iS

Technical Report 786 Cop

00 The Army Communications Objectives

in Measurement System (ACOMS):

'uon Survey Analysis Plan £

Gregory H. Gaertner

Westat, Inc.

Timothy W. Ellg D TIC

U.S. Army Research Institute %ELE CTE l 1

Editors JUN 2 3 1988 p 9U

I..-

a,,

mLEADERSHIp

U.S. Army Research Institute

for the Behavioral and Social Sciences

May 1988 '

A.'t Apipfroed feaori rlane; Sdist auinlite ...

- - - - - - - - -. 5 ,-
UNCLASSIFIED

SECURITY CLASSIfICATION OF THIS PAGE

REPORT DOCUMENTATION PAGE

la REPORT SECURITY CLASSIFICATION lb. RESTRICTIVE MARKINGS

Unclassified --

2a. SECURITY CLASSIFICATION AUTHORITY 3. DISTRIBUTION/ AVAILABILITY OF REPORT

2b. DECLASSIFICATION / DOWNGRADING SCHEDULE Approved for public release;

distribution unlimited.

4 PERFORMING ORGANIZATION REPORT NUMBER(S) S. MONITORING ORGANIZATION REPORT NUMBER(S)

945253 ARI Technical Report 786

Ua. NAME OF PERFORMING ORGANIZATION 6b OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION

(If applicable) U.S. Army Research Institute for the

Westat, Inc. Behavioral and Social Sciences

6C. ADDRESS (City. State, and ZIP Code) 7b. ADDRESS (City, State, and ZIP Code)

1650 Research Blvd. 5001 Eisenhower Avenue

Rockville, MD 20850 Alexandria, VA 22333-5600

8a. NAME OF FUNDING /SPONSORING 8b. OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER

ORGANIZATION (if applicable)

MDA903-85-C-0414

Sc ADDRESS (City, State, and ZIP Code) 10. SOURCE OF FUNDING NUMBERS

PROGRAM PROJECT TASK WORK UNIT

ELEMENT NO. NO. 2Q2b3 NO. ACCESSION NO.

-- _6.3.7.31.A 731A792 2.2.1 R.2

Ti. TiTLE (Include Security Classification)

The Army Communications Objectives Measurement System (ACOMS): Survey Analysis Plan

12. PERSONAL AUTHOR(S)

Gregory H. Gaertner (Westat) and Timothy W. Elig (ARI), editors

13a. TYPE OF REPORT 13b. TIME COVERED 1 4. DATE OF REPORT (Year, Month, Day) 1S. PAGE COUNT

Interim FROM 09/85 ro 04/87 1988 May 184

16 SUPPLEMENTARY NOTATION Timothy W. Elig, Contracting Officer's Representative. Contract work

was requested and funded by the Directorate of Program Analysis and Evaluation, U.S. Army

Recruiting Command, and the Office of the Deputy Chief of Staff for Personnel.

17 COSATI CODES 18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number)

FIELD GROUP SUB-GROUP Advertising Army National Guard Officer acquisition

05 09 -- Analysis Army Reserve Questionnaire

05 08 -- Army Modeling Recruiting (Continued)

19 ABSTRACT (Continue on reverse if necessary and identify by block number)

"--This is the second of two design reports that document the plans for the Army ConinminiCat ions Objectives Measurement Svstem (ACOMS). This report discusses the general plan to

analze yoith and parent survey data and specific plans v opic: tracking responses of

the youth alidience over time; segmentat ions of the youth market; difterentiat ion among

A\mv, Army component, and civilian career alteriiatives; parental inl Itince; aim modt, lint,

th' - !''ts of Army advertising.

llt I i i;t des ign repoi-Lt , \_:iv lCllilli) at i Linoils Objectiy,?.. . ';lslr 'lit .>_st_,, (A:OMS ) :

" t, D4 )' ig-i (AR I Techni l n t kkpol I /i85), d(l sclisse,. t {he, fIli ( o '" , ; i r 'll tlel 'clt., of t} ' .he W ,S

It r ".1I17p ins; and we i ;ht iIII_, fjlt,;t i 01111:1 i re's, ;li 1 ' 1l~ ' I ",. t ioi ,11d pl v '; i t.

Is ) pl ',t; t ti ilt. re.; lts ot tilt :ol'f l ;i't'te.t 'dollli'ute i tloit, tlit it.lrt ol Itlil I. tI ' ,

1 . ((I I i Ii I(

20. DiSTRIBUTION/AVAILABILITY OF ABSTRACT 21. ABSTRACT SECURITY CLASSIFICATION

L:JUNCLASSIFIED/UNLIMITED 0 SAME AS RPT. F OTIC USERS Unc hlss it ied

22a. NAME OF RESPONSIBLE INDIVIDUAL 22b. TELEPHONE (Include Area Code) 22c. OFFICE SYMBOL

Timothy W. Elig 202/274-5610 1 PEIRI -RG

DO FORM 1473, 84 MAR 83 APR edition may be used until exhausted. SECURITY CLASSIFICATION OF THIS PAGE

All other editions are obsolete. UNCLAS SI F I ED

%i L"O'N %
UNCLASSIFIED

SECURITY CLASSIFICATION OF THIS PAGE

ARI Technical Report 786

18. Subject Terms (Continued)

Reserve Officers' Training Corps

Statistics

Survey

Telephone survey

ACOMS

19. Abstract (Continued)

The ACOMS survey is a multiyear telephone survey of a nationally representative sample

of 16- to 24-year-old American youth and their parents. The survey tracks changes in

perceptions, attitudes, and behaviors relevant to Army advertising. Data are being collected continuously through the year, using computer-assisted telephone interviewing (CATI)

technology. Random digit dialing (RDD), involving a modified Waksberg method, is being

used to identify eligible respondents. The 30-minute interview asks youth about responses

to Army advertising, media habits, career plans, and various demographic characteristics " S

Parents of selected 16- to 20-year-old respondents, who meet certain eligibility require-7

ments, are also being interviewed on parallel topics. Survey data will be analyzed separately, as well as in conjunction with other data being collected by the ACOMS system.

Other ACOMS-related reports are identified as follows:

Technical Reports 784, 785, and 787

Research Report 1473

Research Products 88-04, 88-05, 88-06, 88-07, and 88-08

Research Notes 88-17 and 88-18

# /

UNCLASSIFIED S Y IO-

/ i .. URT C ASIIATONO TL.P G
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

WM. DARRYL HENDERSON

EDGAR M. JOHNSON COL, IN

Technical Director Commanding

Research accomplished under contract

for the Department of the Army Acccson For_

Westat, Inc. NTIS C ,

Tic TAB

Technical review by -. r :2, , 1e D

. ..... ... . ..... ".: ..... L .... .

Curtis L. Gilroy

M a r th a S . T e p l i t z k y --- ----

NOTICES -

s TR N a o bon toe " ute ae tha o 'I

and Sl Sc iences, A PER PO.00E

FINAL DISPOSITION. This report may be destroyed when it is no longer needed. Please do not

return it to the U.S. Army Research Institute for the Behavioral and Social Sciences.

NOTE: The findings in this report are not to be construed as an official Department of the Army

position, unless so designated by other authorized documents.

'Ax-A J -
Technical Report 786

The Army Communications Objectives

Measurement System (ACOMS):

Survey Analysis Plan

Gregory H. Gaertner

Westat, Inc.

Timothy W. Elig

Army Research Institute

Editors

Manpower and Personnel Policy Research Group

Curtis L. Gilroy, Chief

Manpower and Personnel Research Laboratory

Newell K. Eaton, Director

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

Office, Deputy Chief of Staff for Personnel

Department of the Army

May 1988

Army Project Number Manpower and Personnel

20263731A792

Approved for publh- release; distribution unlimited. -] I ,F

|S
FOREWORD

To effectively recruit manpower, the U.S. Army uses advertisements to

affect the knowledge, attitudes, and behavioral intentions of youth and such

significant influencers as parents. Army advertising development and execution is guided by a positioning statement and by specific, measurable objectives. This report documents the research plan for the main survey

conducted to measure the achievement of these objectives under the Army

Communications Objectives Measurement System (ACOMS), which supports Army

assessments of advertising program strategies and effectiveness and also

supports both planning for future strategy and increasing the operationai

efficiency of Army advertising programs.

ACOMS has been developed to meet the needs of Army policymakers and

operational managers through a cooperative effort with a Special Advisory

Group (SAG) of representatives from the staffs of the Office of the Deputy

Chief of Stafl for Personnel, the U.S. Army Recruiting Command, the U.S. Army

Reserve Officers' Training Corps Cadet Command, the Oftice of the Chief of the

Army Reserve, and the Army National Guard. Funding for the survey development

was provided by the U.S. Army Recruiting Command.

The participation of the U.S. Army Research Institute (ARI) in this

cooperative effort is part of an ongoing research program designed to enhance

the quality of Army personnel. This work is an essential part of the mission

of ARI's Manpower and Personnel Policy Research Group (MPPRG) to conduct research to improve the Army's capability to effectively and efficiently recruit

its personnel. Specific efforts on ACOMS were undertaken at the direction of

the Deputy Chief of Staff for Personnel. The survey design and plan of analysis were briefed to the SAG at quarterly in-progress reviews and also briefed

in January 1987 to the Deputy Chief of Staff for Personnel, the Commander of

the U.S. Army Recruiting Command, the Chief of the Army Reserve, and the

Director of the Army National Guard.

The ACOMS survey was conducted between October 1986 and December 1987.

Results of the survey effort are forthcoming.

EDGAR M. JOHNSON

Technical Director

v
ACKNOWLEDGMENTS

Army Special Advisory Group, staff officers, and special advisors:

LTC James Simon, LTC Terry White (ODCSPER) (Chair); COL Donald Borden,

COL Henry Brummett, Mr. Tom Evans, LTC Art Mark, LTC Ron Morsch, LTC Jesse

Brokenburr, Dr. Gerald Klopp, MAJ Rick Halek, CPT(P) Douglas McLiverty, CPT

John Perry, CPT Gary Pickens (USAREC); LTC John Anderson, LTC Al Resnick, MAJ

Thomas Loggie (USAROTCCC); LTC Rockwell, LTC Jesse Wall, MAJ Harry Simpson,

Mr. Lou Brodsky (OCAR); SGM Gene Wallace (ARNG); Dr. Zahava Doering, Dr.

Michael Laurence, Ms. Vonda Kiplinger (DMDC); Dr. Paul Gade, Dr. Curtis Gilroy

(ARI).

Statistical Sampling Advisory Panel:

Graham Kalton, University of Michigan; Joseph Sedransk, University of

Iowa; Raj Singh, U.S. Bureau of Census.

ARI project staff:

Timothy Elig and Allyn Herzbach.

Westat project staff:

Veronica F. Nieva, Gregory H. Gaertner, Michael D. Rhoads, Joseph

Waksberg, Michael J. Wilson, Bruce F. Allen, Sandra Baxter, Jansen B. Davis,

Richard C. Dulaney, Susan M. Englehart, Nancy L. Gay, James B. Greenlees,

Edward Hoke, Catherine C. Howard, Jan Hull, Linda J. Keil, Dori Driesberg,

David Lincoln, Robin McEntire, Leyla Mohadjer, Karen Molloy, Carin N. Rauch, t

Steve Roey, Jeannie Whalen, Data Preparation Staff, Secretarial Staff,

Telephone Center Management and Interviewers.

I

'

vi3
PREFACE

This paper reports on the planned analysis of data collected for Project

Image Watch-Dog, "Army Communications Objectives Measurement System (ACOMS)"

and addresses the market for the Army personnel accessioning system responsible each year for obtaining from the non-prior-service youth market over

200,000 volunteers for the enlisted and warrant officer force. In addition,

the U.S. Army Reserve Officers' Training Corps (ROTC) Cadet Command is responsible for attracting over 37,000 high-quality youth as college freshmen at

4-year colleges. To effectively recruit in the youti market, various components of the U.S. Army use advertisements to produce changes in the knowledge,

attitudes, and behavioral intentions of youth and such significant influencers

as peers and parents. ACOMS was designed to provide a measurement and analysis system to support army assessments of advertising program effectiveness,

assessments of advertising strategy efficiencies, management of the advertising program, and planning and development of new marketing strategies and

segmentations.

The planning for this research was initiated in 1984. ACOMS developed

out of work performed for a series of advertising effectiveness conferences

directed by the U.S. Army Recruiting Command (USAREC) at the request of the

Deputy Chief of Staff for Personnel (DCSPER), who met with the Commander of

USAREC, the Chief of the Army Reserve, the Director of the Army National

Guard, and the Deputy Chief of Staff of Training and Doctrine Command for ROTC

in November of 1984 to review the results of these conferences. These officers approved the mission requirements for ACOMS prepared by their staffs as

well as the basic research plan for ACOMS prepared by the U.S. Army Research

Institute (ARI). The DCSPER directed ARI to develop and monitor research

plans and necessary contract efforts for ACOMS with guidance from a Special

Advisory Group (SAG) from involved Army offices. The Defense Manpower Data

Center was added in a special technical advisory capacity before the first

meeting of the SAG.

The SAG was intimately involved in refining the mission requirements for

ACOMS throughout the procurement process that led to the selection by ARI in

1985 of Westat, Inc., as the ACOMS contractor. Scientists from Westat and the

Army community, together with many advisors, developed and refined The Army

Communications Objectives Measurement System (ACOMS): Survey Design (ARI

Technical Report 785) and The Army Communications Objectives Measurement

System (ACOMS): Survey Analysis Plan (ARI Technical Report 786). In addition

to guidance from the SAG, plans for ACOMS benefited from advice concerning

sampling, weighting, and estimation from a Statistical Advisory Panel. The

report describes the efforts of many people on the Westat Project Team and in

the Army community.

The ACOMS system involves more than just surveys or other methods of data

collection. The analysis and reporting agenda for ACOMS were set by Army

users to ensure that, in addition to their own in-house analyses, research

prorbirt- woiild regulr1, flow to users and systematically address their needs.

Research products include quarterly reports, annual tabulation volumes, and

interim reports on interpretive analyses that are summarized in an annual

report.

vii
These reports are forthcoming for the period of ACOMS data collection,

October 1986 through December 1987.

The text of the current report is substantially unchanged since it was

released as Manpower and Personnel Policy Research Group Working Paper 87-2,

January 1987.

TIMOTHY W. ELIG

ARI Senior Scientist and

Contracting Officer's Representative

viii
THE ARMY COMMUNICATIONS OBJECTIVES MEASUREMENT SYSTEM (ACOMS) SURVEY

ANALYSIS PLAN

EXECUTIVE SUMMARY

Requirement:

To improve the efficiency and effectieness of Army advertising

communications.

Procedure:

Development of the ACOMS project design has been a collaborative enterprise involving the Westat Project Team, the Army Research Institute Contracting Officer's Representative, the ACOMS Special Advisory Group (SAG) composed

of representatives from the staffs of the Office of the Deputy Chief of Staff

for Personnel, the U.S. Army Recruiting Command, the U.S. Army Reserve Officers' Training Corps Cadet Command and the Office of the Chief of the Army

Reserve, and the Statistical Advisory Panel, a group of experts providing the

project with advice concerning sampling, weighting, and estimation. The design process began in September 1985 and continued until the start of survey

data collection in October 1986.

The ACOMS project design was developed through consideration of a number

of factors. Project issues and questions were formulated by a conceptual

model of advertising effectiveness, a modified Hierarchy of Effects model. A

review of background documents and interviews with Army personnel provided a

user orientation in defining the analytic issues being addressed by the system. This preliminary exploration revealed the diversity and complexity of

objectives that the project is expected to fulfill. Project 3bjectives were

broadened from assessing the effectiveness of Army communications objectives

to include analyses pertaining to Army advertising strategy and market

segmentation.

The ACOMS development effort has focused on specifying the design and

analysis plan for the survey component of ACOMS, by far the largest component

of the system. Because of the multiple constituencies interested in ACOMS,

the relative priorities among population groups--and hence, the sampling

design--were major design issues. Similarly, because of concerns for cost and

respondent burden, extensive discussion occurred on the relative priorities

among various question domains and on the allocation of specific questions to

particular subpopulations. In addition, the design efforts for both the

sample and the questionnaires were affected by requirement to maintain comparability (in certain key aspects) to the Youth Attitude Tracking Survey (YATS), which covers many of the same areas related to an enlistment

decision-making.

ix
Findings:

ACOMS was designed and implemented to meet Army objectives through a

three-pronged "Fit-Exposure-Change" approach involving a mix of data collection and analytic approaches. In this report, the design of change measures

is discussed. The measurement of change, based on an on-going telephone

survey of a probability sample of youth and their parents, is central to the

ACOMs project. Youth and parental samples were designed to collect information on a variety of topics including the youth's plans for the future, media

habits, and reactions to Armed Forces advertising.

Utilization of Findings:

ACOMS data collection was initiated according to plan in October 1986,

and the first product discussed in this plan of analysis was delivered in

February 1987. As planned, data is being used to track changes over time in

levels of advertising recall and subsequent effects on the knowledge, attitudes, intentions, and actions of youth and their parents. In addition, the

analysis of ACOMS data permits a better understanding of the lagged and cumulative effects of advertising and of the relationships between advertising and

other factors that influence the eventual enlistment decision.

Thus, the ACOMS effort is contributing to current development of behavioral and economic models of enlistment decision-making by the Army

Research Institute. As better models of the enlistment decision process are S.

developed, more effective marketing stra:egies can be applied to help the Army

attain its annual recruiting goals.

S"

A second set of goals involves the use of ACOMS data to assess the Army's %

advertising strategy. ACOMS is examining the extent to which the Army's intended messages are actually being received, and perceived by, their target

audiences. Advertising strategy is also being supported by the analysis of

ACOMS data to refine the definitions of the Army's major market segments.

Reactions to advertising, media habits, and other variables are being analyzed

for the major demographic segments of interest to the Army's recruiting categories, broken down by major regions. Data analysis is assisting in identifying and validating new segments defined in both attitudinal and demographic

terms. This information is important in determining the nature and extent of

the advertising to be directed at each segment.

ACOMS data is also being used to examine "brand differentiation"--i.e.,

comparison of image elements--at several levels: differentiation among the

Active Army, Reserve, National Guard, and ROTC attributes; differentiation

between the Army and other services' attributes; and differentiation between

the Army's position and distinctive advantages vis-a-vis civilian alternatives

(i.e., college and civilian employment). This information is holping the Army

make decisions on relative emphasis of various communications about attributes

and offers of Army compcnents.

x
pp

THE ARMY COM.MUNICATIONS OBJECTIVES MEASUREMENT SYSTEM (ACOMS): SURVEY

ANALYSIS PLAN

CONTENTS

Page

CHAPTER 1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . !

Gregory H. Gaertner

Overview .................................... 1

A Strategy for Assessing the Effects of

Army Advertising .......... .................. 3

Objectives and Messages ......... ................. 10

Messages and Advertisements ..... ............... 13

Advertisements and Audience Exposures .. .......... 13

Audiences and ACOMS ....... ................... 13

ACOMS Main Interview Data Sources ... ............. 16

The Youth and Parental Samples ..... ............... 16

The Overall Youth Sample ..... ................ 17

The Primary Male Sample and Primary

Female Sample ........ ..................... 17

The Parental-linked Sample ..... ............... is

The Longitudinal Sample ...... ................. i

The Secondary Male Sample and Secondary

Female Sample ........ ..................... i

The Hispanic Supplementary Sample ... ............ 19

The Youth Interview ........ .................... 19

The Parental Interview ....... ................... 28

Summary ........... .......................... 29

References ........... ......................... 30

CHAPTER 2. TRACKING AUDIENCE RESPONSE ......... ................. 33

Gregory H. Gaertner, Linda J. Keil, and Nancy L. Gay

Introduction and Overview ...... ................. 33

Methods of Tracking Audience Response .... .. ........... 34

Estimating Levels of Audience Response .... ......... 34

Scale Construction .......... ................... 34

Trend Analysis ........ ..................... 36

Panel Analysis ......... .. ..................... 36

Tracking Prospect Market Segments ... ............. 37

Module-By-Module Description of Analytic

Activities .......... ........................ 38

Intentions to Enlist ........ .................... 38

Scale Construction ....... ................... 39

Importance of Army and Component Attributes ........ 39

Scale Construction ....... ................... 41

Perceptions of Army and Component Attributes .... ........ 43

Scale Construction ....... ................... 45

Enlistment-Related Behaviors ..... ................ 51

Scale Construction ....... ................... 51
CONTENTS (Continued)

Page

CHAPTER 2. (Continued)

Recall of Advertisements ...... .................. .. 53

Specific Variables ...... ................... .. 53

Scale Construction ...... ................... .. 53

Knowledge of Offers ....... .................... .. 56

Scale Construction ...... ................... .. 57

Media Habits ......... ........................ .. 57

Specific Variables ...... ................... .. 57

Scale Construction ...... ................... .. 59

Scheduling and Deliverables ..... ................ .. 61

Quarterly Reports and Briefings .... .............. . 61

Executive Summary ....... .................... .. 61

Data Tables Section ...... ................... ... 64

Information Cards and Appendices ... ............ . 65

Annual Reports ........ ....................... .. 65

References ......... ......................... .. 66

CHAPTER 3. MARKET SEGMENTATION ANALYSES ..... ................ 67

Michael J Wilson

Introduction and Overview .... .............. . 67

Approach ........... .......................... 68

Analysis of Current Market Segmentation

Strategies ......... ........................ .. 69

Tabular Descriptions ...... .................. .. 69

Multivariate Description of Current Market

Segments ......... ......................... . 71

Assessing the Stability of Existing Market

Segmentations ........ ....................... .. 74

Development of New Market Segmentations .. .......... . 75

Identification of Natural Respondent Groupings ..... .. 76

Generalization of Clusters to Reserve Components . . .. 78

Assessment of Market Segment Stability . ......... 79

Year 2 Segmentation Analysis ..... ................ 79

References ......... ......................... .. 81

CHAPTER 4. BRAND DIFFERENTIATION ANALYSES ..... ............... 83

Michael J Wilson

Introduction and Overview ...... ................. 83

Approach .............. .......................... 84

Tabular and Correlational Descriptions ... ........... 85

Multivariate Analysis of Perceptions/Beliefs .... ........ 87

References ............ ......................... 94

1 1

xii
CONTENTS (Continued)

Page

CHAPTER 5. ANALYSIS OF PARENTAL INFLUENCE . ............ 9

Sandra J. Baxter

Introduction and Overview ..... ................. .. 95

Approach .......... .......................... . 96

Dyadic Analysis ....... ..................... .. 97

Parents as Targets of Army Advertising ..... ........... 9F

Knowledge and Attitudes of Parents and Youth .. ........ .. 99

Youth Career Preferences of Parents ... ............ .. 100

The Parental Influence Process ..... ............... . 101

References .......... ......................... . 104

CHAPTER 6. MODELING THE EFFECTS OF ARMY ADVERTISING ... .......... .. 107

Gregory H. Gaertner

Introduction and Overview ...... ................. .. 107

Approach .......... .......................... . 107

Analysis Steps (Year 1) ...... .................. .. 108

Univariate Results ........ ..................... .. 108

Intentions and Propensity ...... ................ .. 108

Perceptions/Importances and Main Message .. ........ 109

Behaviors ......... ........................ . 109

Recall of Advertisements ..... ................ .. 109

Knowledge of Offers ....... ................... ... 09

Media Habits ........ ...................... . .0

Bivariate Results .......... ..................... >0

Exposure/Media Habits and Recall ..... ............ 1

Recall and Knowledge ...... .................. . 11'

Exposure/Recall/Knowledge and

Importance/Perceptions ...... ................. .. 113

Perceptions/Importance and Intentions/

Behaviors ......... ....................... . 113

Intentions/Propensity and Behavior ... ........... .. 115

Multivariate Models of the Effects of Advertising ..... .. 115

Cognition of Army Communications Objectives ....... 116

Attitude Formation ....... ................... .. 118

Combined Models of Cognition and Attitude

Formation ......... ....................... .. 121

A Linked Model of Cognition, Attitude Formation,

and Conation/Behavior ...... ................. .. 123

A Path Analytic Model for the Effects of

Advertising ......... ....................... 125

Analysis Steps (Year 2) ...... .................. . 128

References........................................ 130

Referenc s . . . . .. . . .. .. . . . . . . .. .a .a .a . . 130
CONTENTS (Continued)

Page

CHAPTER 7. PLAN FOR THE INTEGRATED ANALYSIS OF FIT,

EXPOSURE, AND CHANGE ........ .................... .. 133

Gregory H. Gaertner and Sandra J. Baxter

Introduction ......... ........................ .. 133

Message Content Analysis ....... .................. .. 133

Sample .......... ......................... . 134

Sites .......... .......................... . 134

Stimulus Packages ........ .................... .. 134

Materials ......... ........................ . 137

Pilot Test ......... ....................... .. 137

Analysis ......... ........................ . 139

Schedule ......... ........................ .. 140

Exposure to Army Advertising ...... ................ .. 142

Plan for the Analysis of Fit, Exposure and Change ..... .. 146

Analysis ......... ........................ . 152

Summary and Conclusions ...... .................. .. 153

Reference .......... ......................... . 154

CHAPTER 8. WORK PRODUCTS, DELIVERABLES AND SCHEDULES . ......... . 155

Gregory H. Gaertner

References .......... ......................... . 158

BIBLIOGRAPHY ............ .............................. . 159

APPENDIX MESSAGE ANALYSIS QUESTIONNAIRE ..... ............... .. 161

LIST OF TABLES

Table 1. Allocation of perceptions to service and

career alternatives ....... ..................... .. 12

2. Module rotation pattern ...... ................... .. 22

3. Assignment of respondents to perception modules

by component (Primary Male Sample only); includes

Primary Male Analytic Sample (PMAS) and college

juniors and beyond) ....... ..................... . 24

4. Smallest detectable change over time assuming

allocation shown in Table 3 and response

proportion equals 50% ....... .................... .. 27

5. Messages directed to prospect markets as measured

by the perceptions of the Army attributes

questions .......... .......................... . 62

xiv
CONTENTS (Continued)

Page

LIST OF TABLES (Continued)

Table 6. Messages directed to prospect markets as addressed

by the knowledge of Army offers questions .. .......... . 63

7. Bivariate relations between modules in the

Hierarchy of Effects Model ....... .................. .. 112

LIST OF FIGURES

Figure 1. Expanded Fishbein-Azjen hierarchy of effects

model for ACOMS ............ ....................... 5

2. Enlistment decision sequences for differentially

motivated respondents .............................. 8

3. Integrated Fit-Exposure-Change analytic

framework .......... .......................... . 11

4. Table shell for Quarterly Report Data Table 1 ......... . 40

5. Table shell for Quarterly Report Data Tabie 2 ......... . 42

6. Table shell for Quarterly Report Data Table 3 ......... . 46

7. Table shell for Quarterly Report Data Table 4 ......... . 47

8. Table shell for Quarterly Report Data Table 5 ......... . 48

9. Table shell for Quarterly Report Data Table 6 ......... . 49

10. Table shell for Quarterly Report Data Table 7 ......... . 52

11. Table shell for Quarterly Report Data Table 8 ......... . 54

12. Table shell for Quarterly Report Data Table 9 ......... . 55

13. Table shell for Quarterly Report Data Table 10 ... ....... 58

14. Table shell for Quarterly Report Data Table 11. ... ....... 60

15. Hypothetical results of a correspondence analysis

on current market segments ..... ................. . 73

16. "Bostonian's map" of the United States .. ........... . 88

17. Hypothetical results of a multidimensional

scaling analysis of Perceptions/Beliefs .. ........... ... 90

xv
CONTENTS (Continued)

Page

LIST OF FIGURES (Continued)

Figure 18. Hypothetical results of a correspondence

analysis of Perceptions/Beliefs and three

career alternatives ....... .................... .. 92

19. Cognition of Army advertising ..... ............ . . 117

20. The attitude formation process .... ............... ... 119

21. Linked models of cognition and attitude formation ..... . 122

22. Linked cognition, attitude formation, and

conation/behavior models ....... .................. .. 124

23. List of Army ads by medium and sequence showing

those with similar content ..... ................. ... 135

24. Fourteen attributes of Army ads for Sort Board

cards, and shorthand labels ..... ................ .. 136

25. Privacy Act notification to be used on signs

and hand-out cards ........ ..................... .. 138

26. Schedule of activities for message analysis .. ........ . 141

27. Sample data form for characteristics of

listening audiences ....... .................... .. 144

28. Flow of activities to acquire and append exposure

data to the Army Communications Objectives

Measurement Survey (ACOMS) data base ... ............ . 145

29. Matrix 1: Attribute by site matrix

(14 attributes X 6 sites) involved in

computing message weights ...... ................. .. 148

30. Matrix 2: Attribute by advertisement matrix

(14 attributes X 15 advertisements) involved

in computing message weights ...... ................ .. 149

31. Matrix 3: Advertisement by Area of

Dominant Influence (ADI)/ethnicity matrix

(15 advertisements X 1284 ADIs) involved in

computing message weights ...... ................. .. 150

' xvi
CONTENTS (Continued)

Page

LIST OF FIGURES (Continued)

Figure 32. Matrix 4: Attribute by Area of Dominant

Influence (ADI)/ethnicity matrix

(14 attributes X 1284 ADIs) involved in

computing message weights (product of

matrices 2 and 3) ........ ..................... . 151

33. Initial schedule for data collection activities . ...... .. 157

xvii

~z ?~* • 
THE ARMY COMMUNICATIONS OBJECTIVES MEASUREMENT

SYSTEM (ACOMS): SURVEY ANALYSIS PLAN

1. INTRODUCTION

Gregory H. Gaertner

Overview

The Statement of Work for the Army Communications Objectives

Measurement System (ACOMS) specifies that one deliverable will be "a

Formal Plan for the Analysis." This analysis plan is intended to

"[tie] together all elements of data collection and directly (link]

them to how the data are to be analyzed... [The analysis plan] is

intended to be the basic reference guide for the operationalization of

ACOMS and to pull together the efforts of the various Tasks and

Subtasks" (Request for Proposals Amendment No. 3, p. 17a).

