8 % 8 % 2 % 0 % 10 % 6 % 6 %

Traffic 20 % 12 % 20 % 29 % 10 % 19 % 17 %

Other 26 % 17 % 21 % 14 % 10 % 31 % 21 %

Table 27

Post-Release Offenses

Female Subjects

Arrested for New Offense

 Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Discip. InNew offense 4 % 8 % 3 % 12 % 12 % 9 % 6 %

Offense Type

(n=5) (n=4) (n=3) (n=2) (n=3) (n=4) (n=21)

Drug-related 0 % 25 % 0 % 50 % 0 % 50 % 19 %

Violent 0 % 0 % 33 % 0 % 0 % 0 % 5 %

Robbery 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Property 40 % 50 % 0 % 0 % 33 % 25 % 29 %

Forgery, fraud 0 % 0 % 33 % 50 % 0 % 0 % 10 %

Traffic 20 % 25 % 33 % 0 % 33 % 0 % 19 %

Other 40 % 0 % 0 % 0 % 33 % 25 % 19 %
167

Table 28

Post-Release Offenses for Supervised Subjects 

Male Subjects

Subjects Who Were Supervised Upon Release

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=430) (n=375) (n=523) (n=37) (n=55) (n=104) (n=1,524)

Discip. InSupervised 89 % 79 % 88 % 84 % 76 % 75 % 85 %

Subjects With New Offense or Revocation

(n=382) (n=296) (n=459) (n=31) (n=42) (n=78) (n=1,288)

New offense 14 % 16 % 11 % 19 % 17 % 14 % 14 %

Revocation 8 % 8 % 6 % 6 % 10 % 12 % 7 %

Not arrested 78 % 76 % 84 % 74 % 74 % 74 % 78 %

Offense Type

(n=85) (n=70) (n=75) (n=8) (n=11) (n=20) (n=269)

Drug-related 5 % 10 % 8 % 25 % 9 % 20 % 9 %

Violent 11 % 11 % 9 % 13 % 27 % 0 % 10 %

Robbery 2 % 4 % 7 % 0 % 0 % 0 % 4 %

Property 7 % 13 % 9 % 0 % 9 % 5 % 9 %

Forgery, fraud 6 % 6 % 1 % 0 % 0 % 0 % 4 %

Traffic 14 % 9 % 15 % 25 % 9 % 15 % 13 %

Revocation 36 % 33 % 35 % 25 % 36 % 45 % 35 %

Other 19 % 14 % 16 % 12 % 9 % 15 % 16 %

Note: Due to rounding, columns may not sum to 100 percent.
168

Table 29

Post-Release Offenses for Supervised Subjects 

Female Subjects

Subjects Who Were Supervised Upon Release

(n=113) (n=49) (n=97) (n=16) (n=24) (n=43) (n=342)

Supervised 87 % 67 % 91 % 75 % 63 % 81 % 82 %

Subjects With New Offense or Revocation

(n=98) (n=33) (n=88) (n=12) (n=15) (n=35) (n=281)

New offense 5 % 6 % 3 % 17 % 20 % 6 % 6 %

Revocation 3 % 6 % 9 % 0 % 7 % 3 % 5 %

Not arrested 92 % 88 % 88 % 83 % 73 % 91 % 89 %

Offense Type

(n=8) (n=4) (n=11) (n=2) (n=4) (n=3) (n=32)

Drug-related 0 % 0 % 0 % 50 % 0 % 33 % 6 %

Violent 0 % 0 % 9 % 0 % 0 % 0 % 3 %

Robbery 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Property 25 % 25 % 0 % 0 % 25 % 33 % 16 %

Forgery, fraud 0 % 0 % 9 % 50 % 0 % 0 % 6 %

Traffic 13 % 25 % 9 % 0 % 25 % 0 % 13 %

Revocation 38 % 50 % 73 % 0 % 25 % 33 % 47 %

Other 25 % 0 % 0 % 0 % 25 % 0 % 9 %

Note: Due to rounding, columns may not sum to 100 percent.
169

Table 30

Post-Release Drug and Alcohol Use

Supervised Male Subjects

Monthly Rate of Urinalysis

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=343) (n=260) (n=430) (n=26) (n=38) (n=68) (n=1,165)

Discip. InMean 2. 57 2. 29 2. 64 2. 46 2. 26 2. 47 2. 51

Std. dev. 1. 97 1. 80 1. 99 2. 34 2. 25 1. 88 1. 96

Drug/Alcohol Use While Under Supervision

(n=382) (n=296) (n=459) (n=31) (n=42) (n=78) (n=1,288)

Used drugs/alcohol 31 % 34 % 28 % 16 % 36 % 33 % 31 %

Method of Detection of Drug/Alcohol Use

(n=119) (n=100) (n=129) (n=5) (n=15) (n=26) (n=394)

Positive UA 68 % 78 % 70 % 80 % 80 % 73 % 72 %

UA miss/refusal 21 % 18 % 19 % 20 % 7 % 23 % 19 %

Admit drug use 10 % 2 % 10 % 0 % 7 % 4 % 7 %

Positive breathalyzer 1 % 2 % 1 % 0 % 7 % 0 % 1 %

Positive Urinalysis by Drug Type

(n=81) (n=78) (n=90) (n=4) (n=12) (n=19) (n=284)

Alcohol 5 % 3 % 6 % 0 % 8 % 0 % 4 %

Amphetamine 2 % 1 % 2 % 25 % 8 % 0 % 2 %

Barbiturate 1 % 1 % 1 % 0 % 0 % 0 % 1 %

Benzodiazepine 0 % 3 % 0 % 0 % 0 % 0 % 1 %

Cocaine 47 % 50 % 47 % 50 % 25 % 58 % 48 %

Heroin 0 % 4 % 1 % 0 % 0 % 0 % 1 %

Marijuana 27 % 24 % 21 % 25 % 42 % 11 % 24 %

Opiate 10 % 5 % 13 % 0 % 0 % 16 % 10 %

Multiple 7 % 9 % 9 % 0 % 17 % 16 % 9 % 1

Note: 236 male subjects were not under post-release supervision. Due to rounding, columns may not sum to 100 percent.

 22 male subjects tested positive for two drugs (20 used cocaine in combination with another drug, and 2 used 1

amphetamines in combination with another drug); 3 subjects tested positive for three drugs (cocaine in combination with

two other drugs); and one subject tested positive for four drugs (cocaine, heroin, LSD, and alcohol).
170

Table 31

Post-Release Drug and Alcohol Use 

Supervised Female Subjects

Monthly Rate of Urinalysis Testing

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=86) (n=26) (n=80) (n=11) (n=14) (n=33) (n=250)

Discip. InMean 2. 50 2. 66 2. 97 2. 65 2. 63 2. 34 2. 66

Std. dev. 1. 93 2. 39 2. 04 2. 13 1. 52 1. 79 1. 98

Drug/Alcohol Use While Under Supervision

(n=98) (n=33) (n=88) (n=12) (n=15) (n=35) (n=281)

Used drugs/alcohol 19 % 21 % 19 % 17 % 20 % 23 % 20 %

Method of Detection of Drug/Alcohol Use

(n=19) (n=7) (n=17) (n=2) (n=3) (n=8) (n=56)

Positive UA 84 % 43 % 71 % 100 % 100 % 38 % 70 %

UA miss/refusal 11 % 29 % 24 % 0 % 0 % 25 % 18 %

Admit drug use 5 % 29 % 6 % 0 % 0 % 38 % 12 %

Positive breathalyser 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Positive Urinalysis Tests by Drug Type

(n=16) (n=3) (n=12) (n=2) (n=3) (n=3) (n=39)

Alcohol 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Amphetamine 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Barbiturate 6 % 0 % 0 % 0 % 0 % 0 % 3 %

Benzodiazepine 6 % 0 % 0 % 0 % 0 % 0 % 3 %

Cocaine 69 % 0 % 83 % 50 % 100 % 67 % 69 %

Heroin 0 % 0 % 0 % 0 % 0 % 0 % 0 %

Marijuana 13 % 100 % 0 % 0 % 0 % 33 % 15 %

Opiate 6 % 0 % 17 % 50 % 0 % 0 % 10 %

Multiple 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1

Note: 61 female subjects were not under post-release supervision. Due to rounding, columns may not sum to 100 percent.

 No female subjects tested positive for a combination of two or more drugs. 1
171

Table 32

Violations of Supervision and Post-Release CCC Placement 

Supervised Male Subjects

Percent Having a Violation of Supervision, Excluding Drug Use Violations

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=382) (n=296) (n=459) (n=31) (n=42) (n=78) (n=1,288)

Discip. InViolated 19 % 18 % 17 % 10 % 21 % 22 % 18 %

Post-Release CCC Placement

Placed in CCC 4 % 3 % 3 % 0 % 7 % 4 % 4 %

Note: 236 male subjects were not under post-release supervision.

Table 33

Violations of Supervision and Post-Release CCC Placement

Supervised Female Subjects

Percent Having a Violation of Supervision, Excluding Drug Use Violations

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=98) (n=33) (n=88) (n=12) (n=15) (n=35) (n=281)

Discip. InViolated 9 % 15 % 8 % 8 % 13 % 11 % 10 %

Post-Release CCC Placement

Placed in CCC 1 % 3 % 2 % 0 % 0 % 3 % 2 %

Note: 61 female subjects were not under post-release supervision.
172

Table 34

Drug and Alcohol Treatment and Self-Help Participation 

Supervised Male Subjects

Drug and Alcohol Treatment

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=377) (n=293) (n=450) (n=31) (n=42) (n=76) (n=1,269)

Discip. InContract only 44 % 39 % 51 % 29 % 38 % 46 % 45 %

Non-contract only 2 % 4 % 3 % 6 % 5 % 8 % 3 %

Contract and non- 2 % 2 % 5 % 0 % 5 % 5 % 3 %

 contract

No treatment 52 % 55 % 41 % 65 % 52 % 41 % 49 %

Missing = 19

Self-Help Groups (AA/NA)

(n=376) (n=290) (n=448) (n=31) (n=41) (n=76) (n=1,262)

Participated 18 % 23 % 26 % 16 % 24 % 18 % 22 %

Missing = 26

Note: 236 male subjects were not under post-release supervision.

Table 35

Drug and Alcohol Treatment and Self-Help Participation 

Supervised Female Subjects

Drug and Alcohol Treatment

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=93) (n=31) (n=87) (n=12) (n=14) (n=35) (n=272)

Discip. InContract only 49 % 39 % 60 % 42 % 50 % 46 % 51 %

Non-contract only 1 % 3 % 3 % 0 % 0 % 11 % 3 %

Contract and non- 2 % 6 % 1 % 8 % 7 % 0 % 3 %

 contract

No treatment 47 % 52 % 36 % 50 % 43 % 43 % 43 %

Missing = 9

Self-Help Groups (AA/NA)

(n=94) (n=31) (n=86) (n=12) (n=14) (n=35) (n=272)

Participated 21 % 19 % 34 % 25 % 43 % 46 % 29 %

Missing = 9

Note: 61 female subjects were not under post-release supervision.
173

Table 36

Employment and Cohabitation Status 

Supervised Male Subjects

Employment Status

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=378) (n=293) (n=452) (n=31) (n=40) (n=75) (n=1,269)

Discip. InFull-time/entire 41 % 41 % 49 % 32 % 33 % 47 % 44 %

 period

Full-time/partial 29 % 34 % 29 % 26 % 25 % 20 % 29 %

 period

Part-time 10 % 9 % 7 % 13 % 13 % 7 % 9 %

Ineligible 6 % 4 % 5 % 3 % 8 % 8 % 5 % 1

Unemployed 15 % 12 % 10 % 26 % 23 % 19 % 13 %

Missing = 19

Cohabitation Status

(n=382) (n=295) (n=455) (n=31) (n=42) (n=76) (n=1,281)

With spouse 17 % 20 % 22 % 16 % 12 % 26 % 20 %

With common- 13 % 14 % 15 % 19 % 12 % 17 % 15 %

 law spouse or 

 paramour

Neither 69 % 65 % 63 % 65 % 76 % 57 % 65 %

Missing = 7

Note: 236 male subjects were not under post-release supervision. Due to rounding, columns may not sum to 100 percent.

 Subjects were categorized as ineligible if they were involved in a school program, retired or disabled, or detainees who 1

had very little time available “on the streets” to be employed.
174

Table 37

Employment and Cohabitation Status 

Supervised Female Subjects

Employment Status

Comparison Treatment Total

DAP Non-DAP Completed Dropout Discharge complete

(n=97) (n=31) (n=87) (n=12) (n=14) (n=34) (n=275)

Discip. InFull-time/entire 26 % 26 % 34 % 17 % 21 % 26 % 28 %

 period

Full-time/some 34 % 32 % 36 % 25 % 36 % 44 % 35 %

 period

Part-time 14 % 13 % 18 % 25 % 14 % 18 % 16 %

Ineligible 10 % 6 % 5 % 17 % 7 % 6 % 8 % 1

Unemployed 15 % 23 % 7 % 17 % 21 % 6 % 13 %

Missing = 6

Cohabitation Status

(n=97) (n=32) (n=87) (n=12) (n=15) (n=35) (n=278)

With spouse 12 % 12 % 10 % 17 % 7 % 11 % 12 %

With common- 13 % 12 % 3 % 0 % 7 % 9 % 9 %

 law spouse or

 paramour

Neither 74 % 75 % 86 % 83 % 87 % 80 % 80 %

Missing = 3

Note: 61 female subjects were not under post-release supervision. Due to rounding, columns may not sum to 100 percent.

 Subjects were categorized as ineligible if they were involved in a school program, retired or disabled, or detainees who 1

had very little time available “on the streets” to be employed.
175

Table 38

Traditional Models of Failure as Arrest: Supervised and Unsupervised Subjects

Male and Female Subjects

Model of Arrest with: L DF Diff. DF 2 †

Base Variables 2049.9 38 --- ---

Base and Additional Background Variables 2041.6 45 8.3 7

Base and Change Assessment Variables 2044.1 44 5.8 6

Base and Additional Treatment Variables 2047.5 41 2.4 3

Base and Supervision Variables 2041.9 41 8.0** 3

Final Model Variables 2029.8 49 20.1** 11

Male Subjects

Model of Arrest with: L DF Diff. DF 2 †

Base Variables 1836.4 37 --- ---

Base and Additional Background Variables 1826.8 44 9.6 7

Base and Change Assessment Variables 1830.3 43 6.1 6

Base and Additional Treatment Variables 1832.9 40 3.5 3

Base and Supervision Variables 1830.9 40 5.5 3

Final Model Variables 1823.0 48 13.4 11

Notes to Table:

Difference in L between traditional base model and model in question. † 2

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01

NOTE: The blocks for additional treatment variables and supervision variables exclude variables

not available for unsupervised subjects. 
176

Table 39

Traditional Models of Failure as Arrest: Supervised Subjects Only

Male and Female Subjects

Model of Arrest with: L DF Diff. DF 2 †

Base Variables 1682.2 38 --- ---

Base and Additional Background Variables 1672.8 45 9.4 7

Base and Change Assessment Variables 1673.4 44 8.8 6

Base and Additional Treatment Variables 1670.6 47 11.6 9

Base and Supervision Variables 1666.7 43 15.5*** 5

Base and Post Release Behavior Variables 1546.4 43 135.8*** 5

Final Model Variables 1521.1 55 161.1*** 17

Male Subjects

Model of Arrest with: L DF Diff. DF 2 †

Base Variables 1506.4 37 --- ---

Base and Additional Background Variables 1494.3 44 12.1 7

Base and Change Assessment Variables 1497.1 43 9.3 6

Base with Additional Treatment Variables 1494.3 46 12.1 9

Base with Supervision Variables 1492.9 42 13.5*** 5

Base and Post Release Behavior Variables 1377.2 42 129.2*** 5

Final Model Variables 1363.9 54 142.5*** 17

Notes to Table:

Difference in L between traditional base model and model in question. † 2

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01
177

Table 40

Traditional Models of Failure as Arrest or Revocation‡

Male and Female Subjects

Model of Arrest or Revocation with: L DF Diff. DF 2 †

Base Variables 2427.4 38 --- ---

Base and Additional Background Variables 2413.6 45 13.8* 7

Base and Change Assessment Variables 2418.6 44 8.8 6

Base with Additional Treatment Variables 2374.5 47 52.9*** 9

Base with Supervision Variables 2375.0 42 52.4*** 4

Base and Post Release Behavior Variables 2066.1 43 361.3*** 5

Final Model Variables 1983.9 64 443.5*** 26

Male Subjects

Model of Arrest or Revocation with: L DF Diff. DF 2 †

Base Variables 2143.4 37 --- ---

Base and Additional Background Variables 2131.8 44 11.6 7

Base and Change Assessment Variables 2135.1 43 8.3 6

Base with Additional Treatment Variables 2098.1 46 45.3*** 9

Base with Supervision Variables 2103.2 41 40.2*** 4

Base and Post Release Behavior Variables 1824.7 42 318.7*** 5

Final Model Variables 1755.6 63 387.8*** 26

Notes to Table:

Difference in L between traditional base model and model in question. † 2

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01

 Only includes subjects who were under supervision and thus had a probability of revocation ‡

greater than zero.
178

Table 41

Traditional Models of Failure as Evidence of Drug Use‡

Male and Female Subjects

Model of Drug Use with: x DF Diff. DF 2 †

Base Variables 3012.8 38 --- ---

Base and Additional Background Variables 3006.0 45 6.8 7

Base and Change Assessment Variables 3004.8 44 8.0 6

Base and Additional Treatment Variables 2854.0 47 158.8*** 9

Base and Supervision Variables 2934.1 42 78.7*** 4

Base and Post Release Behavior Variables 2921.4 42 91.4*** 4

Final Model Variables 2745.5 63 267.3*** 25

Male Subjects

Model of Drug use with: x DF Diff. DF 2 †

Base Variables 2585.3 37 --- ---

Base and Additional Background Variables 2576.6 44 8.7 7

Base and Change Assessment Variables 2578.0 43 7.3 6

Base with Additional Treatment Variables 2447.5 46 137.8*** 9

Base with Supervision Variables 2513.5 41 71.8*** 4

Base and Post Release Behavior Variables 2510.1 41 75.2*** 4

Final Model Variables 2355.2 62 230.1*** 25

Notes to Table:

Difference in x between traditional base model and model in question. † 2

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01

 Only includes subjects who were under supervision and who had urine testing, thus had a ‡

probability of drug use greater than zero.
179

Table 42

Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Traditional Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -3.2054*** 0.6236 . -3.2548*** 0.6391 .

COMPDAP -0.1506 0.1870 0.860 -0.2483 0.1954 0.780

GRAD12MO -0.2497 0.2949 0.779 -0.4186 0.3187 0.658

GRAD9MO -0.4604** 0.2089 0.631 -0.5320** 0.2204 0.587

INCOMPTX -0.0221 0.2723 0.978 -0.2413 0.3025 0.786

DISCIPTX 0.0113 0.3206 1.011 -0.1370 0.3344 0.872

WITHDRTX 0.3772 0.3788 1.458 0.2147 0.3913 1.239

EBLACK -0.1130 0.1491 0.893 -0.0891 0.1509 0.915

ERACEOTH 0.2778 0.2328 1.320 0.2072 0.2359 1.230

EHISP -0.2493 0.1528 0.779 -0.2566* 0.1534 0.774

EFEM -0.3300*** 0.1252 0.719 -0.3257*** 0.1253 0.722

EPRIORCM 0.4429*** 0.1029 1.557 0.4222*** 0.1033 1.525

ERECVIOL 0.2695** 0.1153 1.309 0.2722** 0.1167 1.313

EPASTVIO -0.0328 0.1113 0.968 -0.0712 0.1123 0.931

TIMESRVD 0.0308 0.0369 1.031 0.0271 0.0369 1.028

ECJSPVNY 0.1940 0.2155 1.214 0.2223 0.2195 1.249

ECJSPVNM -0.2070 0.4083 0.813 -0.2232 0.4138 0.800

AGERLSE -0.0487*** 0.0104 0.952 -0.0455*** 0.0104 0.956

ESUPILL -0.0359 0.0758 0.965 -0.0351 0.0760 0.965

EWORKJOB 0.0014 0.1729 1.001 -0.0104 0.1749 0.990

ELEGITUN -0.0707 0.3191 0.932 -0.0522 0.3208 0.949

EUNEMP 0.0232 0.2391 1.023 0.0103 0.2405 1.010

EJOB_UNK -0.1396 0.4954 0.870 -0.1168 0.5010 0.890

GRADEA 0.0207 0.0367 1.021 0.0194 0.0365 1.020

COC_FRQ 0.0013 0.0391 1.001 0.0014 0.0397 1.001

CRK_FRQ -0.0546 0.0415 0.947 -0.0573 0.0416 0.944

HAL_FRQ 0.0042 0.0585 1.004 -0.0115 0.0593 0.989

HER_FRQ 0.0278 0.0429 1.028 0.0120 0.0433 1.012

OPIA_FRQ -0.0020 0.0607 0.998 0.0027 0.0609 1.003

POT_FRQ -0.0593 0.0420 0.942 -0.0574 0.0424 0.944

BARB_FRQ 0.0387 0.0586 1.039 0.0437 0.0591 1.045

STIM_FRQ -0.0194 0.0466 0.981 -0.0138 0.0466 0.986

DEPLOGTA 0.0188 0.0209 1.019 0.0190 0.0209 1.019

DEPLOGTD 0.0169 0.0355 1.017 0.0091 0.0355 1.009
180

Table 42 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.0017 0.1034 0.998

ECCCFAIL 0.2585** 0.1177 1.295

SUP_REL 0.1990 0.2001 1.220

ECOHO1_6 0.2173 0.2221 1.243

ECOHO7 0.4607* 0.2765 1.585

ECOHO8 0.2614 0.2268 1.299

ECOHO9 -0.3405 0.2887 0.711

ECOHO10 0.2854 0.1972 1.330

ECOHO11 -0.3884* 0.2342 0.678

ECOHO12 -0.1007 0.1648 0.904

ECOHO13 -0.0877 0.1752 0.916

D_T2 0.4280 0.2623 1.534 0.4340* 0.2626 1.543

D_T3 0.4751* 0.2624 1.608 0.4826* 0.2627 1.620

D_T4 0.6071** 0.2586 1.835 0.6205** 0.2589 1.860

D_T5 0.8791*** 0.2497 2.409 0.8988*** 0.2501 2.457

D_T6 0.6786*** 0.2614 1.971 0.7050*** 0.2618 2.024

-2 Log Likelihood

Intercept Only 2176.541 2176.541

Intercept and Covariates 2049.924 2029.842

Concordant Pairs 69.4% 71.4%

Somer’s D 0.420 0.458

Hosmer-Lemeshow

Goodness of Fit 9.7344 with 8 DF (p=0.2842) 11.431 with 8 DF (p=0.1785)

* p < .10

** p < .05

*** p < .01
181

Table 43

Arrests, Male and Female Subjects, Supervised Subjects Only

Traditional Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -2.4360*** 0.6766 . -2.5776*** 0.7021 .