ACOMS is intended to operate as the cornerstone of the Army's

efforts to assess its advertising program, producing estimates of how

accurately advertising executions fit the Army's communication objectives, how much audience exposure advertisements and their embedded

messages obtain, and how much change in youth plans and perceptions

results from advertising. As such, the ACOMS data collection and

analysis system draws from multiple data sources: analysis of the

message content of Army advertisements, analyses of syndicated sources

of exposure data, and interviews with youth and selected parents.

The analysis plan is based on the Statement of Work, Westat's

original proposal and responses to questions, a series of design memos

produced by Westat staff and guidance supplied by U.S. Army Research

Institute (ARI) and the U.S. Army Recruiting Command (USAREC) and by

the ACOMS Special Advisory Group (SAG) during the design phases. Much

of the material to be presented here was outlined in a detailed briefing presented to the SAG on July 8, 1986.

The analysis plan has two major overarching goals: (a) to summarize the important design decisions made during the design phases, and

(b) to provide analytical roadmaps for the upcoming phases. We

discuss each of these purposes in turn.

Since the analysis plan follows an extensive design phase during

which ARI, the SAG and USAREC have worked collaboratively with Westat

to make sample and instrument design decisions, and set basic analysis

priorities, this document provides a useful occasion for summarizing

these basic design decisions. These decisions have involved defining

the ACOMS youth and parent interview samples and specifying criteria

for inclusion and exclusion, defining and implementing procedures for

collecting additional data apart from the primary ACOMS interviewing

frames, retaining comparability with existing research samples and

instruments such as the Youth Attitude Tracking Study (YATS II)

(Research Triangle Institute, 1985) and the New Recruit Survey, the

design of the interviews, reporting deliverables, priorities for

analysis and scheduling.

,J1
A second purpose of the analysis plan is to develop analytical

roadmaps for the upcoming phases. This will involve an overview of

the basic ACOMS conceptual model as presented in the proposal and

modified subsequently, and a review of how each of the major classes

of variables in the model relates to the ACOMS data sources and data

collection procedures. Further, we will review the basic analysis

priorities decided on by the SAG. Then for each of the priority analyses, separate chapters in the analysis plan can:

(1) Elaborate which variables/scales will be used in the

analysis;

(2) Indicate the first analytical steps to be taken;

(3) Indicate likely classes of results of the first steps and

what they would mean; and

(4) Indicate the further steps that might be taken based on

these original results.

The ACOMS Survey Analysis Plan begins with this first chapter

which provides an overview of the ACOMS conceptual model, data sources

and analytic deliverables and summarizes the basic design decisions

reached during the design phase.

Chapters 2 through 6 of The ACOMS Survey Analysis Plan discuss

the substantive chapters of the annual reports. Chapter 7 describes

the ACOMS integrated model while Chapter 8 outlines the ACOMS data

timeframe. The chapters can be summarized thus:

(1) Chapter 2 (Gaertner, Keil & Gay, 1988): Tracking Audience

Response includes discussion of scale construction, trending

analysis, and quarterly report table shells and narratives;

(2) Chapter 3 (Wilson, 1988): Market Segmentation Analyses

reviews various strategies for segmenting the target audience, analytical techniques for segmentation, and likely

segmentation classes and what they might imply;

(3) Chapter 4 (Wilson, 1988): Brand Differentiation Analyses 0

reviews strategies for identifying the images of the various

components and services and other civilian activities held

by youth and parental respondents and identifying dimensions

of knowledgeand attitude differentiation;

(4) Chapter 5 (Baxter, 1988): Analysis of the Parental Influence includes discussion of the analysis of the parental

influencer data, as describing both a cogniti.n,"_'.;.isioi,

process operating parallel to the youth process, and as one

which influences the youth process. The chapter also

includes discussion of the social influences data collected

in the main youth interviews;

2
(5) Chapter 6 (Gaertner, 1988): Modeling the Effects of Army

Advertising includes discussion of the integrated analysis

of the youth interview data across awareness, recall, perceptions, importance, intention and subsequent behavior,

outlining the basic correlational structure of the youth

cognition/decision process;

(6) Chapter 7 (Gaertner & Baxter, 1988): A Plan for the

Integrated Analysis of Fit, Exposure and Change describes

how data drawn from analyses of t e message content of Army

advertising and data from syndicated sourcez of estimates of

exposure to Army advertising can be linked to the main ACOMS

interview data to provide a comprehensive model of fit,

exposure and change. The chapter begins by describing the

methods and measures collected from the message content

analyses. In brief, these data are based on mall-intercept

interviews with a stratified, nonprobabilistic sample of

American youth. These youth were asked questions regarding

the content of active Army print and video advertisements in

terms of the extent to which the advertisements embodied the

Army's communications objectives. Data on exposure to Army

advertising can be drawn from various sources. The chapter

describes these and recommends the acquisition of syndicated

data from the Arbitron Information on Demand system, which 'S

can provide estimates of the rates of viewing during periods

in which Army advertising is aired, broken out by demographic groups. The chapter then proceeds to propose that

these two data sources be combined with the ACOMS main

interview data, and outlines a procedure by which this can

be accomplished. The result is the integrated model for the

analysis of ACOMS fit, change and exposure data; and

(7) Chapter 8 (Gaertner, 1988): Work Products, Deliverables,

and Schedules outlines a timeframe for the presentations and

analysis of ACOMS data.

A Strategy for Assessing the Effects of Army Advertising

The purposes of ACOMS are:

I

(1) To support Army assessments of advertising program

effectiveness in a timely fashion;

(2) To support Army assessments of advertising strategy in an

integrated framework; and "

(3) To support Army advertising management and planning for

future strategy.

This undertaking is ambitious in part because of the sheer size

and sophistication of the Army's communications program, and in part %

because of the diversity of analytic perspectives required. Assessment of program effectiveness requires that ACOMS operationalize the

objectives of the Army's advertising program, and track performance

3
against these objectives. Assessments and support of strategy require

analysis of the dynamics of the advertising influence process and how

Army advertisements target youth and influence populations, position

the Army and its component images and offers vis-a-vis other services

and civilian alternatives, and lead to subsequent enlistment-related

behaviors.

The assessment of effectiveness and strategy in an integrated

framework requires a conceptual model to organize the various results.

In our original ACOMS proposal we presented a conceptual model of the

advertising process based on a modification of the hierarchy of

effects approach. An expanded version of this model is displayed in

Figure 1.

Implicit in the model is the notion that (a) the consumer gains

awareness of a product through advertising messages, which results in

(b) beliefs about the product, in interaction with the consumer's

evaluation of his/her need for the product which in turn affect (c),

the respondent's attitude toward the product, and subsequent intentions and actions toward the product. Section (a) describes a cognitive process, part (b) a process of attitude formation, and part (c) a

conative process.

Two additional processes are posited by the model. The first, a

process of normative formation, is included in Fishbein and Azjen's

(1975) subsequent expansions of the hierarchy of effects model. In

it, prevalent social norms regarding the purchase as perceived by the

respondent (belief) and the respondent's motivation to comply

(evaluation) combine to affect overall perception of social norms

regarding purchase behavior, which in turn affect-purchase intentions.

This process is posited to be independent of the respondent's own

isolated judgment. A second process, parallel to the youth decision

process, is posited to be operating for the parent. Since the empirical assessment of the effects of influential others has not previously

been attempted in a hierarchy of effects framework, the causal paths

connecting characteristics of parents and youths are not specified in

detail.

This description of the consumer decision process has roots

ranging from the attention-interest-desire-action (AIDA) formula of

E.K. Strong in 1925, to more refined models in recent years, such as

those of Lavidge and Steiner (1961), McGuire (1969), and Fishbein and

Azjen (1975). Fishbein and Azjen (1975) and Aaker (1975) present

detailed summaries of these developments. Measures of all of the

foregoing classes of variables are provided in the ACOMS interviewing

frames. The modules from the interviews tapping the variables are

indicated in Figure 1.

In the current context, the hierarchy of effects model suggests

important measures for consideration in measuring the effects of Army

advertising. Respondent demographics are likely to include region,

race and ethnicity, and sex at a minimum. For life stage, variables

will include age, employment and marital status, income (or income of

household) and stage of education at a minimum. (These variables have

4
WCd

2 c

CA

* 0

(U

o M O

CL <F i [T

5~ 0
all been shown to be related to other consequent variables in the

model. For details, see Gade et al., 1984; Weltin et al., 1984; and

Bray et al., 1985). Finally, media habits and exposure are hypothesized as relatively independent of the particular executions of interest in Army advertising. These variables are postulated to affect

respondent awareness of the Army gained through exposure to Army

advertising. This level of knowledge is hypothesized to affect

respondent beliefs about advertising messages (i.e., attributes of the

Army). In Fishbein and Azjen's (1975) model as adapted here, these

beliefs take the form of agreement that a given attribute is offered

by the Army (or component, other service, college or civilian job).

These likelihoods are similar to expectancies in Vroom's model of job

satisfaction (Vroom, 1965; House, 1971), and the model has much in

common with the decision process explored in Johnson (1983). As we

have developed the attributes (see below) these beliefs tend to relate

to positive outcomes (for example, the likelihood that the Army offers

opportunities for job training, funding for college, challenge and

personal development, or patriotic service).

Corresponding to these beliefs are evaluations of the importance

of these attributes. As operationalized, attributes are rated by the

respondent to be of varying levels of importance. The model assumes

that these importance scores depend on a set of underlying needs which

can be assessed through a needs-based segmentation analysis, as may be

seen in Chapter 3 (Wilson, 1988).

The youth's attitudes toward enlistment are hypothesized to arise

as the products of beliefs about what the Army offers and the corresponding personal importance of those attributes. Thus attitudes will

be positive when the respondent believes that positively evaluated

outcomes relating to military service are likely, indifferent when

perceived outcomes are not seen as important, or important positive

outcomes seen as only moderately likely, and negative when negatively

evaluated outcomes are likely.

By implication, in the Fishbein and Azjen formulation beliefs

about attributes are specific to the referent (Army, component, other

service, etc.) while importances are specific to the respondent. In

this sense, attributes are postulated to inhere in the product. We

have modified this position a bit in operationalizing ACOMS, however.

Because enlistment represents a "purchase" where the product's

attributes cannot be tested in advance, and the benefits of which are

likely to be highly subjective, we have included personal referents in

the belief statements. So, for example, we ask the youth whether the

Army offers him or her each attribute.

These attitudes are hypothesized in turn to affect behavioral

intentions (including Army propensity), which in turn are expected to

affect behaviors. It is important to note that these actions may

include enlistment, but are also likely to include intermediate

actions like seeking information, asking for advice, seeing a

recruiter and so on. As the model suggests, these intermediate

actions can feed back on beliefs and evaluations.

6
We hypothesize that attitudes, intentions and actions will generally form consistent sets, but that the character of these sets will

depend on geodemographic and needs-based segments. One area for

research will be to discover whether and how effects from attitudes to

intentions to behaviors and back to attitudes will cumulate for

particular respondents and types of respondents. While the Fishbein

and Azjen and similar expectancy-based models have been criticized as

excessively rational, we make no assumptions of rationality. Rather,

the hypothesis that youth will act to maximize their expected utilities (in the form of importance-belief products) is one of several

testable hypotheses.

We have modified the model presented in the original proposal to

include two additional inputs to the decision process. Fishbein and

Azjen have argued that independent of their own decision processes

taken in isolation, consumers assess prevalent social beliefs regarding aspects of purchase behavior. These beliefs, as assessed by the

consumer, combined with the consumer's motivation to comply with them

lead to an effect of social norms, which is posited to operate

independent of the consumer's own utilities.

A second social influence on the enlistment decision can be

assessed more directly: the influence of the parent. The sampling

and data collection procedures of ACOMS have been designed to interview parents of a subset of ACOMS respondents on their exposure to

Army advertising, and beliefs and evaluations of Army attributes

parallel to the youth interviews. The parent is also asked about his

or her preferences for the target youth's future and how he or she has

attempted to influence the youth's planning.

As an example of the application of the model to a likely empirical case, consider the enlistment-decision process of a respondent for

whom getting funding for college is of primary importance. Prior to

engaging in search behavior, the youth's belief that military service

can provide funding for college may be negative or weak. As the time

to make college-related decisions approaches, he/she may engage in a

variety of tentative actions exploring how this funding can be

obtained. To the extent that the respondent comes to believe that the

Army College Fund (ACF) or the new GI Bill can provide such funding,

his/her attitude toward the service may improve, leading to further

intentions and actions to seek out a recruiter or guidance counselor

to discover the amounts and limitations on such funding. This action

may refine or confirm initial beliefs, leading to additional actions

and so on. This step sequence is cepicted in Figure 2, top panel.

Not displayed in the figure is the possibility that the youth may also

be assessing the experiences and beliefs of his or her classmates, or

seeking (or receiving unsought) the advice of his/her parents, all of

which may affect the search and decision process. The bottom panel

depicts a more truncated search process for a respondent to whom

patriotism is of primary importance. To the extent that the respondent believes that service in the military represents an expression of

patriotism, this resultant positive attitude might lead, fairly

directly, to a recruiter visit and a subsequent enlistment when

7

WRY= 4 4-~ 4.F
Decision Sequence for College-Funding Motivated Respondent

&lj that sevice provides college funding possibilities

is lo re

AMd towird enlistmntn Intenion lto sftk A~nn Seeking m r . information is - out gudance couselr

Imprtnc of college .

funding is i

stage..2: 4

BI that swc

pmvides college

funding possabilies

AZWW toward enlismnnt bum=j to seek A~gflto visit we more m= ~ infomation is - ' wu

Xmaaa of cofege funding reuains hi~b

Decision Sequence for Patriotism-Motivated Respondent

&&f t miblay

service is an

expression of

pamoun

~favonhie_____ Intention Action of

to m"diWY Service to enil entisunent

Im of saving

one's councy

Figure 2. Enlistment decision sequences for

differentially motivated respondents.

8,%
enlistment becomes a practical possibility. Again, to greater and

lesser extents, parental and social influence may have effects. .

Some implications for the current research bear mention. First,

there are obviously features of "readiness" associated with life stage

that must be exogenous to the decision process. That is, there are

circumstances (e.g., need for college funding in the first example,

and ability to make significant life choices in the second) which

allow or impel the youth to make decisions for which information was

already available. Second, the two groups of respondents depicted in

Figure 2 are likely to come from different demographic and motivational backgrounds. Third, advertising is likely to affect them very

differently. For the respondent seeking college funding, an effective

advertising strategy may seek to educate him/her on potential Army

benefits, to encourage small subsequent action steps and to weigh

enlistment as a viable choice among alternatives. For the respondent

seeking to express his/her patriotism, the task of advertising is

likely to be more inspirational. Obviously, the task of advertising

for other segments (for example, influencers) is still different.

As a result of the Army's careful planning process, the objectives of Army communications programs can be translated rather

directly into hypotheses about this model and changes in it resulting

from advertising-based interventions. Further, these hypothesized

effects may take place at a variety of points in the model. For

example, one message of USAREC advertising is that "with Army College 4

Fund you can go to college 'older, wiser and richer.'" This message,

if effective, will raise the subjective probability (either generally

or among those especially interested in college funding) that the Army

offers the opportunity to gain funding for college (a belief). If the

basic model is correct, this heightened belief will, for those who

attach a positive importance to obtaining col -ge funding, result in

improved attitudes toward the enlistment, and an intention to seek

more information about service in it. This intention may result in

subsequent action, leading to a sequence of additional actions. To

pursue our other earlier example, the motivational message that the

Army is "the best way to serve your country" may have the aim of reinforcing the belief in the Army as an expression of patriotism or of

heightening the importance of patriotism either generally or for a

particular segment.

These are, however, only the most obvious effects of advertising,

and only the most obvious applications of the model. An additional

effect of advertising may be to decouple attributes from the Army, for

example, by dispelling the belief that one loses out on career

opportunities by choosing the Army. If this advertising is effective,

it would not be by changing the importance of career concerns, but

rather by reducing the presumed association between military service

and career disadvantage. In fact, this is one of USAREC's communications objectives. A further effect of advertising might not be on

levels of variables in the model but rather on relationships between

them. An example might be to strengthen the link between intention

and action by making available better methods for gaining information

9
on the Army, allowing curious respondents to search for information

more easily.

In terms of the original Statement of Work, the conceptual model

described bears mainly on the analysis of change--given advertising

messages and exposure to them, the model examines how advertising contributes to the enlistment decision process. However, the task of

ACOMS is broader than this, seeking to assess the nature of messages

embodied in advertising executions and further seeking to assess the

exposure of prospect populations to the various Army messages in the

advertisements.

Taken as a whole, the ACOMS Fit-Exposure-Change analysis involves

the sequential meshing of many conceptual elements: messages with

advertisements, advertisements with audience exposures, audience exposures with the ACOMS measures, and the ACOMS measures with all of the

above. Each of the elements represents an array (of objectives,

messages, etc.), and the meshing of any pair of them can be conceptualized as assessing the degree of correspondence between these two

arrays. The various arrays involved in an integrated Fit-ExposureChange assessment are displayed in Figure 3. In fact, the situation

is, if anything, more complex than this, since different service components, active Army, Army Reserve (USAR), Army National Guard (ARNG)

and Army Reserve Officers' Training Corps (ROTC), will represent

different matrix sequences.

In assessi-ag the correspondence among these arrays, a variety of

different data sources will be drawn upon. In the next sections we

delineate the elements and data sources involved in the overall

assessment.

Objectives and Messages

As a result of the Army's careful planning process, we began with

a good sense of the correspondence between objectives and messages.

This correspondence was enhanced through the Multiattribute Utility

Assessment analyses conducted with total Army, active Army, National

Guard and ROTC, and in consultation with the Contracting Officer's

Representative (COR) and SAG.

These activities resulted in a matrix of communications

objectives/messages per component, displayed in Table I. The matrix

arrays 17 attributes by three Army service components--active Army,

U.S. Air Force and Army Reserve National Guard. ROTC is handled

separately, since many of the attributes do not easily apply to it.

The attribute descriptions in the stubs of the rows are presented

as they are asked in the ACOMS main interviews and will also be used

in the message analyses proposed for ACOMS (see Chapter 7, Gaertner &

Baxter, 1988). An entry of 'X' indicates that the attribute in that

row is a communication objective for the component identified in the

column. Thus, while there are 17 attributes, no component is associated with more than 14 of them. There are 10 attributes common to all

the components (except ROTC).

10
II

Apwwt s ~ " s I I I I

A s c I I I f I z

101

Aw Lls ms All .
x x 0 . x 

- a

ccI

24x x A 3- 1 ), x x 31 M M M - -1 

-

X X - -

xJX *9 w x x w x w 24 M x 3C -

40-

34 x x 31 x x x x w M x w 314

x W m xx 3K IK 4 x K W

aa

to

0 3 30 -3. 0 a a. o 7 c1:

-6 a 6 a- .

a ~ ~ a t. UUS 0. o S. z~I IU -O PO ,= -1 C -

-12a - Oa S
We do not claim that this listing is inclusive of all the communications objectives of all of the components, since considerations

of interview length and respondent burden required us to severely

limit the number of objectives associated with any component. These

attributes do represent major current communications objectives of the

Army.

Messages and Advertisements

A second level of correspondence addresses whether the messages

are being effectively portrayed in advertising executions. Although

this correspondence usually is seen as an issue in expert judgment, we

intend to supplement this expert judgment with message analyses of

Army advertising executions as described in Chapter 7 (Gaertner &

Baxter, 1988).

Advertisements and Audience Exposures

Several sources of data are available and required to assess the

correspondence between the advertisements and the degree of exposure

they receive, geographically, chronologically and in terms of key

market segments. These include:

(1) Syndicated data showing frequency and reach obtained in

various geographic and market segments for TV and print

executions;

(2) Leads taken from the Rapid Electronic Advertising Coupon

Transmission (REACT) data base can also be used as measures

of exposure to Army print advertising as direct responses at

the county level; and

(3) Advertising budget at the national and local levels will be

utilized to assess relative levels of effort directed at

particular executions and areas of dominant interest.

As with the products of step 2, these exposure data can be compared with the intended messages to determine achievement of exposure

goals for each communications objective. The most important uses of

ACOMS, however, emerge in the next steps.

Audiences and ACOMS

The fourth step of the integrated Fit-Exposure-Change analysis is

to chart the effects of exposure on prospect audience segments in

terms of the ACOMS measures. In the original proposal, we presented

the major analytic components of ACOMS: cross-sectional, trend,

longitudinal and dyadic analyses. The inclusion of the results of the

previous analytical steps heightens the scope and power of ACOMS

analyses by extending the secondary and modeling capabilities of the

analysis.

13 '. '
q

As examples:

(1) By comparing the results of step 1, the matrix of objectives

and messages with the knowledge, beliefs and attitudes of

ACOMS respondents, we can assess the gross accomplishment of

Army communications objectives. Thus, if a particular

message was intended to be portrayed as supportive of a

particular component, we can use the ACOMS data to determine

whether such an effect was found. If moreover, the message

was intended to appeal to a particular audience, this, too,

can be assessed using the cross-sectional analytic

components;

(2) By comparing the results of step 2, messages and advertisements with effects on ACOMS respondents, we can assess

the relative effectiveness of particular executions and

campaigns. While we do not expect evaluations of particular

executions to form a major part of ACOMS analytic activities, ACOMS data can be an important evaluative tool, especially using quasi-experimental designs (see Chapter 7,

Gaertner & Baxter, 1988);

(3) By comparing the results of step 3, the reach and frequency

data with effects on ACOMS respondents, we can assess the

gross and marginal effects of exposure and changes in it on

the behavior of potential recruits and influencers. Thus,

with the exposure data from syndicated services at the

county level attached monthly to individual respondents, we

can track the effects of changes in advertising mix and

levels through exposure to recall and subsequent attitude

change; and

(4) By comparing the results of step 4, the trends and histories

of individuals in the enlistment decision process with the

results of steps 1, 2, and 3, we can provide feedback on and

input to the process of defining and prioritizing objectives, messages and advertisements. These results can be

used to evaluate advertising mixes and audience segmentation, and the overall strategy of Army advertising, both for

the total Army and its components.

The discussion above suggests several information requirements

for an integrated assessment of the Army's advertising program.

First, a listing of the Army's communication objectives is required,

and has been accomplished (see Table 1). These attributes provide the

core of ACOMS. Operationally, in terms of the model, we need to

assess the extent to which each attribute is important to the respondent, and the extent to which respondents think each attribute is

offered by the active Army and its components, by other services, by

military service generally, and by civilian alternatives. In combination, these measures should estimate the attractiveness of the respective components, services, and civilian alternatives.

14
Attractiveness alone, however, is not sufficient to establish the

effectiveness of an advertising program. Thus, a second information

requirement of the model is to track the subsequent behavior of the

prospect markets to determine whether the advertising results in

enlistment-related behaviors. Since advertising, especially for highrisk purchases, is unlikely to result directly in a "sale" (in this

case, enlistment) it is necessary to collect data on smaller,

incremental, steps toward enlistment. Further, since the advertising

process (and the enlistment process) is likely to proceed slowly, it

is necessary to track target respondents over time. Thus, ACOMS

includes a longitudinal component which tracks the behaviors of interviewed youth over an extended period.

Further, the attractiveness of the Army and behaviors motivated

by that attractiveness are not the results of target youths deciding

their futures in social isolation. Influential figures, in particular, parents, are likely to be intimately involved in the decisionmaking process, as the model suggests. This third information

requirement relating to the attitudes of influential others is operationalized in ACOMS by the respondent's assessment of the influence of

friends and family, and also by separate interviews of parents of half

of the youth sample.

A fourth information requirement of the model is measurement of

the extent to which respondents have been exposed to Army advertising.

In part, exposure is assessed in the youth interview by asking about

the respondent's recall of Army (and other) advertising, and by asking

about the respondent's media habits. Both of these approaches, however, are deeply flawed as measures of exposure. Recall is influenced

not only by exposure, but also by memory decay, comprehension, and

respondent demographics. In fact, one major empirical question for

ACOMS is the extent to which exposure translates into recall. Media

habits predict the actual exposure of prospect youths to advertising

during specific periods weakly, at best. Thus, the primary means of

assessing exposure will be through the use of syndicated data on the

reach and frequency of Army advertising execution exposure to particular demographic segments and geographic markets at defined points in

time.

A fifth information requirement of the model is to assess the

kinds of Army advertising messages to which the prospect youths are 70

exposed. Army advertising executions are far from homogeneous--the

various components attempt to communicate both a common core image and

offer, and distinctive images and offers to prospect populations.

Further, even for the active Army, different executions focus on

particular aspects of the active Army image and offers, and are

intended to be aired at different times corresponding to hypothesized

prospect market motivations. Thus, ACOMS will employ message content

analyses to assess the extent to which various Army print and television advertising executions embody the various Army advertising objectives. Linked with the exposure data, these measures of relative

message content will provide vital input to analysis of the effects of

advertising.

15
p.

In sum, the information needs of ACOMS require drawing data from

multiple sources, and even within the youth interviews, require the 0

collection of data on multiple content areas (the classes of variables

in the model) and multiple advertising stimuli (the active Army, ROTC,

USAR and ARNG, other services, and civilian alternatives). In sections to follow, we outline the data collection procedures designed

for ACOMS to respond to these challenges.

ACOMS Main Interview Data Sources

In the next section, we provide an overview of the youth and

parental interview samples and the youth and parental questionnaire

structures as proposed and subsequently modified. A fuller description of the ACOMS sample selection procedures can be found in

Chapter 3 of The ACOMS Survey Design (Mohadjer & Waksberg, 1988).

The Youth and Parental Samples

Sample design for ACOMS was guided by two main criteria--the

objectives of the research and the need to retain comparability with

extant research (e.g., the Youth Attitude Tracking Study, and the New

Recruit Survey). In line with these dual objectives, the final youth

sample design for ACOMS consists of 16- to 24-year-old males and

females who have neither served nor contracted to serve in the Armed 'a

Forces and have not yet graduated from college. This overall sample

is broken into four groups:

(1) The Primary Male Sample (PMS): (annual n-9600)

Male high school diploma graduates and those currently

enrolled in regular high school or college;

(2) The Secondary Male Sample (SMS): (annual n-850)

Male high school non-completers not currently enrolled in

regular high school or college;

(3) The Female Sample (FS): (annual n-1950)

Female high school dipioma graduates and those currently

enrolled in regular high school or college (Primary Female

Sample (PFS)) and high school non-completers not currently

enrolled in regular high school or college (Secondary Female

Sample (SFS)); and

(4) Supplementary Samples: (annual n-2000)

Male Hispanics in all of the above categories (annual n-3 0 0) a'

and the ADI supplement (PMS only), described further in

Chapter 7 (Gaertner & Baxter, 1988).

Two additional supplements contemplated in the Statement of Work

were dropped during the design phase. These included the supplementary sample of four-year college students and a sample of VoTech s :..,dents. It was further decided that given the final sample

design, supplementation of Black youth would probably not be required.

16
In addition, the PMS and PFS samples are further subdivided. All

16- to 20-year old PMS/PFS sample members become part of a parentallinked sample from which target youths will be selected for an additional interview of a predesignated parent. Half of the parentallinked target youths are designated for participation in a longitudinal sample to be reinterviewed annually.

We discuss each of the main sample groups and the operationalization of their eligibility requirements separately.

The Overall Youth Sample

While the definition of eligibility requirements is apparently

straightforward, the operational definition of the sample is a bit

more complex. Age is defined by date of birth rather than selfreported years of age. Each potential respondent between 16 and 24

years of age is asked whether he/she has served in the military (Army,

Navy, Air Force, Marines, the Reserve or the National Guard), or is

currently in the Delayed Entry Program. These are excluded. Finally,

youth are asked the highest grade they completed and received credit.

Those indicating they have graduated from college are excluded.

Youth currently enrolled in college present special problems in

establishing household residency since it is desirable to avoid double

eligibility for students living away from home while at college. In

collaboration with the SAG, it was decided that students living in

college-sponsored housing would be reached through their parents (and

therefore excluded in the screening interview) while those living on

their own would be eligible for interview directly (and therefore not

traced through the-ir parents.)

The Primary Male Sample and Primary Female Sample

In addition to the age and prior service qualifications of the

overall sample, eligibility for PMS/PFS requires an assessment of

educational attainment. High school diploma graduates are defined as

those completing a regular high school diploma, thug excluding those

with certificates from General Educational Development (GED) and Adult

Basic Education (ABE). The latter are classified as SMS unless they

are enrolled in a two- or four-year college. Current enrollment

during the school year is ascertained directly, and, as above,

requires enrollment in a regular high school or a two- or four-year

college, thus excluding enrollment in training and vocational-technical non-degree programs (the latter are considered SMS if they did not

receive a regular high school diploma). Enrollment status for youths

interviewed from July I through September I is defined by their plans

for September.

The PMS and PFS encompass many of the Army's prime recruiting

groups, not only for the active Army but also for ROTC, ARNG and USAR,

and thus conform to the research objectives for ACOMS. However, the

inclusion of youth with two or more years of college or youth taking a

college ROTC course in the primary samples is at variance with the

17
market for enlisted personnel. Consequently, for more precise reporting of enlisted market groups, we define a subset of the PMS as the

Primary Male Analytic Sample (PMAS), consisting of PMS members who

have not yet begun their junior year in college and are not enrolled

in a college ROTC program.

None of the sample groups exactly parallels the sample definition

for the Youth Attitude Tracking Study II (YATS II). The male sample

of YATS II is approximated by a combination of the PMAS and SMS

samples. For females, a similar parallel sample is constructed by

excluding from the FS respondents who have begun their junior year in

college and who have enrolled in a college ROTC program. A category

in the quarterly reports (Recruiting Market - PMAS + SMS or PFAS +

SFS) is included to provide comparability to the YATS II sample.

Note, however, that the YATS II sample is restricted to a September

and October interviewing period.

The Parental-linked Sample

PMS and PFS eligibles between the ages of 16 and 20 inclusive are

designated for participation in the parental-linked sample. The

randomly selected parent is interviewed regarding his/her own awareness of and attitudes toward Army and other military advertising, Army

offers and images, as well as hopes for and attempts to influence the

target youth. For households with more than one PMS/PFS eligible, one

is designated randomly as the target youth for the parental interview.

For a randomly chosen half of the parental-linked sample, the

eligible parent is female; for the other half, the parent is male.

Eligible parents do not have to live in the same household as the

target youth. "Parent" does not necessarily imply biological parent,

but may include step-parents, foster parents, guardians or adult

relatives--the respondent is allowed to self-define the parent, where

appropriate. If a parent of the designated sex cannot be found or is

not available, the target youth is dropped from the parental-linked

sample.

The Longitudinal Sample

A randomly chosen half of the parental-linked target youths is

selected for participation in the longitudinal sample. Longitudinal

sample youths will be re-interviewed annually in the years following

their original interviews. Their original interviews are slightly

longer than those of nonlongitudinal sample participants, since tracing information and information on social influences are collected

from them as well.

The Secondary Male Sample and Secondary Female Sample

Logically, these samples represent the complements of their

respective Primary Samples--that is, youths not currently enrolled in

regular high school or college and not having completed a regular high

school diploma. These youths will not participate in the parentallinked or longitudinal samples.

18
The Hispanic Supplementary Sample

The screening for the main samples is expected to yield 670

Hispanic youth annually. An additional 300 Hispanic youths will be

selected each year from telephone exchanges containing high densities

of Hispanics using a list secured for ACOMS from Donnelly.

The Youth Interview

The youth interview provides the major measures of the dynamics

and effects of Army advertising available in ACOMS. A hard copy of

the interview can be found in Chapter 4 of The ACOMS Survey Design

(Gaertner, Nieva, & Allen, 1988). In accordance with the conceptual

model which has guided the development of ACOMS, the youth interview

is divided into 14 modules, in addition to the screener. They are, in

interview order:

(1) Education and Employment which verifies and elaborates the

information on the respondent's education collected in the

screener, and elicits employment history and measures of

course content and school performance useful for assessing

quality;

(2) Intentions and Propensity which asks for the respondent's

plans for the next few years, constructed to parallel and

supplement measures of Army propensity in YATS;

(3) Behaviors which elicits information on the respondent's

activities relative to enlistment, employment and/or college

enrollment;

(4) Social Influences which asks-for the respondent's assessments of the attitudes of friends, parents and others toward

enlistment (asked only of parental-linked target youths);

(5) Importance of Attributes which assesses the importance to

the respondent of attributes defined by the Army's communications objectives. These items correspond to the evaluation component of the hierarchy of effects model;

(6) Media Habits which elicits information on the amounts of

television, radio and print material the respondent is

regularly exposed to, and the types of TV programs viewed

and radio programs heard (asked only of a randomly selected

half of all youth respondents);

(7) Knowledge-Recall which asks for unaided and aided recall of

Army (by component) and other service advertising, presented

in random order. The respondent is also asked where the

advertising was seen or heard and what its main message was;

19
(8) Attitude toward Army Advertisin which ascertains how much

the youth likes and believes the advertisements he/she has

seen or heard;

(9) Knowledge-Slogan Recognition which asks whether the respondent can identify slogans utilized in Army, other services

and joint-service advertising presented in random order

(asked only of a randomly selected half of all youth

respondents);

(10) Perceptions/Beliefs which asks whether the Army (by component), other services, military service in general and/or

college and civilian employment offer the attributes defined

by the Army's communications objectives presented in random

order. These items correspond to beliefs in the hierarchy

of effects model;

(11) Knowledge-Awareness which asks for the respondent's level of

knowledge concerning Army offers (asked only of a randomly

selected half of all youth respondents);

(12) Demographics which elicits information on respondent's

ethnicity, marital status, Social Security number, socioeconomic background, and current residence location;

(13) Parental Location Information which elicits information

required to contact parents (asked only of parental-linked

target youth); and

(14) Tracking Information which elicits information required to

trace youth selected for inclusion in the longitudinal

sample, including anticipated changes in telephone number,

names and phone numbers of employer and two others likely to

know respondent's whereabouts (asked only of longitudinal

sample).

While the modules provide measures of the concepts of the hierarchy of effects model, they appear in different order from the model.

Our general strategy is to ask questions regarding future plans and

behaviors before it becomes apparent (through the interview itself)

that a main interest is in Army advertising, in order to minimize

potential agreement bias. In addition, to precisely replicate the

Army active and Reserve propensity measures in YATS II, it was necessary to place the intentions/propensity measures early in the

interview.

The list of modules and items is much longer than can be obtained

from any given respondent in a half-hour telephone interview. Several

strategies are being utilized to ensure that necessary information is

obtained while minimizing respondent burden.

First, certain modules are asked of particular groups of respondents. Tracing and social influence is only being asked of target

20

k24C~V ' A ~ ~ . .'. -.
youths in the parental-linked sample, since these respondents allow

the fullest test of the social/normative aspects of the conceptual

model.

Second, certain other modules are asked only of subsets of

respondents. Thus, Slogan Recognition, Knowledge-Awareness and Media

Habits modules are distributed among respondents as shown in Table 2.

Respondents are assigned randomly to each of the three modules with a

probability of 0.333. Half are not assigned a second module from this

group of three. The remaining half receive one of the two remaining

modules with equal probability. The result is that each module is

asked of half of the sample, and each pair is asked of one-sixth, so

that correlations between items in different modules can be assessed.

This results in the average respondent receiving 1.5 modules of the

three, a substantial time savings. However, this solution does create

potential problems in analyzing the data since some responses will be

missing by design.

Third, some sections within modules are randomly assigned to

subsets of respondents, most particularly sections relating to

Perceptions/Beliefs of Army attributes. The reasoning goes as

follows. In order to assess whether respondents believe that various

Army components, other services, work and college offer the attributes

defined by the Army's communications objectives, we need ratings of

the extents to which the attributes are perceived as descriptive of

each component. To obtain these ratings exhaustively would require

that we ask respondents to rate the attributes in ten separate

contexts (active Army, ROTC, ARNG, USAR, Navy, Marines, Air Force,

Military Service generally, College, and Work). Given that typically

14 attributes are queried, this would represent an intolerable respondent burden. Further, not all groups of respondents are of equal

recruiting priority to the various components--ROTC has greater interest in college students, while ARNG and USAR have greater interest in

high school graduates who are not currently enrolled. Finally, as

with the Slogan Recognition, Media Habits, and Knowledge-Awareness

(above), several of the analyses require correlations across components, as well as within them--thus each pair of components must be

rated by some set of respondents.

In collaborsri~n with the SAG, an approach to allocating respondents to sets of erceptions questions was developed. This allocation

scheme has the fo~lowing characteristics:

(1) All respondents are asked about at least two components--

usually active Army and one other;

(2) Respondents are asked about their current enrollment

statuses, their educational attainments and (if appropriate)

their plans for further education. These responses are used

to form six main groups of respondents:

(a) Current College Juniors and Seniors: includes youth

currently enrolled in third or fourth year of a four

year college or university;

21

ZSM~~',~Z ~%
p.

Table 2

Module Rotation Pattern

Second Module

First Media Slogan Knowledge- None oalModule Habits Recognition Awareness

Media 0.0% 8.3% 8.3% 16.7% 33.3% 0

Habits

Slogan

Recog- 8.3 0.0 8.3 16.7 33.3

nition I

Knowledge 8.3 8.3 0.0 16.7 33.3 -

Awareness

Total 16.7 16.7 16.7 50.0 100.0

i"

22l

.

oU.

4.

I

II

22 I

U)
(b) Current College Freshman and Sophomores: includes

youth currently enrolled in first or second year of a

two or four year college or university; 0

(c) High School Diploma Students, College-oriented:

includes high school students intending to go to

college;

(d) High School Diploma Students, Work-oriented: includes

high school students not intending to go to college;

(e) High School Diploma Graduates Not Currently Enrolled in

College: includes high school diploma graduates who

are not currently enrolled in a two or four year r

college or university and who have not completed more

than two years in a four year college or university;

and

(f) High School Non-completers (SMS, SFS): includes youth

who have not received a regular high school diploma and

who have not received any college credit;

(3) All Primary Male Analytic Sample (PMAS) and Primary Female

Analytic Sample (PFAS) eligibles are asked the active Army

perceptions questions (Groups 2 through 5);

(4) College juniors and seniors (Group 1) are asked two sets of

questions--those pertaining to ROTC and one other component,

branch, college or civilian job;

(5) College freshmen and sophomores (Group 2) are asked two

modules--either active Army or ROTC and one other. Onethird are asked active and ROTC, one-third active and one

other, and one-third ROTC and one other;

(6) College-oriented high school students (Group 3) are asked

three modules--active, ROTC and one other;

(7) Work-oriented high school students and high school graduates 5'

not currently enrolled (Groups 4 and 5, respectively) are

asked active Army and one other set; and

(8) SMS/SFS respondents are allocated as Groups 4 and 5.

Table 3 presents the resultant expected distribution of PMS

respondents, making some assumptions about the distribution of respondents to the five groups. That is, if we assume that about 10% of all

PMS respondents are college juniors and seniors, 24% are college

freshmen and sophomores, 11% high school students college-oriented, 5%

13% high school students work-oriented, and 42% high school graduates

not enrolled, we would expect (for example) about 970 sets of

responses to the ROTC questions annually from college juniors and I

seniors, and so on. It should be emphasized that these are expected

numbers--actual distributions will depend on the validity of the

23 S
Table 3

Assignment of Respondents to Perception Modules by Component (Primary

Male Sample Only; Includes Primary Male Analytic Sample (PMAS) and

College Juniors and Beyond)

n" 970 Attained 3 yrs.+ College 10% Of Total

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE VORK SERVICE

ANNUAL 970 121 121 121 121 121 121 121 121

QUARTERLY 243 30 30 30 30 30 30 30 30

MONTHLY 81 10 10 10 10 10 10 10 10 -a

n- 2330 Freshmen and Sophomores* 24% Of Total

.1'

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

.NNUAL 1553 1553 194 194 194

QUARTERLY 388 388 49 49 49 49 49 49 49 49

MONTHLY 129 129 16 16 16 16 16 16 16 16

I

n- 1035 H.S. Students College Bound 11% Of Total 45% Of High School

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 1035 1035 279 279 79 79 79 79 79 79

QUARTERLY 259 259 70 70 20 20 20 20 20 20

MONTHLY 86 86 23 23 7 7 7 7 7 7 I

n- 1265 H.S. Students Work Bound 13% Of Total 55% Of Not in College

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 1265 141 141 141 141 141 141 141 141 141

QUARTERLY 316 35 35 35 35 35 35 35 35 35

MONTHLY 105 12 12 12 12 12 12 12 12 12

n- 4000 HS Grads Not Enrolled** 42% Of Total

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 4000 444 444 444 444 444 444 444 444 444.

QUARTERLY 1000 ill ill il ill ill Ii ill ll I 

MONTHLY 333 37 37 37 37 37 37 37 37 37

.

I

a

j..o_.. - __o - - _ ,, -. - ,__ 'i ' -t ' - " m * ' - ' %. % ,'." " ". '. '.'.1
Table 3 (continued)

n- 9600 TOTALS

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 7853 4143 1180 1180 980 980 980 980 980 980

QUARTERLY 1963 1036 295 295 245 245 245 245 245 245

MONTHLY 654 345 98 98 82 82 82 82 82 82

Rules for allocating respondents to modules were as follows:

(1) All respondents are asked at least two modules -- usually Active Army and one

(2) Respondents will be asked whether they intend to go to college or into the workfocce. L

well as their current educational statu. Thus for purposes of module assignment. -e

will establish five groups of respondents -- those attaining junior year in college or

beyond, current college students in first and second year, college bound high school

students, work bound high school students, and high school students not current:v

enrolled.

(3) Questions for perceptions of Active Army are asked of all PMAS respondents. Thus. a!

college bound, workforce bound non-enrolled and those in freshman and sophomore ien-s

are asked the Active Army module.

(4) College Freshmen and Sophomores are asked 2 modules -- Active or ROTC and one otx :

One-third will be asked Active and ROTC, one-third will be asked Army and one o:ne :

one-third will be asked ROTC and one other. Distribution among other modules is is

shown.

(5) College-bound H.S. Students are asked 3 modules -- Active, ROTC and one ocher. 7k,

module distributions for the other component activities are as shown.

(6) Workforce bound respondents are divided among ARNG and USAR, and evenly divided among

the other services, work and college.

Includes all post-High School education and training ** Excludes Juniors and beyond

25

4N wAI
assumptions underlying the distribution to groups, and the performance

of the random assignment. The assumptions were based on extrapolations from the Current Population Survey and the most recent YATS.

It is useful to explore the effects of these sample sizes on the

statistical power of the PMS. In Table 4 we present the smallest

detectable difference (between months, quarters or years, depending on

the row) in the estimate of a proportion of 0.5 for the sample sizes

as depicted in Table 3. Table 4 gives the smallest difference which

will lead to a rejection of the null hypothesis of no difference over

time (assuming P-pl-P 2-0.5).

The smallest detectable difference was arrived at as follows.

Assume that we have two samples, one for the previous period, and one

for the current period (annual, quarterly or monthly, depending on the

row) each producing estimates of some proportion (p1 and P2, respectively). We can ask what is the smallest difference between p1 and P2

which will cause us to reject the null hypothesis of no difference.

This is, of course, the same as asking what is the largest difference

which will not cause us to reject the null hypothesis. Any difference

within the 95% confidence interval surrounding the difference will not

lead to rejection. Thus, the null hypothesis is that the estimated

proportions are equal to the proportion in the universe, P-pl-P 2 . The

widest confidence interval is at P-0.5, and the confidence interval is

two times the standard error of (pl-P 2 ). This works out to 2p times

the square root of 2/n, in a simple random sample. Formally, the

variance of the difference is the sum of the variances of each proportion (since the samples are independent) so that 2*sqr (Var(pl-p2))-

2*sqrt(Var(pl)+ var (p2))-2*sqrt(2*Var(pl))-2*sqrt(2*p /n)-2p*sqrt

(2/n)). However, since respondents for each set of questions represent a sub-sample of the total sample and since we are allocating V

respondents to question sets differentially depending on group, there

is a design effect associated with the allocation. This design effect

increases as the proportion giving a set differs by group. Thus, the

design effect is highest for ROTC (2.4), next highest for USAR and

ARNG (1.13), and nearly negligible for active and other components

(1.03). To account for the design effect, we multiply the confidence

interval by the root of the design effect.

As should be apparent, almost any substantively meaningful annual

difference will be statistically significant in the sample as a whole

whether for active (0.017), ROTC (0.036), USAR and ARNG (0.044 or

0.046) or even other components (0.045 or 0.048). Confidence intervals for quarterly and even monthly differences are still reasonably

tight for active and ROTC. Monthly and quarterly differences within

segments become precarious, especially for other components. However,

since quarterly estimates for these other components are not of critical importance, this design tradeoff seems appropriate.

26
w

Table 4

Smallest Detectable Change Over Time Assuming Allocation Shown in

Table 3 and Response Proportion Equals 50%

n- 970 Attained 3 yrs.+ College 10% Of Total

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE ORK SERI'E

ANNUAL 0.048 0.135 0.135 0.135 0.135 0.135 0.135 0.135 0

QUARTERLY 0.095 0.269 0.269 0.269 0.269 0.269 0.269 0.269 0

MONTHLY 0.165 0.467 0.467 0.467 0.467 0.467 0.467 0.467 -

n- 2330 Freshmen and Sophomores* 24% Of Total

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SER*:Z:

ANNUAL 0.038 0.038 0.106 0.106 0.106 0.106 0.106 0.106 0.106 0 126

QUARTERLY 0.075 0.075 0.213 0.213 0.213 0.213 0.213 0.213 0.213 3 2:3

MONTHLY 0.130 0.130 0.369 0.369 0.369 0.369 0.369 0.369 0.369 0 2

n- 1035 H.S. Students College Bound 11% Of Total 45% Of High S.hool

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE JORK SERV:CO

A.NNUAL 0.046 0.046 0.089 0.089 0.166 0.166 0.166 0.166 0.166 3 -6

QUARTERLY 0.092 0.092 0.177 0.177 0.333 0.333 0.333 0.333 0.333 0 3

MONTHLY 0.160 0.160 0.307 0.307 0.577 0.577 0.577 0.577 0.577 0

n- 1265 H.S. Students Work Bound 13% Of Total 55% Of Not in College

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 0.042 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125

QUARTERLY 0.083 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.25)

MONTHLY 0.144 0.413 0.433 0.433 0.433 0.433 0.433 0.433 0.433 0.433

n- 4000 HS Grads Noe Enrolled** 42% Of Total

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 0.023 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070 0.070

QUARTERLY 0.047 0.141 0.141 0.141 0.141 0.141 0.141 0.141 0.141 0.141

MONTHLY 0.081 0.244 0.244 0.244 0.244 0.244 0.244 0.244 0.244 0 24-z

n- 9600 TOTALS

ACTIVE ROTC ARNG USAR NAVY MARINES USAF COLLEGE WORK SERVICE

ANNUAL 0.017 0.036 0.046 0.046 0.048 0.048 0.048 0.048 0.048 0.048

QUARTERLY 0.034 0.071 0.092 0.092 0.096 0.096 0.096 0.096 0.096 0.096

MONTHLY 0.059 0.124 0.159 0.159 0.167 0.167 0.167 0.167 0.167 0.16-

27

. .. . ,,,', . ...€ €€i. .p
The Parental Interview

The parental interview, being conducted with a predesignated

parent of target youths 16- to 20-years-old, was constructed in parallel with the youth interview. It contains eight modules in the

following order:

(1) Parental Influence which probes whether the parent has

discussed military service with the target youth, his/her

expectations for him/her and beliefs that military service

is a good or bad idea for most young men and women;

(2) Importance of Attributes repeats the items in the youth

version which assess the importance of attributes, this time

asking the parent about the importance of these attributes

for the target youth;

(3) Media Habits items are identical to those asked of the youth

and focus on the amounts of television, radio and print

material the parent is regularly exposed to;

(4) Knowledge-Recall questions are also identical to those in

the youth questionnaire and ask for unaided and aided recall

of Army and other service advertising with questions in

random order;

(5) Attitudes toward Army Advertising items address how much the

parent likes and believes the advertisements he or she has

heard or seen using the same items as those in the youth

interview;

(6) Perceptions are probed with the identical questions asked of

the target youth regarding the extent to which the Army (by

component), other services, military service in general,

college and civilian employment offer the target youth the

attributes defined by the Army's communications objectives;

(7) Knowledge Awareness items assess the parent's knowledge, as

in the youth interview, of Army benefits and programs; and

(8) Demographics items elicit information on the parent's

ethnicity, marital status, socio-economic background and

military experience.

In essence, the parental interview adds one module to the youth

questionnaire (Parental Influence), and drops Education and Employment, Intentions/Propensity, Behaviors, Social Influences, Slogan

Recognition and the Parental Location and Tracking modules from the

youth interview. Except in the Importance module (where the referent

for the items is changed to the youth), nearly all question wordings

are identical to those found in the youth questionnaire. Further,

where random assignment to or within modules is performed for the

youth interview, the parent's interview will be assigned to the same

modules and sections as the target youth. Thus, if the target youth

28
- . v .

was asked questions on perceptions of Army and college attributes, the h

parent is also asked the perceptions questions for Army and college

attributes. Further, if the youth was asked media habits and

knowledge-awareness questions, the parent is asked the same modules.

Summary

ACOMS represents a sampling, data collection and analytic effort

of marvelous complexity and sophistication, designed to serve a wide

variety of needs, audiences, and schedules, with a corresponding variety of work products and formats. In this chapter we have provided an

overview of the analysis plan as a whole, reviewed the conceptual

model underlying the effort, and summarized some of the major design

decisions reached in collaboration with the SAG and the COR during the

design phase. These decisions have included the definition of major

sample elements, the construction of the instrument, and basic analytical priorities and schedules. Later chapters describe in detail the

major content areas, methods, and deliverables relating to the youth

and parental interviews.

V

29. %%

, 29

5-

~ V 5- '.~..*. ~% 5- ~ a, a°
REFERENCES

Aaker, D. A. (Ed.). (1975). Advertising management Englewood Cliffs, NJ:

Prentice-Hall.

Fishbein, M., & Azjen, I. (1975). Belief, attitude, intntion and behavior:

An introduction totheorv and research. Reading, MA: Addison-Wesley.

Gade, P. A., Elig, T. W., Nogami, G. Y., Hertzbach, A., Weltin, M., & Johnson,

R. (1984). Motives, incentives, and key influencers for enlistment,

reenlistment, and attrition in the U.S. Army. (PUTA WP 84-19).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Gaertner, G. H., & Baxter, S. J. (1988). Plan for the integrated analysis of

fit, exposure, and change model. In G. H. Gaertner, & T. W. Elig (Eds.),

The Army Communications Objectives Measurement System (ACOMS): Survey

analysis plan (ARI Technical Report 786). Alexandria, VA: U.S. Army

Research Institute for the Behavioral and Social Sciences.