COMPDAP -0.1641 0.2068 0.849 -0.2076 0.2185 0.813

GRAD12MO -0.2520 0.3042 0.777 -0.2137 0.3389 0.808

GRAD9MO -0.5114** 0.2323 0.600 -0.4099* 0.2503 0.664

INCOMPTX -0.1910 0.3309 0.826 -0.2926 0.3676 0.746

DISCIPTX 0.0000 0.3693 1.000 -0.2959 0.3936 0.744

WITHDRTX 0.3918 0.4130 1.480 0.3547 0.4568 1.426

EBLACK -0.1061 0.1591 0.899 -0.1186 0.1658 0.888

ERACEOTH 0.2857 0.2381 1.331 0.1488 0.2565 1.160

EHISP -0.1302 0.1569 0.878 -0.1625 0.1625 0.850

EFEM -0.2820** 0.1397 0.754 -0.3192** 0.1447 0.727

EPRIORCM 0.4631*** 0.1080 1.589 0.4190*** 0.1126 1.520

ERECVIOL 0.1512 0.1311 1.163 0.1359 0.1377 1.146

EPASTVIO 0.0130 0.1244 1.013 -0.0432 0.1313 0.958

TIMESRVD 0.0472 0.0384 1.048 0.1054*** 0.0399 1.111

ECJSPVNY 0.0996 0.2262 1.105 0.0385 0.2490 1.039

ECJSPVNM -0.0585 0.4201 0.943 -0.0415 0.4679 0.959

AGERLSE -0.0587*** 0.0116 0.943 -0.0564*** 0.0113 0.945

ESUPILL -0.0624 0.0845 0.940 -0.1051 0.0885 0.900

EWORKJOB -0.1144 0.1852 0.892 0.1772 0.1981 1.194

ELEGITUN -0.0746 0.3407 0.928 -0.5095 0.3514 0.601

EUNEMP -0.2133 0.2702 0.808 -0.3226 0.2832 0.724

EJOB_UNK 0.3189 0.5154 1.376 0.5281 0.5630 1.696

GRADEA 0.0177 0.0408 1.018 0.0470 0.0424 1.048

COC_FRQ -0.0096 0.0435 0.990 0.0060 0.0460 1.006

CRK_FRQ -0.0685 0.0479 0.934 -0.0920* 0.0493 0.912

HAL_FRQ 0.0232 0.0634 1.023 0.0092 0.0662 1.009

HER_FRQ 0.0172 0.0487 1.017 -0.0094 0.0508 0.991

OPIA_FRQ -0.0357 0.0687 0.965 -0.0441 0.0747 0.957

POT_FRQ -0.0594 0.0471 0.942 -0.0552 0.0492 0.946

BARB_FRQ 0.0780 0.0632 1.081 0.0776 0.0660 1.081

STIM_FRQ -0.0407 0.0521 0.960 -0.0130 0.0536 0.987

DEPLOGTA 0.0533** 0.0233 1.055 0.0533** 0.0233 1.055

DEPLOGTD 0.0076 0.0395 1.008 0.0050 0.0400 1.005
182

Table 43 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.1379 0.1203 0.871

ECCCFAIL 0.1235 0.1377 1.131

UARATE -0.1477*** 0.0468 0.863

HHSE_STR -0.7069 0.7396 0.493

Post-Release Variables

ESPOUSE -0.5367*** 0.1957 0.585

ECOM_LAW 0.1650 0.1701 1.179

SUPVVIOL -0.0312 0.0632 0.969

DIRTY 0.6347*** 0.1834 1.886

EMP_HRS -0.0438*** 0.0049 0.957

ECOHO1_6 0.1515 0.2504 1.164

ECOHO7 0.1973 0.3788 1.218

ECOHO8 0.1925 0.2771 1.212

ECOHO9 -0.4091 0.3515 0.664

ECOHO10 0.4151* 0.2318 1.514

ECOHO11 -0.4751* 0.2606 0.622

ECOHO12 0.1567 0.1854 1.170

ECOHO13 0.1192 0.1910 1.127

D_T2 0.3111 0.2866 1.365 0.3639 0.2914 1.439

D_T3 0.4246 0.2828 1.529 0.4872* 0.2884 1.628

D_T4 0.4438 0.2848 1.559 0.5361* 0.2923 1.709

D_T5 0.8082*** 0.2700 2.244 0.9025*** 0.2785 2.466

D_T6 0.7039** 0.2782 2.022 0.8213*** 0.2871 2.273

-2 Log Likelihood

Intercept Only 1798.026 1798.026

Intercept and Covariates 1682.162 1521.068

Concordant Pairs 70.7% 83.0%

Somer’s D 0.444 0.675

Hosmer-Lemshow

Goodness of Fit 8.4068 with 8 DF (p=0.3948) 16.380 with 8 DF (p=0.0373)

* p < .10

** p < .05

*** p < .01
183

Table 44

Arrests or Revocations, Male and Female Subjects

Traditional Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -3.0183*** 0.5327 . -3.3434*** 0.6664 .

COMPDAP -0.0746 0.1684 0.928 -0.1634 0.1870 0.849

GRAD12MO -0.2665 0.2535 0.766 -0.1965 0.3039 0.822

GRAD9MO -0.3808** 0.1840 0.683 -0.1700 0.2182 0.844

INCOMPTX -0.0361 0.2573 0.965 -0.0794 0.2993 0.924

DISCIPTX 0.1603 0.3016 1.174 -0.3386 0.3461 0.713

WITHDRTX 0.2856 0.3621 1.331 0.4614 0.4059 1.586

EBLACK -0.0516 0.1358 0.950 0.0256 0.1510 1.026

ERACEOTH 0.2547 0.2141 1.290 -0.0881 0.2428 0.916

EHISP -0.1014 0.1280 0.904 -0.0674 0.1374 0.935

EFEM -0.2332** 0.1057 0.792 -0.3042*** 0.1160 0.738

EPRIORCM 0.3437*** 0.0867 1.410 0.2801*** 0.0950 1.323

ERECVIOL 0.1224 0.1095 1.130 0.1411 0.1211 1.151

EPASTVIO 0.0843 0.0986 1.088 -0.0258 0.1108 0.975

TIMESRVD -0.0045 0.0328 0.995 0.0814** 0.0351 1.085

ECJSPVNY 0.1178 0.1671 1.125 -0.0182 0.1918 0.982

ECJSPVNM 0.0699 0.3072 1.072 0.1777 0.3560 1.194

AGERLSE -0.0255*** 0.0086 0.975 -0.0275*** 0.0090 0.973

ESUPILL -0.0288 0.0670 0.972 -0.0628 0.0738 0.939

EWORKJOB -0.1033 0.1484 0.902 0.3503** 0.1675 1.420

ELEGITUN -0.0915 0.2636 0.913 -0.5850** 0.2851 0.557

EUNEMP -0.2782 0.2222 0.757 -0.5042** 0.2483 0.604

EJOB_UNK 0.2652 0.4086 1.304 0.4583 0.4784 1.581

GRADEA 0.0124 0.0319 1.012 0.0280 0.0340 1.028

COC_FRQ 0.0008 0.0343 1.001 -0.0047 0.0385 0.995

CRK_FRQ -0.0066 0.0351 0.993 -0.0602 0.0384 0.942

HAL_FRQ 0.0642 0.0503 1.066 0.0323 0.0552 1.033

HER_FRQ 0.0187 0.0370 1.019 -0.0288 0.0406 0.972

OPIA_FRQ 0.0063 0.0526 1.006 -0.0164 0.0612 0.984

POT_FRQ -0.0533 0.0370 0.948 -0.0709* 0.0410 0.932

BARB_FRQ 0.0117 0.0515 1.012 0.0134 0.0561 1.013

STIM_FRQ -0.0610 0.0417 0.941 0.0122 0.0448 1.012

DEPLOGTA 0.0225 0.0186 1.023 0.0260 0.0195 1.026

DEPLOGTD 0.0261 0.0311 1.026 0.0315 0.0339 1.032

Treatment Variables

ENRGENY -0.2749 0.1741 0.760

ENRSUPY -0.1932 0.2781 0.824

ETSYES -0.0427 0.0886 0.958

ECTRONLY -0.2767 0.1735 0.758

EOTHONLY -0.0780 0.2925 0.925

EBOTH -0.9689*** 0.3083 0.379

ETXMISS 1.6965*** 0.4793 5.455

EAAYES 0.1294 0.1800 1.138

EAAMISS -0.1694 0.3012 0.844
184

Table 44 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.1841 0.1161 0.832

ECCCFAIL 0.2301** 0.1173 1.259

UARATE -0.1938*** 0.0435 0.824

HHSE_STR -0.2925 0.4667 0.746

Post-Release Variables

ESPOUSE -0.4205** 0.1710 0.657

ECOM_LAW -0.0984 0.1617 0.906

SUPVVIOL 0.1286*** 0.0399 1.137

DIRTY 1.1800*** 0.1531 3.254

EMP_HRS -0.0490*** 0.0043 0.952

ECOHO1_6 0.0529 0.2138 1.054

ECOHO7 0.4958* 0.2873 1.642

ECOHO8 -0.0374 0.2440 0.963

ECOHO9 -0.5005* 0.2876 0.606

ECOHO10 0.4079** 0.1957 1.504

ECOHO11 -0.3035 0.2050 0.738

ECOHO12 0.2234 0.1554 1.250

ECOHO13 -0.1573 0.1689 0.854

Time Variables

D_T2 0.2598 0.2411 1.297 0.3256 0.2541 1.385

D_T3 0.6349*** 0.2267 1.887 0.7299*** 0.2410 2.075

D_T4 0.6514*** 0.2283 1.918 0.7872*** 0.2442 2.197

D_T5 0.8282*** 0.2242 2.289 0.9670*** 0.2412 2.630

D_T6 0.7604*** 0.2294 2.139 0.9316*** 0.2469 2.539

-2 Log Likelihood

Intercept Only 2548.969 2548.969

Intercept and Covariates 2419.674 1989.642

Concordant Pairs 67.7% 85.6%

Somer’s D 0.378 0.723

Hosmer-Lemshow

Goodness of Fit 9.5458 with 8 DF (p=0.2984) 12.177 with 8 DF (p=0.1435)

* p < .10

** p < .05

*** p < .01
185

Table 45

Drug Use, Male and Female Subjects

Traditional Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -1.8589*** 0.4415 -2.2986*** 0.5255 .

COMPDAP -0.1051 0.1432 0.900 -0.3060** 0.1575 0.736

GRAD12MO -0.4602** 0.2286 0.631 -0.6677*** 0.2585 0.513

GRAD9MO -0.1703 0.1494 0.843 -0.3858** 0.1752 0.680

INCOMPTX -0.1492 0.2172 0.861 -0.4054* 0.2495 0.667

DISCIPTX -0.1963 0.2835 0.822 -0.3719 0.3044 0.689

WITHDRTX -0.4859 0.4090 0.615 -0.5971 0.4295 0.550

EBLACK 0.1767 0.1241 1.193 0.2656** 0.1323 1.304

ERACEOTH 0.3186 0.2075 1.375 0.1969 0.2209 1.218

EHISP 0.2257** 0.1023 1.253 0.2876*** 0.1077 1.333

EFEM -0.2862*** 0.0856 0.751 -0.2747*** 0.0909 0.760

EPRIORCM 0.1692** 0.0686 1.184 0.1761** 0.0729 1.193

ERECVIOL 0.0893 0.0961 1.093 0.1417 0.1017 1.152

EPASTVIO 0.0727 0.0852 1.075 0.0184 0.0905 1.019

TIMESRVD -0.0943*** 0.0321 0.910 -0.0591* 0.0315 0.943

ECJSPVNY 0.1290 0.1525 1.138 0.0644 0.1580 1.067

ECJSPVNM 0.0208 0.2847 1.021 0.0235 0.2924 1.024

AGERLSE -0.0049 0.0073 0.995 0.0028 0.0077 1.003

ESUPILL 0.0302 0.0550 1.031 0.0191 0.0586 1.019

EWORKJOB -0.1143 0.1268 0.892 0.0092 0.1337 1.009

ELEGITUN 0.1532 0.2164 1.166 0.1208 0.2326 1.128

EUNEMP -0.0998 0.1727 0.905 -0.1925 0.1827 0.825

EJOB_UNK -0.0442 0.3681 0.957 -0.0403 0.3768 0.960

GRADEA -0.0033 0.0273 0.997 0.0088 0.0288 1.009

COC_FRQ -0.0089 0.0288 0.991 -0.0175 0.0305 0.983

CRK_FRQ 0.0649** 0.0270 1.067 0.0453 0.0287 1.046

HAL_FRQ 0.0106 0.0439 1.011 0.0137 0.0456 1.014

HER_FRQ 0.0356 0.0298 1.036 -0.0085 0.0320 0.991

OPIA_FRQ 0.0407 0.0436 1.042 0.0641 0.0466 1.066

POT_FRQ 0.0596* 0.0326 1.061 0.0761** 0.0349 1.079

BARB_FRQ -0.0601 0.0438 0.942 -0.0710 0.0466 0.931

STIM_FRQ -0.0205 0.0351 0.980 -0.0059 0.0369 0.994

DEPLOGTA -0.0167 0.0160 0.983 -0.0246 0.0165 0.976

DEPLOGTD 0.0663** 0.0273 1.069 0.0269 0.0287 1.027

Treatment Variables

ENRGENY -0.3344** 0.1339 0.716

ENRSUPY -0.0374 0.1753 0.963

ETSYES -0.0021 0.0723 0.998

ECTRONLY -0.2598* 0.1557 0.771

EOTHONLY -0.1778 0.2539 0.837

EBOTH 0.7955*** 0.2261 2.216

ETXMISS 0.9226* 0.5018 2.516

EAAYES 0.2098 0.1516 1.233

EAAMISS -0.2454 0.2672 0.782
186

Table 45 — Continued

 Base Model Final Model 

Variable b se(b) OR b se(b) OR 

Supervision Variables

ECCCNO -0.1568 0.1026 0.855

ECCCFAIL 0.4266*** 0.0952 1.532

UARATE 0.0565* 0.0302 1.058

HHSE_STR 0.2063 0.3687 1.229

Post-Release Variables

ESPOUSE -0.2335** 0.1086 0.792

ECOM_LAW -0.0191 0.1123 0.981

SUPVVIOL 0.2331*** 0.0360 1.262

EMP_HRS -0.0078*** 0.0030 0.992

ECOHO1_6 0.0089 0.1841 1.009

ECOHO7 -0.1791 0.2810 0.836

ECOHO8 0.1746 0.2046 1.191

ECOHO9 0.0359 0.2076 1.037

ECOHO10 -0.1355 0.1785 0.873

ECOHO11 0.2614* 0.1565 1.299

ECOHO12 0.0619 0.1307 1.064

ECOHO13 -0.1677 0.1387 0.846

Time Variables

D_T2 -0.2751* 0.1439 0.759 -0.1778 0.1509 0.837

D_T3 -0.2985** 0.1499 0.742 -0.1047 0.1571 0.901

D_T4 -0.6677*** 0.1711 0.513 -0.4169** 0.1779 0.659

D_T5 -0.5514*** 0.1693 0.576 -0.2345 0.1763 0.791

D_T6 -1.3578*** 0.2317 0.257 -1.0287*** 0.2378 0.357 

-2 Log Likelihood

Intercept Only 3274.165 3274.165

Intercept and Covariates 3012.751 2745.484

Concordant Pairs 71.8% 79.9%

Somer’s D 0.447 0.605

Hosmer-Lemeshow

Goodness of Fit 8.6707 with 8 DF (p=0.3708) 15.403 with 8 DF (p=0.0518)

* p < .10

** p < .05

*** p < .01
187

Table 46

Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Bloom Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -4.7408*** 0.6099 . -4.9127*** 0.6536 .

SUBJECT 0.0086 0.1564 1.009 -0.2111 0.1779 0.810

EBLACK -0.3514*** 0.1356 0.704 -0.2695* 0.1411 0.764

ERACEOTH 0.6252*** 0.2069 1.869 0.5156** 0.2181 1.675

EHISP -0.1022 0.1249 0.903 -0.1271 0.1340 0.881

EFEM -0.3742*** 0.1110 0.688 -0.4427*** 0.1225 0.642

EPRIORCM 0.3991*** 0.0945 1.491 0.3419*** 0.0964 1.408

ERECVIOL -0.1196 0.1316 0.887 -0.0882 0.1369 0.916

EPASTVIO 0.0774 0.1148 1.080 0.0120 0.1196 1.012

TIMESRVD 0.0337 0.0334 1.034 0.0265 0.0354 1.027

ECJSPVNY 0.5287 0.3293 1.697 0.7695** 0.3380 2.159

ECJSPVNM -0.5544 0.6476 0.574 -0.7845 0.6618 0.456

AGERLSE -0.0421*** 0.0092 0.959 -0.0410*** 0.0097 0.960

EWORKJOB 0.1666 0.1906 1.181 0.1131 0.1935 1.120

ESUPILL 0.0846 0.0691 1.088 0.0741 0.0720 1.077

ELEGITUN -0.2922 0.3628 0.747 -0.2327 0.3669 0.792

EUNEMP -0.2704 0.2732 0.763 -0.4049 0.2809 0.667

EJOB_UNK -0.0203 0.5800 0.980 0.3003 0.5912 1.350

GRADEA 0.1344*** 0.0332 1.144 0.1084*** 0.0342 1.115

COC_FRQ -0.0328 0.0379 0.968 0.0025 0.0409 1.003

CRK_FRQ 0.1531*** 0.0355 1.165 0.1322*** 0.0376 1.141

HAL_FRQ -0.1645*** 0.0638 0.848 -0.1445** 0.0655 0.865

HER_FRQ -0.0080 0.0405 0.992 -0.0149 0.0423 0.985

OPIA_FRQ -0.0081 0.0534 0.992 -0.0015 0.0559 0.999

POT_FRQ -0.1890*** 0.0372 0.828 -0.1760*** 0.0393 0.839

BARB_FRQ 0.0762 0.0529 1.079 0.0352 0.0568 1.036

STIM_FRQ 0.0393 0.0443 1.040 0.0541 0.0460 1.056

DEPLOGTA 0.0502** 0.0209 1.052 0.0553** 0.0217 1.057

DEPLOGTD 0.0214 0.0336 1.022 -0.0168 0.0353 0.983
188

Table 46 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.3314*** 0.1024 0.718

ECCCFAIL 0.5299*** 0.1174 1.699

SUP_REL 0.5628** 0.2201 1.756

ECOHO1_6 0.9334*** 0.1717 2.543

ECOHO7 0.6424** 0.2537 1.901

ECOHO8 0.0678 0.2378 1.070

ECOHO9 -0.8381** 0.3555 0.433

ECOHO10 -0.2990 0.2347 0.742

ECOHO11 0.2998* 0.1705 1.350

ECOHO12 -0.1219 0.1788 0.885

ECOHO13 -0.0297 0.1720 0.971

D_T2 0.6656*** 0.2344 1.946 0.6847*** 0.2369 1.983

D_T3 -0.0011 0.2741 0.999 0.0912 0.2770 1.096

D_T4 1.3205*** 0.2192 3.745 1.4254*** 0.2231 4.159

D_T5 0.5317** 0.2522 1.702 0.6738*** 0.2558 1.962

D_T6 0.6988*** 0.2462 2.011 0.8460*** 0.2500 2.330

-2 Log Likelihood

Intercept Only 2547.506 2547.506

Intercept and Covariates 2324.885 2250.129

Concordant Pairs 60.2% 61.1%

Somer’s D 0.238 0.258

Hosmer-Lemeshow

Goodness of Fit 63.538 with 8 DF (p=0.0001) 115.88 with 8 DF (p=0.0001)

* p < .10

** p < .05

*** p < .01
189

Table 47

Arrests, Male and Female Subjects, Supervised Subjects Only

Bloom Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -4.6073*** 0.6606 . -5.0224*** 0.7329 .

SUBJECT 0.0740 0.1755 1.077 0.0513 0.2019 1.053

EBLACK -0.4005*** 0.1416 0.670 -0.3572** 0.1619 0.700

ERACEOTH 0.7238*** 0.2099 2.062 0.5983** 0.2521 1.819

EHISP 0.0494 0.1296 1.051 -0.1261 0.1608 0.882

EFEM -0.4048*** 0.1200 0.667 -0.5766*** 0.1520 0.562

EPRIORCM 0.4203*** 0.0975 1.522 0.2698** 0.1068 1.310

ERECVIOL -0.2627* 0.1522 0.769 -0.1450 0.1653 0.865

EPASTVIO 0.0778 0.1276 1.081 -0.0630 0.1475 0.939

TIMESRVD 0.0286 0.0358 1.029 0.1628*** 0.0404 1.177

ECJSPVNY 0.5208 0.3345 1.683 0.5627 0.3605 1.755

ECJSPVNM -0.3971 0.6560 0.672 -0.5105 0.7019 0.600

AGERLSE -0.0446*** 0.0100 0.956 -0.0486*** 0.0106 0.953

ESUPILL 0.0491 0.0745 1.050 -0.1634* 0.0888 0.849

EWORKJOB 0.1035 0.1993 1.109 0.3222 0.2199 1.380

ELEGITUN -0.3710 0.3935 0.690 -1.0597** 0.4151 0.347

EUNEMP -0.4716 0.2985 0.624 -0.5314 0.3270 0.588

EJOB_UNK 0.4310 0.5959 1.539 0.8633 0.6566 2.371

GRADEA 0.1508*** 0.0362 1.163 0.1618*** 0.0414 1.176

COC_FRQ -0.0235 0.0411 0.977 0.0691 0.0478 1.072

CRK_FRQ 0.1793*** 0.0381 1.196 0.0402 0.0475 1.041

HAL_FRQ -0.2197*** 0.0710 0.803 -0.0863 0.0724 0.917

HER_FRQ -0.0108 0.0442 0.989 -0.0546 0.0522 0.947

OPIA_FRQ -0.0359 0.0569 0.965 0.0202 0.0675 1.020

POT_FRQ -0.1825*** 0.0402 0.833 -0.1456*** 0.0471 0.864

BARB_FRQ 0.1058* 0.0554 1.112 0.0351 0.0657 1.036

STIM_FRQ 0.0066 0.0486 1.007 0.0656 0.0529 1.068

DEPLOGTA 0.0761*** 0.0225 1.079 0.0806*** 0.0253 1.084

DEPLOGTD 0.0274 0.0357 1.028 -0.0143 0.0413 0.986
190

Table 47 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.3479*** 0.1241 0.706

ECCCFAIL 0.2631* 0.1514 1.301

UARATE -0.2892*** 0.0473 0.749

SUP_REL . . .

HHSE_STR -1.0163 0.9531 0.362

ESPOUSE -0.8565*** 0.2421 0.425

ECOM_LAW 0.2866 0.1989 1.332

SUPVVIOL -0.1684** 0.0703 0.845

DIRTY 1.2820*** 0.1756 3.604

EMP_HRS -0.0562*** 0.0048 0.945

ECOHO1_6 0.9160*** 0.2056 2.499

ECOHO7 1.0124*** 0.3106 2.752

ECOHO8 -0.1738 0.2886 0.840

ECOHO9 -1.8516*** 0.5401 0.157

ECOHO10 -0.2706 0.2910 0.763

ECOHO11 0.3417* 0.1964 1.407

ECOHO12 0.3164 0.2024 1.372

ECOHO13 0.3602* 0.1943 1.434

D_T2 0.6092** 0.2455 1.839 0.5372** 0.2645 1.711

D_T3 -0.0916 0.2925 0.912 -0.0117 0.3080 0.988

D_T4 1.3306*** 0.2287 3.783 1.5265*** 0.2486 4.602

D_T5 0.3838 0.2737 1.468 0.6799** 0.2934 1.974

D_T6 0.7726*** 0.2556 2.165 1.0556*** 0.2771 2.874

-2 Log Likelihood

Intercept Only 2246.057 2246.057

Intercept and Covariates 2008.106 1626.307

Concordant Pairs 59.1% 76.3%

Somer’s D 0.218 0.557

Hosmer-Lemshow

Goodness of Fit 86.909 with 8 DF (p=0.0001) 93.517 with 8 DF (p=0.0001)

* p < .10

** p < .05

*** p < .01
191

Table 48

Arrests or Revocations, Male and Female Subjects

Bloom Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -4.3224*** 0.5391 . -5.6530*** 0.8100 .