Gaertner, G. H., Nieva, V. F., & Allen, B. F. (1988). The ACOMS

Questionnaires. In V. F. Nieva & T. W. Elig (Eds.), The Army

Communications Objectives Measurement Systems (ACOMS): Survey design

(ARI Technical Report 785). Alexandria, VA: U.S. Army Research

Institute for the Behavioral and Social Sciences.

House, R. J. (1971). A path goal theory of leader effectiveness.

Administrative Science Quarterly, 16, 321-338.

Johnson, R. M. (1983). Modeling the individual enlistment decision. (PUTA WP

83-29). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences,

Lavidge, R. J., & Steiner, G. A. (1961). A model for predictive measurements

of advertising effectiveness. Journal of Marketing, 25, 59-62.

McGuire, W. J. (1969). An information-processing model of advertising

effectiveness. Paper presented at the Symposium of Behavioral Management

Science in Marketing, Center for Continuing Education, The University of

Chicago, Chicago, IL.

Mohadjer, L., & Waksberg, J. (1988). The ACOMS Sample Design. In V. F. Nieva

& T. W. Elig (Eds.), The Army Communication Objectives Measurement System

(ACOMS): Survey design (ARI Technical Report 785). Alexandria, VA:

U.S. Army Research Institute for the Behavioral and Social Sciences.

Research Triangle Institute. (1985). Youth Attitude Tracking Study II, Wave

16--Fall 1985 (Contract MDA903-83-C-1072). Arlington, VA: Defense

Manpower Data Center.

Vroom, V. H. (1965). Motivation in management. New York: American

Foundation for Management Research.

30e
Weltin, M. M., Elig, T. W., Johnson, R. M., & Hertzbach, A. (1984). Attitudes

of high school seniors toward military enlistment. (PUTA WP 84-24).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Wilson, M. J. (1988). Market segmentation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Wilson, M. J. (1988). Brand differentiation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

3

13

~ P.

~? ~ ~ .
2. TRACKING AUDIENCE RESPONSE

Gregory H. Gaertner, Linda J. Keil, and Nancy L. Gay

Introduction and Overview

The purpose of the tracking activities is to provide timely feedback to the Army on levels of audience response to Army advertising

and interpretation of observed changes in response across time. This

feedback takes the form of quarterly reports to the Special Advisory

Group (SAG), delivered in briefing and narrative form six weeks after

the close of each quarter. In this chapter we describe our plans for

the quarterly reports and the statistical and methodological developments which support these reports.

In overview, the variables and scales being tracked are drawn

from the hierarchy of effects model described in Chapter 1 (Gaertner,

1988). Thus, for key market segments, we will report on enlistmentrelated intentions, importance and perceptions of Army attributes,

enlistment-related behaviors, recall of advertising (aided and

unaided), knowledge of Army offers, and media habits. The identification of key market segments and the interpretation of the results is

aided by the Army's dual-market concept, trimester advertising plans,

and quarterly trends in recruiting missions. Since these identify key

quarterly markets and the knowledge and perceptions advertising is

intended to support in these key markets, the quarterly reports provides both an assessment of advertising conducted in the previous

quarter and a market analysis of the receptivity of key prospect

markets in the upcoming quarter.

In addition, the tracking effort relates closely to the market

segmentation activities being conducted under the Army Communications

Objectives Measurement System (ACOMS) described in Chapter.3 (Wilson, lot

1988), and provides the basis for models of the effects of Army

advertising discussed in Chapter 6 (Gaertner, 1988).

Data collected in the ACOMS Youth interviews is the focus of the

analyses described in this chapter. During the first year of data

collection, the analyses are primarily cross-sectional comparisons

across prospect market segments. Beginning in the second year of data

collection, longitudinal data will be added as youth are reinterviewed.

This chapter is divided into two main sections. In the first, we

specify the plan for the tracking analysis; in the second, we describe

the format, content, and timing of the deliverables that are being

produced. The general description and schedule of deliverables is

contained in Chapter 8 (Gaertner, 1988). Because the format and content of the quarterly reports is shown in tables incorporated in the

text of this chapter, we have shifted some discussion of them from

Chapter 8 to the end of this chapter. In describing the analysis

plan, we first provide a general overview of the approach we are

taking in developing scales of tracked variables and in identifying

and describing trends in result" Second, we will define the prospect

33

V.~~~~~~r or_~~ '~~! .~ ; ~'
IIJ

market segments that will be tracked and explain their relationship to

the Army's dual-market concept, trimester advertising plans, and quarterly recruiting missions. Third, we specify the variables being

tracked and the initial steps, anticipated results, and likely next

steps to be taken in each of the main analytical activities. Discussion of the deliverables follows and includes detailed information

about the format and content of the quarterly reports, annual report

chapters, and briefings.

Methods of Tracking Audience Response

The focus of the tracking analysis is on interpreting and predicting change in audience response to Army advertising over time.

The methods involved in the tracking analysis can be summarized in

terms of three foci: (a) estimating levels of audience response,

(b) summarizing the various measures of audience response, and

(c) detecting trends in measures and summaries. We can discuss each

method in more detail.

Estimati r Levels of Audience Response

The preliminary step in the tracking activity is to produce estimates of intentions, importance, perceptions, behaviors, recall,

knowledge, and media habits for various groups of youth. Educational

groupings and groups based on age, gender, recruiting brigade and

Reserve Officers' Training Corps (ROTC) region provide the in: ial

segments. Since most of the response variables of interest are at a

categorical level of measurement, these estimates can be produced

simply by crosstabulating the response variables by the groupings.

For reporting purposes, the response variables can be dichotomized and

the proportion in some response category reported by group. Thus, for

example, we can report proportion viewing a type of television show,

or proportion answering a knowledge question-correctly, for college

students, college-oriented high school students, work-oriented college

students, and so on.

We should note that because of the complex nature of the ACOMS

sample, standard errors of proportions will differ from those which

would be computed on the basis of ordinary statistical formulae. The

balanced half-sample repeated replication method (BRR) for estimating

variances is being used (McCarthy, 1969; Mohadjer, Chu, Morganstein, &

Rhoads, 1986) and was a topic in the analyst training which was provided under ACOMS. Similarly, tests for the significance of differences among segments and over time require BRR. Since the basic data

are crosstabular, we expect to use a modified log-linear analysis procedure which provides a chi-square test for independence of crosstabulated variables. These procedures will be covered in a future analyst

training session.

Scale Construction

Literally hundreds of measures of audience response are available

from the ACOMS youth interviews, and even the quarterly summaries

34
report on nearly 100 variables. For ease of summary and interpretation, an initial focus of analytical effort will be on developing

scales of higher order constructs such as media habits, recall of

advertising, and others. The scaling activity will begin following

the second quarter of data collection. Building and refinement of

scales will be a continuing activity as cross-sectional data accumulate and longitudinal data become available.

Scale construction is an iterative process beginning with a

priori expectations about which variables will form scales. The

process can be described in the form of four basic steps.

(1) The hierarchy of effects model served as the basis for

defining the separate question modules that are included in

the ACOMS interview. The first step in the scaling process,

therefore, is to identify variables within each module that

may be expected to form scales;

(2) Correlational and factor analytic techniques can then be

used to test the accuracy of these a priori expectations.

These two analytic techniques will indicate which variables

in each module reliably cluster together and the underlying

dimensional structure on which the clusters rest;

(3) Scaling expectations can then be refined on the basis of

these results using item analysis. Some of the variables

initially included in the scaling may be dropped and others

may be added in attempts to identify the set of variables

within each module that forms the best scale(s). Once

again, correlational and factor analytic techniques will be

used to test the scales' reliability; and

(4) Finally, correlations among scales can be computed providing

some evidence of construct validity. The modeling utility

of the scales is addressed in Chapter 6.

Throughout, the scale construction activity is likely to be

driven by compromises between simplicity and accuracy. On one hand,

fewer scale summaries of particular constructs are preferred over more

of them. Thus, we would prefer to have a single index of, for example, knowledge of Army offers. On the other hand, however, if the

scaling results suggest that the intercorrelations of knowledge of

offers items differ by component, it is misleading to sum the various

knowledge items to produce a single knowledge of offers index.

Further, this summed scale would be unlikely to correlate with other

items of interest reliably or to track consistently over time. This

requirement does not imply that a single scale is useless if levels of

knowledge of offers differ by component, but rather only if the correlations among items differ, since the latter might imply that knowledge of, for example, ROTC offers could not be predicted from knowledge of Reserve offers.

35
Trend Analysis

Interpretation of aggregate level change will require crosssectional comparisons of variables and scales over time. The goal

will be to identify emerging trends in the responses of prospect

market groups to Army advertising. Charting predictable seasonal

trends as they relate to advertising plans and recruiting missions

will also be required, both as an end in itself and also to distinguish between seasonal and secular trends. This latter analysis,

obviously, cannot begin until the second year.

In the first year, analysis of trends from quarter to quarter

will not, in principle, differ from analysis of differences among segments within a quarter, simply because we will not have enough data

points to treat time as a continuous variable. Thus, methods such as

log-linear modeling and analysis of variance which can accommodate

discrete variables will be employed for the trending analysis.

Beginning in the second year of data collection, more sophisticated trending methods can be utilized. We propose the use of

statistical process control. This method essentially defines confidence boundaries around a set of data points. Interpretive rules are e

defined for deciding when observed changes represent emerging trends

in the data as opposed to isolated events or random fluctuation.

Names of these rules, such as "runs of 8" and "trends of 6," demonstrate the importance of numerous datapoints in the interpretation of

emerging trends. Thus, the utility of statistical process control

will increase over time.

The analysis of quarter by quarter change described above will

focus primarily on prospect market segments within the Primary Male

Sample (PMS). The sample size for these groups will be sufficiently

large to permit quarterly analysis. It should be noted that analysis

of certain additional groups, such as Hispanics and Blacks, can only

be done on an annual basis because the sample sizes will be too small

on a quarterly basis to supply reliable findings.

Panel Analysis

As longitudinal data become available beginning in the second

year of data collection, panel analysis techniques will be used to

explore questions related to individual level change. As with the

trend analysis, greater confidence will be achieved in panel analysis

results as data accumulate. Although panel analyses can be informatively conducted following two waves of data collection, it is generally agreed that three waves are needed for estimation of the reliability of measures.

Analysis of the panel data will not only allow us to evaluate

issues of individual change relevant to the hierarchy of effects model

such as lagtime between intention to enlist and enlistment-related

actions but it will also permit exploration of th3 stability of

prospect market segments and the effects of segment shifts on individual's responses. For example, a high school student's shift from

36

"I 1 cz-Cyz,,
work-orientation to college-orientation may be expected to be accompanied by appropriate changes in the importance and awareness of particular Army offers such as money for college.

One important goal of the panel analysis will be to identify

predictable life-cycle changes and explore their effects on response

to Army advertising by market segment. Results of the longitudinal

analysis will then be compared to the cross-sectional data to determine if cross-sectional samples accurately represent life-cycle

change. This activity will be part both of the tracking activity and

the modeling effects of advertising discussed in Chapter 6 (Gaertner,

1988).

Tracking Prospect Market Segments

The Army's dual-market concept generates expectations about the

effects of each trimester's promotional advertising on specific

prospect market segments. For each prospect group, we would anticipate increases in perceptions of specific Army attributes and knowledge of relevant recruiting offers during the time the group is

focused on as a key market segment. Attention to change across quarters in the knowledge and perceptions of the prospect market segments

will permit close examination of these expectations. It will also

allow tracking of the relationship between the knowledge and perception of Army attributes and enlistment-related propensity and behavior, information that is crucial for identifying the lag time between

advertising and behavior. Attention to changes in media habits and

advertising recall will also be of value in determining where improvements can be made in the media advertising plan.

Using the information we currently have available, we have mapped

the trimester advertising plan onto the quarterly recruiting missions

to identify primary prospect market segments for each quarter. Since

trimesters and quarters do not overlap perfectly, there are relatively

strong secondary prospect markets for some quarters as well. The main

prospect market is comprised of four groups within the PMS: (a) high school graduates not currently enrolled in college or vocational

training, (b) work-oriented high school students, (c) college-oriented

high school students, and (d) college freshmen and sophomores. The

current primary prospect market segments for each quarter are:

(1) Fall quarter (October, November, December)--High school

graduates not currently enrolled. College freshmen and

sophomores and college-oriented high school students are

strong secondary prospects during the latter part of this

quarter;

(2) Winter quarter (January, February, March)--College-oriented

high school students;

(3) Spring quarter (April, May, June)--Work-oriented high school

students; and

37
(4) Summer quarter (July, August, September)--High school

graduates not intending to enroll in college.

The quarterly prospect listed above are subj'ect to change as the

Army's marketing plans change.

Although the four grcups will be the main focus of the tracking

effort, additional market segments will be monitored as well. The

entire youth sample will be categorized by sex. In addition, the

Primary Male Analytic Sample (PMAS) will be grouped first by brigade

and second by one-year age groupings. Thus, questions concerning sex

differences in the full sample as well as area and age differences

among PMAS youth will also receive analytic attention on a quarter by

quarter basis.

Module-By-Module Description of Analytic Activities

This section specifies in some detail the specific variables in

each module that are being tracked. It also includes sample questions

to be addressed by scale construction, trend and panel analysis within

each module.

Intentions to Enlist

Two batteries of questions are being used to measure intention to

enlist--questions measuring unaided responses indicating Army enlistment intentions, and aided responses in which the youth states that

enlistment in the Army is a probable or definite likelihood.

Unaided Intention

IP-I: Now let's talk about your plans for the next few

years. What do you think you might be doing? (If answers

"Joining the Military or Service") IP-3: You said you might

be joining the military. Which branch of the service would

that be? (If answers "Army") IP-4: Which type of service

would that be? (Code "Active," "Reserve," "National

Guard").

Aided Intention

IP-8: How likely is it that you will be serving on active

duty in the Army?

IP-9: How likely is it that you will be serving in the Army

National Guard?

IP-10: How likely is it that you will be serving in the

Army Reserve?

IP-IIA: How likely is it that you will receive an officer's

commission through participation in the Army Reserve

Officers' Training Corps or Army ROTC?

38

A~ .A. .A
Response categories for unaided intention range from "definitely"

(scored 1), "probably" (scored 2), "probably not" (scored 3),

"definitely not" (scored 4).

For the unaided intention questions, a general intention to

enlist in the Army is measured by a response pattern indicating that

the respondent intends to join the military and specifically the Army.

For the aided intei.cions questions, a general intention to enlist in

the Army is considered positive if the respondent answered "definitely" or "probably" to any of the components. The question wordings

and scoring for aided intention are similar to parallel questions in

Youth Attitude Tracking Study II (YATS II), (Research Triangle

Institute, 1985), modified for Army use. Figure 4 presents the

quarterly reporting table shell for intentions.

Scale Construction

Scale construction activities will be directed first to validating the aided general intention measure, by determining whether

respondents describing themselves likely to enlist in one component

are likely to enlist in any Army component. A second activity will be

to determine whether aided intention is closely associated with

unaided intention, both for general Army enlistment and for the

particular components. A third activity will be to determine whether

Army enlistment intentions are closely associated with intentions to

enlist in other services. These latter two scales will not be

reported quarterly, at least initially.

Importance of Army and Component Attributes

The ACOMS youth questionnaire asks all youth to rate the importance of 19 attributes of the Army and its components in regard to his

or her plans for the near future. Army components include (a) active

Army (Active); (b) Army Reserve (USAR); (c) Army National Guard

(ARNG); and (d) Reserve Officers' Training Corps (ROTC). The

importance statements are:

IA-l: In thinking about your plans for the next year,

please tell me how important it is that you have opportunities for the following things?

(1) Having a wide variety of opportunities to find a job

you can enjoy? (Active) (USAR) (ARNG) (ROTC)

(2) Having a physical challenge? (Active)

(3) Having an experience you can be proud of? (Active)

(USAR) (ARNG) (ROTC) ..

(4) Having a stepping-stone between high school and

college? (Active)

(5) Developing leadership skills? (Active) (USAR) (ARING)

(ROTC)

39 'h

.A A A P JL ~,. ! _*, . .. A~
r-4r

I L

I!1

JI

ii Ir-4

40<

* C.

-r * _ WIV 7

1":1
I

(6) Working with the latest high-tech equipment? (Active) a'

(7) Helping your career development? (Active) (USAR)

(ARNG)

(8) Developing self-confidence? (Active) (USAR) (ARNG)

(ROTC)

(9) Developing your potential? (Active) (USAR) (ARNG)

(10) Having a mental challenge? (Active) (USAR) (ARNG)

(11) Becoming more mature and responsible? (Active) (USAR)

(ARNG)

(12) Training in useful skill areas? (Active) (USAR) (ARNG)

(13) Working with highly-trained people? (Active) (USAR)

(ARNG)

(14) Earning money for college or vocational education?

(Active) (USAR) (ARNG)

(15) Serving your country?

(16) Having interesting and exciting weekends? (USAR)

(ARNG)

(17) Working part-time? (USAR) (ARNG)

(18) Living in your own hometown? (USAR) (ARNG)

(19) Being able to make changes and use your own judgment

(ROTC)

The statements were presented in order with a random start to -

minimize systematic biases associated with response set. Response

categories ranged from "not at all important" (scored 1) to "very

important" (scored 5). Figure 5 presents the quarterly report table

shell for importance of attributes. (Importance data for ROTC is

reported in Table 10 which also reports ROTC perceptions data. A more %

detailed account of this table is given in the next section.)

Scale Construction %

Scale construction activities will initially be directed to two

related problems -- what is the structure of needs of American youth,

and how do these needs relate to perceptions of the Army and its

components? The progress toward answering these two questions is V

likely to be iterative, beginning with factor analyses of the impor- "

tance items for the PMAS, comparing these with corresponding results

for perceptions of the Army and its components, then observing whether I

observed importance factors are constant across recruiting market

41

-- %- .-. .... , --..
i ..

ilV

|iI

il

i i I

ii I

I!,,

! &

* Il ..- ! '

:~ :1
isegments and whether perceptions and importance factors correspond

within as well as across segments.

This iteration is made possible by the parallel construction of

the perceptions and importance items, which was, in turn, suggested by

the hierarchy of effects model. Thus for all questions asking perceptions of the Army's components, we have corresponding questions asking

how important this attribute is to the respondent.

The first issue in importance scale construction is descriptive--

how do the various attributes hang together? The dual market theory

suggests that motivations will be divided into instrumental (e.g.,

skill training, job characteristics) and future-enabling (e.g.,

developing potential, leadership skills). Other clusterings might

separate comparatively instrumental values from patriotic ones, or

educational from occupational values. Other arguments have separated

factors pulling recruits into the service from those pushing them from

their current situations. The first step in scale construction will

be to factor analyze the importances to assess what clusterings of the

attributes are most descriptive.

A second issue involves relating structures of perceptions to

those of importance. As noted in previous sections, Fishbein and

Azjen (1975) place critical reliance on the products of importance and

perceptions (in their terms evaluations and beliefs, respectively) in

predicting attitudes and subsequent behavior. Thus, assessing correspondence between the structures of youth values and perceptions is a

critical analytical task for subsequent model development.

Perceptions of Army and Component Attributes

The ACOMS youth questionnaire asks the respondent to agree or

disagree with statements describing attributes of the Army and its

components as well as the military in general, other services, college

and work. Since not all of the attributes were equally relevant to

all of the Army components and since space in the interview was at a

premium, the SAG allocated perceptions to the Army components. The

statements used in the first quarter of data collection- were later

modified slightly. The statements presented are those used in the

second quarter and following (relevant components in parentheses).

The statements are:

The (SERVICE) offers...

(1) A wide variety of opportunities to find a job you can enjoy.

(active and other services) (USAR) (ARNG) (ROTC)

(2) A physically challenging environment. (active and other

services)

(3) An experience you can be proud of. (active and other

services) (USAR) (ARNG) (ROTC)

43
(4) An advantage over going right from high school to college.

(active and other services)

(5) An opportunity to develop leadership skills. (active and

other services) (USAR) (ARNG)

(6) The chance to work with the latest high-tech equipment.

(active and other services)

(7) A great value in your civilian career development. (active

and other services) (USAR) (ARNG)

(8) An opportunity to develop self-confidence. (active and

other services) (USAR) (ARNG) (ROTC)

(9) The opportunity to develop your potential. (active and

other services) (USAR) (ARNG)

(10) A mentally challenging experience. (active and other

services) (USAR) (ARNG)

(11) An opportunity to become more mature and responsible.

(active and other services) (USAR) (ARNG)

(12) Many opportunities for training in useful skill areas.

(active and other services) (USAR) (ARNG)

(13) Many chances to work with highly-trained people. (active

and other services) (USAR) (ARNG)

(14) An opportunity to obtain money for college or vocational

school. (active and other services) (USAR) (ARNG)

(15) An opportunity to serve America while living in your own

hometown. (USAR) (ARNG)

(16) Interesting and exciting weekends. (USAR) (ARNG)

(17) An excellent opportunity for part-time work. (USAR) (ARNG)

(18) Leadership and management training. (ROTC)

(19) A college elective that can be taken together with other

college courses. (ROTC)

(20) An officer's commission in the active Army, Army Reserve, or

the Army National Guard. (ROTC)

(21) The opportunity to use your college acquired skills. (ROTC)

(22) The opportunity to make changes and use your own judgment.

(ROTC)

44 p
The statements are presented in order with a random start to

minimize systmatic biases associated with response set. Response

categories range from 1 to 5 where 1 means strongly disagree, 2 means

disagree, 3 is neither agree nor disagree, 4 is agree and 5 is

strongly agree. Each respondent is asked about two or at most three

components/services/other. These sets of attribute statements are "chained" together such that statements which are common to each set

are asked at the same time. For example, if the respondent receives

active Army and Reserve statements, the interviewer says, "The Army

offers you a physically challenging environment," then follows with,

"How about the Reserve?" (During the first quarter of data collection, responents were asked the full set of attributes for one

component/service/other before continuing to the next component/

service/other.)

The format for the perceptions tables varies somewhat among the

Army components. The active Army table is in the same format as the

other tables in the quarterly report. The tables for the Reserve and

National Guard report only totals for the main recruiting market

groups because sample sizes are not sufficient to allow reliable estimates for the smaller subcategories. The main recruiting markets

consist of the Primary Male Analytic Sample (PMAS), the Secondary Male

Sample (SMS), the Primary Female Analytic Sample (PFAS), the Secondary

Female Sample (SFS), the Total Recruiting Market, and the Total

Primary Male Analytic Sample (PMAS). The ROTC table reports data for

the ROTC sample which includes only College Juniors and Seniors,

College Freshmen and Sophomores and High School Students Collegeoriented because these groups represent the prime ROTC market. The

table also presents data by ROTC region rather than recruiting

brigade, and reports both ROTC perceptions and corresponding importance items for the ROTC sample. Figures 6 through 9 present the

table shells for the quarterly reports on perceptions.

Scale Construction

Rather than asking the respondent to provide his or her image of

the military in general or the Army and its components in particular,

the statements elicit the respondent's perception of the accuracy of

major military advertising copy points. The scale construction activity should produce preliminary measures of the extent to which the

Army's advertising and that of the components has been able to project

distinctive images of their offers and attributes.

The first scale construction step will be to factor analyze the

perceptions measures for all services. While the discussion which

follows assumes factor analytic procedures, multidimensional scaling

may be more appropriate. Both methods will be explored. Further, in

all likelihood, smaller factor analyses will likely precede the large

one described below. Correlations among perceptions of attributes

within component can be estimated quite directly. Correlations among

perceptions across components/services can be estimated because

perceptions of each pair of components/services was asked of some

subset of respondents. Before proceeding, two caveats should be

noted. First, no respondents were asked solely about other services,

45
S.

SW

.1 * 1;

I :1

I

II

ii S's

1?

4

:1 3 1-

4 

U

I:' V

~I 

*

~

-

I ~I 

,

F 1 10 t

11 5 )**~

ls.i

; 

I

~ ii 

I

1 

* 5'

I * 0'

0-4

a, -%

p I ____________________ *~

(A

1 

-

I .0

________ 

j-s ii l t d I #~'

I II!! ~ I 

-

-

I a a a * 

-

U

!J~ * ~ I z ~ 3 :~ 2 2 

~

46

' 

-

- d V 

. V~VvE.~

~ ~l * 'V % ''~q
*1 * a A III

Li I

III

1;

~; 

5V

i ~.; p.

~ ii * S

I ;t

- ~ Jj 

4

I ~ 

0

~ I ~ -4 -'I 'I

a j ~ 

00

~ :1 

0

* Ia: 

0

:2 -. 0 ~ 04 p. ~ 0

-4 

v 3~.41 0

1~) 4.

Cu

Ii4 .4-

* -p 0

'4-4

4 t.~

_________________ 

0 4 -- S

: *~

-

-0 A -4

I.

.4

I

S

& * 

S

47 I.

p.
- ~ *M~ ~.

I

__ 

I

K!- I

ii

1; = -S !

* -.

V

-c

!

II 

p

A'

~ A' 2= N

I, ~ -% II

~! LI Cu .4 - a

1 Cu :1 Cu

-~ .ti I

-~ 0 4,.

* 'U.

4' 4,..

4.

.4.

~ -4 4. t '.4I 1-I

Cu -'4

- I p

'I iI a U ~ I 1! -4 4.

-4

______ * '2) I-

* '2)

'-4 I-- - - Cu 

4

4',

-qii**~ 

-I I - - - u '.4..

a Ii! ~

I

48 .4.

'.4

d .4. .4~ * .4'. .~* ~* '4. ~w -- ~,-- 

'
. w w i . xP. r i I ' -, J' A. ? C'0 ,.' F i'.'. . C ' ' . * ... '' ,.r : , ' " .. *-, '. .. 4 % .J ~ .~

i

1:1

" I

- h

-iil : :

.- i l

:2

I..

-* I l . -

, ii!i; ' -' .' I i
so that the correlations between Navy attributes and Air Force

attributes, for example, cannot be estimated without strong assumptions. Second, this overall correlation matrix may have to be

smoothed if the correlation matrix is not positive semi-definite.

Several possible classes of results of the factor analysis

suggest themselves. First, it may be that youth favorable to any

service or component will be favorable to all of them, and that youth

favorable to any attribute will favor all of them (and the converse).

This result would reflect a relatively undifferentiated picture of

military service, either in terms of service images or attributes of

military life ("Military life is either good or bad"). A factor analysis of all attributes for all components/services would reflect a

single dominanc factor. In a sense, this represents the simplest of

results, and the nullest of null hypotheses. Another cause of these

results might be response set. Our modifications to the Fall 1988

instrument were in part devoted to minimizing this possibility.

Beyond this simple case, the results might increase in complexity

several ways. First, it may be that respondents are "brand loyal"--

that is, for example, respondents favorable about any Army attribute

are favorable about all of them. Extended to all services, this state

of affairs would be reflected in factors being extracted from the

overall factor analysis which referred not to attributes but rather to

services, yielding an "Army" (or component) factor, an "Air Force"

factor, and so on. Second, it may be the case that respondents who

saw an attribute in one service would see it in all of them (and the

converse), yielding factors which were attribute specific rather than

service specific. This would suggest a fairly differentiated picture

of the attributes of the military ("military life offers some things

but not others"), but little differentiation among services. Third,

it may be that the factor analysis will show distinctive service by

attribute clusters, a highly differentiated picture ("The Army offers

skill training and money for education, while the Navy offers ... ").

Fourth and finally, it may be that different market segments will

perceive different service by attribute clusters. Thus, for example,

work-oriented prospects might think that the Army offers distinctive

skill training while college-oriented prospects might think the Army

has a distinctive money for college offer. These latter results are

the basis of the dual market theory.

The classes of results above are presented in increasing order of

complexity, from one factor (an undifferentiated attitude toward the

military), to a few factors clustered by service (brand loyalty) or by

attribute (a differentiated view of the military as a whole), to

multiple service by attribute factors (distinctive service attribute -.

packages), and finally multiple contingent service by attribute

factors (different service attribute packages by market segment). 0

Scale construction results should mirror this complexity, while

attempting to provide a parsimonious description of the results. %

50
Enlistment-Related Behaviors

Enlistment-related behaviors are being assessed using the

following battery of questions:

BE-l: In the past six months, have you talked with anyone

about possibly joining the Army? (If answers "yes") BE-2:

With whom have you talked? (If answers "recruiter") BE-8:

Was the recruiter an Army recruiter?

BE-7: In the past six months, have you talked to an Armed

Forces recruiter about military service? (If answers "yes")

BE-8: Was the recruiter you spoke with an Army recruiter?

BE-1O-12: In the past six months, have you...

a. responded to an Army ad by calling a toll-free number

or sending for a gift?

b visited an Army recruiting station?

c. taken a written test used for the Army, such as the

Armed Services Vocational Aptitude Battery?

Figure 10 presents the table shell for the monthly reports on

enlistment-related behaviors.

Scale Construction

The intent of the scale construction activity for enlistmentrelated behaviors is to develop a set of milestones measuring progress

toward enlistment. Our approach in building this scale will be to sum

the responses and determine which of the measures (if any) aoes not

seem to fit an overall scale.

In addition, the interview asks for respondent behaviors with

respect to entering college and securing civilian employment.

College-related behaviors include thinking or talking about going to

college, taking admissions tests, or submitting college applications.

Job-related behaviors include thinking or talking about getting a

full-time job, visiting employers, or applying for a job. While these

behaviors will not be reported quarterly, we expect to construct milestone scales for college- and employment-related behaviors which will

parallel the scale for enlistment-related behaviors. These scales in

combination should allow us to describe the process of posthigh school

life choices for ACOMS respondents, and provide clues for likely and

unlikely prospect markets.

51
WW V D UW.W4W

VVN hW VjFb 2 ~'~~~.

,p

,',

,5p

= ] ' 

I

• i .I

" 1. * 

!a 

I I I, :

El 

... 0.

'"."
V 2 771 W77 77

K77 c..k7.. r..4

Recall of Advertisements

Specific Variables

Two sets of questions are included in the quarterly reports on

recall of advertisements. First, unaided recall is being tracked as

shown in the table shell in Figure 11. Four questions are used in

tabulating the results contained in this table:

(1) KR-I: Thinking about all forms of advertising, for which

military service or services do you recall seeing or hearing

any advertising?

(2) KR-2: (Asked if the respondent indicates recall of ROTC

advertising) You mentioned seeing or hearing advertising

for the Reserve Officers' Training Corps. For which military service or services was this advertising? %

(3) KR-3: (Asked if the respondent indicates recall of National

Guard advertising) You mentioned seeing or hearing advertising for the National Guard. For which military service

or services was this advertising?

(4) KR-4: (Asked if the respondent indicates recall of advertising for the Reserve) You mentioned seeing or hearing

advertising for the Reserve. For which military service or

services was this advertising?

Overall Recall levels for each of the branches/components of the

military is being tracked by combining the above unaided responses

with aided measures of recall elicited in questions KR-5 through KR13. These nine questions are asked of respondents who did not spontaneously mention recalling advertising for the particular

branches/components shown in the table shell in Figure 12. Each of

the questions is worded: "Do you recall seeing or hearing any advertising for the <BRANCH/COMPONENT>?"

Scale Construction

The scales to be constructed will depend heavily on the preliminary analysis. It may be, for example, that recall of military

advertising depends heavily on which branch is being considered. It

may even be that recall of Army advertising depends heavily on which

component is being considered. In these cases, we expect to develop,

as necessary, multiple measures of recall, branch- and/or componentspecific.

Tracking unaided recall of each of the branches' and components'

advertising will indicate the relative differences among recallability

of advertising for each of the branches and components. Additionally,

responses to aided recall questions will tell us how much the provision of branch/component names helps increase recallability for each.

Another important question addressed by analysis of the recall items

is the nature of differences among market segments in levels of

53

% %% % % % %

X!.
- - - - . F - s . . . m~~~F~FUJ' PJ~~J '~

S

.C~C

S

4.

1; S

'I

2

i

I I

S

'a

I -~

-

3 V

I Cu

S

Cu .5.,.

Cu .5.'

-'4.

__________ 5% A-i

- U I 0

o.

U.. I

-a- .

:W. C -

I' C

I 0 I. S

* IN

w

Cu

:1 I: j 71-:

35 - ~ :i ~ ~ 0 0 5 I

-~ 0 Ii Ii u~-I j 2 1 - . 0 - ! - - - .4. a . a * I- * ~ s..: .E

1 S - - -. .4

- .. S S

?q~C

54 S

-w

C... - ~ p, *.* j.*~p~5

'.p.5,~. 5 4 'a...' 454 .4--' .5-.'.., -4 -, -' - *~ * C ~ ~ 4.~. ~ ~K5 '

K J~~* p~.. ~ * -

'4 .P.A4'FC.'4 -5's *. ~ - .
" 

I

= iV

. I I:

__- !

. 55 
unaided or overall recall of advertising for specific military

branches and Army components and how recall varies over time within

market segments.

Questions addressed by scaling include the overall question of

what level of unaided recall of military advertising exists among 16-

to 24-year-olds and how much recall is increased by the provision of

branch and component names. Differences in the recall scales among

market segments over time will be of interest at both the aggregate

and individual levels.

Knowledge of Offers

The ACOMS youth interview asks a number of questions regarding

respondent knowledge of Army offers, the answers to which can be

scored as correct or incorrect. These questions, which will be

tracked and reported quarterly, are (the correct answers are displayed

in parentheses):

Active Army Knowledge

KA-7: Is it possible to earn money for college by enlisting

in the Army? (Yes)

KA-I: (If answers "yes" to KA-7) How much do you think can

be earned through Army education benefits? ($15,000+)

KA-3: (If answers "yes" to KA-7) Do you think Army education benefits are more, less or about the same as the Navy,

Air Force or Marines offer? (More)

KA-4: Please tell me whether or not each of the following

offers the "GI Bill"? [List includes Army, Navy, Air Force,

Marines.] (Each Offers)

'Z

KA-5: What is the minimum number of years that a new

recruit has to serve on active duty in the Army? (2 Years)

KA-6: Is it possible to sign up for the Army and start

serving up to one year later? (Yes)

Army Reserve and Army National Guard Knowledge

KA-8: Are 17-year-old high school juniors eligible to join

the Army Reserve or Army National Guard? (Yes)

KA-9: (If answers "no" to KA-8) Is high school graduation

required before joining the Army Reserve or Army National

Guard? (No)

KA-10: Who sponsors the "Scholar-Athlete Award Program?"

Is it the Marine Corps, National Guard, Army Reserve or Army

National Guard? (Army Reserve)

56 I

N N AW ,K NN
KA-I1: Can qualified people who join the Army Reserve or

Army National Guard receive money for college? (Yes)

KA-12: (If answers "yes" to KA-Il) What is the maximum

amount of money for college that qualified people who join

the Army Reserve or Army National Guard can receive under

the "GI Bill"? ($4,000-$5,999)

Rather than presenting the responses for these questions in the

quarterly reports, we will simply show the proportion answering each

question correctly. Figure 13 presents the table shell for these

items in the quarterly report. We expect that we will track knowledge

of offers throughout the ACOMS project, but that the actual items

being tracked may change over time.

Scale Construction

One summary measure to be produced might be the sum of the

correct answers given to all of the questions above. Alternatively,

the scale results may suggest that knowledge of offers clusters in

terms of active versus Reserve and National Guard. In this case

separate knowledge scales would be constructed.

The knowledge questions provide a measure of the comprehension of

the offers of various components as they are described in advertisements--nearly all of the messages are copy-points currently or potentially in Army advertising. Questions of interest will relate to

whether knowledge of offers varies seasonally and/or across key market

segments or as copy points are added or deleted.

Media Habits

Specific Variables

Two sets of media habits questions are being regularly tracked--

one having to do with likely exposure to Army advertising, and a

second having to do with media consumption patterns.

The former proxy exposure measures include asking the respondent

about the various types of television shows he or she frequently

watches and about the various types of radio programs he or she frequently listens to. The question sets for television and radio habits

are:

H-12: Do you frequently watch any of the following types

of TV shows?

Sports

Suspense or mystery

General drama

Music or music video

Situation comedy

TV movies

Talk shows

167
! a I * [.--,

hi~

ij Iii -,0

w A

, : . ! ! I., -

!j

I 58

... i . ... -- .. ..... ... ,. . ..,,,- .,, ,,,,....., .'..',.', .,..,.+.q , .. l' . s ' ..'n , • . , . , ., '> .. " " ,'e - , ' ' ' * U;-- ' t" i - ' 
MH-26: Do you frequently listen to any of the following

types of radio programs?

News

Classical music

Pop

Country

Sports

Talk shows

Rock & roll

"Easy listening"

Figure 14 shows the table shell for the quarterly reporting of

media habits responses. These measures represent proxy measures of

exposure to various types of media programming.

A second set of media habits questions relates to patterns of

media consumption--that is, the levels and balance of media regularly

monitored by the respondent. The questionnaire asks the youth whether

he/she regularly watches television, listens to the radio, watches

programs on a VCR, and reads magazines or newspapers and how many

hours weekly for each. While these results will not be reported

quarterly, they will have several potential uses. First, they may be

used in the segmentation analyses to define groups partly on the basis

of media consumption. Second, they relate to the overall modeling of

advertising effects (Chapter 6, Gaertner, 1988). Third, we may use

these measures to refine the analysis of exposure data from syndicated

sources (Chapter 7, Gaertner & Baxter, 1988).

Scale Construction

In general, media habits scaling results may be specific to

medium (e.g., print readers, television viewers, radio listeners) or

perhaps can be summarized in a single index. That is, both for proxy

exposure and media consumption patterns, it may be sensible to speak

of an overall level of media and/or advertising exposure, or it may be

more useful to break down the results in terms of print, television

and radio media habits and exposure.

Questions to be addressed by examining patterns of media consumption include: What level of attention (in terms of time) do youth

devote to media? How does the level differ across media? Are there

differences among market segments in level of mass media attention?

Are there segment differences in orientation to specific media? Are

there seasonal trends in media orientation? Are there differences in

level of media orientation at the individual level over time? Do

c-nese differences correspond with cross-sectional observations (e.g.,

age and educational groupings)?

Results of the scaling of likely exposure to Army advertising

will address questions about levels of exposure among 16- to 24-yearolds given the Army's current advertising campaign. The relationship

between likely exposure and education, sex, age, and region of residence will also be explored both in general and by medium. Other

59
A. -

M

J.

2. B S a -

£ a. *BJ.

V.

1

* B bBBBB~

*1 V . I

IA I

2 1

j <~ *

2 *-. 

__I_______ __________________________________________________ (B

_____ I ________________ ~lt

Cu

2-

. Cu

IL 1 cu B

* .

I * .

* 0

'I I C-

* 2

I *

:1 1 ~

* . Cu

I! I

2 o &

* -

-~ ,4B

_________________________________________________________________ Cu

U C- S

I I 1. U B~ *~ j. ~ 3 0 0 0 0

a ; : ~ II !i ~ * * 1 ~ * - -

a. 5.1 :~ I 2

60 p

4%

*d .f J **~~B ~ B ~ \~B ~,f J* JB ~ ~ ~ ~ ~ - ~ .~ ~
I

questions that will be addressed include: What are the differences in

likely exposure to ads of each of the Army's components? How does the

level of likely exposure vary across seasons?

Scheduling and Deliverables

The main deliverables associated with tracking audience response

are quarterly reports and briefings and annual report chapters. The

first quarterly report was presented February 18, 1987 and contains

data analysis from the first quarter of data collection (October,

November, and December, 1986). Thereafter, reports will be presented

mid-May, mid-August, and mid-November, 1987 with the annual report

scheduled for October 30, 1987. Quarterly reports are briefed to the

SAG and presented in narrative form. Subsequent years will continue

this quarterly and annual schedule of report production.

The following subsections describe our plans for the format and

content of these deliverable items.

Quarterly Reports and Briefings

The quarterly reports consist of two main sections: (a) an

executive summary which highlights important findings from the quarter

in terms of the key prospect markets for that quarter and the next

defined by the dual market concept, the quarterly accession missions

and the Army's advertising plans, and (b) a section of "top-line" data

tables, providing results in summary form. The quarterly reports will

also be accompanied by information cards and appendices to aid in

interpretation.

Executive Summary

The first main section of the quarterly reports will focus on

interpretation of the findings. As described earlier, we can use the

dual-market concept to organize and interpret the main findings in N.

each quarter.

The dual-market concept in combination with cyclic changes in

accession missions defines target markets for each quarter--high

school degree graduates (HSDGs) in quarter 1, college-oriented in 6

quarter 2, work-oriented in quarter 3 and HSDGs not intending to

enroll in college in quarter 4. The dual market concept also posits

perceptions critical to the respective motivations of these key target

markets, such as college-oriented high school students attracted by

offers of college funding and normative appeals, work-oriented high

school students by future-enabling work-oriented messages. In Table 5

we have mapped the Army attributes against these target market motivations. Finally, the dual-market concept helps to define the sorts of

knowledge critical to enlistment related decision-making for the

target markets. Table 6 maps the the knowledge questions asked in the

ACOMS interview against the market segments defined by the dual market

concept. 0

61

L.' A %N- .
Table 5

Messages Directed to Prospect Markets as Measured by the Perceptions of

the Army Attributes Questions

Target Markets

College-Oriented J Both I Work-Oriented

An opportunity to aAn opportunity to Many opportuniobtain money for develop leadership ties for training

college or vocation- skills (ROTCc in useful skill

al school wording: leader- areas

ship and manageAn advantage over ment training) A great value in

going right from your civilian career

high school to aAn opportunity to development

college develop selfconfidence A wide variety of

The opportunity to opportunities to find

make changes and use aA mentally a job you can enjoy

your own judgment challenging exper-

(ROTCc only) ience Interesting and

exciting weekends

A college elective A physically

that can be taken challenging An opportunity to

together with other environment serve America while

college courses living in your own

(ROTC only) An experience you hometown

can be proud of

An officer's commission in the bThe chance to

active Army, Army work with the

Reserve, or the Army latest high-tech

National Guard equipment

(ROTCc only)

An opportunity to

The opportunity to become more mature

use your college and responsible

acquired skills

(ROTCc only) Many chances to work with highly

trained people

The opportunity to

develop your potential

An excellent

opportunity for

part -time work

aAlthough targeted to both market segments, these messages are expected

to have stronger appeal to the college-oriented segment. bAlthough

targeted to both market segments, this message is expected to have

stronger appeal to the work-oriented segment. cReserve Officers

Training Corps

62
V.' r, W72.777;

Table 6

Messages Directed to Prospect Markets as Addressed by the Knowledge of

Army Offers Questions

Target Markets

College-Oriented I Both I Work-Oriented

Is it possible to earn What is the minimum Are 17-year-old high

money for college by number of years that school juniors

enlisting in the Army? a new recruit has to eligible to join the

serve on active duty Army Reserve or Army

How much do you think in the Army? National Guard?

can be earned through

Army education Is it possible to Is high school

benefits? sign up for the Army graduation required

and actually start before joining the

Do you think Army serving up to one Army Reserve or Army p

education benefits are year later? National Guard?

more, less or about

the same as the Navy, Who sponsors the

Air Force, or Marines "Scholar-Athletic

offer? Award" program? Is

it the Marine Corps,

Please tell me whether National Guard,

or not each of the Army Reserve, Air

following services Force, or Navy?

offers the "GI Bill"?

- Army

- Air Force

-Navy

- Marines

Can qualified people

who join the Army

Reserve or Army

National Guard

receive money for

college?

What is the maximum

amount of money for

college that qualified

people who join the Army

Reserve or Army

National Guard can

receive under the

"GI Bill"?

63

%-%C

%* 2 0 % -v!.-Z i . .. C
The dual-market concept in combination with the ACOMS interview

data allow retrospective and prospective assessment of the Army's

advertising on a quarterly basis. Retrospectively, the theory

suggests that if the advertising were successful, the work-oriented

knowledge of offers and perceptions of Army attributes should increase

in the first quarter for non-enrolled high school graduates, that the

college-oriented knowledges and perceptions of college-oriented high

school students should increase in the second quarter, and that the

work-oriented knowledges and perceptions of work-oriented high school

students should increase in the third quarter. These expectations may

be excessive, but they do provide measurable milestones against which

we can assess advertising performance.

The dual-market theory also provides diagnostic indicators of the

likely receptivity of target markets in upcoming quarters. Since, for

example, much of the accession mission of quarter 2 is allocated to IliA high school seniors, the extent to which first quarter results

indicate favorable perceptioLs among coliege-orieL Led high schIool

students should be of interest.

We should note several caveats in these retrospective and

prospective assessments. First, it is not clear how much perceptions

of Army attributes will vary from quarter to quarter, or how much

advertising can affect them. Obtaining answers to these questions is

why ACOMS was developed. Second, the target markets are themselves

not fixed and immutable. It is likely, for example, that many high

school seniors will, in the early part of the school year, consider

themselves "college-oriented" before the exigencies of college admissions and funding have been seriously considered. Third, the question

of how favorable is favorable enough is a real one and will only

become clearer with time. Nonetheless, the process of hypothesizing

and tracking results in line with the dual-market theory should allow

much clearer assessments of Army advertising than would otherwise be

the case.

Data Tables Section

The data tables section will feature the 11 full data tables and

the 3 tables of monthly PMAS totals, and any additional tables

approved by the Contracting Officer's Representative (COR) and the

SAG. Copies of the 11 data table shells are shown in Figures 4

through 14. These tables provide estimates of levels of media habits,

recall, knowledge, perceptions, enlistment-related behaviors and

intentions to enlist in the Army broken out by market segments as

shown. The PMAS monthly totals are for enlistment-related intentions,

perceptions of active Army and unaided recall of military advertising.

The main focus of this section is to provide "quick readings" of

where respondents stood with respect to critical variables during the

quarter. However, summary statements about observed changes across

quarters will also be included in this section when appropriate. This

section may expand as scaling results become available. Tables and/or

graphical representations of scales with accompanying text may be

added when available, after discussion with the COR and the SAG.

64

%
A

Information Cards and Appendices

In addition to the two main sections, each quarterly report %

includes information cards for each of the 11 main data tables. These

cards provide information on respondents, special notes and cautions

and a question key which details "at a glance" the exact wording of

each question item and its accomopanying table column headings and

variable names. A set of appendices containing useful reference materials on ACOMS sampling, the weighting of data, computation of standard errors, and a list of reference sources is also included. The

quarterly reports are reproduced on three-hole punched paper so that

one binder will hold all reports for one year plus the entire set of

reference appendices.

Annual Reports

The tracking results for the year will also provide a chapter in

the annual reports. This chapter can have several purposes. The

first is to summarize the trends for the year in terms of media

habits, knowledge, recall, perceptions, intentions and behaviors _n a

more holistic way than is possible in the quarterly reports. A second

is to compare the results for the current year to those in the previous year as these data become available. This activity will be of

particular interest as the panel data become available. A third

purpose is to address topics that are distinctively related to

tracking--estimates of seasonality of particular items and scales, and

estimates of secular trends which are noncyclic.

.1

65

0~S d*'dr
REFERENCES %

Fishbein, M., & Azjen, I. (1975). Belief, attitude, intention and behavior:

An introduction to theory and research. Reading, MA: Addison-Wesley.

Gaertner, G. H. Introduction. In G. H. Gaertner, & T. W. Elig (Eds.).

(1988). The Army Communications Objectives Measurement System (ACOMS)"

Survey analysis plan (ARI Technical Report 786). Alexandria, VA: U.S.

Army Research Institute for the Behavioral and Social Sciences.

Gaertner, G. H. (1988). Modeling the effects of Army advertising. In G. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement Systems (ACOMS): Survey design (ARI Technical Report 786).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Gaertner, G. H., & Baxter, S. J. (1988). Plan for the integrated analysis of

fit, exposure, and change model. In G. H. Gaertner, & T. U. Elig (Eds.),

The Army Communications Objectives Measurement System (ACO.MS): Survey

analysis plan (ARI Technical Report 786). Alexandria, VA: U.S. Army

Research Institute for the Behavioral and Social Sciences.

McCarthy, P. J. (1969). Pseudo-replication: Half samples. Reviews of the

International Statistical Intitute, 37, 239-264.

Mahadjer, L., Chu, A., Morganstein, D., & Rhoads, M. (1986). Estimation and

analysis of survey data using SAS procedures: WESVAR, NASSREG, and

NASSLOG. Paper presented at the meeting of the American Statistical

Association, Chicago, IL.

Research Triangle Institute. (1985). Youth Attitude Tracking Study II, Wave

16--Fall 1985 (Contract %DA903-83-C-1072). Arlington, VA: Defense

Manpower Data Center.

Wilson, M. J. (1988). Market segmentation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis lIan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

. -e

.J
3. MARKET SEGMENTATION ANALYSES

Michael J Wilson

Introduction and Overview

M_.e.et segmentation is the process of classifying individuals

into homogeneous groups based on the similarity or dissimilarity of

respondent characteristics (Sands & Warwick, 1981). As such, segmentation analyses form an important part of the overall Army Communications Objectives Measurement System (ACOMS) analysis. Efficient

segmentation of the youth market can help the Army locate promising

prospect groups, tailor advertising executions and placements to them

and track the achievement of communications objectives.

Several unique features of the Army complicate segmentation

analysis. First, the Army's market segments are defined in terms of

respondent attitudes and buying motives and in terms of sociodemographic attributes of the respondent such as entry Test Score

Category (TSC), race, gender, age, etc., or both. Second, the various

Army components can be expected to require different segments and

segmentation strategies regardless of whether the segments are defined

demographically or attitudinally. Third, the task requires that we p

both analyze the existing segmentation strategies of the Army and its

components, and develop new segmentation strategies in support of Army

advertising. In this chapter we outline our approach to these

challenges. %

The market segmentation analyses discussed in this chapter constitute a natural extension of the activities described in Chapter 2

(Gaertner, Keil, & Gay, 1988). Continuing this earlier focus, the

segmentation analyses will consider how key current market segments

vary with regard to media habits, knowledge of active Army and component offers, recall of Army advertisements (aided and unaided), perceptions of active Army, Reserve Officers' Training Corps (ROTC),

Army Reserve (USAR) and Army Natioral Guard (ARNG) attributes, and

enlistment-related intentions and behaviors. The market segmentation

analyses will focus directly on the multivariate characteristics of

market segments while simultaneously extending analytic activities to

include the assessment of new segmentation strategies. Specifically,

the questions the market segmentation analyses will address are:

(1) How well do current predominantly sociodemographic segmentations isolate the needs, perceptions, propensities, and

behaviors of American youth?

(2) How do important market segments differ among components?

Do segments defined for the active Army differ in attitudes

toward reserve components?

(3) Do alternative more efficient segmentation strategies exist? A

More specifically, can sociodemographic and attitudinal data

be combined to produce homogeneous respondent clusters?

67

€ '.: . '; , ' ' : :_' . ' = ' " ' .. ' "€....1 .. .. .. . . .. ..... , . . .
(4) How do these combined sociodemographic and attitudinal

segmentation strategies differ among the various components?

(5) How stable are the segmentation results over time?

(6) How can the various segments defined be utilized to target

advertising messages and placements and to support

recruiting missions?

These questions will structure the ACOMS market segmentation

analyses. They address, first, the utility of existing segmentations

overall and differentially by component, and, second, the development

of new segmentation strategies based upon respondent needs, habits,

perceptions, propensities, and behaviors, again, overall and by component. The last question constitutes an overarching concern of any

market segmentation analysis--how the results can be used to direct

advertising, marketing and sales efforts.

We begin this chapter by describing our approach to the segmentation analyses. Then a more detailed account of year 1 analyses is

presented. This discussion is divided into two parts: (a) assessing

current segmentation strategies, and (b) developing new segmentation

strategies. For each, we first discuss a general approach to be

applied to active Army segmentations and then analytic procedures for

extending the results to the ROTC, ARNG and USAR. The discussion of

year I activities is then augmented by an overview of year 2 analytic

extensions.

Approach

Our approach to the market segmentation analyses is divided into

two separable, but related, parts. The first is designed to describe

current market segmentation strategies. In performing these investigations, we will: (a) develop tabular descriptions of current

segments, (b) describe and summarize relationships between market

segments and important demographic, attitudinal, and intentional/

behavioral characteristics, (c) assess differences in knowledge and

perceptions of Army components in terms of existing segments and

(d) assess the stability of descriptive findings throughout the data

collection year. This assessment of current market segments is

closely integrated with the tracking efforts described in Chapter 2

(Gaertner et al., 1988).

The second set of activities, directed toward the development of

*new market segments, will proceed somewhat differently. Rather than

beginning with established market segments and moving to a comprehensive description of their relevant demographic, attitudinal, and

intentional/behavioral characteristics, this second approach will

begin with an examination of salient respondent characteristics and

then work "backwards" to a formulation of market segments. Outlined

as a series of steps, this analysis will: (a) classify respondents

into "natural" clusters or groups based on attitudes and values

(b) describe the clusters in terms of targetable characteristics such

68

- , .r.. - - ,p . ... . . ... . . , ... . . o .,', .', '... . - . ".''..- -.
as demographics or future plans (i.e., work-oriented, collegeoriented) in order to define the new market segmentations, and (c)

assess the stability of new segment characteristics throughout the

data collection year.

In addition to the close relationships between the segmentation

analyses and the tracking results noted above, the segmentation analyses can also be seen as the obverse of the brand differentiation analyses described in Chapter 4 (Wilson, 1988). If market segmentation

describes the structure of demand for Army enlistment and the ways in

which youth view the Army, brand differentiation describes the structure of supply of Army enlistment and the ways that Army components

hope to be viewed by prospect youth. The dynamics of this joint positioning are addressed more directly in Chapter 6 (Gaertner, 1988).