SUBJECT 0.0310 0.1468 1.031 0.0470 0.1783 1.048

EBLACK -0.2380* 0.1280 0.788 -0.0555 0.1544 0.946

ERACEOTH 0.5307*** 0.1990 1.700 0.0747 0.2504 1.078

EHISP 0.0372 0.1130 1.038 -0.0044 0.1433 0.996

EFEM -0.3839*** 0.1041 0.681 -0.5640*** 0.1336 0.569

EPRIORCM 0.3801*** 0.0847 1.462 0.2277** 0.0970 1.256

ERECVIOL -0.2462* 0.1299 0.782 -0.0982 0.1474 0.906

EPASTVIO 0.1896* 0.1061 1.209 -0.0086 0.1295 0.991

TIMESRVD -0.0205 0.0323 0.980 0.1008*** 0.0379 1.106

ECJSPVNY 0.4161* 0.2472 1.516 0.4082 0.2877 1.504

ECJSPVNM -0.2731 0.4813 0.761 -0.3348 0.5558 0.715

AGERLSE -0.0253*** 0.0082 0.975 -0.0315*** 0.0091 0.969

ESUPILL 0.0716 0.0639 1.074 -0.0817 0.0782 0.922

EWORKJOB 0.0763 0.1702 1.079 0.5015** 0.2061 1.651

ELEGITUN -0.2278 0.3099 0.796 -1.1146*** 0.3526 0.328

EUNEMP -0.5025** 0.2520 0.605 -0.7051** 0.2966 0.494

EJOB_UNK 0.3856 0.5220 1.471 0.8129 0.6431 2.254

GRADEA 0.1155*** 0.0310 1.122 0.1150*** 0.0359 1.122

COC_FRQ -0.0111 0.0347 0.989 0.0430 0.0428 1.044

CRK_FRQ 0.1638*** 0.0325 1.178 0.0232 0.0416 1.023

HAL_FRQ -0.1220** 0.0573 0.885 -0.0348 0.0612 0.966

HER_FRQ 0.0005 0.0367 1.000 -0.0667 0.0448 0.936

OPIA_FRQ -0.0295 0.0492 0.971 0.0431 0.0602 1.044

POT_FRQ -0.1409*** 0.0347 0.869 -0.1389*** 0.0431 0.870

BARB_FRQ 0.0557 0.0493 1.057 -0.0292 0.0592 0.971

STIM_FRQ -0.0100 0.0414 0.990 0.0945** 0.0478 1.099

DEPLOGTA 0.0584*** 0.0193 1.060 0.0701*** 0.0228 1.073

DEPLOGTD 0.0170 0.0309 1.017 -0.0044 0.0378 0.996

Treatment Variables

ENRGENY -0.3654 0.2452 0.694

ENRSUPY -0.4669 0.4668 0.627

ETSYES -0.2501*** 0.0952 0.779

ECTRONLY -0.0316 0.2053 0.969

EOTHONLY -0.4145 0.3920 0.661

EBOTH -1.6182*** 0.3980 0.198

ETXMISS 2.1752*** 0.5168 8.804

EAAYES 0.3546 0.2184 1.426

EAAMISS -0.3911 0.3822 0.676
192

Table 48 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.5462*** 0.1168 0.579

ECCCFAIL 0.5429*** 0.1306 1.721

UARATE -0.2807*** 0.0471 0.755

HHSE_STR -0.1225 0.5193 0.885

Post-Release Variables

ESPOUSE -0.6226*** 0.2067 0.537

ECOM_LAW -0.0143 0.1861 0.986

SUPVVIOL 0.0572 0.0448 1.059

DIRTY 1.6011*** 0.1589 4.958

EMP_HRS -0.0586*** 0.0044 0.943

ECOHO1_6 0.5712*** 0.1948 1.770

ECOHO7 1.3046*** 0.2667 3.686

ECOHO8 -0.3408 0.2721 0.711

ECOHO9 -1.6491*** 0.4284 0.192

ECOHO10 0.0319 0.2236 1.032

ECOHO11 0.1580 0.1765 0.171

ECOHO12 0.3185* 0.1723 1.375

ECOHO13 0.0655 0.1797 1.068

D_T2 0.4156* 0.2134 1.515 0.3721 0.2405 1.451

D_T3 0.2139 0.2265 1.239 0.3355 0.2517 1.399

D_T4 1.1331*** 0.1961 3.105 1.4360*** 0.2241 4.204

D_T5 0.3364 0.2299 1.400 0.7456*** 0.2576 2.108

D_T6 0.6886*** 0.2163 1.991 1.0871*** 0.2455 2.966

-2 Log Likelihood

Intercept Only 2819.505 2819.505

Intercept and Covariates 2596.770 1963.616

Concordant Pairs 61.1% 83.0%

Somer’s D 0.246 0.677

Hosmer-Lemeshow

Goodness of Fit 72.063 with 8 DF (p=0.0001) 70.558 with 8 DF (p=0.0001)

* p < .10

** p < .05

*** p < .01
193

Table 49

Drug Use, Male and Female Subjects

Bloom Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -2.9136*** 0.4422 . -3.4254*** 0.5628 .

SUBJECT -0.0771 0.1251 0.926 -0.4615*** 0.1490 0.630

EBLACK -0.0273 0.1042 0.973 0.0912 0.1140 1.095

ERACEOTH 0.7463*** 0.1622 2.109 0.7629*** 0.1817 2.144

EHISP -0.0367 0.1145 0.964 0.1192 0.1212 1.127

EFEM -0.3207*** 0.0867 0.726 -0.3756*** 0.0972 0.687

EPRIORCM 0.2377*** 0.0670 1.268 0.2603*** 0.0732 1.297

ERECVIOL 0.0503 0.1016 1.052 0.1529 0.1083 1.165

EPASTVIO 0.0148 0.0148 1.015 -0.1528 0.0990 0.858

TIMESRVD -0.1135*** 0.0299 0.893 -0.0484 0.0297 0.953

ECJSPVNY 0.0166 0.1746 1.017 -0.0698 0.1855 0.933

ECJSPVNM 0.0840 0.3321 1.088 0.1086 0.3486 1.115

AGERLSE -0.0031 0.0031 0.997 0.0118 0.0076 1.012

ESUPILL 0.1595*** 0.0547 1.173 0.1199** 0.0593 1.127

EWORKJOB 0.0499 0.1395 1.051 0.2870* 0.1498 1.332

ELEGITUN 0.1461 0.2481 1.157 -0.0098 0.2744 0.990

EUNEMP -0.1770 0.1988 0.838 -0.2820 0.2103 0.754

EJOB_UNK -0.0362 0.4278 0.964 0.0583 0.4518 1.060

GRADEA 0.0659** 0.0286 1.068 0.0735** 0.0310 1.076

COC_FRQ -0.0248 0.0295 0.975 0.0735 0.0310 1.001

CRK_FRQ 0.1230*** 0.0279 1.131 0.0667** 0.0307 1.069

HAL_FRQ -0.0849 0.0491 0.919 -0.0500 0.2406 0.976

HER_FRQ -0.0023 0.0317 0.998 -0.0617* 0.0344 0.940

OPIA_FRQ -0.1192** 0.0482 0.888 -0.0622 0.0520 0.940

POT_FRQ 0.0656** 0.0332 1.068 0.1131*** 0.0365 1.120

BARB_FRQ -0.0050 0.0442 0.995 -0.0479 0.0484 0.953

STIM_FRQ -0.0356 0.0380 0.965 -0.0330 0.0407 0.968

DEPLOGTA -0.0262 0.0174 0.974 -0.0288 0.0181 0.972

DEPLOGTD 0.0856*** 0.0291 1.089 0.0290 0.0317 1.029

Treatment Variables

ENRGENY -0.3882** 0.1733 0.678

ENRSUPY 0.0957 0.2426 1.100

ETSYES -0.0327 0.0693 0.968

ECTRONLY -0.3821** 0.1751 0.682

EOTHONLY 0.0620 0.2800 1.064

EBOTH 0.5580** 0.2651 1.747

ETXMISS 1.1141** 0.5658 3.047

EAAYES 0.3223** 0.1640 1.380

EAAMISS -0.2797 0.2893 0.756
194

Table 49 — Continued

 Base Model Final Model 

Variable b se(b) OR b se(b) OR 

Supervision Variables

ECCCNO -0.2272** 0.0955 0.797

ECCCFAIL 0.4395*** 0.0980 1.552

UARATE 0.0754** 0.0302 1.078

HHSE_STR 0.9603*** 0.3686 2.613

Post-Release Variables

ESPOUSE -0.0916 0.1134 0.912

ECOM_LAW -0.1608 0.1248 0.851

SUPVVIOL 0.1805*** 0.0375 1.198

EMP_HRS -0.0089*** 0.0029 0.991

ECOHO1_6 0.4975*** 0.1522 1.645

ECOHO7 -0.3812 0.2933 0.683

ECOHO8 0.2822 0.1976 1.326

ECOHO9 -0.0255 0.2146 0.975

ECOHO10 0.0163 0.1686 1.016

ECOHO11 0.3707** 0.1480 1.449

ECOHO12 -0.1081 0.1490 0.897

ECOHO13 -0.3794** 0.1527 0.684

D_T2 0.0102 0.1376 1.010 0.1634 0.1447 1.178

D_T3 -0.3502** 0.1570 0.705 -0.1023 0.1644 0.903

D_T4 -0.5054*** 0.1686 0.603 -0.2004 0.1760 0.818

D_T5 -0.6777*** 0.1822 0.508 -0.3442* 0.1891 0.709

D_T6 -0.5389*** 0.1782 0.583 -0.1951 0.1853 0.823

-2 Log Likelihood

Intercept Only 3464.186 3464.186

Intercept and Covariates 3156.096 2897.069

Concordant Pairs 68.0% 76.3%

Somer’s D 0.371 0.535

Hosmer-Lemeshow

Goodness of Fit 22.125 with 8 DF (p=0.0047) 23.91 with 8 DF (p=0.0024)

* p < .10

** p < .05

*** p < .01
195

Table 50

Arrests, Males and Females, Supervised and Unsupervised Subjects

Complete, Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT 6.0448*** 0.5777

COMPLETE 1.1203*** 0.3101

EBLACK 0.1701 0.1524

ERACEOTH -0.3030 0.2480

EHISP 0.3047** 0.1397

EFEM 0.2728*** 0.1128

EPRIORCM -0.4165*** 0.0930

ERECVIOL -0.2713** 0.1186

EPASTVIO 0.0696 0.1089

TIMESRVD -0.0152 0.0349

ECJSPVNY -0.2460 0.2033

ECJSPVNM 0.2921 0.3798

AGERLSE 0.0434*** 0.0098

ESUPILL 0.0458 0.0714

EWORKJOB 0.0099 0.1574

ELEGITUN 0.1060 0.2896

EUNEMP -0.0138 0.2226

EJOB_UNK 0.0719 0.4436

GRADEA -0.0149 0.0333

COC_FRQ -0.0043 0.0368

CRK_FRQ 0.0557 0.0389

HAL_FRQ -0.0118 0.0548

HER_FRQ -0.0187 0.0407

OPIA_FRQ 0.0279 0.0574

POT_FRQ 0.0525 0.0406

BARB_FRQ -0.0561 0.0543

STIM_FRQ 0.0102 0.0427

DEPLOGTA -0.0113 0.0200

DEPLOGTD 0.0066 0.0330

Supervision Variables

ECCCNO -0.0450 0.0991

ECCCFAIL -0.2348** 0.1159

SUP_REL -0.2123 0.1943

Post-Release Variables

ECOHO1_6 -0.1232 0.2113

ECOHO7 -0.4653* 0.2731

ECOHO8 -0.2596 0.2225

ECOHO9 0.3394 0.2584

ECOHO10 -0.3102 0.1912
196

Table 50 - Continued

Final Model

Variable b se(b)

ECOHO11 0.3093* 0.2054

ECOHO12 0.0210 0.1519

ECOHO13 0.1590 0.1627

Selection Bias Variables

COVARIAN -0.6877*** 0.2348

SIGMA 1.7163*** 0.0973

* p < .10

** p < .05

*** p < .01
197

Table 51

Arrests, Males and Females, Supervised Subjects Only

Complete, Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT 5.4181*** 0.6098

COMPLETE 1.2406*** 0.3377

EBLACK 0.1977 0.1617

ERACEOTH -0.3340* 0.2565

EHISP 0.2062 0.1478

EFEM 0.2219* 0.1241

EPRIORCM -0.4426*** 0.0988

ERECVIOL -0.1830 0.1314

EPASTVIO -0.0052 0.1193

TIMESRVD -0.0470 0.0363

ECJSPVNY -0.1706 0.2218

ECJSPVNM 0.2178 0.4133

AGERLSE 0.0485*** 0.0107

ESUPILL 0.0915 0.0789

EWORKJOB 0.0016 0.1742

ELEGITUN 0.3090 0.3192

EUNEMP 0.1520 0.2506

EJOB_UNK -0.3050 0.4861

GRADEA -0.0213 0.0373

COC_FRQ 0.0072 0.0407

CRK_FRQ 0.0789* 0.0445

HAL_FRQ -0.0271 0.0610

HER_FRQ -0.0103 0.0447

OPIA_FRQ 0.0759 0.0652

POT_FRQ 0.0518 0.0448

BARB_FRQ -0.0918 0.0599

STIM_FRQ 0.0219 0.0471

DEPLOGTA -0.0353 0.0220

DEPLOGTD 0.0026 0.0363

Supervision Variables

ECCCNO 0.0612 0.1130

ECCCFAIL -0.1967 0.1294

UARATE 0.0911* 0.0399

HHSE_STR 0.4974 0.5655

Post-Release Variables

ESPOUSE 0.5493*** 0.1626

ECOM_LAW -0.1436 0.1488

SUPVVIOL -0.0923 0.0603

DRUGVIOL 0.0348 0.0416

PRCTFULL 0.0044*** 0.0018

ECOHO1_6 -0.1696 0.2236

ECOHO7 -0.1244 0.3306

ECOHO8 -0.2630 0.2581

ECOHO9 0.3789 0.2996

ECOHO10 -0.3923* 0.2147

ECOHO11 0.2177 0.2191
198

Table 51 — Continued

Final Model

Variable b se(b)

ECOHO12 -0.0759 0.1676

ECOHO13 0.0822 0.1730

Selection Bias Variables

COVARIAN -0.7738*** 0.2555

SIGMA 1.6808*** 0.1046

* p < .10

** p < .05

*** p < .01
199

Table 52

Arrests or Revocations

Male and Female Subjects, Complete

Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT 5.4249*** 0.5092

COMPLETE 1.0047*** 0.2525

EBLACK 0.1977 0.1617

ERACEOTH 0.1047 0.1228

EHISP 0.1318 0.1062

EFEM 0.1899** 0.0882

EPRIORCM -0.2830*** 0.0708

ERECVIOL -0.1560 0.0987

EPASTVIO -0.0439 0.0866

TIMESRVD -0.0327 0.0267

ECJSPVNY -0.1098 0.1530

ECJSPVNM 0.0348 0.2843

AGERLSE 0.0221*** 0.0072

ESUPILL 0.0890 0.0579

EWORKJOB -0.1328 0.1300

ELEGITUN 0.3665 0.2315

EUNEMP 0.1937 0.1894

EJOB_UNK -0.1672 0.3728

GRADEA -0.0125 0.0263

COC_FRQ -0.0037 0.0298

CRK_FRQ 0.0789 0.0445

HAL_FRQ -0.0327 0.0444

HER_FRQ -0.0079 0.0315

OPIA_FRQ 0.0471 0.0463

POT_FRQ 0.0380 0.0324

BARB_FRQ -0.0490 0.0434

STIM_FRQ 0.0296 0.0346

DEPLOGTA -0.0212 0.0159

DEPLOGTD 0.0015 0.0263

Treatment Variables

ENRGENY 0.1279 0.1241

ENRSUPY 0.0965 0.1960

ETSYES 0.0638 0.0691

ECTRONLY 0.3161** 0.1416

EOTHONLY 0.1034 0.2426

EBOTH 0.4696* 0.2505

ETXMISS -1.3987*** 0.4023
200

Table 52— Continued

Final Model

Variable b se(b)

EAAYES -0.0705 0.1490

EAAMISS -0.0042 0.2559

Supervision Variables

ECCCNO 0.1279 0.0937

ECCCFAIL -0.2213*** 0.0939

UARATE 0.1321*** 0.0324

HHSE_STR -0.1062 0.3471

Post-Release Variables

ESPOUSE 0.4651*** 0.1227

ECOM_LAW -0.0305 0.1168

SUPVVIOL -0.2102*** 0.0387

DRUGVIOL -0.0705*** 0.0266

PRCTFULL 0.0062*** 0.0013

ECOHO1_6 -0.0377 0.1643

ECOHO7 -0.4128* 0.2235

ECOHO8 -0.1003 0.1918

ECOHO9 0.4016 0.2159

ECOHO10 -0.3520 0.1566

ECOHO11 0.2177** 0.1525

ECOHO12 -0.0550 0.1223

ECOHO13 0.2441 0.1301

Selection Bias Variables

COVARIAN -0.8639*** 0.2243

SIGMA 1.3728*** 0.0684

* p < .10

** p < .05

*** p < .01
201

Table 53

Drug Use

Male and Female Subjects Who Had Urine Testing During Follow-Up, Complete

Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT 6.0021*** 0.5711

COMPLETE 0.6829** 0.3157

EBLACK -0.1604 0.1485

ERACEOTH -0.3313 0.2500

EHISP -0.2704** 0.1190

EFEM 0.3390*** 0.0989

EPRIORCM -0.1956*** 0.0794

ERECVIOL -0.2066* 0.1139

EPASTVIO -0.0236 0.1003

TIMESRVD 0.0682** 0.0343

ECJSPVNY -0.0310 0.1807

ECJSPVNM -0.0558 0.3369

AGERLSE 0.0028 0.0085

ESUPILL -0.0307 0.0644

EWORKJOB -0.0796 0.1483

ELEGITUN -0.1024 0.2558

EUNEMP 0.1753 0.2021

EJOB_UNK 0.1391 0.4309

GRADEA -0.0279 0.0315

COC_FRQ 0.0381 0.0332

CRK_FRQ -0.0552* 0.0318

HAL_FRQ -0.0516 0.0501

HER_FRQ -0.0041 0.0352

OPIA_FRQ -0.0315 0.0511

POT_FRQ -0.0769** 0.0380

BARB_FRQ 0.0805 0.0505

STIM_FRQ 0.0135 0.0401

DEPLOGTA 0.0302* 0.0181

DEPLOGTD -0.0403 0.0311

Treatment Variables

ENRGENY 0.2905** 0.1420

ENRSUPY 0.0312 0.1886

ETSYES 0.0208 0.0776

ECTRONLY 0.3103 0.1772

EOTHONLY 0.2332 0.2859

EBOTH -0.8881*** 0.2507

ETXMISS -1.1132 0.5817
202

Table 53 — Continued

Final Model

Variable b se(b)

EAAYES -0.3056 0.1736

EAAMISS 0.4010 0.3082

Supervision Variables

ECCCNO 0.2735*** 0.1138

ECCCFAIL -0.5684*** 0.1095

UARATE -0.0553* 0.0332

HHSE_STR -0.2364 0.3927

Post-Release Variables

ESPOUSE 0.2684** 0.1182

ECOM_LAW 0.0064 0.1233

SUPVVIOL -0.2421*** 0.0400

PRCTFULL 0.0065*** 0.0015

ECOHO1_6 0.0412 0.1981

ECOHO7 0.1732 0.2971

ECOHO8 -0.2384 0.2238

ECOHO9 0.0199 0.2305

ECOHO10 0.1693 0.1967

ECOHO11 -0.2956* 0.1674

ECOHO12 -0.1123 0.1401

ECOHO13 0.1800 0.1496

COVARIAN -0.5354 0.4060

SIGMA -0.8999*** 0.1323

* p < .10

** p < .05

*** p < .01
203

Table 54

Traditional Models of CCC Outcomes

Male and Female Subjects

Model of CCC Failure with: x DF Diff.† DF 2 

Base Variables 1021.7 33 --- ---

Base and Additional Background Variables 1008.6 40 13.1*

7

Base and Change Assessment Variables 1005.1 39 16.6**

6

Base and Additional Treatment Variables 1018.8 36 2.9

3

Full Model Variables 992.0 46 29.7*** 13

Male Subjects

Model of CCC Failure with: x DF Diff.† DF 2 

Base Variables 864.4 31 --- --- 

Base and Additional Background Variables 851.6 38 12.8* 7

Base and Change Assessment Variables 845.9 37 18.5*** 6

Base and Additional Treatment Variables 861.6 34 2.8 3

Full Model Variables 833.1 44 31.3*** 13

Difference in x between traditional base model and model in question. † 2

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01
204

Table 55

CCC Failure, Male and Female Subjects

Traditional Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -0.2040 0.6392 . -0.5544 0.6818 .