Analysis of Current Market Segmentation Strategies

The analysis of current market segmentations will be the first of

the year I activities and will proceed in a three-stage, incremental

fashion. The steps to be taken are:

(1) Tabular descriptions of market segments in terms of their

demographic, psychological, and behavioral characteristics

as appropriate;

(2) Examination of the results for the various Army components

to determine whether unified or separate segmentation

strategies are required; and

(3) Assessment of the stability of findings obtained in steps I

and 2 throughout the data collection year.

Within each of these steps, initial analytical efforts will be

directed to the active Army. Subsequent activities within each step

will seek to replicate and extend the findings to provide results _

which are of relevance to the ROTC, USAR and ARNG.

In the sections following, these steps are described in greater

detail.

Tabular Descriptions

As discussed in Chapter 2, an initial cut at the active Army segmentation strategy is based on three factors: the dual-market concept

(i.e., work-oriented and college-oriented high school seniors), the

trimester advertising strategy which varies the mix of advertised

communications between "core" and "targeted" messages, and the differential education and quality foci of quarterly recruiting missions.

Thus the main active Army prospect market is comprised of four market

segments within the Primary Male Analytic Sample (PMAS):

(1) High school graduates not currently enrolled in college or

vocational training,

69

, " " " . , ,. .- % 
.. K 1V 

(2) Work-oriented high school students,

(3) College-oriented high school students, and

(4) College freshmen and sophomores.

These segments will constitute one of the more important current

market segmentations examined in the analysis. In order to distinguish this segmentation from others discussed, it will be termed the

main active Army prospect segmentation. Crosstabulations of major

ACOMS variables by main active Army prospect segmentation will be made

available in the quarterly reports.

The active Army also segments its prospect market according to

gender, race, age and recruiting brigade. These segmentations will

also be examined and, except for race, which is unavailable because of

the small numbers of nonwhites in the quarterly sample, provided in

the quarterly reports.

The segmentation strategies of the reserve components appear to

yield similar groupings, but emphasize different markets. Among

youth, for example, the ROTC defines high school juniors and seniors

and college freshmen and sophomores as its main markets. ROTC results

will also be examined in terms of the ROTC recruiting regions. Additional segments defined in terms of college major may not be available

under ACOMS. ARNG and USAR main markets include nonprior service men,

ages 17 and up, with secondary markets including prior ser-ice and inservice men. USAR results will also be examined by recruiting

brigade. ARNG has expressed special interest in racial breakdowns,

and in college students aged 21 and older. Special interest groups

identified in terms of doctors, physician assistants and present

members of ARNG/USAR may not be available under ACOMS.

Additional cutting variables for the segmentation analyses of

reserve components not routinely available through the quarterly

reports will be developed collaboratively with the Contracting

Officer's representative (COR) and the respective components.

The first step in our analysis of the currently utilized market

segmentations will be the examination of tabulations of these segments

against: (a) demographic characteristics; (b) media habits;

(c) knowledge of Army and component offers; (d) perceptions of the

active Army, ROTC, USAR and ARNG; (e) importance of attributes;

(f) enlistment intentions, and; (g) enlistment-related behaviorz.

Much of this information will be presented as a part of the

tracking activity described in Chapter 2 (Gaertner et al., 1988).

Further, the beginning analyses can be conducted simultaneously for

the active Army and for the reserve components.

For gender, race and age segmentations, demographic descriptions

will be of limited utility since demographic characteristics form the

basis of the segment groupings. On the other hand, the main active

Army prospect segmentation constitutes a mixture of demographi

70

10,, ,', ,-. .,' .. .' .- .. _% %._ , - - % " -. - %- 
(educational attainment) and attitudinal or aspirational classifications (work-oriented and college-oriented). In this case, it will be

important to focus on demographic description as well as characteristics such as media habits, enlistment intentions/behaviors (behaviors)

and perceptions and importances (attitudes).

These tabulations of segments by respondent characteristics will

allow preliminary assessment of the ability of a particular segmentation to isolate specific youth prospects. For example, the active

Army's dual-market concept assumes work-oriented and college-oriented

high school students have differential receptivity to training and

college benefit messages. Bivariate assessment of the main prospect

market segments will provide evidence as to whether various segments

recall messages homogeneously; whether they have similar enlistment

intentions; and whether they manifest differential beliefs about the

Army. In a like manner, descriptions of differential media habits

across age segments will provide information regarding the differential probabilities of youth even being exposed to advertisement

messages. In short, the bivariate segmentation analyses will establish the ability of present segmentations to isolate important demographic, attitudinal, and intentional/behavioral groups in the

prospect market.

Two caveats are in order. First, as noted in Chapter 2, questions regarding perceptions of ROTC, ARNG and USAR were not directed

to all respondents, but rather to selected subsets. Thus, biva;riate

relationships between segments and reserve component perceptions will

be delayed until adequate case bases are available -- perhaps the

third quarter of 1987. Second, in spite of the large size of the

ACOMS youth sample, adequate case bases for rare segments (e.g.,

Hispanics) will not be available on a quarterly basis. Thus, segmentations involving race will also likely be delayed until the third

quarter of 1987.

Together, these comprehensive descriptions of market segments

will provide a firm baseline for establishing who is targeted by the

various segmentations and how they view the Army. In addition, this

baseline will be useful as an anchor for considering changes in the

composition of market segments through time due either to seasonal

fluctuation or structural change in the manpower marketplace.

Multivariate Description of Current Market Segments

Upon completion of the tabular descriptions of market segments,

multivariate descriptions will be developed. The tabular descriptions

generated above are useful for summarizing segment composition on one

or two variables at a time. They become increasingly complex and

difficult to interpret, however, as additional variables enter into

table construction. Higher order descriptions, therefore, will

utilize correspondence analysis (Lebart, Morineau, & Warwick, 1984)

for the multivariate description of market segments. A few comments

regarding this technique should serve as both an introduction to this

71
relatively new statistical approach and as an illustration of how the

multivariate descriptions of current markets segmentations will

proceed. DI

Correspondence analysis is a multivariate descriptive statistical

technique related to multidimensional scaling, log-linear modeling,

canonical correlation and factor analysis. Like these techniques,

correspondence analysis is a statistical technique for summarizing

large amounts of data in as compact a manner as possible. Factor

analysis, for example, is a technique for summarizing the information

contained in a large number of variables using a smaller number of

factors. Analogously, correspondence analysis is a technique that

summarizes graphically the information contained in large contingency

tables.

Conceptually, correspondence analysis is a method for expressing

the rows and columns of an n-way contingency table as profiles.

Specifically, row profiles are described by the dimensions defined by

table columns and column profiles are described by the dimensions

defined by table rows. An example should clarify:

Assume we wished to analyze the relationship between the main

active Army prospect segmentation and five importance measures (e.g.,

having a physical challenge, earning money for college or vocational

school, having a mental challenge, working with high-tech equipment,

and having weekend excitement). Traditional contingency analysis

would be very complex in the case of this six-way table. Correspondence analysis, however, simplifies the search for interpretable relationships and might produce output like that shown in Figure 15.

In this hypothetical figure, we see the four main active Army

prospect market segments and the five importance measures displayed in

a two-dimensional grid defined by axes I and 2. Recalling that row

profiles are defined in terms of contingency table columns, the

segments can be interpreted in terms of importance dimensions. In

order to accomplish this interpretation, the dimensions defined by

axes 1 and 2 must be identified (much as is done during a factor

analysis).

In the present hypothetical case, axis 1 might be interpreted as a money for school/work environment continuum and axis 2 as a

physical/mental challenge continuum. On this grid we see the placement of market segments. As we have arrayed the segments here, high

school work-oriented and graduates not currently enrolled seem to form one homogeneous group while high school college-oriented and college

studen13 form another group. This is not entirely unexpected.

The ways in which segments within these groups distinguish themselves is interesting as well. The hypothetical figure suggests that

college-oriented students place roughly equal emphasis on mental

challenge and school money while college students focus more specifically on money. We also see that work-oriented students place relatively greater emphasis on job characteristics than the nonenrolled

segment. A final piece of information summarized in this figure is

72

- .,
AXIS 2 I

Physical Challenge 0

Not Currently Enrolled

O In School

Work-Bound

• High Tech Equipment we.end Excement

AXIS 1

In College

0 School Money

0 College-Bound

*Mental Challenge

Figure 15. Hypothetical results of a correspondence

analysis on current market segments.

73
the finding that weekend excitement is not a salient importance

attribute for any of the segments. Its position near the origin

signifies that it contributes little in defining the grid for the

segments.

By producing such summaries of multivariate tabular information,

correspondence analyses can address whether segmentations isolate

distinctive groups of respondents and can summarize relationships

between segments and characteristics of interest. The market segmentation illustrated in Figure 15 very clearly distinguished between

importance orientations across segments. In addition, this example

also provided a summary of the relationships between segments and

those importance orientations.

In the case of ACOMS segmentation analyses, correspondence analysis has two features that particularly rccommend its use. Most obviously, the technique produces dual displays of row (segment) and

column (criterion) geometries, each having a similar interpretation.

This is very important in the case of ACOMS as the linkages between

market segments and respondent perceptions, attitudes, and behaviors

is only incompletely known.

Second, correspondence analysis has highly flexible data requirements. Unlike techniques such as canonical correlation, and factor

analysis, correspondence analysis does not require interval-level

data. This technique was explicitly developed for the analysis of

contingency table data, a flexibility very important in the present

ACOMS application.

Correspondence analysis, then, will be used to describe the relationships between existing market segments and key attitudinal and

behavioral indicators. We would anticipate conducting these analyses

first for active Army segments, including the main active Army

prospect segmentation, gender, age, brigade and race (as data become

available). Column (criterion) variables will be, in order of priority, perceptions, importance, intentions, behaviors, media habits, and

knowledge. As case bases permit, these criterion variables can be

utilized in combination as well. We would then go on to produce corresponding analyses for the other components to assess whether existing segments isolate important attitudinal and behavioral differences

or whether additional segments seem necessary.

Assessing the Stability of Existing Market Segmentations

The final step in the evaluation of currently used market segmentations will be the assessment of their stability over time. It is

quite possible that, as a function of changing advertising targeting

and messages throughout the year, perceptions about the Army and

recall of messages will vary over time. In fact, the sizes of the

segments 

oriented high 

themselves 

school 

may 

students 

vary through 

early in 

the 

the 

year. 

year 

For 

may 

example, college- I

become workoriented as the college entrance process proceeds. It is vital,

therefore, that ongoing analyses be performed on all market segments

in order to detect and evaluate change throughout the first year.

74

r% r d']
Through successive replications of the tabular descriptions

discussed above as well as correspondence analyses, the stability of

perceptions, attitudes, media habits, intentions/propensities, and

behaviors within market segments can be tracked. These replications

will provide two important kinds of information. First, they will

provide an initial indication of the stability of findings during any

particular period. Individual analyses are capable of producing

unrepresentative results. To the degree that successive analyses

reinforce each other, we can be more assured that findings are not

based upon mere random fluctuations in any one period.

The second important function performed by the successive replications will be the incremental accumulation of information regarding

seasonal variation in market segment composition. It is expected, for

example, that recall of Army messages will vary as a function of the

messages aired. As the content of messages and/or their mix is

changed throughout the year, we would expect to register this change

through replication of the analyses.

Development of New Market Segmentations

The development of new market segmentations will follow as a

natural extension of the investigation of current market segmentations. Much of the information gained in the initial analyses will be

used here as the starting point for development of new segmentations.

From correspondence analysis, for example, we will have indications as

to the salience of the various importance, beliefs, and attitude

measures to respondents. While this second set of analytic steps will

proceed from the findings of the first, the steps taken are different.

Instead of beginning with established market segments and working

toward a description of them in terms of their multivariate relationships to key respondent characteristics, the development of new

segmentations will begin by analyzing key characteristics and work

back to the demographic identification of new market segments. The

analysis will consist of the following four steps:

(1) Identification of natural respondent groupings displaying

homogeneous and distinct beliefs, attitudes, habits,

behaviors, and intentions profiles;

(2) Description of respondent groupings in terms of market

segment characteristics;

(3) Generalization and modification of the segmentation for use

by reserve components; and

(4) Assessment of the stability of new market segments.

These steps are detailed below.

75

vv% %

N M,
Identification of Natural Respondent Groupings

A variety of respondent behaviors, attitudes, and intentions are

of potential interest for the targeting of advertising. Among those

of greatest interest are importance of attributes and perceptions of

the various components. We expect to begin the development of new

segments focusing on the perceptions of active Army. We will then

replicate and extend these analyses using perceptions of the reserve

components.

The first step in the development of new market segments will be

the determination of whether respondents naturally group themselves

into relatively homogeneous clusters.

For example, it is reasonable to assume that a subset of ACOMS

respondents hold strongly negative views about the active Army while

another subset of respondents will be very favorably disposed toward

Army attributes. It would be useful to identify these two groups and

target advertising as appropriate. Even more important, though, is

the identification of individuals in the middle who may constitute the

largest potential pool of Army enlistees.

It is further reasonable to suppose that these three groups would

differ in terms of the structure of their perceptions of the Army.

For example, we might expect that respondents with extreme attitudes

toward the Army (favorable or unfavorable) might evidence a strong

halo effect and not discriminate among Army attributes, while respondents with moderate attitudes might be disposed to favor some

attributes and dislike others. Thus, these results would suggest two

nested segmentation strategies. An initial cut would separate extreme

attitudes from moderate ones, and a second cut would target appeals to

different segments within the moderate group. Procedurally, we would

run separate factor analyses of active Army perceptions within groups

of differing overall favorability toward the Army. If our suppositions were correct, we would only expect more than one factor to

emerge among those with moderate opinions, and the factor analysis in

this group would define its characteristic market structure. This

market structure might be similar to the one depicted in Figure 15.

In order to be useful, this latter segmentation would require a multidimensional analysis showing, for different sets of respondents in the

moderate group their distinctive values, behaviors and demographic

backgrounds. Of course, the factor analysis might not yield the same

results if the criterion variables were perceptions of, for example,

the USAR. We would expect to replicate this basic analysis for the

various components.

The classification of respondents into a limited number of meaningful

groups (for example, groups within the moderate opinion set) will

primarily rely on the technique of cluster analysis (Aldenderfer &

Blashfield, 1935). "Clustcr analysis" is the generic name for a wide

variety of techniques used for the purpose of creating classifications

or taxonomies. These procedures empirically form clusters or groups

of similar entities. More specifically, clustering techniques are

multivariate statistical procedures that begin with information about

76

* V'S5~*%* ~' v~~X~A'C.' Z.eZ Z "~
all respondents and attempt to classify them into a small number of

relatively homogeneous groups.

The basic data used for a cluster analysis are measures of the

similarity between cases on variables of interest (i.e., their profile

similarity). That is, the input required for analysis is the relative

response of each case to questions vis-a-vis the responses of all

other cases. This is not the kind of information collected by ACOMS.

The first step in the cluster analysis, then, is the transformation of

survey responses into measures of relative similarity. In this step,

two popular similarity measures will be used. These are the correlation coefficient between cases and the Euclidean distance between

cases.

The correlation between cases J and K is defined as

(xij - xj)(Xik - Xk) rjk

r~~k 2 2__ _ _ _ _ _ _ _ _ _ _ _

I(xij Yx)2 (xik . xk)

where xii is the value of variable I for case J, x. is the mean of all

values o the variables used for case J, and n is he number of

variables.

The Euclidean distance between case J and K is defined as

dp 2

djk - xij - Xik)

i-l

where d is the distance between cases J and K, and xij is the value

of the j h variable for the Jth case.

Returning to the previous example, the space defined by the

factor analysis of perceptions of the active Army for respondents with

moderate opinions would form a satisfactory frame for computing

distances between cases. The distance between two cases would be

defined as the root of the sum of the squares of their differences on

each dimension. Distance, then, would be the opposite of similarity.

Following the transformation of survey responses into a format

suitable for input, cluster analyses will begin. As noted above,

cluster analysis is the generic name for a family of multivariate

statistical techniques. The techniques are distinguished primarily by

the methods they use for the formation of clusters. In general, the

methods fall into one of three groups: linkage methods, errors sums

of squares or variance methods, and centroid methods. All require the

same data input but differ in how they measure the distances between

clusters in the successive steps of their iterative algorithms. The

choice of which method to use in a particular application will be

reached following extensive diagnostic procedures. These analyses

77
will group respondents according to the similarities of their profiles* In the example of active Army perceptions, moderate respondents would be grouped according to their similarity of position in

the space defined by the factor analysis. Each cluster will contain

respondents having relatively similar perceptions and the clusters,

themselves, will define the differences among respondents.

We expect that the process of moving from cluster analysis

results to new segments will be iterative. That is, we expect to

define a comparatively large number of clusters and then use these

clusters in correspondence analyses with other variables of interest

to determine parsimonious yet effective clusters. In the example, we

would expect to relate the perceptions clusters to measures of importance of attributes, propensity, enlistment-related behaviors, and the

like. Then, as clusters do not appear to differentiate in terms of

other variables of interest, they could be collapsed, forming a more

inclusive segment. The collapsing process would stop when all remaining clusters differentiated among other variables of interest.

Alternatively, the clustering could continue by including relevant criterion variables in the profiles. (Obviously, the question of

whether a variable should operate as a correlate of a cluster or as

part of the profile, thereby helping to define the cluster, is a

matter of analytical judgment and projected utility.) In the active

Army perceptions example, we might decide to include importance of

attributes in the clustering algorithm, thereby measurilng configurations of importance/perceptions. Clusters might include those for

whom work values were important but not seen as offered by the active

Army, important and perceived, unimportant and not perceived, and

where challenge variables were important but not seen as present, etc.

As the new segmentations are developed, they can then be assessed

in much the same way as existing segments. It is expected that a

number of respondent clusters will correspond to multiple respondent

characteristics. Media habits, for example, may correspond closely to

age, or enlistment intentions may be roughly approximated by family

income. Such relationships can increase the utility of the segmentation since the additional variables add additional handles for targeting advertising messages and placements. Since a number of different

key respondent characteristics will be used in forming profiles and

subsequent clusters, it is likely that several new segmentation

strategies will be developed, each having a particular perspective on

the partitioning of American youth.

Generalization of Clusters to Reserve Components

As clusters defining market segments are developed and refined,

we' expect to adapt them for use by the ROTC, USAR and ARNC. To be

sure, it is unlikely that one new segmentation will satisfy the

disparate needs of the various components. Nonetheless, it is hoped

in the interests of parsimony and the capacity to project an overall

Army image that the segmentation strategies of the various components

will have many elements in common.

78
Assessment of Market Segment Stability I

The final stage in the development of new market segmentations

will be an assessment of their stability over time. It is important

that any new segmentations developed are proved to have known characteristics that are stable over time or at least fluctuate seasonally

in known manners. As in the assessment of current market segmentation

strategies, it is necessary that replications of the above analyses be

performed periodically. These replications will be conducted as

discussed earlier.

Year 2 Segmentation Analysis

The year 1 analyses will be continued in the second year as well

as expanded to consider issues of seasonality and longitudinal change.

Year 1 analyses will be replicated (except as modified on the basis of

findings) in order to retain continuity in research results across the

years of ACOMS data collection. More fundamentally, however, the

analyses will be repeated in order to more precisely establish the

patterns and amplitudes of seasonal fluctuation in market segment

characteristics.

With the beginning of the second year of ACOMS data collection,

it will become possible to compare findings across years. To the

degree that first quarter 1987 findings agree with those of the first

quarter in 1988, increased confidence may be placed in the results and

our conclusions. In a like fashion, if fluctuations in segment com-position over quarters in the two years are essentially similar, we will

be in a good position to track and assess seasonal fluctuations among

market segments.

Longitudinal data collection begins during the second year of the

ACOMS project. Using longitudinal data, it will become possible to

determine the changes that have taken place in respondents in their

media habits, message recall, perceptions about the Army, and enlistment intentions and behaviors. The collection of these data is

invaluable for a number of reasons. First, it will allow assessment

of period effects. If, for example, in comparing the cross-sectional

(not longitudinal) attitudes toward the Army of 18-year-olds in 1987

and 1988 we find a substantial difference, we cannot determine whether

the difference is due to an actual change in attitudes or due to

inaccuracies in measurement during 1987, 1988, or both. If, on the

other hand, we compare the 1988 cross-sectional findings with those

for longitudinal sample 18-year-olds and find substantial agreement,

it becomes possible to assert that a true shift in attitudes has taken

place. This calibration function of the longitudinal sample is vital

for correct inference.

The longitudinal sample will also be invaluable for the study of

changes in the attitudes, perceptions, and beliefs about the Army as

individuals mature. Currently, longitudinal information is not available on individuals specific to their attitudes and plans regarding

the Army. The longitudinal study of changes occurring in market

segments will provide insight into the dynamic interaction between

79
market segment composition and the changing marketing strategies and

needs of the Army. It may be possible to define segments empirically

in terms of life-cycle processes moving toward (or away from) enlistment, college enrollment, work, etc. Such a segmentation would be

invaluable in targeting advertising messages not only crosssectionally but over time. Thus, for example, direct mail advertising

could be directed especially to particular kinds of prospects in

particular stages of a potentially multiyear enlistment process.

I

I'

L

,.

80
REFERENCES

Aldenderfer, M. S., & Blashfield, R. K. (1985). Cluster analysis. Beverly

Hills, CA: Sage Publications.

Gaertner, G. H. (1988). Modeling the effects of Army advertising. In C. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement Systems (ACOMS): Survey design (ARI Technical Report 786).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Gaertner, G. H., Keil, L. J., & Gay, N. L. (1988). Tracking audience

response. In G. H. Caertner & T. W. Elig (Eds.), The Army Communications

Objectives Measurement System (ACOMS): Survey analysis plan (ARI

Technical Report 786). Alexandria, VA: U.S. Army Research Institute for

the Behavinoral and Soci.l Sciences.

Lebart, L., Morineau, A., & Warwick, K. M. (1984). Multivariate descriptive

statistical analysis: Correspondence analysis and related techniques for

large matrices. New York: John Wiley.

Sands, S., & Warwick, K. (1981). What product benefits to offer to whom, an

application of conjoint segmentation. California Market Review, 24, 69-

74.

Wilson, M. J. (1988). Brand differentiation analyses. In G. H. Caertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Pehavioral and Social Sciences.

81
4. BRAND DIFFERENTIATION ANALYSES

Michael J Wilson

Introduction and Overview

Before we can fully explain the processes through which Army

advertising affects individuals or model how advertising changes

enlistment intentions, we must first understand how individuals

perceive the Army. The brand differentiation analyses discussed in

this chapter are designed to provide a comprehensive understanding of

not only how Army opportunities are perceived, but also how the Army

is perceived in relation to selected other service and civilian career

alternatives.

The data used in these analyses is taken from the Perceptions/

Beliefs module of the Army Communications Objectives Measurement

System (ACOMS) questionnaire. In this module, youths are asked to

respond (on a five-point scale ranging from "strongly disagree" to

"strongly agree") to a number of statements concerning opportunities

offered by the Army, its components, the other military services,

military service in general, civilian employment, and college attendance. The statements describe opportunities for mental and physical

challenge, personal growth and maturity, skill training and work with

high-tech equipment, and opportunities for gaining experience and

monetary benefits that will be helpful in civilian life. Together,

these perceptions and beliefs about opportunities offered by 10 military and civilian alternatives will be used as the basis for characterizing how respondents differentiate among them. No single respondent is asked to provide his/her perceptions and beliefs about all 10

alternatives. The actual alternatives considered by any single

respondent is determined according to an allocation algorithm.

Before proceeding, a few comments should be made regarding terminology used in this chapter. For discussion purposes, each of the 10

alternatives is considered a brand in the same way that Ford and

Mercury automobiles constitute distinct brands or alternative purchase

choices. In some cases, brands overlap. For example, the Army and

Navy are distinct brands subsumed by the larger corporate entity, the

military in general. This does not pose a difficulty as one of the

brand differentiation research goals is to determine whether and how

the individual services distinguish themselves from the more global

image of the military in general. In what follows, therefore,

"brands" and "alternatives" will be used interchangeably to refer to

the several military services and civilian job and education choices

evaluated by ACOMS respondents. Additionally, since the characteristics of brands queried relate primarily to opportunities offered by

each, we will often refer to beliefs as perceived "opportunity structures" or "opportunity space."

The brand differentiation analyses will highlight the differences

in perceived opportunities among the various career alternatives.

These analyses will lead to the "positioning" of the Army vis-a-vis

the other alternatives. The simultaneous positioning of the Army with

83
respect to both opportunities and other brands will clearly show in r

what ways the Army is viewed as essentially similar to other services

and where it distinguishes itself. Finally, the brand differentiation

analyses will be continually replicated throughout the period of data

collection to provide information on changes in the perceptions of the

Army and other brands throughout the calendar year. As a component of

the ACOMS analysis plan, the brand differentiation analyses will prove

invaluable as (a) a description of how youth perceive the Army and

other career alternatives and (b) a method for tracking changes in

youth perceptions.

The remainder of this chapter is divided into three sections. In

the section immediately following, we describe our general approach to

brand differentiation analyses. Next, a more detailed account of

year i analyses is presented followed by a brief discussion of year 2

extensions.

Approach

Our approach to the study of brand differentiation in the Army

recruiting marketplace during year 1 will consider differentiation

modeling in three steps. First, basic or summary or descriptive

information will be generated about cross-brand opportunity perceptions and within-brand opportunity structures. In this effort, tabular and correlational techniques will be used to investigate differences in perceptions/beliefs regarding opportunities across brands as

well as the correlational structure among perceived opportunities

within brands. These investigations will provide important initial

data summaries upon which the multivariate analyses will build. The

tabular descriptions of perceptions/beliefs by career alternatives

will highlight where military and civilian opportunities are perceived

as essentially similar and also where different. Descriptions based

on correlations among perceptions/beliefs within each career alternative serve a second purpose. This information will describe interrelationships among perceived opportunities within each of the 10 career

alternatives offered to ACOMS respondents.

In the second step of the brand differentiation analysis, multivariate statistical techniques (i.e., multidimensional scaling

(Davison, 1983) and correspondence analysis (Lebart, Morineau, and

Warwick, 1984) will be used to describe more comprehensively how

respondents perceive the opportunity structures for each of the ACOMS

career alternatives. The first use of multivariate techniques will be

uiacnted toward gaining a general understanding of how youths view

opportunity structures across brands. Using multidimensional scaling

techniques to construct perceptual maps of career opportunities, we

will determine the major salient dimensions used by respondents to

evaluate career alternatives as well as the importance of various

potential opportunities with regard to salient criteria. As a scaling

and data reductiu,. mcthod, mrnltidimersior-l -1]ing Thares many characteristics with factor analysis. The scaling of perceptions will

uncover underlying or latent dimensions which organize respondent

84
evaluations of opportunities. Additionally, multidimensional scaling

will produce graphic output illustrating the relationships between

observed perceptions/beliefs and latent factors. I

Following the careful examination of perceptual maps for each of

the 10 career alternatives, we will use correspondence analysis to

determine the relative images of alternative brands vis-a-vis their

opportunity structures. Correspondence analysis will produce maps

similar to those produced by multidimensional scaling. Instead of

being confined to the representation of opportunity space alone, however, correspondence analysis will additionally position career alternatives within this space. This positioning analysis will show which

opportunities are most closely associated with particular alternatives

and how the alternatives differ.