COMPDAP -0.1922 0.2060 0.825 -0.2128 0.2095 0.808

GRAD12MO -0.6231** 0.3201 0.536 -0.7034** 0.3298 0.495

GRAD9MO -0.3689* 0.2078 0.691 -0.3914* 0.2148 0.676

INCOMPTX 0.0203 0.2922 1.021 -0.0094 0.2981 0.991

DISCIPTX 0.4080 0.4181 1.504 0.4250 0.4262 1.530

WITHDRTX -0.0489 0.5476 0.952 -0.0790 0.5499 0.924

EBLACK 0.1603 0.2018 1.174 0.1236 0.2055 1.132

ERACEOTH -0.0463 0.3534 0.955 -0.0253 0.3591 0.975

EHISP -0.0088 0.1506 0.991 -0.0379 0.1532 0.963

EFEM -0.2613** 0.1168 0.770 -0.2764** 0.1224 0.759

EPRIORCM 0.2765*** 0.0982 1.318 0.2780*** 0.1004 1.320

ERECVIOL -0.0345 0.1492 0.966 -0.0624 0.1534 0.940

EPASTVIO 0.1487 0.1243 1.160 0.1636 0.1274 1.178

TIMESRVD -0.0409 0.0453 0.960 -0.0358 0.0461 0.965

ECJSPVNY -0.1286 0.1853 0.879 -0.1561 0.1900 0.856

ECJSPVNM 0.2809 0.3354 1.324 0.3053 0.3443 1.357

AGERLSE -0.0144 0.0104 0.986 -0.0140 0.0107 0.986

ESUPILL 0.1058 0.0795 1.112 0.1028 0.0810 1.108

EWORKJOB 0.0260 0.1769 1.026 0.0308 0.1796 1.031

ELEGITUN -0.7181** 0.3579 0.488 -0.7370** 0.3649 0.479

EUNEMP 0.2017 0.2486 1.224 0.2617 0.2524 1.299

EJOB_UNK 0.2379 0.4789 1.269 0.1962 0.4867 1.217

GRADEA -0.0404 0.0400 0.960 -0.0399 0.0410 0.961

COC_FRQ -0.0517 0.0411 0.950 -0.0464 0.0419 0.955

CRK_FRQ 0.0218 0.0416 1.022 0.0101 0.0427 1.010

HAL_FRQ -0.0224 0.0648 0.978 -0.0129 0.0657 0.987

HER_FRQ 0.0793* 0.0436 1.082 0.0806* 0.0446 1.084

OPIA_FRQ 0.0085 0.0617 1.009 -0.0048 0.0629 0.995

POT_FRQ -0.0011 0.0451 0.999 0.0062 0.0460 1.006

BARB_FRQ -0.0104 0.0595 0.990 -0.0103 0.0602 0.990

STIM_FRQ -0.1103** 0.0507 0.896 -0.1145** 0.0515 0.892

DEPLOGTA 0.0279 0.0226 1.028 0.0186 0.0241 1.019

DEPLOGTD 0.0948** 0.0395 1.099 0.0785* 0.0411 1.082
205

Table 55 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Other Background Factors

EPSTDGTX -0.0120 0.0848 0.988

EPSTETOH -0.2362 0.2015 0.790

EDIAGNO 0.1149 0.1364 1.122

EDIAGDEP -0.1871 0.2444 0.829

EDIAGASP -0.0459 0.1548 0.955

EDIAGBTH 0.4157* 0.2291 1.515

EPSTMHTX -0.0099 0.1023 0.990

Motivation for Change Variables

ECLUST1 0.3028** 0.1533 1.354

ECLUST2 -0.3752** 0.1867 0.687

ECLUST3 -0.1463 0.1848 0.864

ECLUST4 0.4177** 0.1959 1.518

ECLUST5 0.2140 0.2221 1.239

ECLUST6 -0.3567 0.3349 0.700

-2 Log Likelihood

Intercept Only 1294.811 1294.811

Intercept and Covariates 1194.420 1173.060

Concordant Pairs 69.5% 71.7%

Somer’s D 0.395 0.438

Hosmer-Lemeshow

Goodness of Fit 5.6426 with 8 DF (p=0.6872) 12.668 with 8 DF (p=0.1238)

* p < .10

** p < .05

*** p < .01
206

Table 56

CCC Failure, Male and Female Subjects

Bloom Model

Base Model Final Model

Variable b se(b) OR b se(b) OR

Base Model Variables

INTERCPT -1.1610 0.7660 -1.7871** 0.8272

SUBJECT -0.1922 0.1826 0.825 -0.2423 0.1872 0.785

EBLACK 0.2926 0.2752 1.340 0.2312 0.2775 1.260

ERACEOTH -0.2139 0.5131 0.807 -0.1603 0.5164 0.852

EHISP -0.2164 0.1882 0.805 -0.2679 0.1924 0.765

EFEM -0.3816*** 0.1438 0.683 -0.3003** 0.1505 0.741

EPRIORCM 0.2802*** 0.1066 1.323 0.2736*** 0.1113 1.315

ERECVIOL -0.1102 0.1796 0.896 -0.1374 0.1844 0.872

EPASTVIO 0.1879 0.1448 1.207 0.2118 0.1493 1.236

TIMESRVD -0.0424 0.0485 0.958 -0.0514 0.0504 0.950

ECJSPVNY -0.2644 0.2379 0.768 -0.2750 0.2438 0.760

ECJSPVNM 0.2650 0.4386 1.303 0.2705 0.4492 1.311

AGERLSE -0.0213* 0.0114 0.979 -0.0219* 0.0117 0.978

ESUPILL 0.2496*** 0.0878 1.283 0.2454*** 0.0909 1.278

EWORKJOB 0.2403 0.2298 1.272 0.1540 0.2366 1.167

ELEGITUN -0.7897* 0.4675 0.454 -0.8068* 0.4759 0.446

EUNEMP 0.4064 0.3140 1.501 0.4356 0.3208 1.546

EJOB_UNK -0.1406 0.6922 0.869 -0.0379 0.7142 0.963

GRADEA 0.0059 0.0476 1.006 0.0229 0.0496 1.023

COC_FRQ -0.1351*** 0.0475 0.874 -0.1369*** 0.0490 0.872

CRK_FRQ 0.1085** 0.0473 1.115 0.0848* 0.0498 1.089

HAL_FRQ -0.1781** 0.0767 0.837 -0.1377* 0.0786 0.871

HER_FRQ 0.0659 0.0509 1.068 0.0787 0.0528 1.082

OPIA_FRQ -0.0597 0.0700 0.942 -0.0642 0.0725 0.938

POT_FRQ 0.0432 0.0521 1.044 0.0524 0.0549 1.054

BARB_FRQ 0.0836 0.0627 1.087 0.0826 0.0651 1.086

STIM_FRQ -0.1031* 0.0593 0.902 -0.0983 0.0608 0.906

DEPLOGTA 0.0204 0.0261 1.021 0.0139 0.0282 1.014

DEPLOGTD 0.1800*** 0.0464 1.197 0.1478*** 0.0498 1.159
207

Table 56 — Continued

Base Model Final Model

Variable b se(b) OR b se(b) OR

Other Background Factors

EPSTDGTX -0.0219 0.1006 0.978

EPSTETOH -0.3051 0.2469 0.737

EDIAGNO 0.1951 0.1593 1.215

EDIAGDEP -0.4460 0.3293 0.640

EDIAGASP 0.0485 0.1815 1.050

EDIAGBTH 0.3327 0.2648 1.395

EPSTMHTX -0.2323* 0.1223 0.793

Motivation for Change Variables

ECLUST1 0.5299*** 0.1641 1.699

ECLUST2 -0.5362** 0.2299 0.585

ECLUST3 -0.2394 0.1918 0.787

ECLUST4 0.4635** 0.2342 1.590

ECLUST5 0.1286 0.2429 1.137

ECLUST6 -0.3225 0.3314 0.724

-2 Log Likelihood

Intercept Only 1137.353 1137.353

Intercept and Covariates 1004.207 973.289

Concordant Pairs 66.4% 68.0%

Somer’s D 0.331 0.364

Hosmer-Lemshow

Goodness of Fit 18.32 with 8 DF (p=0.0190) 25.631 with 8 DF (p=0.0012)

* p < .10

** p < .05

*** p < .01
208

Table 57

CCC Failure, Male and Female Subjects, Complete

Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT -0.1970 0.3965

COMPLETE -0.0796 0.2791

EBLACK 0.0755 0.1214

ERACEOTH 0.0004 0.2120

EHISP -0.0145 0.0873

EFEM -0.1557* 0.0674

EPRIORCM 0.1595*** 0.0556

ERECVIOL -0.0377 0.0894

EPASTVIO 0.1004 0.0744

TIMESRVD -0.0336 0.0260

ECJSPVNY -0.0938 0.1122

ECJSPVNM 0.1931 0.2043

AGERLSE -0.0103* 0.0061

ESUPILL 0.0644 0.0466

EWORKJOB 0.0117 0.1036

ELEGITUN -0.3394* 0.1953

EUNEMP 0.1291 0.1463

EJOB_UNK 0.0543 0.2929

GRADEA -0.0274 0.0232

COC_FRQ -0.0260 0.0240

CRK_FRQ 0.0075 0.0246

HAL_FRQ -0.0100 0.0374

HER_FRQ 0.0527** 0.0260

OPIA_FRQ -0.0023 0.0361

POT_FRQ -0.0069 0.0260

BARB_FRQ -0.0074 0.0343

STIM_FRQ -0.0597** 0.0288

DEPLOGTA 0.0114 0.0137

DEPLOGTD 0.0421* 0.0230

Other Background Factors

EPSTDGTX -0.0077 0.0491

EPSTETOH -0.1399 0.1123

EDIAGNO 0.0547 0.0774

EDIAGDEP -0.1520 0.1351

EDIAGASP -0.0076 0.0886

EDIAGBTH 0.2563* 0.1335

EPSTMHTX -0.0004 0.0585
209

Table 57 - Continued

Final Model

Variable b se(b)

Motivation for Change Variables

ECLUST1 0.1615* 0.0899

ECLUST2 -0.2104** 0.1038

ECLUST3 -0.0719 0.1076

ECLUST4 0.2249* 0.1282

ECLUST5 0.1231 0.1285

ECLUST6 -0.1974 0.1932

COVARIAN -0.0266 0.3147

 

* p < .10

** p < .05

*** p < .01
210

Table 58

Traditional Models of Employment Outcomes

Male Subjects

Model of Arrest with: r DF Diff. F† DF 2 

Base Variables .12 32 --- --- 

Base and Additional Background Variables .13 39 1.9 7

Base and Change Assessment Variables .13 38 2.2** 6

Base and Additional Treatment Variables .16 41 6.0*** 9

Base with Supervision Variables .19 36 25.1*** 4

Base and Post Release Behavior Variables .16 36 13.6*** 4

Full Model Variables .24 63 5.8*** 31

Female Subjects

Model of Arrest with: r DF Diff. F† DF 2 

Base Variables .19 32 --- ---

Base and Additional Background Variables .24 39 2.0** 7

Base and Change Assessment Variables .21 38 .9 6

Base and Additional Treatment Variables .29 41 3.3*** 9

Base with Supervision Variables .21 36 1.2 4

Base and Post Release Behavior Variables .24 36 3.6*** 4

Full Model Variables .39 60 2.2*** 28

Notes to Table:

Difference in F between traditional base model and model in question. †

* Significant at p < .10

** Significant at p < .05

*** Significant at p < .01
211

Table 59

Employment, Male Subjects

Traditional Model

 Base Model Final Model

Variable b se(b) b se(b)

Base Model Variables

INTERCPT 28.9880*** 9.7275 31.1511***11.7475

COMPDAP -4.7479 3.2558 -1.4176 3.2076

GRAD12MO 6.8039 4.6655 7.6667 4.9449

GRAD9MO 1.0064 3.3279 -1.2716 3.6508

INCOMPTX -0.7225 5.4825 3.8089 5.7456

DISCIPTX -12.1673* 7.1087 -2.9298 6.9049

WITHDRTX -12.1044 7.8443 -6.0142 7.6433

EBLACK -2.9191 2.8175 -3.4784 2.7798

ERACEOTH -5.0171 4.5967 -1.5629 4.5358

EHISP -2.7351 2.2407 -2.1440 2.1592

EPRIORCM -2.1987 1.4511 -1.6160 1.3893

ERECVIOL -2.5504 2.3005 -2.9855 2.2016

EPASTVIO -0.4744 1.9754 1.4067 1.8920

TIMESRVD 1.7559*** 0.6041 1.3406** 0.5844

ECJSPVNY 0.4576 3.6917 -0.1537 3.5564

ECJSPVNM -0.7546 6.8442 2.6929 6.6083

AGERLSE 0.0117 0.1553 -0.1408 0.1508

ESUPILL -4.1514*** 1.3080 -3.7893*** 1.2462

EWORKJOB 8.8585*** 2.9656 8.4418*** 2.8475

ELEGITUN -6.9466 6.0237 -7.5520 5.7750

EUNEMP -4.0649 4.1255 -1.5922 3.9668

EJOB_UNK 0.6873 8.6356 -2.4449 8.2787

GRADEA 1.7266*** 0.5989 1.9288*** 0.5750

COC_FRQ -0.6574 0.6667 -0.7420 0.6391

CRK_FRQ -0.1397 0.7253 0.2243 0.7004

HAL_FRQ -2.4919** 0.9875 -2.0709** 0.9471

HER_FRQ -1.9161*** 0.7386 -1.2945* 0.7091

OPIA_FRQ 0.0534 1.0900 0.3028 1.0457

POT_FRQ 0.3520 0.7277 -0.0248 0.7025

BARB_FRQ -0.4755 1.0050 -1.1064 0.9584

STIM_FRQ 1.1121 0.7913 0.7238 0.7586

DEPLOGTA 0.3374 0.3584 0.3072 0.3492

DEPLOGTD 1.4265** 0.6021 1.8886*** 0.5852
212

Table 59 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Motivation for Change Variables

ECLUST1 -4.3391* 2.4184

ECLUST2 2.8107 2.5269

ECLUST3 -0.6487 2.5118

ECLUST4 -0.1627 3.2289

ECLUST5 3.4910 3.3681

ECLUST6 -0.2364 4.1331

Treatment Variables

ENRGENY -1.9615 2.3342

ENRSUPY 5.8666 3.5946

ETSYES 1.8097 1.5434

ECTRONLY 10.6332** 5.0352

EOTHONLY 5.1938 6.9086

EBOTH 1.8855 7.0250

ETXMISS -28.8633 18.1778

EAAYES 2.5506 4.2879

EAAMISS 2.2318 7.9856

Supervision Variables

ECCCNO -6.3523*** 2.1549

ECCCFAIL -6.0824*** 2.2321

UARATE 0.2027 0.6528

HHSE_STR 7.1196 6.1718

Post-Release Variables

ESPOUSE 2.7810 1.9862

ECOM_LAW -1.1263 2.1549

SUPVVIOL -4.8482*** 1.0276

DRUGVIOL -1.1688* 0.6348

ECOHO1_6 -10.7246*** 3.9251

ECOHO7 0.3506 5.6580

ECOHO8 -1.0738 4.5840

ECOHO9 0.4155 4.3406

ECOHO10 -0.8845 3.5866

ECOHO11 1.2534 3.2579

ECOHO12 1.7959 2.6678

ECOHO13 4.5604 2.7820

R-square 0.1238 0.2381

Adjusted R-square 0.0998 0.1959

* p < .10

** p < .05

*** p < .01
213

Table 60

Employment, Female Subjects

Traditional Model

 Base Model Final Model

Variable b se(b) b se(b)

Base Model Variables

INTERCPT -18.1320 25.5520 -34.9104 29.6574

COMPDAP -1.6120 9.5452 4.4894 9.4709

GRAD12MO 1.4758 11.8266 -17.7553 13.1370

GRAD9MO 5.2333 10.2356 -5.2152 11.1940

INCOMPTX 9.2328 11.2147 -1.6556 11.8636

DISCIPTX 3.2188 15.0151 5.5858 14.5952

WITHDRTX -9.9643 15.8853 -6.1062 15.6109

EBLACK -5.7266 8.2061 -6.3921 8.3974

ERACEOTH 6.2170 14.7012 7.2205 15.0828

EHISP -15.1066** 6.0649 -12.2818** 6.0289

EPRIORCM 1.6899 3.1259 2.6031 3.0649

ERECVIOL -7.3324 8.2581 -9.9475 8.2552

EPASTVIO 7.6958 8.6831 12.6088 8.6087

TIMESRVD 3.8931 2.3631 0.9551 2.4593

ECJSPVNY -1.8024 9.2954 -2.9381 9.8612

ECJSPVNM 9.3431 16.9679 10.2751 17.8340

AGERLSE 0.3051 0.4169 0.0393 0.4339

ESUPILL -0.1664 3.0356 1.7705 3.0027

EWORKJOB 12.8315** 6.4468 18.0217*** 6.4765

ELEGITUN 10.8900 10.4673 23.7031** 10.4613

EUNEMP -1.0021 9.1731 -4.7113 9.3345

EJOB_UNK -30.7304 17.7794 -52.2313***18.2290

GRADEA 2.1674 1.4791 2.1355 1.5048

COC_FRQ 2.8502 1.4889 2.8587** 1.4613

CRK_FRQ 0.6521 1.3554 1.6786 1.3846

HAL_FRQ 3.3112 2.5466 3.9732 2.5676

HER_FRQ -1.0010 1.6868 0.7605 1.7733

OPIA_FRQ 1.9957 2.1947 0.1654 2.1874

POT_FRQ 2.6858 1.5535 2.8965 1.5113

BARB_FRQ 1.4788 2.0555 2.7277 2.0991

STIM_FRQ 0.7587 1.5980 1.7658 1.6052

DEPLOGTA -0.7855 0.8454 -0.4133 0.8716

DEPLOGTD -4.8396*** 1.5075 -4.6802*** 1.4696
214

Table 60 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Other Background Factors

EPSTDGTX -8.0701** 3.1985

EPSTETOH -2.6913 7.8299

EDIADNO 3.5047 4.6410

EDIAGDEP 2.9908 6.5546

EDIAGASP -7.5541 6.4994

EDIAGBTH 0.3519 7.6459

EPSTMHTX -9.0855*** 3.1244

Treatment Variables

ENRGENY 2.1950 6.2035

ENRSUPY -12.1973 7.9493

ETSYES 10.7616*** 3.4218

ECTRONLY -7.7252 9.0857

EOTHONLY 4.1375 15.9861

EBOTH -23.9320 16.8020

ETXMISS 24.1327 27.5755

EAAYES 29.2638** 11.8031

EAAMISS -37.6599 22.4596

Post-Release Variables

ESPOUSE -5.9017 6.0354

ECOM_LAW -2.8794 6.2510

SUPVVIOL -3.0339 3.1748

DRUGVIOL -3.5556 2.0597

ECOHO1_6 -8.2872 9.1853

ECOHO7 0.3558 21.0413

ECOHO8 9.5539 10.5283

ECOHO9 11.3059 9.0468

ECOHO10 -16.7597 10.1613

ECOHO11 6.1236 8.2996

ECOHO12 -0.7174 7.5298

ECOHO13 -1.1123 7.2006

R-square 0.1931 0.3864

Adjusted R-square 0.0763 0.1957

* p < .10

** p < .05

*** p < .01
215

Table 61

Employment, Male Subjects

Bloom Model

 Base Model Final Model

Variable b se(b) b se(b)

Base Model Variables

INTERCPT 18.0146 10.2451 21.0744 12.7373

SUBJECT -3.5604 2.8745 1.8861 2.9951

EBLACK 3.0948 2.6951 0.0421 2.7033

ERACEOTH -8.9317** 4.3530 -1.4294 4.4435

EHISP -4.9315** 2.2753 -2.9729 2.2203

EPRIORCM -0.4662 1.4890 0.2588 1.4555

ERECVIOL 1.8407 2.5948 2.0221 2.4858

EPASTVIO -7.6954*** 2.1527 -5.6721*** 2.0801

TIMESRVD 2.2669*** 0.6135 1.4912** 0.6016

ECJSPVNY 1.2142 4.4424 0.0261 4.2645

ECJSPVNM -3.2037 8.4249 -0.2077 8.0704

AGERLSE -0.0860 0.1588 -0.2757* 0.1570

ESUPILL -5.4151*** 1.3133 -3.7362*** 1.2719

EWORKJOB 11.8585*** 3.5387 13.6494*** 3.4174

ELEGITUN -6.9966 7.3456 -5.7371 7.0227

EUNEMP -6.9066 4.6378 -2.8907 4.5137

EJOB_UNK 2.8166 10.8210 -7.7734 10.3919

GRADEA 2.0291*** 0.6811 2.4063*** 0.6581

COC_FRQ -0.6309 0.6916 -1.5554** 0.6713

CRK_FRQ -1.0787 0.7983 -0.1806 0.7925

HAL_FRQ 0.3992 1.0212 0.3392 0.9999

HER_FRQ -1.6510** 0.7822 -1.3699* 0.7675

OPIA_FRQ 0.5870 1.0225 0.1373 1.0290

POT_FRQ 1.3623 0.7713 0.3937 0.7539

BARB_FRQ -3.2637*** 0.9803 -2.7819*** 0.9526

STIM_FRQ 1.1009 0.8298 0.2699 0.8005

DEPLOGTA 0.3667 0.3969 0.4476 0.3867

DEPLOGTD 1.2818** 0.6544 2.7600*** 0.6624
216

Table 61 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Motivation for Change Variables

ECLUST1 -13.7879*** 2.5427

ECLUST2 2.0891 2.9414

ECLUST3 -3.0012 2.3318

ECLUST4 -0.9989 3.7778

ECLUST5 4.8392 3.3196

ECLUST6 11.7391*** 3.6201

Treatment Variables

ENRGENY -1.8194 2.9068

ENRSUPY 2.6520 5.3936

ETSYES 3.9286*** 1.5117

ECTRONLY 14.9758*** 5.7312

EOTHONLY 14.3607* 8.2256

EBOTH 4.6274 8.3332

ETXMISS -49.8283** 20.5906

EAAYES -0.0096 4.7608

EAAMISS 9.8603 8.9572

Supervision Variables

ECCCNO -4.5489** 2.0410

ECCCFAIL -6.1504*** 2.3266

UARATE 0.1417 0.6870

HHSE_STR 20.8516*** 6.0948

Post-Release Variables

ESPOUSE 3.3081 2.2198

ECOM_LAW -3.8566 2.4917

SUPVVIOL -3.3162*** 1.0871

DRUGVIOL -1.3010* 0.6834

ECOHO1_6 -6.4278* 3.8913

ECOHO7 2.3430 5.1635

ECOHO8 -3.1430 4.6208

ECOHO9 -4.5410 4.1592

ECOHO10 -3.9675 3.6514

ECOHO11 1.0290 3.0413

ECOHO12 1.4886 2.9491

ECOHO13 4.4582 3.0243

R-square 0.1507 0.2682

Adjusted R-square 0.1311 0.2310

* p < .10

** p < .05

*** p < .01
217

Table 62

Employment, Female Subjects

Bloom Model

 Base Model Final Model

Variable b se(b) b se(b)

Base Model Variables

INTERCPT -9.1822 23.4301 -40.8729 27.1713

SUBJECT -0.6381 7.5064 2.4555 7.5681

EBLACK -4.4442 7.2187 -1.9871 7.5018

ERACEOTH 9.5151 12.2625 6.7541 12.9251

EHISP -8.6656 4.9906 -11.3521** 5.4915

EPRIORCM -0.4632 3.0889 1.8176 2.9491

ERECVIOL -1.2212 6.5812 -0.8017 6.8201

EPASTVIO 12.1045 8.8515 7.9503 8.5550

TIMESRVD 3.0405 2.4398 -0.0019 2.4832

ECJSPVNY 1.4795 11.2743 5.2046 11.0227 

ECJSPVNM 6.2776 21.2466 -0.5006 20.4219

AGERLSE -0.2444 0.4089 -0.3086 0.4289

ESUPILL -1.0723 2.9837 -0.1124 2.9739

EWORKJOB 6.0379 6.3938 16.9472*** 6.5986

ELEGITUN 16.9356 10.8024 28.5125***10.6952

EUNEMP 4.9202 9.3432 -5.2177 9.1034

EJOB_UNK -45.3106** 18.6578 -64.1265***18.7402

GRADEA 3.6815*** 1.1565 4.2940*** 1.3039

COC_FRQ 6.1302*** 1.4930 5.0228*** 1.4809

CRK_FRQ -2.9981** 1.2838 -0.2952 1.4137

HAL_FRQ 1.8972 2.6475 2.1343 2.6173

HER_FRQ -2.8573** 1.3575 0.7411 1.6200

OPIA_FRQ 2.3736 2.1726 0.2313 2.1311

POT_FRQ 2.7747 1.4710 2.9582** 1.4253

BARB_FRQ 4.5700** 2.0172 3.6906* 2.1072

STIM_FRQ 4.0786** 1.6667 2.8735* 1.6205

DEPLOGTA -2.1973*** 0.8324 -1.3691 0.8802

DEPLOGTD -6.6055*** 1.5892 -5.7293*** 1.5056
218

Table 62 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Other Background Factors

EPSTDGTX -7.2670** 3.1555

EPSTETOH -3.2428 7.7473

EDIAGNO -0.2233 4.6179

EDIAGDEP -0.8293 6.2872

EDIAGASP -14.3311** 6.1159

EDIAGBTH -3.2677 7.3209

EPSTMHTX -6.6580** 3.0297

Treatment Variables

ENRGENY 2.1505 5.6322

ENRSUPY -4.4092 7.9864

ETSYES 6.0215** 3.0279

ECTRONLY -9.2331 9.6198

EOTHONLY 2.5367 17.5175

EBOTH -13.4968 15.9204

ETXMISS 19.8380 28.5482 

EAAYES 18.2420 12.6264

EAAMISS -23.9299 23.8895

Post-Release Variables

ESPOUSE -7.6487 5.7708

ECOM_LAW -3.0093 6.0982

SUPVVIOL -3.1191 3.3753

DRUGVIOL -6.1659** 1.9143

ECOHO1_6 -0.6759 7.9887

ECOHO7 -16.0996 14.9781

ECOHO8 10.9226 9.2785

ECOHO9 10.6195 8.3168

ECOHO10 - 7.1430 8.6438

ECOHO11 2.1817 8.8995

ECOHO12 4.4315 6.9258

ECOHO13 -3.3400 6.9230

R-square 0.4454 0.6078

Adjusted R-square 0.3791 0.4988

* p < .10

** p < .05

*** p < .01
219

Table 63

Employment, Male Subjects, Complete

Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT -32.1110 33.0520

COMPLETE 17.2258 20.7844

EBLACK -10.0642 8.2328

ERACEOTH -0.7299 13.6061

EHISP -7.3069 6.1439

EPRIORCM -4.4277 4.0312

ERECVIOL -7.8160 6.2269

EPASTVIO 4.7354 5.3460

TIMESRVD 4.2533*** 1.6597

ECJSPVNM 5.9392 18.5640

ECJSPVNY -1.7189 10.0180

AGERLSE -1.2592*** 0.4273

ESUPILL -12.8703*** 3.5538

EWORKJOB 33.2225*** 8.0514

ELEGITUN -67.5976*** 14.6025

EUNEMP 8.6471 11.0839

EJOB_UNK 14.6921 24.1302

GRADEA 7.0702*** 1.6470

COC_FRQ -0.2528 1.8236

CRK_FRQ -1.6577 1.9754

HAL_FRQ -2.8671 2.7733

HER_FRQ -2.3122 2.0165

OPIA_FRQ -1.4065 3.0048

POT_FRQ -0.9506 2.0245

BARB_FRQ -1.9828 2.7868

STIM_FRQ 0.4170 2.1667

DEPLOGTA -0.2646 0.9916

DEPLOGTD 5.0719*** 1.6508

Motivation for Change Variables

ECLUST1 -3.2774 7.0053

ECLUST2 3.2775 7.2702

ECLUST3 2.7983 7.3101

ECLUST4 -1.9732 10.0991

ECLUST5 4.8922 9.6808

ECLUST6 1.5480 12.0130

Treatment Variables

ENRGENY -8.2659 6.6498

ENRSUPY 12.7788 10.5683
220

Table 63 — Continued

Final Model

Variable b se(b)

ETSYES 8.5452** 4.3213

ECTRONLY 36.2152*** 12.5768

EOTHONLY 28.2776 18.4097

EBOTH 7.6860 18.6364

ETXMISS -108.8187*** 43.7235

EAAYES 15.4717* 11.5112

EAAMISS -5.6524 21.1216

Supervision Variables

ECCCNO -22.9065*** 6.0379

ECCCFAIL -13.6094* 6.2678

UARATE 0.6791 1.8480

HHSE_STR 39.2389* 21.6417

Post-Release Variables

ESPOUSE 10.4433* 5.8034

ECOM_LAW -4.1095 6.1736

SUPVVIOL -13.0330*** 2.9608

DRUGVIOL -3.6384** 1.7792

ECOHO1_6 -31.4436*** 11.0559

ECOHO7 10.9644 16.2027

ECOHO8 -4.7308 13.0873

ECOHO9 -7.2790 12.2725

ECOHO10 -5.6929 10.4097

ECOHO11 2.0520 9.1687

ECOHO12 9.8891 7.5761

ECOHO13 12.8270* 7.7957

COVARIAN -0.1731 0.2322

SIGMA 96.3157*** 4.0892

* p < .10

** p < .05

*** p < .01
221

Table 64

Employment, Female Subjects, Complete

Heckman Model

Final Model

Variable b se(b)

Base Model Variables

CONSTANT -247.2121** 136.5464

COMPLETE -25.4524 39.4746

EBLACK 2.2213 16.5481

ERACEOTH -22.7347 29.3515

EHISP -41.9586*** 14.0349

EPRIORCM 6.5783 6.4969

ERECVIOL -29.1517* 16.8577

EPASTVIO 27.5197 17.8484

TIMESRVD -2.7949 4.7383

ECJSPVNY -5.5018 20.6673

ECJSPVNM 15.8639 37.3754

AGERLSE -0.6517 0.9272

ESUPILL 3.5440 6.2005

EWORKJOB 46.4459*** 15.0039

ELEGITUN 33.7117 22.3265

EUNEMP -14.6981 20.1353

EJOB_UNK -113.9005*** 46.5668

GRADEA 3.2357 3.1933

COC_FRQ 4.7075 3.0739

CRK_FRQ 3.3980 2.9440

HAL_FRQ 11.7363** 5.4208

HER_FRQ 2.5719 3.7321

OPIA_FRQ 1.5998 4.4917

POT_FRQ 6.2249** 3.0237

BARB_FRQ 5.9649 4.3535

STIM_FRQ 4.3169 3.2972

DEPLOGTA -0.5535 1.7808

DEPLOGTD -11.6221*** 3.0589

Other Background Factors

EPSTDGTX -19.9376*** 6.9881

EPSTETOH -14.6600 14.8143

EDIAGNO 7.5648 9.8947

EDIAGDEP -6.2374 12.6390

EDIAGASP -20.5639 14.2575

EDIAGBTH 10.7854 15.9964

EPSTMHTX -21.8755*** 6.6828

Treatment Variables

ENRGENY 10.5016 12.8803

ENRSUPY -41.1975** 17.9066

ETSYES 23.5507*** 7.9883

ECTRONLY 6.9478 20.9722

EOTHONLY -20.2095 31.0567

EBOTH -65.6195 44.6805

ETXMISS 51.4462 64.7839

EAAYES 129.7196 126.6303

EAAMISS -216.0057 252.5413

Post-Release Variables

ESPOUSE -24.3226* 13.1360

ECOM_LAW 4.4116 12.8448

SUPVVIOL -1.7698 6.5649

ECOHO1_6 -42.0943* 18.5261
222

Table 64 — Continued

Final Model

Variable b se(b)

ECOHO7 -13.8106 40.0700

ECOHO8 16.0893 21.5139

ECOHO9 41.2103** 20.4657

ECCCNO -5.8564 12.4212

ECOHO10 -34.4378** 20.0645

ECOHO11 19.0277 17.4111

ECOHO12 5.0852 15.2581

ECOHO13 12.0990 14.4356

COVARIAN -0.0127 0.5828

SIGMA 72.0446*** 5.7980

* p < .10

** p < .05

*** p < .01
223

Table 65

Arrests, Male and Female Subjects, Supervised and Unsupervised Subjects

Treatment Subjects Only

Sites Only Final Model

Variable b se(b) OR b se(b) OR

INTERCPT -3.7881*** 0.4897 . -2.6817* 1.4784 .

COMPLETE -0.4423** 0.1727 0.643 -0.2799 0.2224 0.756

DISCIP 0.2810 0.2514 1.324 0.0207 0.2778 1.021

WITHDRAW 0.3040 0.2968 1.355 0.2885 0.3323 1.334

Institution Indicators

ELEXSITM -0.6851 0.4232 0.504 -0.7925 0.7767 0.453

EBUTSITM -0.2426 0.3431 0.785 -0.4267 0.5725 0.653

EFAISITM 0.3371 0.3397 1.401 0.0296 0.3824 1.030

ELOFSITM -0.1472 0.7058 0.863 0.2392 0.9370 1.270

EMCKSITM 0.5373 0.5754 1.711 0.6146 0.7299 1.849

EMNASITM 0.4834 0.3814 1.622 0.1112 0.4689 1.118

EOXFSITM 0.4879 0.3293 1.629 -0.2089 0.4691 0.811

EPHXSITM 0.3505 0.4938 1.420 0.3088 0.5535 1.362

ERCHSITM -0.0178 0.4647 0.982 -0.1593 0.5630 0.853

ESEASITM -0.3702 0.5885 0.691 -0.0316 0.6891 0.969

ESHESITM 0.3125 0.3919 1.367 -0.0099 0.4822 0.990

ETALSITM 0.0708 0.3446 1.073 -0.4333 0.6046 0.648

ETRMSITM 0.1709 0.5775 1.186 0.9381 0.8191 2.555

EYANSITM -0.3609 0.3871 0.697 0.0681 0.5431 1.071

EMRGSITM -0.9267* 0.5174 0.399 -0.2475 0.7867 0.781

COHTIME 0.0091 0.0129 1.009 0.0269 0.0323 1.027

TIMETORL -0.0360** 0.0165 0.965 -0.0173 0.0359 0.983

Base Model Variables

EBLACK -0.0805 0.2332 0.923

ERACEOTH 0.1058 0.3443 1.112

EHISP 0.0777 0.2379 1.081

EPRIORCM 0.3510** 0.1596 1.420

ERECVIOL 0.3634* 0.1924 1.438

EPASTVIO -0.0407 0.1868 0.960

TIMESRVD 0.0988* 0.0596 1.104

ECJSPVNY -0.2744 0.2691 0.760

ECJSPVNM 0.4202 0.4721 1.522

AGERLSE -0.0606*** 0.0180 0.941

ESUPILL -0.0768 0.1289 0.926

EWORKJOB 0.0357 0.3099 1.036

ELEGITUN 0.2187 0.5643 1.244

EUNEMP 0.2437 0.4209 1.276

EJOB_UNK -1.0386 0.9131 0.354

GRADEA 0.0112 0.0537 1.011

COC_FRQ 0.0369 0.0659 1.038

CRK_FRQ -0.1486** 0.0727 0.862

HAL_FRQ -0.0014 0.0921 0.999

HER_FRQ 0.1084 0.0678 1.114

OPIA_FRQ -0.0817 0.1035 0.922

POT_FRQ -0.0417 0.0701 0.959

BARB_FRQ 0.0870 0.0976 1.091

STIM_FRQ -0.0952 0.0766 0.909

DEPLOGTA -0.0351 0.0349 0.966

DEPLOGTD 0.0309 0.0560 1.031
224

Table 65 — Continued

Sites Only Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.1687 0.1822 0.845

ECCCFAIL 0.4493** 0.1926 1.567

SUP_REL -0.0108 0.3480 0.989

Post-Release Variables

ECOHO1_6 0.3816 0.5568 1.465

ECOHO7 -0.0306 0.5019 0.970

ECOHO8 0.5803 0.3707 1.787

ECOHO9 -0.5831 0.4524 0.558

ECOHO10 0.7502*** 0.2731 2.117

ECOHO11 -0.8075 0.4929 0.446

ECOHO12 0.0401 0.3809 1.041

ECOHO13 0.3438 0.4303 1.410

Time Variables

D_T2 0.4500 0.3774 1.568 0.4720 0.3829 1.603

D_T3 0.3015 0.3920 1.352 0.3494 0.3977 1.418

D_T4 0.2073 0.4050 1.230 0.3089 0.4108 1.362

D_T5 0.8413** 0.3616 2.319 0.9977*** 0.3688 2.712

D_T6 0.3854 0.3985 1.470 0.5700 0.4053 1.768

-2 Log Likelihood

Intercept Only 913.772 913.772

Intercept and Covariates 879.189 803.653

DF 24 61

Concordant Pairs 65.0% 78.1%

Somer’s D 0.342 0.584

Hosmer-Lemeshow

Goodness of Fit 15.218 with 8 DF (p=0.0550) 4.8296 with 8 DF (p=0.7756)

* p < .10

** p < .05

*** p < .01

NOTE: Cases from Danbury, Dublin, LaTuna, and Three Rivers were deleted due to no failures,

and cases from Alderson deleted due to quasi-complete separation.
225

Table 66

Arrests, Male and Female Subjects, Supervised Subjects Only

Treatment Subjects Only

Sites Only Final Model

Variable b se(b) OR b se(b) OR

INTERCPT -3.6895*** 0.5370 . -2.1701 1.6661 .

COMPLETE -0.4389** 0.1903 0.645 -0.0694 0.2713 0.933

DISCIP 0.3557 0.2839 1.427 -0.1829 0.3401 0.833

WITHDRAW 0.3368 0.3199 1.400 0.3133 0.4058 1.368

Institution Indicators

ELEXSITM -0.2787 0.4326 0.757 -0.6892 0.8726 0.502

EBUTSITM -0.2248 0.3748 0.799 -0.8250 0.6591 0.438

EFAISITM 0.0480 0.4124 1.049 -0.5022 0.4848 0.605

ELOFSITM -0.0449 0.7147 0.956 0.0387 1.0276 1.039

EMCKSITM 0.5300 0.5862 1.699 0.7149 0.8125 2.044

EMNASITM 0.5305 0.3940 1.700 0.2956 0.5320 1.344

EOXFSITM 0.6815** 0.3387 1.977 -0.1814 0.5045 0.834

EPHXSITM 0.4443 0.5043 1.559 0.8566 0.5956 2.355

ERCHSITM 0.2144 0.7060 1.239 0.3642 0.8628 1.439

ESEASITM -0.9344 0.9712 0.393 -0.2416 1.0603 0.785

ESHESITM 0.3916 0.4071 1.479 -0.0228 0.5679 0.977

ETALSITM 0.2337 0.3746 1.263 -0.3054 0.6448 0.737

ETRMSITM -0.0319 0.6945 0.969 1.3015 0.9730 3.675

EYANSITM -0.2352 0.4194 0.790 0.1192 0.6454 1.127

EMRGSITM -1.3239 0.7052 0.266 -0.9232 0.9619 0.397

COHTIME 0.0038 0.0138 1.004 0.0323 0.0355 1.033

TIMETORL -0.0374** 0.0174 0.963 -0.0038 0.0391 0.996

Base Model Variables

EBLACK 0.0107 0.2745 1.011

ERACEOTH -0.1936 0.4114 0.824

EHISP 0.1077 0.2786 1.114

EPRIORCM 0.3576* 0.1945 1.430

ERECVIOL 0.0297 0.2363 1.030

EPASTVIO 0.3554 0.2206 1.427

TIMESRVD 0.1707*** 0.0637 1.186

ECJSPVNY -0.6832** 0.3149 0.505

ECJSPVNM 0.9882* 0.5217 2.686

AGERLSE -0.0873*** 0.0219 0.916

ESUPILL -0.0050 0.1571 0.995

EWORKJOB 0.2660 0.3506 1.305

ELEGITUN -0.0271 0.6039 0.973

EUNEMP 0.1205 0.5062 1.128

EJOB_UNK -1.0000 0.9732 0.368

GRADEA 0.0514 0.0690 1.053

COC_FRQ 0.0312 0.0818 1.032

CRK_FRQ -0.2479*** 0.0946 0.780

HAL_FRQ -0.0194 0.1100 0.981

HER_FRQ 0.1129 0.0805 1.119

OPIA_FRQ -0.2269* 0.1343 0.797

POT_FRQ -0.0724 0.0852 0.930

BARB_FRQ 0.2308** 0.1164 1.260

STIM_FRQ -0.0677 0.0942 0.935

DEPLOGTA 0.0075 0.0400 1.008

DEPLOGTD 0.0053 0.0654 1.005
226

Table 66 — Continued

Sites Only Final Model

Variable b se(b) OR b se(b) OR

Supervision Variables

ECCCNO -0.3837* 0.2280 0.681

ECCCFAIL 0.2013 0.2245 1.223

UARATE -0.0954 0.0795 0.909

Post-Release Variables

ESPOUSE -0.5624* 0.3067 0.570

ECOM_LAW 0.1971 0.2978 1.218

SUPVVIOL 0.0849 0.1107 1.089

DIRTY 0.3433 0.3126 1.410

EMP_HRS -0.0518*** 0.0084 0.950

ECOHO1_6 0.4346 0.6407 1.544

ECOHO7 -0.4526 0.6882 0.636

ECOHO8 0.8136* 0.4603 2.256

ECOHO9 -0.8550 0.6158 0.425

ECOHO10 1.1181*** 0.3393 3.059

ECOHO11 -1.0942* 0.5829 0.335

ECOHO12 0.2009 0.4805 1.222

ECOHO13 0.4901 0.4837 1.633

Time Variables

D_T2 0.3833 0.4193 1.467 0.4222 0.4368 1.525

D_T3 0.3415 0.4259 1.407 0.4149 0.4459 1.514

D_T4 0.1314 0.4524 1.140 0.3563 0.4776 1.428

D_T5 0.8431** 0.3969 2.324 1.2044*** 0.4279 3.335

D_T6 0.5051 0.4265 1.657 0.9099** 0.4593 2.484

-2 Log Likelihood

Intercept Only 762.944 762.944

Intercept and Covariates 730.505 584.937

DF 24 66

Concordant Pairs 66.6% 87.3%

Somer’s D 0.372 0.760

Hosmer-Lemeshow

Goodness of Fit 4.3931 with 8 DF (p=0.8200) 11.767 with 8 DF

(p=0.1619)

* p < .10

** p < .05

*** p < .01

NOTE: Cases from Danbury, Dublin, LaTuna, and Three Rivers deleted due to no failures and

cases from Alderson deleted due to quasi-complete separation. CCC placement during supervision

(HHSE_STR) variable deleted due to small sample size.
227

Table 67

Arrests or Revocations, Male and Female Subjects, Treatment Subjects Only

Sites Only Final Model

Variable b se(b) OR b se(b) OR

INTERCPT -3.5532*** 0.4380 . -2.2143 1.4347 .

COMPLETE -0.3167** 0.1528 0.729 0.0039 0.2368 1.004

DISCIP 0.3276 0.2334 1.388 -0.3928 0.3030 0.675

WITHDRAW 0.1800 0.2668 1.197 0.4671 0.3538 1.595

Institution Indicators

EALDSITM -0.6540 0.4397 0.520 -0.5836 0.7422 0.558

EDANSITM -0.7320 0.9885 0.481 -1.0540 1.1137 0.349

EDUBSITM -0.5136 0.9760 0.598 0.2137 1.1297 1.238

ELEXSITM -0.0069 0.3531 0.993 0.0034 0.7793 1.003

EBUTSITM -0.2477 0.3346 0.781 -0.7538 0.6699 0.471

EFAISITM -0.1318 0.3885 0.877 -1.6557 0.4719 0.519

ELATSITM -0.0758 0.9808 0.927 -0.3335 1.1177 0.716

ELOFSITM -0.0305 0.5944 0.970 0.7376 0.8509 2.091

EMCKSITM 0.7359 0.4715 2.087 -0.1984 0.7127 0.820

EMNASITM 0.7073** 0.3219 2.029 0.3056 0.4458 1.357

EOXFSITM 0.6152 0.3079 1.850 0.3186 0.5113 1.375

EPHXSITM 0.7448 0.4022 2.106 1.3566** 0.5351 3.883

ERCHSITM 0.2874 0.5973 1.333 1.5374** 0.7450 4.652

ESEASITM -0.5860 0.7162 0.557 -0.0309 0.9076 0.970

ESHESITM 0.8224*** 0.3073 2.276 0.6897 0.4620 1.993

ETALSITM 0.1792 0.3345 1.196 0.1442 0.6495 1.155

ETRMSITM 0.3744 0.5132 1.454 0.2269 0.7911 1.255

ETRVSITM -0.2931 0.9753 0.746 -1.2439 1.1185 0.288

EYANSITM -0.1947 0.3533 0.823 0.5979 0.5151 1.818

EMRGSITM -1.0004* 0.5166 0.368 -1.2278 0.8312 0.279

TIMETORL -0.0395*** 0.0141 0.961 -0.0139 0.0327 0.986

COHTIME 0.0024 0.0112 1.002 0.0061 0.0305 1.006

Base Model Variables

EBLACK 0.2048 0.2462 1.227

ERACEOTH -0.4369 0.3743 0.646

EHISP 0.1500 0.2331 1.162

EFEM

EPRIORCM 0.1446 0.1601 1.156

ERECVIOL 0.1683 0.1982 1.183

EPASTVIO 0.3025 0.1876 1.353

TIMESRVD 0.1627*** 0.0549 1.177

ECJSPVNY -0.3901 0.2470 0.677

ECJSPVNM 1.1693*** 0.4126 3.220

AGERLSE -0.0383** 0.0165 0.962

ESUPILL -0.0563 0.1306 0.945

EWORKJOB 0.3438 0.2617 1.410

ELEGITUN -0.4237 0.4722 0.655

EUNEMP -0.1207 0.4283 0.886

EJOB_UNK -0.1300 0.6441 0.878

GRADEA -0.0242 0.0534 0.976

COC_FRQ 0.0382 0.0664 1.039

CRK_FRQ -0.1077 0.0643 0.898

HAL_FRQ 0.0267 0.0943 1.027

HER_FRQ 0.0124 0.0677 1.013

OPIA_FRQ -0.1012 0.1033 0.904

POT_FRQ -0.0918 0.0691 0.912

BARB_FRQ 0.1437 0.0925 1.155
228

Table 67 — Continued

Sites Only Final Model

Variable b se(b) OR b se(b) OR

STIM_FRQ -0.0958 0.0759 0.909

DEPLOGTA -0.0349 0.0309 0.966

DEPLOGTD 0.0806 0.0567 1.084

Treatment Variables

ENRGENY -0.1267 0.2577 0.881

ENRSUPY 0.5322 0.3812 1.703

ETSYES -0.2560 0.1692 0.774

ECTRONLY -0.1171 0.2090 0.890

EOTHONLY 0.8942 0.3739 2.445

EBOTH -0.9315** 0.3780 0.394

EAAYES 0.7140** 0.3103 2.042

EAAMISS -1.0270 0.5179 0.358

Supervision Variables

ECCCNO -0.6034** 0.2465 0.547

ECCCFAIL 0.3574 0.2098 1.430

UARATE -0.1605** 0.0726 0.852

HHSE_STR -1.6848 0.9720 0.185

Post-Release Variables

ESPOUSE -0.4634 0.2765 0.629

ECOM_LAW -0.0896 0.2888 0.914

SUPVVIOL 0.2642*** 0.0754 1.302

DIRTY 1.1931*** 0.2538 3.297

EMP_HRS -0.0631*** 0.0073 0.939

ECOHO1_6 -0.0908 0.5432 0.913

ECOHO7 -0.2940 0.5151 0.745

ECOHO8 0.2199 0.4121 1.246

ECOHO9 -0.6566 0.4265 0.519

ECOHO10 0.7141** 0.2975 2.042

ECOHO11 -0.6531 0.3732 0.520

ECOHO12 0.6516 0.3827 1.919

ECOHO13 0.4079 0.4160 1.504

Time Variables

D_T2 0.1724 0.3712 1.188 0.4196 0.4233 1.521

D_T3 0.7880** 0.3334 2.199 1.1563*** 0.3923 3.178

D_T4 0.5967 0.3478 1.816 1.0397** 0.4104 2.828

D_T5 0.9349*** 0.3319 2.547 1.4639*** 0.3977 4.323

D_T6 0.6983** 0.3482 2.010 1.2127*** 0.4139 3.362

-2 Log Likelihood

Intercept Only 1168.728 1139.942

Intercept and Covariates 1111.686 804.256

DF 29 80

Concordant Pairs 67.4% 89.3%

Somer’s D 0.376 0.796

Hosmer-Lemeshow

Goodness of Fit 6.0254 with 8 DF (p=0.6444) 9.1881 with 8 DF (p=0.3267)

* p < .10

** p < .05

*** p < .01
229

Table 68

Drug Use, Male and Female Subjects

Treatment Subjects Only

Sites Only Final Model

Variable b se(b) OR b se(b) OR

INTERCPT -2.1676*** 0.3347 . -3.1879*** 1.2173 .