The third step in year 1 analytic activities will consist of a

constant monitoring of changes in perceptions about career alternative

opportunities throughout the calendar year. At least quarterly, analysis steps one and two will be replicated in order to determine

whether perceptual changes have occurred in either the salience of

specific opportunities or the relative positionings of career alternatives. P

During the second year of ACOMS data collection, analyses will be

extended to consider both seasonality and longitudinal issues. As the

data come available, quarter-by-quarter brand differentiation analyses

can be compared across years. To the degree that changes in year 2

correspond with those observed in year 1, we can confidently conclude

that such fluctuations are seasonal and, consequently, begin their

parameterization. If fluctuations do not coincide between the two

years, we will search for causal mechanisms.

The second year of ACOMS data collection also signals the beginning of longitudinal data collection. Once such data are available,

we will begin the process of separating period from cohort effects and

studying the process of individual changes in brand perceptions as

ACOMS respondents mature.

Each of these steps in our approach to the study of brand differentiation is discussed in greater detail in later sections of this

chapter

Tabular and Correlational Descriptions

As the first step in the brand differentiation analyses, extensive tabular and correlational descriptions will be developed of

respondent perceptions and beliefs about the opportunities presented

by the 10 ACOMS career alternatives. The tabular and correlational

descriptions are designed to: (a) investigate the distributions of

perceptions/beliefs individually across career alternatives, and

(b) summarize any associations that may exist among the perceptions/

beliefr variables within individual career alternatives.

85
Tabular descriptions will be developed for each perceptions/

beliefs question. That is, each ACOMS question asking respondents

about brand opportunities (i.e., the Army offers a wide variety of

opportunitiis to find a job you can enjoy, a physically challenging

environment, etc.) will be crossed with all appropriate career alternatives. For example, a table may contain as column entries the

response categories (i.e., the values one to five) for perceptions/

beliefs regarding opportunities to develop leadership skills while the

rows will identify different career alternatives. In this way,

perceptions of each brand will be displayed in a fashion facilitating

comparison across alternatives. These tabular descriptions will

facilitate identification of those areas where perceptions of specific

alternatives agree and diverge. In the description of civilian career

development, for example, respondents may generally think that college

attendance and a civilian job further civilian career development

while military service does not. Such information is important for

the understanding of how the Army, and the military in general,

appeals to youth. Such preliminary descriptive information will aid

in locating those areas where military and civilian opportunities are

viewed by respondents as essentially different. This identification

is the primary purpose of the tabular analyses.

A data difficulty must be mentioned here. The ACOMS questionnaire does not query identically regarding available opportunities for

all career alternatives. As noted in the introduction, there are a

total of 17 generic perceptions/beliefs questions. This summarization

excludes Reserve Officers' Training Corps (ROTC). Questions about

this Army component are centered about eight opportunity attributes,

four of which overlap to a greater or lesser extent with the larger

list of fourteen. The difficulties associated with ROTC brand differentiation analysis are, therefore, unique and largely of a technical

nature. For this reason, they will not be highlighted in this

overview of our analysis plan. For all services and military service

in general, respondents are asked to rate their perceptions on 14 of

them. When rating civilian employment and college attendance opportunities, the total number of questions asked drops to 12 and 8, respectively. The implication of this is that not all perceptions/beliefs

tables will contain rows corresponding to the civilian employment and

college attendance options.

The second stage of initial opportunity structure description

utilizes product-moment correlations to assess patterns of associations among perceptions/beliefs questions. This task is designed to

determine whether the opportunities are distinct or correlated as

perceived by respondents and, further, to establish whether the correlational structures is invariant with respect to career alternatives.

In the course of ACOMS questionnaire development, an extensive list of

Army opportunity attributes was assembled. While these opportunities

largely correspond to particular advertising "messages," it is important to deterr'i"r whether the recruiting marketplace distinguishes

between the offered opportunities. When evaluating the Army, for

example, respondents may consider opportunities for developing selfconfidence, personal potential, and maturing as essentially the same.

86

~~%
Correlational analyses will be conducted separately for each of

the 10 brands considered. These analyses will determine the degree to

which beliefs about one opportunity covary with beliefs about others,

within brands. As an initial description of opportunities within

alternatives, these analyses will reveal the underlying dimensions

upon which career alternatives are evaluated. In addition, comparisons of correlation matrices across brands will provide indications

of the differing ways in which alternative career choices are evaluated. An example should clarify these points.

Suppose that in the correlational analysis of perceptions/

beliefs regarding the Army we find the opportunities to develop one's

potential and skill training are highly correlated, but in the percep- •

tions regarding civilian employment, skill training is highly correlated with working with highly trained people. Both correlations rest

on skill training but the thrust of the relationships appears different. In the civilian case, training seems associated with a second

training-related attribute, while for the Army it is related to individual development. The conclusion that might be reached in this

circumstance is that enlistment and employment are evaluated

(perceived) according to different criteria. Such a finding contributes to an understanding of brand differentiation. Examination of

such correlational descriptions provide an initial picture of the

"mind set" used by respondents when considering career alternatives.

Following completion of the tabular and correlational descriptions, analysis will move to more comprehensive multivariate evaluations of respondents perceptions and beliefs regarding alternative

career opportunity structures.

Multivariate Analysis of Perceptions/Beliefs

The first step in multivariate brand differentiation analysis

will be the development of "perceptual maps" which plot respondent

perceptions/beliefs about the 10 ACOMS career alternatives. Construction of these maps will utilize the technique of multidimensional

scaling (MDS), the generic name for a collection of techniques

designed to portray psychological relations (perceptions) among

stimuli (opportunities) as relations among points in a multidimensional space. Input for MDS corresponds generally to the requirements

discussed in the previous chapter in the case of cluster analysis.

That is, some measure of case similarity (regarding perceived opportunities) is required. It is expected that a measure such as the

Euclidean distance will be used. To restate, the Euclidean distance

between case J and K is defined as

d ~ p2 djk i(Xij x - ik)

where d is the distance between cases J and K, and xij is the value

of the j h variable for the Jth case.

As an example of what this technique yields, consider Figure 16.

87

i Z Z C - el !
VT.J

Xp

N.Y..

PA..

INN. IN .O I

Figure 16. Bostonian's map of the United States

(from SheGard, 1966).

88
This figure presents the Bostonian's map of the United States.

It was obtained by Shepard (1966) when he surveyed New England university students about the relative proximity of the contiguous American O

states. The figure illustrates the mapping orientation of MDS.

Specifically, it demonstrates that MDS produces a psychological,

rather than objective, characterization of stimuli. In this example,

the map produced by MDS clearly reflects a bias which overemphasizes

New England and the mid-Atlantic states. The geographic relations

between these states are much more accurately reproduced than those

for midwestern and western states. Given the fact that this map is

produced using the perceptions of undergraduate students in New

England, the natural ethnocentricity is not unexpected.

In order to more directly demonstrate the utility of MDS for the

analysis of ACOMS respondent perceptions and beliefs, we have devised

the following hypothetical example of a perceptual map of Army Reserve 6

opportunities. Figure 17 illustrates the results of such a hypothetical MDS analysis. In this figure we observe that the 14 opportunities

rated by respondents are arrayed in a perceptual space defined by

axes I and 2. As with factor analysis, the substantive interpretation

of the axes are arrived at through a consideration of the placement of

the rated orportunities and the similarity of opportunities, is

signaled by close proximity.

In this hypothetical case we might define axis I as a skill

acquisition dimension ranging from fewer skill training opportunities

on the left to greater opportunities on the right. The vertical axis

could be characterized as ranging from personal growth opportunities

(top) to individual inertia (bottom). While we have no expectation

that the perceptual map is empirically accurate, the figure serves to

highlight the information content available from such multivariate

descriptions.

Continuing the example, we see that only the skill training and

personal growth dimensions are required to "explain" variation in

perceptions and beliefs regarding Army Reserve opportunities. These

dimensions, then, constitute the only salient dimensions upon which

the recruiting marketplace evaluates Army Reserve messages. This

information is useful, but perceptual maps yield additional insights.

If we examine the placement of the 14 various appeals along the

two dimensions, several interesting findings emerge. First, the

opportunities for a proud experience and community service are generally of no salience (being near the origin in this mapping). Second, I we see that skill training, money for education, and the value for

future civilian opportunities strongly define one dimension. These

messages are clearly important. Finally, there is an interesting

pattern in what might be considered personal growth and situation

opportunities: challenge and development are on one end of the

continuum 

anchor the 

while 

other. 

enjoyment, 

This dimension 

weekend 

might 

excitement, 

be seen, 

and 

therefore, 

hometown stationing 

as divid-

.

ing opportunities between those associated maintaining the status quo 0 and others related to growth, challenge, and development.

89 I
p.%

Sl!oniero AXIS 2 '

0 Develop Potena'l Mental Challenge

• .ity

•Leaership Skill s

0 Prow-J Skil Traini-ng

AXIS 1"

S nnMoney for Colee

',

40 Vakie for CMllan Career

Wekend$ Work with Trained People

* Mnjoy fCl

.Hypothetical results of a mulCidemensional

scaling analysis of Perceptions/Beliefs.

900r
What has been done to this point, through the construction,

inspection, and interpretation of perceptual maps for each of the 10 k career alternatives investigated by ACOMS, is the identification of

differences among opportunities within a univariate framework and the S

delineation of multivariate opportunity structures within brands. It

remains to be determined how the various ACOMS career alternatives are

perceived as total opportunities. Correspondence analysis .ill be

used for this determination.

Correspondence analysis, as described in Chapter 3, Market

Segmentation Analyses (Wilson, 1988), is a multivariate descriptive

method related to MDS that also summarizes information graphically.

Conceptually, correspondence analysis constitutes a direct extension

of both the tabular and correlational analyses discussed earlier. The

tabular analyses are extended beyond examination of perceptions on a

one-by-one basis. Rather, perceptions regarding all offered opportunities are expressed in one n-way table and summarized graphically as

with MDS. Correspondence aralysis also extends the correlational

studies by considering the interrelations among the various opportunities. As a multivariate technique, correspondence analysis produces

profiles of career alternatives with regard to opportunities while

simultaneously representing opportunities within the space defined by

career alternatives.

Figure 18 should clarify the point. This figure presents a hypothetical example of the output that can be produced by a correspondence analysis. In this figure the opportunity space continues to be

represented as it was for the MDS example (and shown in Figure 17)

with opportunities perceived to be arrayed along two dimensions, skill

acquisition and personal development or growth. Added to this map- -"

ping, however, is the placement of three career alternatives--the

Army, college attendance, and civilian employment--within the space

defined by opportunities. In this example, college is most directly

seen as offering a challenging environment for personal development

and growth and both the Army and civilian employment are perceived as r

less challenging.

Civilian employment is positioned most favorably along the axis

defined by skill training and career value. The Army, in contrast to

both college attendance and civilian employment, is placed in a median

position between training and development. What this implies is that

while college and a civilian job are perceived by respondents as more

distinctly offering subsets of the 14 opportunities, the Army is positioned as a career alternative which offers a wider range of

opportunities.

In this obviously hypothetical figure, we see the information

that correspondence analysis can reveal. By placing the various ACOMS

career alternative within the structure of respondent opportunity

spaces, we can uncover the relative positioning of alternatives. Such

positioning analyses serve to define the relative competitive positions of career alternatives. To the degree, for example, that advertising stresses a strength of the Army that is differentiated from 5

other career alternatives, perceptions of Army service and perhaps

91
AXIS 2

* Self Confidence

OlDevelop Potential 0 Mental Challenge

OMatnty,

College 01 Leadership Skills

0 Army

Civilian Employme*

0 E TrainingAXIS 1

Serve Comnity 0 Money for College

0 Vakie for CMian Career

Weekends 0 Work with Trained People

EnIjoy Job

* Hometown

FL&ure 18 Hypochecical results of a correspondence

analysis of Percepcions/Beliefs and three

career alternatives.

92
enlistment intentions should be favorably influenced. If, however,

messages emphasize opportunities equally attributed to several career

alternatives, the benefit to the Army is less.

Correspondence analyses, therefore, are intended to provide an

overall summary of how ACOMS respondents perceive the Army, its components, the other services, the military in general, and civilian

alternatives with regard to the differential opportunities they offer.

As a vehicle for understanding basic perceptions about the Army and

its competitors, the brand differentiation analyses proposed here will

provide a comprehensive foundation for the modeling analyses described

in Chapter 6 (Gaertner, 1988).

93,

5_

.

*5

'5

5'
REFERENCES

Davison, R. E. (1983). Multidimensional scaling. New York: John Wiley.

p

Gaertner, G. H. (1988). Modeling the effects of Army advertising. In C. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement Systems (ACOMS): Survey analysis plan (ARI Technical Report

786). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

Lebart, L., Morineau, A., & Warwick, K. M. (1984). Multivariate descriptive

statistical analysis: Correspondence analysis and related techniques for

large matrices. New York: John Wiley.

Shepard, R. N. (1966). Metric structures in ordinal data. Journal of

Mathematical Psychology, 3, 287-315.

Wilson, M. J. (1988). Market segmentation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

94
5. ANALYSIS OF PARENTAL INFLUENCE

Sandra J. Baxter

Introduction and Overview

In numerous studies of their attitudes toward military service,

adolescents have described their parents as the primary influencers on

career planning (e.g., Gade et al., 1984; Hicks, Collins, & Weldon,

1979). To explore the attitudes and impact of parents, the design for

Army Communications Objectives Measurement System (ACOMS) includes

telephone interviews with the parents of all 16- to 20-year-old

Primary Male Sample (PMS) and Primary Female Sample (PFS) sample

members. Half of the parents will be mothers and half fathers.

Studies on the role of parents in enlistment decisions (Orkand S

Corporation, 1983) substantiate the targeting of parents as the

intended recipients of some Army advertising. What is not fully

known, however, is how parental attitudes toward military service are

influenced by advertisements and how parental influence, in turn,

shapes youth decisions regarding enlistment.

This chapter discusses the research questions stemming from a

consideration of the parental influence process as explored in the

ACOMS surveys administered to youth and parents. The chapter begins

with a discussion of the approach and the four sets of major research

questions to be examined. The next sections focus on each set of

research questions in turn, detailing the survey items to be used, the

analysis techniques that will be employed, and possible findings.

The plan discussed in this chapter focuses primarily on the analysis of parent and youth data from the first year of ACOMS data

collection. As the fielding period lengthens, trend analyses will be

conducted to search f6r aggregate level changes in the responses of

parents and in parent-youth dyads. Once longitudinal data from the

youth re-interviews are available, panel analyses can be conducted

that incorporate the parent data. We will conduct dyadic trend and

panel analyses on many of the research questions raised in the

sections that follow.

Many of the dyadic analyses described below will generate

hypotheses that can be tested with data from the youth reinterviews.

For example, we might predict that youth not considering military

service but with parents who would like to see them join will eventually broaden their options to include the military. Longitudinal

evidence that such a shift has not occurred would lead to further

dyadic analyses to test at least three rival hypotheses: (a) career

plans are firmly set for many youth by age 16, (b) the youth are

resisting parental influence, or (c) the communication between parent

and youth is very poor. Each hypothesis bears implications for Army

advertising aimed primarily at parents.

95
Aproach

The hierarchy of effects model will guide the analysis of

parental influence. The lower portion of the Fishbein and Azjen model 0

argues that demographic variables are associated with parents' awareness of Army advertising, which in turn shapes their beliefs and evaluations regarding Army service. These result in preferences for the

career choices of their youth, which underlie the influence attempts a

parent makes or does not make.

The Fishbein and Azjen model also displays considerable conceptual similarity in communication processes between youth and parents.

This similarity, coupled with the plan to begin analysis of parental

influence after the first year of data collection, will enable us to

build upon findings in the youth data made during the tracking

(Chapter 2) (Gaertner, Keil, & Gay, 1988), segmentation (Chapter 3)

(Wilson, 1988), brand differentiation (Chapter 4) (Wilson, 1988), and

modeling (Chapter 6) (Gaertner, 1988) analyses. Many of the relationships explored in those analyses can be replicated among the parent

sample because of the high degree of overlap between the youth and

parent questionnaires. As the following comparison of question

modules shows, six of the eight modules in the parent version are

identical to those in the youth version:

Module in Content in

Parent Version Youth Version

Importance of Attributes Identical questions

Media Habits Identical questions

Knowledge/Recall Identical questions

Attitudes toward Army Identical questions

Advertising

Knowledge/Awareness Identical questions

Perceptions/Beliefs Identical questions

Parental Influence Parallel questions in

Behaviors and Social

Influence modules

Demographics Similar questions

Further comparability was gained by programming the questionnaire

administration so that modules or sets of items randomly assigned to

16- to 20-year-old youth were also given to their parents.

As examples of how we can capitalize on aggregate youth data

analyses described in the earlier chapters, we will usa the parent

data to replicate scales constructed on the perceptions items in the

youth survey to discover whether the Army's advertising copy points

have been received by both groups in the same way. We will examine %

demographic and attitudinal groupings that emerged as analytically %,

useful in the youth segmentation analysis to see whether they are of

value for the parent sample. We will also replicate a portion of the

youth analysis directed to modeling the effects of Army advertising.

96

* . ' U A4
No IJ ML' .T'R 

The analysis of parental influence will explore four sets of

research questions, three of which compare youth and parent responses

both in the aggregate and as linked pairs of respondents:

(1) A set focusing on parents as targets of Army advertising;

(2) A set comparing the knowledge and attitudes of parents with

those of their youth;

(3) A set about the career preferences parents hold for their

youth, compared with the preferences youth hold for

themselves; and

(4) A set on social influence from the youth and parent

perspectives.

Within each set, the investigation will begin with univariate

statistics measuring the level and variation of aggregated parent or

youth responses. It will then shift to crosstabular analyses focusing

on pairs of responses given by a respondent, such as relationships

between their demographic characteristics and attitudes or between

attitudes and behavior. Next will come dyadic analyses focusing on p

the similarities and differences in responses of parent and his or her

youth. The analysis will then move to multivariate analyses, ranging

from replication of scales based on the youth data, to a variety of

linear and nonlinear statistical techniques.

Dyadic Analysis

Most of the research questions in this chapter require dyadic

analysis techniques to explore the similarity or difference in

responses between parent and youth pairs. Dyadic techniques are still

relatively simple, due in part to the small number of family studies

that have collected data from two or more members (Klein, 1982). As

more studies rely on paired respondents in a household, analysts will

utilize more sophisticated techniques. Currently, several approaches

to measuring similarity or disparity in reports are available. As

Klein points out, however, few researchers test the validity of more

than one disparity measure in a study, so the differing techniques may

be more widely useful than now thought.

Klein presents five "strategies" for dealing with discrepant

reports about families. Although his interest is in the quality of

family functioning while ours is in the influence process between a

parent and youth, the models are suggestive of dyadic analysis alternatives we could pursue. The models include:

(1) The additive model which simply takes the sum or average of

the reports from the family members surveyed;

(2) The discrepancy score model which views the degree of

discrepancy in reports as an important family property;

97

%
(3) The disjunctive model which treats separate reports as

equally reliable and valid and does not attempt to combine

them;

(4) The weighted model in which some reports are regarded as

more realistic than others and are given more weight; and

(5) The conjunctive model which focuses on the covariance in

reports of two family members across a variety of questions.

The additive model has been depicted as the best when the respondents are talking about concrete behaviors rather than attitudes

(Ballweg, 1969). The discrepancy model appears most appropriate when

the focus is on the degree to which one family member can state the

attitude or perception of another. The weighted model is best when

there is varying credibility of respondents in a household, due

perhaps to a differential pull of social desirability among them

(White & Brinkerhoff, 1978).

The model that offers the greatest analytical insight for the

ACOMS data set appears to be the conjunctive model building on the

covariance of responses to the same question offered by two respondents. Several statistical techniques have been advocated for this

model. We could perform a factor analysis of the pool of responses

from all youth and parents combined on one question, generate the

factor loadings, and calculate a factor score for each parent-youth

pair. We could also create a four-level dependent variable (such as

high advertising credibility parent and youth, high adult and low

youth, etc.) and use discriminant function analysis to look at the

linear combination of predictor variables that best explain the dependent variable pattern. Finally, we could define one dyad member's

response to a particular item as the dependent variable in a multiple

regression equation. The predictors would include the other member's

response to the same item plus other variables which might influence

their covariation.

We will follow Klein's suggestion and utilize several dyadic

analysis models in our examination of parent-youth responses. As we

become more familiar with the dyadic techniques and the level of

similarity or dissimilarity in paired responses, we will develop a

systematic approach to comparing parents' responses to those of their

sons or daughters.

Parents as Targets of Army Advertising

A number of the Army's advertisements are directed at parents, on

the assumption that they influence their youths' thinking about military service. The form and extent of parental influence is examined

in the fourth set of research questions listed above; hc:re, the topics

are parents' own attitudes toward Army advertising and recall (aided

and unaided) of it, their knowledge of Army offers, perceptions of

Army attributes, and media habits.

981
Initially, we will replicate the analysis of these questionnaire

modules conducted for the youth sample as discussed in Chapter 2

(Gaertner, 1988). That is, we will look at the level of response of

the audience of parents as measured by items in the first six question

modules listed earlier, e.g., the proportions of parents believing

that the opportunity for a physical challenge is important to their

son or daughter, regularly viewing television, recalling ads for the

various Army components, reporting that they believe Army advertisements, perceiving the Army to offer a physically challenging environment, and correctly estimating Army college benefits. These analyses

will be conducted on the aggregated parent sample and then on various

subgroups based on variables in the demographic module such as education, age, ethnicity, gender, military experience, and income as well

as brigade. As with the youth analysis, we will use the BRR method

for estimating variances and a modified log-linear procedure for

calculating a chi-square test of independence between crosstabulated

variables.

Continuing to take advantage of the analyses outlined in

Chapter 2, we will replicate the scales constructed on the youth

sample which summarize media habits, advertising recall, awareness of

Army benefits, etc. The thrust here is to develop scales and scale

scores which summarize parental attitudes and knowledge and will serve

as analytical variables in subsequent analyses. The youth scales

provide a convenient starting point for these analyses. We expect to

revise the scales substantially when applied to the parent data. We

will discuss the differences in scale composition between the youth

and parent samples and the implications of these differences for the

Army's audience targeting efforts.

The analysis of parent data will also parallel the market segmentation and brand differentiation activities conducted on the youth

data as summarized in Chapter 3 (Wilson, 1988) and Chapter 4 (Wilson,

1988). We will search for segmentations, based on knowledge, attitude, and demographic variables, that define distinctive subsets of

the parent sample which could be targeted by different Army advertisements. We will also work backwards from items in the Knowledge-Awareness and Perceptions/Beliefs modules to specify the characteristics of

parents who clearly discern differences among the "brands" of service,

and among the competing opportunities of military service, college,

and civilian employment. We will use the same set of statistical

techniques, e.g., correspondence analysis, cluster analysis, and

multidimensional scaling, as used on the youth data.

Knowledge and Attitudes of Parents and Youth

The set of research questions to be addressed here focuses on the

similarities and differences in the knowledge, attitudes, and media

habits of parents and their sampled youth. The main issue is whether

the parent-youth dyads are sufficiently homogeneous that Army advertising directed to one group is appropriate for the other as well.

For example, do parents who describe themselves as regular television

viewers have youth who also describe themselves as frequent watchers,

or do media consumption patterns tend to vary in a household? If the

99
former is true, parents and youth can both be reached by televised

ads; if the latter, one group must be targeted through some other

medium, a more expensive strategy.

p

Parent-youth similarities in knowledge-awareness and knowledgerecall will be interesting to study because the data are not behavioral, as are those on media habits. When we look for similarity in

attitudes toward Army advertising and perceptions/beliefs, which are

clearly attitudinal, a finding of high or low similarity will beg the

question of causality. The important issue of whether parental attitude influenced the youth's, or the obverse, or whether the process

-s genuinely interactive, or whether both attitudes were caused by

some third factor will be explored once the longitudinal youth data

become available.

Youth Career Preferences of Parents

The preferences parents hold about the immediate career they

would like their youth to pursue summarize the subtle and explicit

ways they have been shaping the youth's aspirations since birth. They

are asked:

PI-7: "What would you like to see (youth's name) do in the

future--go to college, get training in a vocational or

technical program, get a full-time job, join the Armed

Services, get married and not work, or something else?"

The parents' responses will be the primary indicators of immediate

career preference for their youth. Openness to the idea of military

service as a specific career option is gauged by a series of questions, beginning with a normative pair asking whether the parent

thinks service in the military is a good idea for "most men" and "most

women." Subsequent items ask explicitly whether the parent has talked

to the youth about enlisting, entering as an officer, signing up for

active duty, and seeing a recruiter.

While the parental responses will be valuable indicators of their

relative support for school, work and military service as career

options for their young adults (and youth generally, in the instance

of military service), their answers will be even more useful when

juxtaposed with the career preferences articulated by their youth.

The youth are asked:

IP-I: "Let's talk about your plans for the next few years.

What do you think you might be doing?"

Unlike their parents, the youth are not read the response categories,

which consist of going to school, working, doing nothing, joining the

military, and other.

The analysis will begin with a simple crosstabulation of the

aggregated parent and aggregated youth responses to reveal gross

similarities and discrepancies in career preferences. A small portion

of the discrepancy will be attributable to the fact that youth are not

100

J"
given any response categories to consider. The remaining discrepancy

will motivate analysis of whether the two generations are at odds over

immediate career expectations or whether there is considerable congruence. The parents, many of whom made career decisions in the late

1960's when military service was disparaged, may be less supportive of

,it than their youth. We will explore sexual stereotypes by comparing

the immediate career expectations of parents with daughter and among

the daughters themselves (does the younger generation consider military service more frequently?), and contrast parent-daughter and

parent-son dyads (is there greater congruence of expectations in the

former than in the latter?).

Several analyses will focus exclusively on the imm- iiate career

preferences of high school students, who are of special analytical

interest since the active Army draws a large proportion of its

enlisted personnel from high school graduates or near-graduates.

These students will have been categorized as college-oriented or workoriented as part of the audience tracking effort; the utility of this

segmentation will have been tested in the analyses outlined in

Chapter 3 (Wilson, 1988). We will crosstabulate the two groups with

the preferences held for them by their parents to determine the degree

of congruence in expectations and the proportion of youth whose

parents express a preference for military service. By stratifying

these analyses according to the education, age, ethnicity, and military experience of the parent, we will be able to specify the parental

characteristics of youth within the two segments who may be especially

open to advertising messages about Army service.

The Parental Influence Process

The final set of research questions focuses on three topics:

(a) the process by which parental attitudes, perceptions, knowledge,

and immediate career preferences for their youth are communicated,

(b) the content of those communications, and (c) their apparent

impact. We perceive these three topics to comprise the influence

process as measured by ACOMS. There are, of course, myriad ways for

parents and youth to "communicate" about the immediate career choices

facing the youth that are not measured by ACOMS, and the ideas and

attitudes exchanged are far more complex than the survey questionnaire

can tap. Nevertheless, the role of parents is known to be so pivotal

in youth decision-making about military service that the survey data ,-P

we can collect will warrant extensive analysis.