COMPLETE -0.1126 0.1469 0.893 -0.2581 0.1964 0.773

DISCIP 0.4284* 0.2263 1.535 0.3106 0.2689 1.364

WITHDRAW -0.4167 0.3049 0.659 0.0724 0.3354 1.075

Institution Indicators

EALDSITM -0.0704 0.2901 0.932 -0.1939 0.5574 0.824

ELEXSITM -0.6854* 0.3574 0.504 -0.5203 0.6937 0.594

EBUTSITM -0.2465 0.2769 0.782 0.1128 0.5293 1.119

EFAISITM 0.3953 0.2532 1.485 0.5640 0.3480 1.758

ELATSITM 1.0413** 0.5313 2.833 1.2254* 0.6948 3.406

ELOFSITM 0.3077 0.4091 1.360 0.3923 0.6377 1.480

EMCKSITM -1.7336* 0.9608 0.177 -2.9868*** 1.0354 0.050

EMNASITM 0.5428** 0.2718 1.721 0.6189* 0.3467 1.857

EOXFSITM 0.6565*** 0.2499 1.928 0.2656 0.4212 1.304

EPHXSITM 0.1472 0.4570 1.159 0.2760 0.5198 1.318

ERCHSITM 0.3795 0.4762 1.462 0.6945 0.5794 2.003

ESEASITM 0.0178 0.4814 1.018 0.8866 0.6066 2.427

ESHESITM 0.1116 0.3100 1.118 -0.1174 0.4014 0.889

ETALSITM -0.1226 0.2921 0.885 0.6181 0.5764 1.855

ETRMSITM 0.3157 0.4233 1.371 -0.2933 0.5842 0.746

ETRVSITM -0.8557 0.9674 0.425 -1.4215 1.0782 0.241

EYANSITM 0.0678 0.2661 1.070 0.0691 0.3934 1.072

EMRGSITM -0.2689 0.3042 0.764 -0.1902 0.5604 0.827 

TIMETORL -0.0288** 0.0119 0.972 -0.0179 0.0284 0.982

COHTIME -0.0002 0.0094 1.000 -0.0142 0.0266 0.986

Base Model Variables

EBLACK 0.3913** 0.2026 1.479

ERACEOTH -0.1190 0.3285 0.888

EHISP -0.0331 0.1967 0.967

EPRIORCM 0.2158* 0.1139 1.241

ERECVIOL 0.2950* 0.1683 1.343

EPASTVIO -0.0588 0.1518 0.943

TIMESRVD -0.0525 0.0626 0.949

ECJSPVNY 0.1812 0.2196 1.199

ECJSPVNM 0.0447 0.3878 1.046

AGERLSE -0.0068 0.0128 0.993

ESUPILL 0.0040 0.0975 1.004

EWORKJOB -0.1963 0.2033 0.822

ELEGITUN -0.1216 0.3711 0.885

EUNEMP -0.1413 0.2877 0.868

EJOB_UNK 0.3812 0.5156 1.464

GRADEA 0.0727* 0.0429 1.075

COC_FRQ -0.0312 0.0504 0.969

CRK_FRQ 0.0904** 0.0452 1.095

HAL_FRQ 0.0157 0.0699 1.016

HER_FRQ 0.0092 0.0511 1.009

OPIA_FRQ 0.1481** 0.0720 1.160

POT_FRQ 0.0430 0.0546 1.044

BARB_FRQ -0.1065 0.0712 0.899
230

Table 68 — Continued

Sites Only Final Model

Variable b se(b) OR b se(b) OR

STIM_FRQ 0.0385 0.0544 1.039

DEPLOGTA -0.0408* 0.0247 0.960

DEPLOGTD 0.0261 0.0443 1.026

Treatment Variables

ENRGENY -0.3075* 0.1893 0.735

ENRSUPY -0.0204 0.2650 0.980

ETSYES 0.1240 0.1498 1.132

EOTHONLY -0.1935 0.3840 0.824

ECTRONLY -0.4595* 0.2494 0.632

EBOTH 0.8917*** 0.3380 2.439

ETXMISS 1.3357 0.8251 3.803

EAAYES 0.3737 0.2506 1.453

EAAMISS -0.9230** 0.4479 0.397

Supervision Variables

ECCCNO 0.0475 0.2227 1.049

ECCCFAIL 0.4522*** 0.1658 1.572

UARATE 0.0912* 0.0497 1.095

HHSE_STR 0.1366 0.5946 1.146

Post-Release Variables

ESPOUSE -0.2595 0.1849 0.771

ECOM_LAW 0.0077 0.1945 1.008

SUPVVIOL 0.3485*** 0.0641 1.417

EMP_HRS -0.0104** 0.0049 0.990

ECOHO1_6 -0.2241 0.4658 0.799

ECOHO7 -0.4429 0.4276 0.642

ECOHO8 -0.1896 0.3449 0.827

ECOHO9 -0.4811 0.3039 0.618

ECOHO10 0.1221 0.2736 1.130

ECOHO11 0.5673** 0.2582 1.763

ECOHO12 0.5646** 0.2785 1.759

ECOHO13 -0.0283 0.3421 0.972

Time Variables

D_T2 -0.1808 0.2143 0.835 -0.0318 0.2345 0.969

D_T3 -0.2046 0.2205 0.815 0.1469 0.2412 1.158

D_T4 -0.4491* 0.2411 0.638 0.0067 0.2613 1.007

D_T5 -0.5054** 0.2501 0.603 0.0778 0.2701 1.081

D_T6 -1.5019*** 0.3657 0.223 -0.8177** 0.3797 0.441

-2 Log Likelihood

Intercept Only 1519.130 1519.130

Intercept and Covariates 1450.624 1206.570

DF

Concordant Pairs 65.7% 83.2%

Somer’s D 0.331 0.671

Hosmer-Lemeshow

Goodness of Fit 4.0023 with 8 DF (p=0.8569) 7.0056 with 8 DF (p=0.5360)

* p < .10

** p < .05

*** p < .01

NOTE: Six cases from Dublin were omitted from the models due to zero cells (i.e., no failures). 

In addition, eight cases from Danbury were deleted due to quasi-complete separation.
231

Table 69

CCC Failure, Male and Female Subjects, Treatment Subjects Only

Sites Only Final Model

Variable b se(b) OR b se(b) OR

INTERCPT -1.4875*** 0.4931 . -0.2713 1.1315 .

COMPLETE -0.5148** 0.2069 0.598 -0.5885** 0.2346 0.555

DISCIP 0.7843** 0.3440 2.191 0.8309** 0.3887 2.295 

WITHDRAW -0.3716 0.4126 0.690 -0.0901 0.4560 0.914

Institution Indicators

EALDSITM 0.2373 0.3831 1.268 0.6862 0.4728 1.986

EBUTSITM -0.7560* 0.4301 0.470 -0.8019* 0.4731 0.448

EDANSITM -0.2715 1.1474 0.762 -0.6516 1.3059 0.521

EDUBSITM 1.3000 0.9780 3.669 1.0322 1.0922 2.807

EFAISITM -0.5884 0.4911 0.555 -0.9504* 0.5684 0.387

ELATSITM 2.0990*** 0.6585 8.158 2.1748*** 0.7872 8.801

ELEXSITM -2.4045*** 0.7553 0.090 -3.5497*** 0.9278 0.029

ELOFSITM -1.1358 1.0135 0.321 -1.1711 1.0615 0.310

EMCKSITM 0.9725 0.7365 2.645 0.7900 0.8034 2.203

EMNASITM 0.4069 0.3980 1.502 0.3167 0.4675 1.373

EMRGSITM -0.2805 0.4215 0.755 0.2242 0.4867 1.251

EOXFSITM 0.1086 0.3834 1.115 0.0009 0.4568 1.001

EPHXSITM 0.0470 0.5752 1.048 0.2782 0.6414 1.321

ERCHSITM -0.2872 0.6564 0.750 -0.3836 0.7342 0.681

ESEASITM -0.4159 0.6640 0.660 0.0506 0.7215 1.052

ESHESITM 0.4053 0.4166 1.500 0.2279 0.5264 1.256

ETALSITM 0.1141 0.3853 1.121 0.5120 0.4477 1.669

ETRMSITM 0.1275 0.6804 1.136 0.1298 0.7272 1.139

ETRVSITM 0.0329 1.0897 1.033 0.4245 1.2375 1.529

EYANSITM 0.2889 0.3388 1.335 0.6600 0.4267 1.935

COHTIME 0.0129 0.0147 1.013 0.0174 0.0169 1.018

TIMETORL 0.0156 0.0167 1.016 0.0312 0.0199 1.032

Base Model Variables

EBLACK 0.2615 0.2878 1.299

ERACEOTH -0.2645 0.4579 0.768

EHISP 0.0362 0.2468 1.037

EPRIORCM 0.4986*** 0.1602 1.646

ERECVIOL -0.3615 0.2743 0.697

EPASTVIO 0.2175 0.2159 1.243

TIMESRVD 0.0195 0.0796 1.020

ECJSPVNY -0.2012 0.2754 0.818
232

Table 69 — Continued

Sites Only Final Model

Variable b se(b) OR b se(b) OR

ECJSPVNM 0.4638 0.4809 1.590

AGERLSE -0.0444*** 0.0178 0.957

COC_FRQ -0.0045 0.0661 0.995

CRK_FRQ -0.0063 0.0671 0.994

HAL_FRQ -0.0896 0.0989 0.914

HER_FRQ 0.1160 0.0725 1.123

OPIA_FRQ -0.0004 0.0923 1.000

POT_FRQ -0.0465 0.0734 0.955

BARB_FRQ 0.0353 0.0940 1.036

STIM_FRQ -0.0936 0.0761 0.911

DEPLOGTA 0.0286 0.0381 1.029

DEPLOGTD 0.1017* 0.0604 1.107

ESUPILL -0.1303 0.1317 0.878

EWORKJOB -0.0911 0.2797 0.913

ELEGITUN -0.8875 0.5657 0.412

EUNEMP -0.1412 0.4332 0.868

EJOB_UNK 0.3611 0.6665 1.435

GRADEA -0.0291 0.0602 0.971

Change Assessment Variables

ECLUST1 0.4184* 0.2426 1.519

ECLUST2 -0.4340 0.2808 0.648

ECLUST3 -0.0834 0.3491 0.920

ECLUST4 0.4168 0.3020 1.517

ECLUST5 0.2385 0.4115 1.269

ECLUST6 0.0017 0.6295 1.002

Additional Background Variables

EPSTDGTX -0.1355 0.1400 0.873

EPSTETOH -0.0290 0.2711 0.971

EDIAGNO 0.0545 0.2188 1.056

EDIAGDEP -0.0816 0.3492 0.922

EDIAGASP -0.2131 0.2521 0.808

EDIAGBTH 0.5325 0.4150 1.703

EPSTMHTX 0.0949 0.1623 1.100

-2 Log Likelihood 

 Intercept Only 633.409 633..409

 Intercept and Covariates 585.988 511.883 

Concordant Pairs 68.6% 79.8

Somer’s D 0.378 0.599

Hosmer-Lemshow

Goodness of Fit 4.85 with 8 DF (p=0.7735) 9.6741 with 8 DF (p=0.2886)

* p < .10

** p < .05

*** p < .01
233

Table 70

Employment, Male Subjects, Treatment Subjects Only

Sites OnlyFinal Model

Variable b se(b) b se(b)

INTERCEP 38.7495*** 7.2791 30.6567 22.6200

COMPLETE 11.3253*** 3.2174 3.3670 3.7303

DISCIP -10.3042* 5.5566 -0.5381 5.5207

WITHDRAW -9.9472* 6.0105 -8.7872 6.0340

Institution Indicators

EBUTSITM 5.5338 5.2200 8.1419 9.2079

EFAISITM -11.4393** 5.5163 -5.0006 6.2276

ELOFSITM 11.9363 9.4912 1.7157 11.7128

EMCKSITM -18.2962 10.0178 -8.4229 10.8313

EMNASITM 12.0731 6.3280 13.2705** 6.4449

EMRGSITM 13.5867** 6.3120 3.5959 9.8491

EOXFSITM -16.6510*** 5.8436 -7.7553 7.6801

ERCHSITM 1.6550 10.3131 -1.2167 10.7080

ESEASITM 4.6932 9.1964 -1.7873 10.5701

ESHESITM 6.5996 6.5081 4.7255 7.2023

ETALSITM -0.7683 5.9878 4.4062 9.4668

ETRMSITM -9.3956 9.6671 -7.9623 11.7833

ETRVSITM -13.5148 13.3971 -14.4127 14.1510

EYANSITM 13.9876*** 5.2701 10.7020 6.5497

COHTIME 0.3379 0.2216 0.1797 0.4598

TIMETORL 0.9370*** 0.2498 0.6076 0.4954

Base Model Variables

EBLACK -1.3437 3.9582

ERACEOTH -4.9109 6.1037

EHISP 1.1529 3.5635

EPRIORCM -1.8591 2.0270

ERECVIOL -7.4356** 3.2583

EPASTVIO 5.5369** 2.8103

TIMESRVD 0.4225 0.9482

ECJSPVNY -2.8101 4.7201

ECJSPVNM 5.4680 8.3580

AGERLSE -0.3028 0.2319

ESUPILL -3.6451 1.9427

EWORKJOB 7.2200 3.9002

ELEGITUN -12.1341 8.3415

EUNEMP 3.4986 5.9480

EJOB_UNK -6.2709 10.3687

GRADEA 1.6459** 0.7792

COC_FRQ -0.7529 0.9695

CRK_FRQ 1.0265 0.9917

HAL_FRQ -0.7901 1.3497

HER_FRQ 0.2400 1.0812

OPIA_FRQ -1.6642 1.5077

POT_FRQ -0.4556 1.0100

BARB_FRQ -0.6734 1.3922

STIM_FRQ 1.4907 1.0988

DEPLOGTA 0.8004 0.5103

DEPLOGTD 0.2564 0.8155
234

Table 70 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Treatment Variables

ENRGENY -3.8281 3.1819

ENRSUPY 7.0113 4.4415

ETSYES 2.6255 2.8161

ECTRONLY 5.2732 6.4047

EOTHONLY 2.9580 9.1898

EBOTH -9.9466 8.8634

ETXMISS -4.9454 22.4388

EAAYES 1.7481 7.0945

EAAMISS -0.3514 13.3841

Supervision Variables

ECCCNO -3.3175 4.0947

ECCCFAIL -10.3152*** 3.5034

UARATE 0.2792 1.1016 

HHSE_STR 2.5239 9.7071

Post-Release Variables

ESPOUSE 4.8795 2.8943

ECOM_LAW -0.2148 3.1480

SUPVVIOL -6.3430*** 1.6151

DRUGVIOL -0.3313 1.0681

ECOHO1_6 -6.1697 8.2326

ECOHO7 3.7840 8.1534

ECOHO8 -0.9205 6.6688

ECOHO9 2.6910 5.5651

ECOHO10 2.8452 4.7694

ECOHO11 -1.8155 5.1634

ECOHO12 4.9572 5.4542

ECOHO13 -2.7609 6.2530

R-square 0.1017 0.3121 

Adjusted R-square 0.0722 0.2068

* p < .10

** p < .05

*** p < .01
235

Table 71

Employment, Female Subjects

Treatment Subjects Only

Sites Only Final Model

Variable b se(b) b se(b)

INTERCEP 61.0837***18.8709 -42.8151 57.7088

COMPLETE 3.3093 6.4204 -15.6595* 8.4658

WITHDRAW -12.6133 10.9331 5.5565 11.1068

DISCIP 2.0879 10.5692 15.8256 11.5945

Institution Indicators

EALDSITF -5.3871 12.0890 -7.5404 24.8843

EDUBSITF 1.2791 13.2501 8.3793 17.0461

EDANSITF 0.2654 14.4700 9.9221 16.0893

ELEXSITF 3.8370 9.8438 -10.7610 25.4852

COHTIME -0.5407 0.5499 -0.6510 1.2074

TIMETORL 0.3497 0.6114 -0.0450 1.1648

Base Model Variables

EBLACK -13.9794 12.3872

ERACEOTH 7.5346 23.2314

EHISP -14.0500 9.4380

EPRIORCM 2.7749 4.5347

ERECVIOL -16.4935 13.2149

EPASTVIO 14.6624 12.1796

TIMESRVD -2.6116 4.4719

ECJSPVNY -21.8618 13.3840

ECJSPVNM 46.8719 24.2315

AGERLSE 0.7641 0.6304

ESUPILL 6.3584 4.2381

EWORKJOB 15.4427 9.3212

ELEGITUN 17.5113 15.6152

EUNEMP -10.4508 13.4391

EJOB_UNK -25.3884 25.8912

GRADEA 3.6736 2.4873

COC_FRQ 0.1853 2.1312

CRK_FRQ 5.9633*** 1.9387

HAL_FRQ 4.3003 3.6152

HER_FRQ 2.4945 2.3689

OPIA_FRQ -0.5656 3.0347

POT_FRQ 4.8097** 2.2622

BARB_FRQ 3.0478 3.0274

STIM_FRQ -0.4217 2.2608

DEPLOGTA 0.0825 1.1768

DEPLOGTD -5.0124** 1.9809
236

Table 71 — Continued

 Base Model Final Model

Variable b se(b) b se(b)

Other Background Factors

EPSTDGTX -11.5537** 4.9696

EPSTETOH 4.2091 11.7114

EDIAGNO 0.2065 6.4146

EDIAGDEP 4.6496 10.5420

EDIAGASP -0.0049 9.5346

EDIAGBTH 13.1529 10.8568

EPSTMHTX -9.8249** 4.7917

Treatment Variables

ENRGENY -18.6746 14.2786

ETSYES 16.5595*** 5.0622

ECTRONLY -8.2499 10.2734

EOTHONLY 13.4910 19.7962

EBOTH -21.2734 25.9017

EAAYES 36.2222** 17.1660

EAAMISS -47.1299 33.2446

Post-Release Variables

ESPOUSE -9.5264 9.7654

ECOM_LAW -4.2805 10.6947

SUPVVIOL 2.2984 5.1067

DRUGVIOL -3.6110 3.1805

ECOHO1_6 -11.9649 22.1765

ECOHO7 1.8024 29.5588

ECOHO8 16.2751 15.3845

ECOHO9 12.9008 12.5676

ECOHO10 -6.6371 15.4663

ECOHO11 5.2367 10.1319

ECOHO12 -9.3290 13.6844

ECOHO13 1.4909 15.1372

R-square 0.0288 0.5846

Adjusted R-square -0.0315 0.2609

* p < .10

** p < .05

*** p < .01

NOTE: ENRSUPY and ETXMISS are not included in this model because no women DAP

subjects had a value of one for these variables.
237

APPENDIX A: SUPPLEMENTARY TABLES

Table A1

HDU Refusals — Male Subjects

Variable b se(b) OR

INTERCPT -1.8906 0.2049 .

DRACEB -0.2613*** 0.0877 0.770

DRACEO -0.5633 0.3400 0.569

DSECLEVM 0.2031 0.1324 1.225

DSECLEVL -0.2288** 0.1160 0.796

DETHNO -0.2333** 0.1184 0.792

DOFFSEVM 0.2871*** 0.1019 1.333

DOFFSEVH 0.1502 0.1388 1.162

DOFFSEVG 0.3300** 0.1309 1.391

DPRIORM -0.1875 0.1336 0.829

DPRIORS 0.0105 0.1064 1.011

DVIOLM 0.2146 0.1180 1.239

DVIOLS 0.2986** 0.1179 1.348

AGE 0.0162*** 0.0040 1.016

-2 Log Likelihood

Intercept Only 4178.965

Intercept and Covariates 4070.609

Concordant Pairs 60.5%

Somer’s D 0.217

Hosmer-Lemeshow

Goodness of Fit 7.6473 with 8 DF (p=0.4687)

** p < .05

*** p < .01
238

Table A2

HDU Refusals — Female Subjects

Variable b se(b) OR

INTERCPT -3.6313 0.4570 .

DRACEB 0.4923** 0.1918 1.636

DRACEO -0.3275 0.6397 0.721

DSECLEVL 0.3694 0.1946 1.447

DOFFSEVM 0.5301** 0.2298 1.699

DOFFSEVH 0.6937** 0.2898 2.001

DOFFSEVG 0.7013 0.3938 2.016

DVIOLM 0.6696** 0.3094 1.953

DVIOLS -0.5518 0.5475 0.576

AGE 0.0224** 0.0096 1.023

-2 Log Likelihood

Intercept Only 830.383

Intercept and Covariates 801.602

Concordant Pairs 63.1%

Somer’s D 0.273

Hosmer-Lemeshow

Goodness of Fit 4.9634 with 8 DF (p=0.7615)

** p < .05

*** p < .01
239

Table A3

Missed Research — Male Subjects

Variable b se(b) OR

INTERCPT -1.8360 0.2694 .

DETHN 0.2197 0.1730 1.246

DRACEB -0.1214 0.1329 0.886

DRACEO -0.0114 0.3691 0.989

DCPTR 1.4919*** 0.1283 4.446

DNONDAP -2.2187*** 0.2168 0.109

AGE -0.0117 0.0069 0.988

-2 Log Likelihood

Intercept Only 2110.361

Intercept and Covariates 1881.339

Concordant Pairs 72.4%

Somer’s D 0.459

Hosmer-Lemeshow

Goodness of Fit 4.9446 with 8 DF (p=0.7635)

** p < .05

*** p < .01
240

Table A4

Missed Research — Female Subjects

Variable b se(b) OR

INTERCPT -2.9544 0.5488 .

DOFFSEVM -0.4313 0.2366 0.650

DOFFSEVH -0.0633 0.3375 0.939

DOFFSEVG -0.1742 0.4852 0.840

DCPTR 1.1466*** 0.2277 3.148

DPRIORA 0.2778 0.2754 1.320

DPRIORB 0.2241 0.2640 1.251

DSECLEVL 0.5111** 0.2367 1.667

AGE 0.0225 0.0137 1.023

-2 Log Likelihood

Intercept Only 605.203

Intercept and Covariates 563.189

Concordant Pairs 68.3%

Somer’s D 0.370

Hosmer-Lemeshow

Goodness of Fit 5.7372 with 8 DF (p=0.6766)

** p < .05

*** p < .01
241

Table A5 

Research Refusals — Male Subjects

Variable b se(b) OR

INTERCPT -2.7777 0.3421 .

DETHN 0.1700 0.2160 1.185

DRACEB -0.2808 0.1660 0.755

DRACEO -0.6562 0.6100 0.519

DCPTR 0.9539*** 0.1723 2.596

DPRIORA -0.3949 0.2410 0.674

DPRIORB -0.1261 0.1933 0.882

DVIOLL -0.2215 0.2450 0.801

DVIOLH 0.3117 0.1951 1.366

DNONDAP -0.2446 0.1797 0.783

AGE 0.0087 0.0083 1.009

-2 Log Likelihood

Intercept Only 1417.312

Intercept and Covariates 1363.944

Concordant Pairs 64.3%

Somer’s D 0.298

Hosmer-Lemeshow

Goodness of Fit 5.2222 with 8 DF (p=0.7336)

** p < .05

*** p < .01
242

Table A6

Missed Intake 1 — Male Subjects

Variable b se(b) OR

INTERCPT -1.7818 0.3693 .

DCPTR -0.6954*** 0.2196 0.499

DNONDAP -1.8146*** 0.4908 0.163

AGE -0.0059 0.0102 0.994

-2 Log Likelihood

Intercept Only 1037.665

Intercept and Covariates 971.820

Concordant Pairs 63.9%

Somer’s D 0.334

Hosmer-Lemeshow

Goodness of Fit 4.5583 with 8 DF (p=0.8036)

** p < .05

*** p < .01
243

APPENDIX B: CODEBOOK OF VARIABLES USED IN TRIAD ANALYSES

Variables Used in Subject Attrition Analyses

Age at Time of Interview

AGE: A continuous variable.

Comparison vs. Treatment Subject

DCOMPTRMT: Dummy variable coded as 1 if comparison subject.

Excluded group is treatment subject.

DAP vs. non-DAP site (site where subject identified)

DNONDAP: Dummy variable coded as 1 if non-DAP site.

Excluded group is DAP site.

Ethnicity

DETHNO: Dummy variable coded as 1 if non-Hispanic.

Excluded group is Hispanic.

History of Violence (Dummy variables)

DVIOLM: Coded as 1 if minor history of violence.

DVIOLS: Coded as 1 if serious history of violence.

Excluded group has no history of violence.

Institution

This was coded as an effects vector that included all institutions where a research subject was

identified.

Offense Severity (Dummy variables)

DOFFSEVM: Coded as 1 if moderate offense severity.

DOFFSEVH: Coded as 1 if high offense severity.

DOFFSEVG: Coded as 1 if great offense severity.

Excluded group is low moderate offense severity.

Prior Commitments (Dummy variables)

DPRIORM: Coded as 1 if minor prior commitments.

DPRIORS: Coded as 1 if serious prior commitments.

Excluded group has no prior commitments.

Race (Dummy variables)

DRACEB: Coded as 1 if black.

DRACEO: Coded as 1 if other race.

Excluded group is white.
244

Recency of Violence (Dummy variables)

DVIOLR: Coded as 1 if violence within past 5 years.

DVIOLP: Coded as 1 if violence more than 5 years ago.

Excluded group has no history of violence. (Note: Recency of violence was not included

in the HDU analyses.)

Security Level of Institution (Dummy variables)

DSECLEVL: Coded as 1 if low security level..

DSECLEVM: Coded as 1 if medium security level.

Excluded group is minimum security level. (Note: female subjects were housed only at

minimum and low security facilities.)

Variables Used in Outcome Analyses

Unless otherwise indicated, all of the nominal variables are coded as effects vectors.

Age at Time of Release from Current Incarceration 

AGERLSE: A continuous variable.

Alcohol Dependency

DEPLOGTA: log odds of alcohol dependence; a continuous variable.

Alcohol Treatment History

EPSTETOH: Coded 1 if there was no previous inpatient or outpatient alcohol treatment.

Excluded group had previous history of alcohol treatment.

Amount of Time Served for Current Offense

TIMESRVD: Number of years served; a continuous variable.

Criminal Justice Status at Most Recent Incarceration

ECJSPVNY: Coded 1 if under supervision at time of incarceration.

ECJSPVNM: Coded 1 if supervision at time of incarceration unknown.

Excluded group not supervised at time of incarceration.

DAP Maturity

COHTIME: number of months between implementation of program and subject’s admission to

program; a continuous variable.

Diagnoses of Depression and Antisocial Personality

EDIAGNO: Coded 1 if neither diagnosis.

EDIAGDEP: Coded 1 if diagnosis of depression only.

EDIAGASP: Coded 1 if diagnosis of antisocial personality only.

EDIAGBTH: Coded 1 if both diagnoses.

Excluded group has missing diagnosis information.

Drug Dependency

DEPLOGTD: log odds of drug dependence; a continuous variable.
245

Drug Treatment History

EPSTDGTX: Coded 1 if no previous inpatient or outpatient drug treatment. 

Excluded group had previous history of drug treatment.

Drug Use

The following variables for different drugs represent the frequency of use during the period of

heaviest use, with the following values: 0 = never used or used fewer than 5 times, 1 = less than

once per month, 2 = 1 to 3 days per week, 3 =1 to 2 days per week, 4 =3 to 4 days per week, and

5 = daily.

BARB_FRQ: barbiturates. 

COC_FRQ: cocaine.

CRK_FRQ: crack.

HAL_FRQ: hallucinogens.

HER_FRQ: heroin.

OPIA_FRQ: opiates.

POT_FRQ: marijuana.

STIM_FRQ: stimulants. 

Education

GRADEA: Number of years of education: A continuous variable.

Employment (Employment status in month before incarceration)

EWORKJOB: Coded 1 if working full- or part-time.

ELEGITUN: Coded 1 if unemployed because in school, a homemaker, retired, or disabled.

EUNEMP: Coded 1 if unemployed but looking for work.

EJOB_UNK : Coded 1 if employment status unknown.

Excluded group is composed of those unemployed because of involvement in illegal drug

use or illegal activities, because the individual has never been employed, or due to other

reasons.

Ethnicity

EHISP: Coded 1 for Hispanics, with non-Hispanics serving as excluded group.

History of Violence

ERECVIOL: Coded 1 if violence occurred less than 5 years ago.

EPASTVIO: Coded 1 if violence occurred 5 or more years ago.

Excluded group had no history of violence.

Illegal Source of Income

ESUPILL: Coded 1 if ever supported self mainly through illegal activity for at least one year. 

Excluded group did not ever support self mainly through illegal activity for at least one

year.

In-Prison Outpatient Treatment

ENRGENY: Coded 1 if received outpatient treatment in prison.

Excluded group did not receive outpatient treatment.

In-Prison Self-Help Group

ENRSUPY: Coded 1 if involved in self-help group (e.g., NA, CA, or AA) in prison.

Excluded group not involved in self-help group.
246

Involved in Post-Release Self-Help Group

EAAYES: Coded 1 if involved in self-help group.

EAAMISS: Coded 1 if self-help group participation unknown/missing.

Excluded group did not participate in self-help after release from prison.

Level of selection bias

COVARIAN—Ordinal variable.

Living with Spouse

ESPOUSE: Coded 1 if living with spouse.

ECOM_LAW: Coded 1 if living with common-law partner.

Excluded group not living with spouse or common-law partner.

Mental Health Treatment History

EPSTMHTX: Coded 1 if subject received no previous inpatient or outpatient mental health

treatment.

Excluded group had previous history of mental health treatment

Monthly Rate of Urine Testing

UARATE: Average number of urinalysis tests per month.

Month Since Release from BOP Custody

D_T2, D_T3, D_T4, D_T5, D_T6: Represents successive months of release, starting with second

month of release. Excluded group is first month of release.

Motivation for Change

ECLUST1: Coded 1 for contemplation cluster.

ECLUST2: Coded 1 for preparation cluster.

ECLUST3: Coded 1 for reluctant cluster.

ECLUST4: Coded 1 for action cluster.

ECLUST5: Coded 1 for precontemplation cluster.

ECLUST6: Coded 1 for uninvolved cluster.

Excluded group has motivation for change information missing.

Placement in Halfway House After Release

HHSE_STR: Time-dependent covariate for event history analyses. In the models of employment,

this is a dummy variable representing whether a halfway house placement occurred during

supervision.

Post-Release Drug Use

A) DIRTY: Time dependent covariate: dummy variable representing whether drug or alcohol use

occurred during the month in question. Used in event history analyses for the traditional and

Bloom models.

B) DRUGVIOL: Ordinal variable: number of drug-related supervision violations throughout the

supervision time period or until arrest or until end of supervision, whichever occurred first. Used

in Heckman model.
247

Post-Release Employment

A) PRCTFULL: Percent of post-release period employed full-time: Used in Heckman model.

B) EMP_HRS: Number of hours worked during each post-release month. Time dependent

covariate used for event history analyses in traditional and Bloom models.

Post-Release Supervision Violations

This consists of any violation of a condition of supervision except one related to drug use. 

SUPVVIOL: Ordinal variable — number of violations of conditions of supervision throughout the

supervision time period or until arrest or until end of supervision, whichever occurred first.

Prior Commitments

EPRIORCM: Coded 1 if had a major or minor prior commitment. 

Excluded group had no prior commitment.

Race

EBLACK: Coded as 1 if black 

ERACEOTH: Coded as 1 if of other race. Whites serve as excluded group.

Received CCC Placement/CCC Outcome

ECCCNO: Coded 1 if did not receive CCC placement.

ECCCFAIL: Coded 1 if failed CCC placement.

Excluded group completed CCC placement.

Received Transitional Services During CCC Placement

ETSYES: Coded 1 if received transitional services.

Excluded group did not receive transitional services.

Release Cohort of Subject

ECOHO1_6 : Coded 1 if released between July 1992 and December 1993.

ECOHO7: Coded 1 if released between January and March 1994.

ECOHO8: Coded 1 if released between April and June 1994. 

ECOHO9: Coded 1 if released between July and September 1994.

ECOHO10: Coded 1 if released between October and December 1994.

ECOHO11: Coded 1 if released between January and March 1995.

ECOHO12: Coded 1 if released between April and June 1995. 

ECOHO13: Coded 1 if released between July and September 1995.

Excluded group is cohort 14—released between October and December 1995.

Sex

EFEM: Coded 1 if female, with males serving as the excluded group.

Supervised After Release

SUP_REL: Time dependent covariate for event history analyses.

Time Served After Release from DAP

TIMETORL: Number of months between DAP discharge and release from custody.
248

Type of Post-Release Treatment

ECTRONLY: Coded 1 if received contract services only.

EOTHONLY: Coded 1 if received non-contract services.

EBOTH: Coded 1 if received both contract and non-contract services.

ETXMISS: Coded 1 if service received is unknown or missing.

Excluded group had no post-release treatment.

Type of Subject (These are dummy-coded variables with non-DAP controls serving as excluded

group)

COMPDAP: Coded 1 if DAP comparison.

INCOMPTX: Coded 1 if DAP incomplete.

WITHDRTX: Coded 1 if DAP dropout.

DISCIPTX: Coded 1 if DAP disciplinary discharge.

GRAD9MO: Coded 1 if completed 9-month DAP.

GRAD12MO: Coded 1 if completed 12-month DAP.

Excluded group is non-DAP control.

Type of Subject (used for Bloom model)

SUBJECT: Coded 1 if DAP subject — comparison or treatment.

Excluded group is non-DAP control.

Type of Subject (used for Heckman model)

COMPLETE: Conditional probability of completing treatment if entered treatment. 

Type of Treatment Subject (used for treatment subjects-only models)

WITHDRAW: Coded 1 if DAP dropout.

DISCIP: Coded 1 if DAP disciplinary discharge.

COMPLETE: Coded 1 if completed 9-month or 12-month DAP.

Excluded group is DAP incomplete.
249

APPENDIX C: MODELS FOR DEALING WITH SELECTION BIAS IN AN EVALUATION OF A PRISON-BASED DRUG TREATMENT PROGRAM

Introduction

The Federal Bureau of Prisons (BOP) wanted to assess whether in-prison modified therapeutic

community treatment programs improve the post-release behavior of drug-involved offenders

following release from the Bureau’s custody. The Bureau sought to learn whether or not

treatment:

 reduced halfway house placement failure.

 increased the percentage of time that offenders were employed full-time during the 6

months following release from BOP custody.

 decreased the rate of relapse to drug use following release from prison and halfway

house confinement.

 decreased the rate of criminal recidivism, defined alternately as:

(1) being arrested during the 6 months following release from BOP custody, and

(2) being arrested or otherwise being revoked from supervised release during the 6 

months following release from BOP custody.

The Bureau was unable to assign subjects randomly to treatment and to no treatment conditions,

so it devised a quasi-experimental design to test for treatment effectiveness. Some Federal prisons

had therapeutic community treatment programs (hereafter referred to as DAP facilities) and

others did not (hereafter non-DAP facilities). Prisoners in DAP facilities did not differ materially

from prisoners in non-DAP facilities, so the two populations were comparable for evaluation

purposes. Within the DAP facilities, some offenders were offered and accepted treatment

(hereafter the DAP treatment group) while others either were not offered treatment or declined

treatment that was offered (hereafter the DAP comparison group). Of course, those offenders

who were housed in non-DAP facilities did not receive treatment (hereafter the non-DAP control

group).

The Bureau wanted to learn whether treatment improved the post-release performance for those

who received treatment. However, the Bureau was concerned that a simple comparison of the

outcomes for offenders who were treated (the DAP treatment group) with the outcomes for

offenders who were not treated (the non-DAP control group and the DAP comparison group)

could be misleading because of selection bias. In this case, the concern was that some unmeasured

factors (such as motivation to change) that affect the decision to enter treatment might also affect

post-release performance, so the relationship between treatment and post-release performance

could be partly or wholly spurious. The Bureau consulted with Abt Associates about procedures

for dealing with this problem. Abt Associates suggested three instrumental variable models for

dealing with this form of selection bias: the Bloom approach, a standard instrumental variables

approach, and a Heckman selection bias approach.

The Bloom approach (1984) is the most straightforward of the three approaches. Because a

prisoner’s assignment to a specific prison had nothing to do with whether he or she needed

substance abuse treatment, selection bias does not affect a comparison between the outcomes for

the non-DAP control group and the combined outcomes for the DAP treatment and comparison

groups. To illustrate this approach, suppose that every prison comprises two populations of

offenders: those who would enter treatment if it were offered to them and those who would not
250

enter treatment if it were offered. When treatment is offered, these populations can be identified,

and when treatment is not offered, they cannot be identified. Let:

P The percentage of a prison population that would accept treatment if given accept

the opportunity. Call this group A.

1-P The percentage of a prison population that would decline treatment if given accept

the opportunity. Call this group B.

F The fraction of group A that would recidivate if treatment were not pro- accept

vided.

F The fraction of group B that would recidivate. decline

Then if treatment were provided to no one:

F = P F +(1-P )F . untreated population accept accept accept decline

This is the expected value of the observed proportion of failures in the non-DAP control group.

Suppose that, on average, treatment reduced the proportion of inmates who recidivate by an

amount D. If treatment were provided to everyone who would accept it:

F = P ((F -D) +(1-P )F treated population accept accept accept decline

This is the expected value of the observed proportion of failures in the combined DAP groups. A

test of treatment effectiveness can be based on the differences between F and treated population

F . Some algebra shows that the expected value of the effect from treatment is: untreated population

D = (F -F )/P . untreated population treated population accept

The Bloom approach affords an estimate of the treatment effect D and a measure of its statistical

significance despite the fact that the treated and untreated groups may have failure rates that differ

from each other for reasons that have nothing to do with the receipt of treatment.

The Bloom approach to evaluating treatment effectiveness is not much complicated by introducing control variables and using regression models. The introduction of control variables has three

benefits. First, by reducing unexplained variance, the regression can reduce the standard error of

estimate for the treatment effect. Second, the control variables can help adjust for any population

difference between DAP and non-DAP facilities. And, third, the parameters associated with

control variables have policy relevance for the Bureau. Thus, the BOP selected the Bloom

approach for the analysis reported in the main study. It will not be discussed further in this

appendix, except to compare it with two other approaches.

The second approach is to use standard instrumental variables (Davidson and MacKinnon, 1993,

for example). Suppose an analyst were to combine data from all three sources (non-DAP controls,

DAP comparisons, and DAP treatment subjects), assign a dummy variable coded one to those

who received treatment and coded zero for those who did not, and then regress the outcome

variable on this dummy variable and any control variables that seem appropriate. The problem
These are important identifying conditions. For identification, we want the instrument to 64

be strongly correlated with the receipt of treatment. The facts that treatment is unavailable to

inmates from non-DAP facilities, that treatment availability varies widely across the DAP

facilities, and that treatment availability increases over time means that the receipt of treatment

can be predicted with fairly good accuracy by knowing the facilities where the inmate was housed

and when he was housed there. The prediction should be strongly correlated with the receipt of

treatment. However, there is little or no reason to believe that the place where the inmates was

housed and when he was housed in that facility should have a strong effect on his subsequent

misconduct following release. Consequently, the instrument should be distributed as independent

of unmeasured variables that also affect recidivism. In technical terms, the model is identified.

251

with this approach is well known. The estimated regression parameter associated with the dummy

variable will be biased and inconsistent if the dummy variable and the error term are not independent. Independence seems unlikely if any unmeasured factor (such as motivation) affects both the

receipt of treatment and the error term.

A solution is to identify an instrumental variable that is highly correlated with the dummy variable

but that is distributed as independent of the error term. One suitable instrument is the estimated

probability of entering treatment, where this instrument might be estimated from a probit model.

The dependent variable in the probit model is a dummy variable indicating whether the offender

entered treatment. This probit model is estimated using just those data from the DAP subjects,

since the non-DAP subjects have a zero probability by definition, so the instrument is set to zero

for them. By substituting the instrument for the dummy variable and estimating the regression, the

parameter estimate associated with the instrument is a consistent estimate of the treatment effect.

The standard instrumental variable approach is more difficult than is the Bloom approach, and we

might ask: Why bother? This is a difficult question to answer in the abstract. Because the

standard instrumental variable approach uses more information than does the Bloom approach, we

might expect that parameter estimates based on the standard instrumental variable approach

would have the potential to be more efficient than would be parameter estimates based on the

Bloom approach. Monte Carlo testing shows that this is not necessarily true, however. 

How these two approaches compare depends partly on the size of the non-DAP sample relative to

the rest of the sample. The Bloom approach can yield relatively poor estimates when the

proportion of non-DAP cases is small, but the estimates improve as the proportion approaches 50

percent of the study sample. Also, the instrumental variable estimates are poor when (1) the

instrument is not highly correlated with the dummy variable, or (2) the instrument is highly

collinear with other variables in the regression. Experience from limited Monte Carlo testing is not

much to go on, but we note two facts. The first is that the non-DAP sample is only about 20

percent of the available data. The second is that the instrument is fairly strong, partly because the

non-DAP subjects ensure a zero value for the instrumental variable, and partly because the

probability of entering treatment varies systematically across the 20 DAP facilities and increases

over time. We return to these issues later. 64

The Heckman selection bias approach (Heckman, 1979; Maddala, 1983) is somewhat more

difficult to apply than is the instrumental variable approach. It requires the analyst to jointly model

the selection into the sample and into the post-release outcome. We describe this approach at

length below. For now, note that the selection bias approach has much in common with the

standard instrumental variable approach, and if the analyst is willing to limit his or her analysis to
The Bureau of Prisons operates a number of facilities across the nation. Some are 65

maximum security, others are medium security, and still others are minimum security. An

inmate’s original placement depends on his or her security requirements, prison space availability,

and geography (so that offenders will be as near their homes as is practicable). Offenders often

begin their terms in high or medium security facilities and progress through less secure facilities

over time. They also transfer from facility to facility for a variety of other reasons, including the

availability of specialized health care and other programs. However, with a few exceptions,

inmates did not transfer from one facility to another to receive substance abuse treatment.

The Bureau developed a procedure to estimate the probability of receiving treatment based on an

inmate’s progression through the prison system. They assigned this probability—PROB in the

text—to every member of the study sample. Our problem was to use PROB to estimate the

probability of selection into the study sample for offenders at the time they were released from

prison. This would be a relatively simple matter if the Bureau could divide the number of DAP

comparison subjects who were selected for the study sample by the total number of DAP

comparison subjects who were released, but at the time of release, the Bureau could no longer

identify the population of DAP comparison subjects. This required us to infer the sampling

probability for a releasee cohort.

252

a linear-additive regression model, there is little to recommend the selection bias approach over

the instrumental variable approach. However, as explained by Maddala (1983, p.261), the

Heckman selection bias model can be used to study more complicated models where treatment

interacts with other variables. For that reason, we developed the selection bias model in this

appendix.

In addition to selection bias, another problem complicates this comparison between those who

were treated and those who were not treated. The Bureau sampled DAP comparison subjects, but

it did not know the probability that an inmate from a DAP facility who declined treatment would

be included in the sample. This is a serious problem because all three of the methods discussed

above require that this sampling probability be known. 65

In summary, the Bureau asked Abt Associates to help it develop statistical models for analyzing

treatment effectiveness in terms of the outcomes identified earlier. It asked Abt Associates to help

it deal with the problem of not knowing the sampling probability for DAP comparison subjects, to

develop model-based adjustments for selection bias that are suitable for the Bureau’s evaluation,

and to demonstrate the use of these models using the Bureau’s data. The Bureau provided those

data, specified the variables that had to be included in the modeling, and specified that the model

had to be linear-additive in those variables and their parameters. The reason for these restrictions

was to ensure that inferences based on the Abt Associates’ models could be compared readily

with inferences based on the models estimated by the Bureau. Two of the Bureau’s interests were

to estimate models that ignore the selection bias problem and to compare results with findings

from models that attempt to adjust for selection bias. Results from this comparison are presented

in the main report.

The analysis reported here is preliminary. It is based on 6 months of follow up data, almost

certainly too short of a period to judge the effectiveness of treatment. But the Bureau continues to

collect data, increasing both the size of its sample and the length of the follow-up period. Given

that future data will provide a much better basis for judging treatment effectiveness, and given
Zi 
 0

1TRi2Xi)1i

253

(8)

that future analysis of those data will afford the opportunity for a more rigorous analysis, this

appendix focuses on methodology rather than on substantive findings. The prospect of future

analysis is the reason why we have developed the selection bias model when, given the constraints

of a linear additive model imposed by the Bureau on this analysis, the instrumental variable model

would seem to do quite well.

This appendix provides a technical exegesis of the statistical models developed by Abt Associates.

There are four models:

  a lognormal survival model (Section 2.0).

  an exponential survival model (also Section 2.0).

  a probit model (Section 3.0).

  a two-limit tobit model (Section 4.0).

Each of these four models has an adjustment for selection bias. The appendix also explains how

Abt Associates dealt with the problem of estimating sampling probabilities for DAP comparison

cases. Finally, it provides computer output of the results from using the Bureau’s data to estimate

the four models, and a copy of the computing algorithm, as attachments.

Recidivism: Survival Models That Are Based on the Lognormal and Exponential Distributions

This section of the appendix develops two mathematical models of recidivism. One is based on a

lognormal survival model and the other is based on an exponential survival model. We discuss

both models (in section 2.1) because they raise similar analytic problems, which have similar

solutions. In section 2.2, we introduce a form of selection bias into both models and develop an

estimation procedure (maximum likelihood) that yields consistent parameter estimates of the

treatment effect, provided the model is true. Deriving those estimates requires a model of the

process by which subjects get into treatment, which we develop in section 2.3, and a procedure

for deriving sampling weights, which we develop in section 2.4.

The Basic Recidivism Model

Upon release from prison (including release from a half-way house), every offender has a

propensity to recidivate. Recidivism means either that the offender was rearrested or that illegal

drug use was detected. These two events are analyzed separately. The propensity to recidivate

can be expressed as a non-negative, increasing function of an underlying latent propensity score,

Z. This score is in turn assumed to be a linear function of a dummy variable (coded 1 when the

offender was treated and coded zero otherwise) and a vector of control variables. Thus, the

propensity score is written:

where:

Z a latent variable, measured on a continuous scale, so that within a specified time i

the probability of recidivism for the i individual decreases as Z increases. th

i
L1 
 N

i

1(Ti)

Ri

(1	0(6))1	Ri

254

(9)

TR a dummy variable coded 1 when the i offender was treated and coded 0 other- i

th

wise.

X a column vector of control variables such as age, gender, and race. i

 a scalar parameter — the constant term. 0

 a scalar parameter — the treatment effect. 1

 a row vector of parameters associated with the control variables. 2

 a random error term, identically and independently distributed as standard normal 1i

across the sample of offenders. We use  as an error term in other equations, so

the superscript “1” is introduced to distinguish error terms across equations.

) A scalar parameter. Alternatively, we might drop ) from (8) and assume that  is

distributed as normal with a mean of zero and variance of ) , but the derivations 2

are simplified by using this first specification.

We eventually adopt two different assumption about how the latent variable Z affects the

distribution of time until recidivism, but it is useful first to define the density and distribution

functions for time until recidivism generically, and then substitute parametric distribution

functions to get the lognormal and exponential models. Let:

t represent time until recidivism. i

1(t ) represent the density function for time until recidivism. i

0(t ) represent the cumulative distribution function for time until recidivism. i

The follow up period lasts 6 months. If recidivism occurs within 6 months, then we observe the

time at which it occurred. Otherwise we observe that recidivism did not occur within those 6

months. The generic likelihood function for recidivism during the first 6 months is written:

where:

L is the generic likelihood function for a survival model with censoring at 6 months. 1

T is the time (in months) until recidivism for the i subject when recidivism is i

th

observed.

R is coded 1 when recidivism happens within the 6-month follow up period and is i

coded 0 otherwise.
1A(t

Ai

) 
 e

	0.5

(ln(t

Ai

)	0	1TRi

	2Xi

)

2

)2

t

Ai

2%)2

i 
 e

Zi

1U(t

Ui

) 
 P





	

ie

	i

t

Ui

(1)d1

The inspection is based on the Allison model, discussed in the main text, which treats 66

each month’s events as independently distributed but censored by occurrence of the first event. 

The Bureau estimated this model.

255

(10)

(11)

(12)

This generic likelihood function is standard for survival models (Kalbfleisch and Prentice, 1980;

Lancaster, 1990). It is readily changed into the likelihood for the lognormal survival model by

substituting the lognormal density and distribution functions into the generic form, and, likewise,

it is transformed into a variation of the exponential survival model by substituting density and

distribution functions based on a modification of the exponential distribution. We take those steps

below.

Based on inspection of the data, it appears reasonable to assume that the time until an arrest

follows a lognormal distribution. In this case, ln(t ) = Z , and the density function for time until 66

i i

an arrest is written:

where:

1 (t ) represents the lognormal density function for the distribution of time until arrest. A Ai

t time of arrest. Ai

Substituting the lognormal density (10) and its distribution function into the generic likelihood

function (9) yields the likelihood function for the lognormal survival model.

Based on preliminary analysis, time until a positive urine screen seems to follow an exponential

distribution. The propensity to recidivate (8) is now written in the form:

Unlike the usual exponential model, this specification has an error term 1 that must be taken into

account in the analysis (see Trussel and Richards, 1985). This introduction of an error term is a

convenient and realistic way to introduce selection bias into the model, although it does complicate the mathematics behind the development of the survival model. Thus, the density function for

the time until recidivism is now written as the integral of a mixture distribution:
Yi 
 01Xi

2i

The models developed here are sometimes called mixture models (Lancaster, 1990), and 67

the () is sometimes called the mixture distribution. Estimates of the parameters in the

distribution of greatest interest to us (e.g., the exponential) are sensitive to the assumptions made

about the mixture distribution (Yamaguchi, 1986). A literature on criminal careers (Spelman,

1994) reports that offense rates have a skewed distribution across offenders, and this finding

might be extended to assume that time until recidivism will be similarly skewed, so that the error

distributions chosen for this analysis have some justification. Others (Schmidt and Witte, 1988;

Rhodes, 1989) have found the lognormal to be a useful distribution for explaining recidivism. 

Nevertheless, future analyses will test the sensitivity of results to alternative assumptions made

about the mixture distribution. For example, by using a power transformations (such as the BoxCox power transformation), the distribution () can be extremely flexible.

256

(13)

where:

1 (t ) represents the density function for the distribution of time until drug use was U Ui

detected.

t time until detection of drug use. Ui

( ) the standard normal density function. 1

The integration removes the unobserved 1 from the distribution. However, the presence of 

 1

67

will not be innocuous in discussions to follow. Equation (12) has no closed-form equivalent

expression and requires numerical integration. Of course, this is also true of its cumulative

distribution function, which requires a second integration over t from 0 to T . Ui i

Introducing Selection Bias

A problem occurs when subjects who receive treatment are selected on a non-random basis. This

may happen because subjects self-select for treatment or because treatment personnel are

selective, or both. To build selection bias into the lognormal and exponential models, we

introduce a second latent variable, the propensity to enter treatment:

Here:

Y a latent variable. The higher the value of Y, the more likely a person will enter i

treatment.

X a column vector of control variables, the same as defined earlier. i

 a scalar parameter. 0

 a row vector of parameters conformable with X. 1
1A(t

Ai

|TRi
1) 
 e

	0.5

(ln(t

Ai

)	0	1TRi

	2Xi

)

2

)2

i

t

Ai

2%)

2

i



01Xi'

ln(t

Ai

)	0	1TRi	2Xi

)i

1	'2

01Xi

1A(t

Ai

|TRi
0) 
 e

	0.5

(ln(tAi)	0	2Xi)

2

)2

i

t

Ai

2%)

2

i

c

01Xi'

ln(t

Ai

)	0	2Xi

)i

1	'2

c 01Xi

257

(14)

(15)

 a random error term that is distributed as standard normal. 2i

and:

when Y  0, then treatment occurs (TR=1), and

when Y < 0, then treatment does not occur (TR=0).

Unless 1 and 

 2 are statistically independent, the variable representing treatment (TR) will not be

independent of 1. It seems unlikely that the two will be independent, because they both are

affected by excluded variables, such as motivation to change behavior. This correlation will cause

the parameter estimate of the treatment effect (1) to be biased and inconsistent unless it is taken

into account in the analysis.

One approach to overcoming this problem is to assume a parametric form for the joint distribution

between  and  , and to take that joint distribution into account in the likelihood functions 1 2

(equation 9). Assuming that the two are distributed as bivariate normal, two cases are pertinent:

the first for time until an arrest and the second for time until detection of drug use. Considering

the first case (the lognormal distribution), the density function expressed previously as equation

(10) is correct only for those cases that come from non-DAP facilities. For people who receive

treatment, we use the conditional density function as represented by equation (7) in place of (10).

and for people who do not enter treatment and were members of the DAP comparison group, we

use the conditional density function represented by (8) in place of (10).

where:

 the standard normal cumulative distribution function.

 the complement of the standard normal cumulative distribution function. c

' the correlation between  and  . 1 2
1U(t

Ui

|TRi
1) 
 P





	

ie

	i

t

Ui

(1|TRi
1)d1

1U(t

Ui

|TRi
0) 
 P





	

ie

	i

t

Ui

(1|TRi
0)d1

258

(16)

(17)

The conditional density functions (7) and (8) have cumulative distribution counterparts, which

must also be substituted into (9). We do not show those distribution functions because they are

just the appropriate specification of the bivariate normal cdf divided by the unconditional

probability that the subject was treated (7) or was not treated (8).

The general approach to deriving this likelihood is explained in Maddala (1983, p. 266). Briefly,

we start with the bivariate normal density involving 1 and 

 2. This can be written as (1 21 )( |

). We integrate this over the appropriate range for 2  to get the joint probability of t and entering A

treatment (equation (7)) or not entering treatment (equation (15)). We divide the results by the

unconditional probability of entering treatment (equation (7)) or not entering treatment (equation

(15)).

In essence, then, the likelihood functions differ depending on whether the subject came from a

non-DAP facility, from a DAP facility but did not enter treatment, or from a DAP facility and

entered treatment. Nevertheless, the generic likelihood (9) holds; we just substitute the correct

density and distribution function depending on whether the subject is a member of the non-DAP

control group, the DAP comparison group, or the DAP treatment group.

The generic likelihood function also has to be modified when the exponential model is used. When

a subject comes from a non-DAP facility, equation (12) represents the density function. When the

subject comes from a DAP facility and receives treatment, we use (16) in place of (12)

and when the subject comes from a DAP facility but does not receive treatment then we use (17)

in place of (12)

where:

( |TR =1) is the normal density function conditional on TR = 1. 1 i i

( |TR =0) is the normal density function conditional on TR = 0. 1 i i

and numerical integration is used to get these conditional distributions, because there is no closedform equivalent expression. The density function for the error terms in (9) and (10) conditional on

TR can be written:
(1|TRi


1) 


P



2
	0	1X1

b(1,2,')d2

P



2
	0	1Xi

(2)d2




 01X1'1

(1	'2)

(01Xi

) (1)

259

(18)

(19)

where:

 represents the density function for the bivariate normal (standard normal in this b

case), and

' represents the correlation between  and  . 1 2

and a similar expression exists for (1 i |TR =0). As before, the density functions have cumulative

distribution (over t ) function counterparts. These must be numerically computed with a double U

integral and substituted, as appropriate, into (9).

The likelihood function is different depending on whether the subject came from the non-DAP

control group, the DAP comparison group, or the DAP treatment group. The generic likelihood

(9) holds; we substitute the correct density and distribution function depending on whether the

subject is a member of the non-DAP control group, the DAP comparison group, or the DAP

treatment group.

Estimating the Probability of Selection Into Treatment

Applying the adjustment described above for selection bias requires an estimate of . Although

the  and  parameters could be estimated jointly, it is easier (although less efficient) to estimate

the  parameters from the probit model (equation (13)) and then maximize the likelihood

expression (equation 9, after the appropriate substitutions) conditional on those estimates of .

Estimation of the probit model was not straightforward. Because the Bureau sampled the DAP

comparison cases, we had to take that sampling into account by including the probability of being

sampled as part of the likelihood function for the probit model. Thus, the probit model needs to be

based on the joint probability of two events: entering treatment or not entering treatment, and

being selected into the study sample. DAP treatment cases were selected with certainty, so they

have a conditional selection probability equal to one, and non-DAP cases do not enter into this

estimation, because those cases have a zero probability of entering treatment. The likelihood for

this model is written:
L2 
 N

i

(01Xi)

TRi PSi 1	(01Xi

) 1	TRi

(01Xi)(PSi(1	(01Xi))

 The solution presented in this section is provisional. We continue to work with the 68

Bureau to develop better ways to estimate the implied sampling weights.

260

(20)

where:

PS is the probability of selection into the study sample for the i case. When the i

th

subject received treatment, the probability is 1, because all treated subjects were

included in the sample.

The logic of this approach is that the probit model represents the probability of occurrence of two

events. In the first event, a subject either is selected for treatment or he is not selected for

treatment. The second event — being included in the sample — is then conditional on the

outcome of the first event. If the subject entered treatment, then he was included in the sample,

but if he did not enter treatment, he was included in the sample with a probability of PS . The i

likelihood function reflect the joint probability of those two events.

Determining PS, the Probability of Selection Into the Study Sample68

Estimation of the likelihood function (20) requires knowing PS , the probability of being selected i

into the study sample given that an offender was from a DAP facility and either entered treatment

or did not enter treatment. As we commented in the introduction to this appendix, the Bureau did

not record this probability. In fact, if would be difficult to do so, because offenders move from

prison to prison, so the DAP comparison group comprises inmates who spent at least some time

in a DAP facility, but did not necessarily spend their entire incarceration periods in a DAP facility.

We sought to identify a justifiable way of determining the probability of estimating PS .i

The key to this estimation was to use an estimate of the proportion of inmates who entered

treatment given (1) the DAP facilities where an inmate was incarcerated during his imprisonment,

(2) how long he spent in each of those facilities, and (3) and the proportion of persons who spent

time in those facilities who entered treatment. The Bureau estimated that probability PROB using i

procedures described in the main report. Given PROB , we can estimate the implied value of PS i i

that makes the proportion of DAP comparison subjects and DAP treatment subject observed in

the data consistent with PROB.

Define:

PROB The probability that the i subject enters treatment, as estimated by BOP. The i

th

probability of being selected into the study sample is 1 conditional on entering

treatment. Thus, the joint probability of entering treatment and being selected

into the study sample is PROB .i
PSi 
 1

1e

M

J

j
1

!1j

ECOHOijM

K

k
1

!2kINSTik!3Xi

L3 
 N

i

PSi (1	PROBi) 1	SiPROBi

S

PSi (1	PROBi) PROBi

261

(21)

(22)

PS (1-PROB ) The probability that the i subject at a DAP facility does not enter treat- i i

th

ment (1-PROB ) times the conditional probability that a subject who does i

not enter treatment is selected for the study sample — PS . PS needs to i i

be estimated.

We assume that PS is a function of the subject’s release cohort (that is, that PS varies over time), i i

the prison from which the subject was recruited (that is, that PS varies across facilities), and some i

other variables. We write the relationship between PS and the independent variables as: i

where:

ECOHO is a dummy variable representing the i subject’s release in cohort (j=1...J). ij

th

Names of variables, as they appear in this appendix, were chosen by BOP

and correspond to usage in the main report.

INST is a dummy variable representing the k institution. ik

th

! parameters to be estimated.

The likelihood function for estimating the ! is:

where:

L The likelihood. 3

S is a dummy variable coded 1 when the i subject enters treatment and coded zero i

th

when he does not enter treatment.

The reader might note that, for purposes of the analysis discussed in this appendix, the estimation

of PS is unnecessary. We could have estimated a linear probability model using PROB as the

dependent variable and X as the independent variables and then developed the likelihood function

(13) based on this linear probability model instead of the probit model. The reason for taking the

extra step of estimating PS is that the Bureau needed the sampling probability, PS, to apply the

Bloom solution discussed earlier. Although we will not discuss findings in detail, we did in fact

estimate a model using this simpler approach, and found that it provided similar results to those

based on the more complicated approach.
Zi 
 01TRi

2Xi3i

L41 
 N

inonDAP CONTROL

(02Xi)

hi 1	(02Xi

) 1	hi

L42 
 N

iDAP COMPARISON

(02Xi|TRi
0)hi 1	(02Xi|TRi
0) 1	hi

L43 
 N

iDAP TREATED

(01TRi2Xi|TRi
1)hi1	(01TRi2Xi|TRi
1) 1	hi

262

(23)

(24)

(25)

(26)

To summarize, the estimation requires three steps. In the first step, we estimate the sampling

weights for the DAP comparison group members. Using those sampling weights, in the second

step we estimate the probability that an offender housed in a DAP facility will be treated.

Conditional on that probability, in the third step we estimate the treatment effect.

A Probit Model of Halfway House Failures

The Bureau chose to analyze failures in halfway house assignments as a dichotomous dependent

variable — failure (coded 1) and success (coded 0). This decision suggested that a probit model

would be an appropriate way to analyze outcomes. As before, we assume that every individual

who is placed in a halfway house has a propensity to fail, expressed as a latent variable:

We have reused notation from above because there seems to be little risk of confusion. The Z

again represents the latent variable, but now it applies to the propensity to fail in a halfway house

confinement.

An inmate fails when:

Z i  0

and he or she succeeds when:

Z < 0 i

Assuming that 3 and 

 2 are distributed as bivariate normal, the likelihood function for estimating

the  can be written as:

for the non-DAP control group, as:

for the DAP comparison group, and as:
(01TRi

2Xi|TRi
1) 
 b(01TRi

2Xi,01Xi,')

(01Xi)

L4 
 L41L42L43

Zi 
 01TRi2Xi4i

263

(27)

(29)

for the DAP treatment group, where:

h equals 1 when the subject failed and equals 0 otherwise. i

( + X |TR =0) represents the probability of failing in a halfway house conditional on the i 0 2i i

th

subject’s not being treated and (0 1 i 2i i + TR+ X |TR =1) represents the probability of failure

conditional on the i subject’s being treated: th

where:

 is the bivariate normal distribution function (standard normal in this case). b

and a similar expression exists for ( + X |TR =0). 0 2i i

The likelihood function is then written:

A Two-Limit Tobit Model of Employment

The Bureau chose to measure post-release employment as the percentage of time employed

during the 6-month follow up period. This could range from 0 percent for those who were never

employed to 100 percent for those who were always employed. Both extremes were observed in

the data.

Although an ordinary least squares regression might be used to analyze this outcome, OLS

regression suffers from three problems when applied in this context. The first problem is that

parameter estimates will be biased and inconsistent because the outcomes have upper and lower

limits, which are not taken into account by the estimation procedure. The second problem is that

the standard errors will be inconsistent, because the error terms will necessarily be

heteroscedastic. The third problem is that selection bias still needs to be taken into account.

Although feasible generalized least squares can be used to deal with all these problems, an

alternative approach is to use a two-limit tobit model (Maddala, p. 160).

As used here, this model assumes that the every offender has a propensity to be employed.

Reusing the earlier notation, we write this propensity as:

The subject is unemployed at all times when
L51 
 N

inonDAP CONTROL

 	0	2Xi

)

E1i 

TEi	2Xi

)

)

E2i

1	

100	0	2Xi

)

1	E1i

	E2i

L52 
 N

iDAP TREATED

 	0	1TRi

	2Xi

)

|TRi
1

E1i 

TEi	0	1TRi	2Xi

)

|TRi
1

)

E2i

1	

100	0	1TRi	2Xi

)

|TRi
1

1	E1i

	E2i

264

(30)

(31)

Z < 0, i

and he is employed full time when

Z > 100, i

and, otherwise, time employed (TE ) equals the latent variable, so: i

TE = Z when Z i i i  0 and Z i  100

The unknown parameters can be estimated by maximum likelihood. As before, we have to

account for three conditions. When the study subject comes from the non-DAP control group, the

likelihood is:

When the subject come from the DAP treatment group, the likelihood is:

and when the subject comes from the DAP comparison group, the likelihood is:
L53 
 N

iDAP COMPARISON

 







	0	2Xi

)

TRi
0)E1i



TEi	0	2Xi

)

|TRi


0

)

E2i

1	

100	0	2Xi

)

|TRi


0

1	E1i

	E2i

L5 
 L51L52L53

265

(32)

where:

E equals 1 when the subject was unemployed for the entire follow up period, and 1i

otherwise equals zero.

E equals 1 when the subject was employed during part (but not all) of the follow up 2i

period, and otherwise equals zero.

As before,  and  represent the conditional distribution and density function, respectively. The

conditional distribution function has already been presented as part of the probit model, and the

density is similar to that for the uncensored part of the lognormal model, except that the dependent variable is in natural rather than logarithmic units. Thus, we do not show them explicitly.

The likelihood function for the two-limit tobit model is written:

Summary

Parameter estimates are not provided in this appendix; they appear in the main report. Researchers

at the Bureau compared the parameter estimates from these selection bias models with estimates

derived from other models that do not take selection bias into account. Findings are discussed in

the main report.

We consider these estimates to be preliminary. The Bureau continues to collect data for more

offenders and for longer follow-up periods. When additional data become available, we will

estimate the models reported in this appendix using those data. For this round of estimation, we

have used models that have a structural form specified by the Bureau. We expect to experiment

with improvements to that structural form in future analyses. Additionally, the models developed

in this paper make some strong assumptions, especially about mixture distributions. Some of these
The derivations in this appendix rely heavily on assumptions about the joint distributions 69

between two error terms. The bivariate normal has played a large role in those assumptions.

Although it is difficult to substitute other distributional assumptions without that substitution

leading to major changes to these models, it is practical to introduce power transformation (such

as the Box-Cox power transformation) to provide more flexibility to the model. Given the

possible sensitivity of such models to distributional assumptions (Yamaguchi, 1986), this seems

like a prudent step to plan for future analysis.

266

assumptions can be relaxed, and we will seek to do this in future analyses. Finally, we continue 69

to search for better ways to weight the data.
267

GLOSSARY OF TERMS

BOP 

Federal Bureau of Prisons

CCC 

Community Corrections Center. These facilities also are referred to as halfway houses. Inmates

can be placed in a CCC either prior to the end of their sentences while under BOP custody or

while under supervision by a Probation officer. Although not applicable to the subjects in this

study, an individual may be placed in a CCC for his or her entire sentence.

DAP

Drug Abuse Program — the BOP Residential Drug Abuse Treatment Programs serving individuals with histories of drug or alcohol abuse.

DAP comparison

Subject group consisting of individuals who were housed at one or more DAP sites at times in

their incarcerations when they could have volunteered for and been accepted into a DAP. 

Non-DAP control

Subject group consisting of individuals who were never housed at a DAP facility or were housed

at such a facility only in the last few months of their incarcerations when it would have been too

late to volunteer for DAP. 

DAP complete

Subject group consisting of individuals who completed the in-prison residential DAP.

DAP incomplete

Subject group consisting of individuals who did not complete the in-prison residential DAP for the

following reasons: (1) they were released from BOP custody or released to a CCC before

completion, (2) they transferred to another institution before completion, or (3) they went out on

writ before completion.

DAP dropout

Subject group consisting of individuals who did not complete the in-prison residential DAP

because they voluntarily dropped out.

DAP disciplinary discharge

Subject group consisting of individuals who did not complete the in-prison residential DAP

because they were discharged for disciplinary reasons (i.e., did not adhere to program rules or 

were found guilty of committing a disciplinary infraction).

DSM-III-R

Diagnostic and Statistical Manual for Mental Disorders (American Psychiatric Association, 1987). 

FCI

Federal Correctional Institution. A BOP facility housing sentenced inmates.
268

FMC

Federal Medical Center. A BOP facility serving inmates with medical problems. 

INS

Immigration and Naturalization Service.

NCIC

National Crime Information Center. This is an automated database maintained by the Federal

Bureau of Investigation with information on Federal and State arrests. 

SENTRY

Automated BOP database with comprehensive information on currently and formerly incarcerated

inmates. This database holds large amounts of information, including background characteristics,

sentence length and conditions, program involvement, disciplinary infractions, institutional

transfers, and Community Corrections Center placements.

TRIAD 

Acronym for this drug treatment evaluation project. TRIAD stands for Treating Inmates’

Addiction to Drugs.

UA

Urinalysis. These tests occur in the halfway house and while under supervision. During a halfway

house placement, urinalysis testing may be completed by halfway house staff, as well as by

treatment providers for cases in which the individual is enrolled in transitional services. 

The standard drugs tested for when an individual is being supervised by a U.S. Probation officer

include cocaine metabolites, opiate metabolites, phencyclidine, amphetamines, barbiturates,

benzodiazepine, and methadone. As of February 1996, marijuana was added to the regular drug

screen test. Other drugs are tested upon special request. The initial screening levels used by

Probation officers were those approved by the U.S. Department of Health and Human Services. 

VCCLEA 

1994 Violent Crime Control and Law Enforcement Act. This law contains provisions for eligible

inmates who complete all phases of the BOP’s DAP to earn as much as a one-year reduction in

their statutory release dates.
269

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