As with the previous two topics, the influence process will be

probed from both the parent and youth perspectives and will rely heavily on dyadic analysis techniques. Unlike most of the analyses

described earlier in this chapter, in which the dyadic analysis

focused on parent and youth responses to the same question, the dyadic

techniques used here will link responses to similar or related items

(e.g., did parent encourage youth to see a military recruiter, did

youth ever talk with a recruiter?). The thrust of the analysis will

continue to be on similarities or discrepancies in reports.

101

4 ' 4''m'4 4 ' -
The behaviors underlying the influence process will be examined

first. Parents are initially asked how frequently they have had

informal talks about the youth's general educational and job plans.

They are then queried about talks regarding enlisting in the Armed

Services, signing up for active duty, the Reserve or the National

Guard, seeing a military recruiter, and about pointing out ads for the

services in the mass media and receiving recruiting materials mailed

to the home. We will first examine the frequencies on these items to

learn the percentage of parents who report having done each activity

and the proportion of parents having informal talks who report any of

the military-oriented influence behaviors. We will then construct

scales (if the items appear linear) or conduct cluster analysis (if

several dimensions seem to underlie the responses). We will also

introduce demographic variables to ascertain the characteristics of

parents associated with high, medium and low scale scores or with

activity patterns located in the four quadrants of the cluster analysis space. The responses of youth will then be incorporated and

dyadic analyses conducted on parent and youth reports about having

talked about joining the Army and having spoken with a military

recruiter.

Understanding the influence process requires not only information

about the frequency of talks between a parent and youth, but also

information about the content of those talks. Parents are asked

whether they generally encourage, discourage, or stay neutral about

the youth's enlisting during talks about military service. Similarly,

youth are asked whether their enlisting would be perceived as a good

or bad idea by their father and by their mother. Analysis of the

parent responses, combined with several demographic variables and

possibly attitude-based segmentations, will show the types of parents

who are supportive of military service. Dyadic analysis linking the

youth responses with them will reveal harmonious or discordant pairs,

and perhaps suggest the types of parents who could use information

about the Army's offers to persuade an uninformed or reluctant youth

about the value of military service.

The final portion of the influence process is the impact: given

the level of communication and its content, did the parent-youth

interaction appear to shape the youth's career plans for the immediate

future? To measure this, parents are asked two questions about the

amount of influence they think they have had on the youth's plans for

the future and on plans about enlisting. The ACOMS survey does not

contain parallel items for the youth. The parents' responses to the

more general question of impact will be used to stratify the dyads,

and we will investigate whether parents who perceive themselves as

influential have greater attitudinal similarity to their youth regarding military service than do parents who report little influence. A

similar analysis will be conducted using parental influence concerning

enlistment as the stratifying variable.

Friends, teachers, school counselors, fellow students, employers,

and coworkers are also potential sources of influence on a youth's

decision process regarding enlistment. Youth are asked, as they are

about their mother and father, whether each of these groups would feel

102
that his or her enlisting in the Army was a good or bad idea. Multivariate analysis will probe the relative support of parents vis-a-vis

the others. We will also identify the two subgroups of youth who

report a strong intention to, or not to, enlist and then examine where

they might be drawing social support. Further discussion of the

analyses on the social influence process is contained in Chapter 6

(Gaertner, 1988).

The parental influence process is inherently dynamic and our

analysis of it will benefit greatly from the youth longitudinal data.

Many of the attitudes and behaviors of parents are better conceptualized as having lagged rather than immediate impact. From this

perspective, similarities between parent and youth documented in the

first year of data collection are the result of an influence process

long underway. To investigate military- and, more specifically, Armyrelated parental influence occurring during the fielding of ACOMS, we

will use several lead-lag statistical analysis techniques (using various time intervals) on the parent and longitudinal youth data bases.

All of the analyses discussed in this chapter will be relevant to the

ACOMS assessment of the Army advertising program, and will suggest how

parents can be more effectively targeted to help the Army achieve its

human resource requirements.

0,

103
REFERENCES

Ballweg, J. (1969). Husband-wife response similarities on evaluative and nonevaluative survey questions. Public Opinion Quarterly, 33, 249-254. 3

Fishbein, M., & Azjen, I. (1975). Belief, attitude, intention and behavior:

An introduction to theory and research. Reading, MA: Addison-Wesley.

Gade, P. A., Elig, T. W., Nogami, G. Y., Hertzbach, A., Weltin, M., & Johnson,

R. (1984). Motives, incentives, and key influencers for enlistment,

reenlistment, and attrition in the U.S. Army. (PUTA WP 84-19).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Caertner, G. H. Introduction. In G. H. Gaertner, & T. W. Elig (Eds.).

(1988). The Army Communications Objectives Measurement System (ACOMS):

Survey analysis plan (ARI Technical Report 786). Alexandria, VA: U.S.

Army Research Institute for the Behavioral and Social Sciences.

Gaertner, G. H. (1988). Modeling the effects of Army advertising. In G. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement Systems (ACOMS): Survey analysis plan (ARI Technical Report

786). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

Caertner, G. H., Keil, L. J., & Gay, N. L. (1988). Tracking audience

response. In G. H. Gaertner & T. W. Elig (Eds.), The Army Communications

Objectives Measurement System (ACOMS): Survey analysis plan (ARI

Technical Report 786). Alexandria, VA: U.S. Army Research Institute for

the Behavioral and Social Sciences.

Hicks, J. M., Collins, T., & Weldon, J. I. (1979). Youth aspirations and

perceptions of ROTC/militarv: A comparison (Research Problem Review 79-

2). Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Klein, D. (1982). The problem of multiple perception in families.

Unpublished manuscript. South Bend, IN: University of Notre Dame.

Orkand Corporation (1983). Parents: perceptions of their influence on youths'

enlistment decisions. Final Report, Contract MDA 903-81-C-0629, for the

Office of Assistant Secretary of Defense for Manpower, Reserve Affairs,

and Logistics, The Department of Defense, Washington, D.C.

White, L. K., & Brinkerhoff, D. B. (1978). Measuring dyadic properties: An

exploratory analysis. International Journal of Sociology of the Family,

8, 219-231.

Wilson, M. J. (1988). Market segmentation analyses. In C. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

104
Wilson, M. J. (1988). Brand differentiation analyses. In C. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

dp

1

sp.

&

'

'pp.

105 ¢

.
6. MODELING THE EFFECTS OF ARMY ADVFRTISING

Gregory H. Gaertner •

Introduction and Overview

One goal of the Army Communications Objective Measurement System

(ACOMS) is to provide an assessment of effects of Army advertising in

an integrated framework. The current research activity begins to

provide this integrated assessment. Previous chapters concentrate on

one subject area (e.g., segmentation, brand differentiation, parental

influence). The focus of this chapter is on bivariate and multivariate analyses, within and across segments, components, services and

time in an attempt to determine how Army advertising affects knowl- 1K

edge, recall, perceptions, attitudes, intentions and ultimately

enlistment-related behaviors. An additional goal will be to assess

how each of these constructs in turn affects others, cumulating toward '.8

youth career choice and life planning, including options provided by

military service.

This chapter focuses on data collected in the ACOMS interviews,

and mainly on the youth interviews. Data components will include

cross-sectional interviews with youth and parents in year 1, and

cross-sectional and longitudinal interviews in subsequent years.

Since the analysis must proceed before the full year's data are

collected, we plan to run preliminary analyses on the first two quarters of data and replicate them on the datz. collected through June 30

of each year for inclusion in the annual reports. Progress briefings

and working papers will be based on preliminary data. Scheduling and

deliverables are described in Chapter 8 (Gaertner, 1988). The anal)-

sis of fit, exposure and change, described in the next volume,

includes information drawn from syndicated exposure data and message

content analyses to be conducted later.

%

This chapter is organized as follows. We begin by describing the

approach to the analysis, and then step through the plan for the analysis in detail. At each step, we discuss likely results and how the

plan responds to them. We begin with the plans for the first year of

analysis, and then move to discuss the additional Extensions and

refinements available in the second year of data coilection.

Approach

The approach to the analysis of advertising effects in this

chapter is incremental. The analysis will begin with a review of the

univariate findings from Chapter 2 (Gaertner, Keil, & Gay, 1988). We

will then examine bivariate relationships to answer questions of

interest, and develop several multivariate models corresponding to

aspects of the hierarchy of effects model presented in Chapter 1

(Gaertner, 1988). These models build toward an inclusive test of the e

latter part of the hierarchy of effects model. In each step, we will

begin by describing overall results, and then note differences by S

segment of the prospect population and further differences by component and service.

107

JU~~~~~~ K,~ -A. P.. A A t-.
This approach relies heavily on the scale construction and trending results described in Chapter 2 (Gaertner, et al., 1988) on tracking. It also relies heavily on the segmentation/brand differentiation

analyses described in Chapter 3 (Wilson, 1988) and Chapter 4 (Wilson,

1988). In the interest of providing a complete but succinct description of the plans for this analytical activity we will briefly summarize the results from previous chapters on which the current effort

will depend. Readers desiring further details on these tracking and

segmentation analyses should consult those chapters.

Since we assume that most of the scales and segmentations will be

developed in other activities, the basic statistical methods to be

utilized for the modeling effort will be correlational and path

analytic in the first year. Cross-lagged panel analysis and event

history analyses will also be conducted in the subsequent years.

Finally, some of the replication analysis within segments will require

analyses of covariance.

Analysis Steps (Year I)

As noted above, modeling the effects of advertising will consist I

of four basic analytical steps during the first year:

(1) Reviewing and extending the univariate results from the

tracking and segmentation/brand differentiation activities;

(2) Presenting bivariate results;

(3) Presenting sub-models representing portions of the hierarchy

of effects model; and

(4) Producing an inclusive hierarchy of effects model.

In the sections to follow we describe these steps in further detaii.

%Univariate Results

The various constructs from the hierarchy of effects model are

measured in the main youth interview. We can summarize the discussion

in Chapter 2 (Gaertner, et al., 1988) as follows.

Intentions and Propensity

Measures of intentions and propensity are elicited with respect

to several stimuli: (a) likelihood of enlistment (by component and

branch of service, aided and unaided); (b) likelihood of college

enrollment; and (c) likelihood of full-time civilian employment. As

with previous measures, early efforts will be devoted to determining

variability seasonally, by segment and by component/service.

108

1%

'k A _' -!k .
IPerceptions/Importances and Main Message

Since the questions on perceptions of Army attributes represent p

the core of the ACOMS measurement system, particular attention will be

directed to the internal structure of perceptions and beliefs regarding the Army and its components, and the importance of communications

objectives to youth respondents. It is reasonable to suppose several

scales of perceptions of the Army (relating, for example, to personal

growth, to educational benefits, and to skill training) will emerge.

One early interest will be to discover whether the importance of

attributes clusters along similar dimensions. A second focus will be

to determine whether perceptions are annually cyclic corresponding to

youth life-cycle changes (e.g., college testing, application and

acceptance, employment search) and whether importance of attributes

varies as would be expected under current segmentation strategies

(e.g., whether college-oriented students place relatively high importance on college benefits, work-oriented on skill training). Further,

if strong brand differentiation exists, perceptions may also differ by

component and service.

Depending on the analysis of the open-ended question on main

message perceived in Army advertising, measures derived from it may

allow analysis of "unaided" perceptions (for example, what do youth

perceive the main intended messages of Army advertising to be, and in

what priority). These may vary seasonally and by segment.

Behaviors

Measures of career choice behaviors are likely to cluster along

several nonorthogonal dimensions--actions relating to enlistment,

actions relating to college search and enrollment, and actions relating to job search. Levels of each of these can be expected to vary %

seasonally, corresponding to youth life cycle changes. Dual market

theories predict the types of behaviors undertaken by youth (workoriented youth searching for employment, college-oriented youth active

in education-related search) and we will test the hypotheses.

Recall of Advertisements

Recall of Army component and other services advertising is

elicited in youth interviews in both unaided and aided forms. Scale

and brand differentiation results may suggest that advertising for

certain components or services is better recalled than others. Tracking questions will reveal whether recall varies seasonally with advertising weight or is more associated with durable characteristics of

the respondent.

Knowledge of Offers

The youth interviews are likely to yield estimates of respondent

knowledge of offers and images per component, and perhaps for the Army

overall. Tracking results should determine whether knowledge is annually cyclic corresponding to Army advertising weights. Brand differentiation analyses should assess how to categorize types of knowledge

109
(e.g., overall knowledge of military or Army offers, knowledge of

Reserve or Reserve Officers' Training Corps (ROTC) offers, etc.) and,

in conjunction with segmentation analyses, who knowledgeable respon- p

dents are likely to be.

Media Habits

Two sorts of media habit measures are likely to result:

(a) proxy measurements of exposure; and (b) measures of the youth's

habits of media consumption in terms of levels of and balance among

media. The former, proxy measures of exposure, can be derived from

the extent to which a respondent views shows, reads publications, or

listens to radio shows on which the Army regularly advertises. The K

latter, measures of media habits, can be derived from the amounts of

time the respondent views television (and which kinds of shows), reads

newspapers/ magazines, and/or listens to radio, both overall and in

proportions to one another as well as tastes in particular kinds of

television and radio programs. Both types of measures may yield

media-specific or overall scales, and the tracking results are likely

to yield estimates of annual cycles of media consumption (for example,

higher radio listening in the spring and summer than in the fall and

winter).

Bivariate Results

Examinations of the bivariate relationships among constructs in

the hierarchy of effects model represent the initial steps in the

testing of the model as a whole. They also provide needed answers to

questions of practical importance in assessments of Army advertising

effectiveness.

Our approach in examining the bivariate relationships among

constructs will consist of several steps:

(1) Assess the form of the relationship (i.e., linear,

exponential, etc.) through scatterplots, analyses of

variance and piece-wise regression;

(2) Assess the strength of the relationship through sizes of

correlation coefficient, tests of significance; and

(3) Assess differences among segments and between youth and

parents through analyses of covariance and/or regression

substitution methods. .5

In Chapter 2 (Gaertner et al., 1988) and in the foregoing discussion, segments were used in isolating differences in univariate distributions of enlistment-related intentions, importance and perceptions of Army attributes, etc. In this section, segments tend to be %

used as specifying contexts for the operation of cognitive, conative %

and decision processes. Operationally, this implies that segments

will be used as moderators and interaction terms when assessing the

bivariate relationships among constructs in the model.

110 I
I

Table 7 displays the constructs in the model in matrix form with

brief notations of the substantive issues addressed by the various

bivariate relationships displayed. We can discuss some of these rela- 0

tionship in more detail.

Exposure/Media Habits and Recall

Some of the media habits measures operate as proxies for exposure, especially those asking whether the respondent regularly views/

reads/listens to vehicles in which the Army disproportionately advertises. The correlation between these measures of exposure and recall

of Army advertisements is of substantive interest to Army advertising

managers. If the relationship is linear, the correlation gives some

sense of how much exposure to the vehicle translates into what level

of cognition of the advertisement. If the relationship has a "step"

form (i.e., at some point, an increase in exposure yields a nonlinear

increase in recall), this result would indicate a threshold for cognition of Army advertising. The correlations between other media habits

measures and recall give a sense of what the marginal utilities of

various kinds of vehicles are in terms of recall. The correlations

between TV, print and radio consumption and recall will provide measures of the differential success of media in securing cognition of

Army advertisements.

The correlations of the exposure proxies and the media habits

(overall and by media) with knowledge, perceptions, propensity and

behavior can be interpreted analogously as above, in yielding thresholds, marginal utility and differential success insights.

Recall and Knowledge

Recall (both aided and unaided) is a commonly used measure of

cognition, "top of the mind" awareness and as a surrogate for advertising impact. In ACOMS, this latter assumption of impact will not be

required, since independent measures of attitudinal and behavioral

change will be coller'ted. In order to assess the relationships

between recall and related attitudes and behaviors, bivariate correlations between recall and the other measures will be produced. 'For

example, the relationship between recall and knowledge of Army offers

is a measure of comprehension of the information content of the Army's

advertising. We would expect that respondents better able to recall

advertising would have better knowledge of the Army's offers, although

this may not be true generally or in some markets. If recall cannot

be shown as linked to knowledge, or to related attitudes and behaviors, its usefulness as a measure of impact might be questionable.

The relationship between recall and perceptions is more difficult

to interpret, since recall may cause perceptions (ccgnition leading to

attitude change) or be caused by perceptions (selective attention to

favorable objects). We can begin to tease out the causal direction

both by path analysis and by panel analysis in the second and following years. The issue of causality is similarly confounded for intentions and behaviors.

ill

.5.
Table 7

Bivariate Relations between Modules in the

Hierarchy of Effects Model

Recall Knowledge Perceptions Intentions Behaviors

Media ATTITUDE

Habits COGNITION FORMATION

Recall COMPREHENSION

Knowledge DIRECTED

of Offers ATTITUDE

CHANGE

Perceptions CONATION

Intentions MOTIVATED

BEHAVIOR

112

A. A. "'' f'* \ ' % , AA4 A 
Exposure/Recall/Knowledge and Importance/Perceptions

Since change in the perceptions of Army attributes is of fundamental importance for ACOMS and to the Army's communications programs,

bivariate relationships between perceptions and the cognition measures

are of particular interest. To the extent that the Army's advertising

program is ono of directed attitude change focused on particular

aspects of Army offers, it is in these bivariate relationships that

initial evidence of advertising impact can be uncovered.

We expect that exposure to, recall of and knowledge of the contents of Army advertising will be associated with more favorable

perceptions of the Army. ACOMS will, however, be able to assess much

more than this minimal result. ,-

First, the Army's trimester advertising plan, supported by the

dual market theory, allows fairly precise targeting of expected

impacts of advertising effects. For example, in the fall the main

advertising emphasis is on skill training, and the major prospect

audience is high school graduates. Thus, as we noted in Chapter 2

(Gaertner, et al., 1988), if the advertising is effective, we should

expect to see increased knowledge of skill training aspects of the

Army's offer among high school graduates. To the extent that skill

training is an effective sales point for this segment, we would

further expect that knowledge of skill training options would produce

favorable perceptions of Army skill training programs. In the winter

and early spring, similar predictions would be made regarding collegeoriented high school seniors and appeals emphasizing college funding.

Thus, we would expect, if targeting were operating as hoped for, that

particular directed knowledge-based appeals would be differentially

effective for different markets in different seasons.

The issue of causal direction impedes unequivocal interpretation,

however. It could be that favorably disposed respondents would have

greater knowledge or recall of appeals of particular relevance. Thus,

respondents favorable to Army skill training appeals would pay particular attention to them. As with recall, panel analyses should help

to untangle the issue of causal direction.

Perceptions/Importance and Intentions/Behaviors

In any assessment of the effectiveness of advertising, this set

of relationships is arguably the most crucial. The hierarchy of

effects model clearly suggests that if respondents are made aware of

favorable aspects of the Army along dimensions of importance to them,

they will become more favorable to enlistment in the Army and, other

things being equal, be motivated to do something about enlistment.

Empirically, the hierarchy of effects model predicts that the

product of perceptions and importance, summed over all attributes,

will be the best predictor of behavioral intention and that neither

perception nor importance would be as efficient as predictors. The

model further suggests that segmentation will occur through systematic

differences among youth in terms of importance (or perceptions) which

113
N..

will identify markets. Thus, the college-oriented youth would place

high importance on money for college, while the work-oriented youth

would be expected to place emphasis on skill training. To the extent

that each sees the Army as fulfilling his/her respective needs, the

product (importance times perception) would be large, and a high

behavioral intention predicted.

A simple example should suggest some of the issues involved in

testing this model. Assume that there are really two markets:

college-oriented prospects who are interested basically in money for

college, and work-oriented prospects who are interested in skill -'

training. To simplify things a bit, assume that the prediction equation for behavioral intention to enlist is:

BI- Pm * Im + PS * Is (1)

where Pm is the perception that the Army offers money for college, Im

is the importance of money for college, Ps is the perception that the

Army offers skill training, and Is is the importance of skill training. Fishbein and Azjen (1975) generally assume that no additional

weights are required to reflect the differential impact of different

perceptions. Rather, the importance scores are expected to substitute

for additional weights. If the Fishbein and Azjen formulation is

correct, equation 1 should have high predictive accuracy. However,

consider some further assumptions. Assume that the youth market

divides into two segments--Sm, youth interested in money for college,

and Ss, youth interested in skill training. For youth in Sm, we can

postulate that Is will be small and Im will be large, so that equation

1 becomes

BI - a, + Im * Pm (la)

and for youth in Ss the equation is

BI - a2 + Is * Ps (Ib)

Empirically, the results would imply an interaction effect of

segment on the association between perception and behavioral intention, particularly if importance were not explicitly entered in the

equation. In fact, a segmentation based on importance of attributes

is likely to yield results similar to la and lb if equation 1 is

correct. Thus, given appropriate assumptions about segmentation in

the market and the bases of that segmentation, equations 1, la and lb

are all reflective of the same dynamics at work, in spite of being

specified in quite different ways.

One goal of the bivariate analyses involving perceptions and

intentions/behaviors will be to test the predictions of the Fishbein

and Azjen formulation. A broader goal will be to develop explanations

for the various forms the results might take.

114
Intentions/Propensity and Behavior

The relationship between intentions and behaviors relating to

enlistment is the jumping off point for the examination of goaldirected search. While this element is not a major initial concern of

the ACOMS analysis, the examination of patterns of search behavior

relating to propensity can provide useful marketing results. It may

be, for example, that certain types of prospects engage in search

behaviors in different ways, or that propensity is associated with

search behavior for only some types of respondents. These results

could help to target the Army's limited marketing resources on especially likely prospect markets.

Multivariate Models of the Effects of Advertising

The Fishbein-Azjen (1975) hierarchy of effects model displayed in

Chapter I has three main parts--a cognitive element, relating to media

habits, exposure and knowledge/awareness; an attitude formation element, relating to perceptions, importances and attitudes toward

enlistment; and a conative element, relating to intentions and behaviors. Building toward a more inclusive test of the model as a whole,

the third major analytical activity in modeling the effects of Army

advertising will be to construct smaller submodels of these cognitive,

attitude formation, and conative processes.

Statistical methods are expected to involve path analysis and

traditional regression and correlation analyses for the main submodels in the first year. To assess differences among segments, or

between parents and youth, two alternative methods may be useful.

First, we anticipate testing the sub-models separately in the various

segments, searching for marked differences among them them. Analyses

of covariance can then be conducted to test for significant differences among segments.

Second, we expect to utilize the method of mean substitution,

developed by Duncan, Featherman and Duncan (1972) to assess the impact

of aggregate differences among segments resulting from differences in

path coefficients. An example may clarify the procedure.

Assume we discover that even though high school students and

graduates have roughly the same magazine readership patterns (i.e.,

the same levels in roughly the same magazines), but that high school

graduates seem to gain more knowledge of Army offers from their reading than the students. Empirically, this would be reflected in a

larger correlation (or steeper slope) relating levels of magazine

readership to knowledge of offers for graduates than for high school

students. We discussed this relationship between exposure and knowledge earlier in terms of comprehension. This difference in comprehension could be assessed directly by an analysis of covariance. How- I

ever, a second method would be to substitute the mean level of readership for high school graduates in the standardized equation predicting

knowledge from readership derived from high school students. If there

were no difference between high school students and high school graduates in terms of the path coefficient, the predicted mean knowledge of

115

-' ~ ~ . W~ ~ ~ ,~ f( , vCVt 'W
high school graduates would be equal to the observed mean. Since by

assumption there is a difference--graduates will have more knowledge

than expected assuming comprehension rates identical to high school

students--the difference would reflect the additional knowledge gained

by high school graduates from their greater rate of comprehension.

This method yields results which can more easily be presented and

explained than the more complex analyses of covariance, but are

equally rigorous.

In the next sections we discuss the four. submodels proposed for

cognition, attitude formation, the combination of the two, and the

model linking the combined sub-models with conation/ behavior.

Cognition of Army Communications Objectives

Measures in this part of the model include media consumption

patterns, proxy exposure, recall (aided and unaided), knowledge of

offers, slogan recognition and main message comprehension. The

initial submodel to be tested is depicted in Figure 19.

Four classes of variables are displayed in the figure: media

consumption, exposure to vehicles in which the Army advertises heavily, recall, and knowledge. The model displays minimal paths. Consumption of a particular medium is expected to result in exposure to

the vehicles utilizing that medium, resulting in recall and knowledge.

Further, the model depicts a fairly simple causal chain: consumption

affects recall only through exposure, and exposure affects knowledge

only through recall. In spite of its apparent simplicity, the model

can address several issues of interest.

First, to the extent that there are differences by medium in the

effects of consumption on exposure, the findings imply differential

efficiency of ad placement for the various media. For example, if

consumers of print media generally do not report exposure to Armyutilized vehicles at the same rates as consumers of television, there

is reason to believe that the television placements are more effective

than the print placements.

Second, if there are differences in the relationships between

exposure to Army-utilized vehicles and recall (aided or unaided) by

medium, these differences would reflect differences in impact of the

various vehicles by medium, an area of obvious interest.

Third, if there are effects of consumption patterns on recall or

knowledge, independent of exposure to heavily utilized vehicles, these

effects might be interpreted as impacts of advertising in vehicles

other than those explicitly queried. These effects could reflect *

unreliability in reporting of exposure to vehicles. They might also

reflect the spurious effects of unmeasured variables. If these

effects are large, the results might suggest a different selection of

heavily utilized vehicles.

116 P

ZF -^ r' " F' ~ J
Caa

- a

UU

6 =,

=V

l U I

1 _ 1 7

"o o o

o a

a 117 -. - 2-2
Fourth, if consumption or exposure affect knowledge, main message

comprehension or slogan recognition independent of recall, the results

would argue in favor of a pervasive process of communication, either ,

interpersonal or not consciously perceived. Alternatively, the

results might argue against the validity of recall measures in assessing advertising impact. To the extent that aided and unaided recall

operate differently in this connection, the results might suggest the

utilization of one or the other in subsequent analyses.

In addition to reviewing findings from the cognition sub-model -'

taken as a whole, we can also replicate these results in the various

segments and for the various components. The various segments are

likely to have different levels of consumption of TV, print and radio,

and therefore different levels of exposure to different Army-utilized d

vehicles. To the extent that their vehicle choices are different, the 0

various segments are likely to have differential rates of exposure

even with similar rates of consumption. Thus, the efficiency of

different vehicles in reaching prospect youths might vary by segment.

Further, it may be the case that specific levels of exposure for some

medium may translate into various levels of recall for different segments. Thus, college-oriented recall may vary most by print exposure, 0

while work-oriented recall might vary more by radio or television

exposure. A similar case might be made for differences among segments

in relating recall or exposure to knowledge, main message and slogan

recognition.

The multivariate results for other Army components are more

limited than those available for active Army. None of the other Army

components advertises extensively on television or radio so that the

proxy exposure measures will be less useful. We do, however, have %

exposure to print vehicles and aided and unaided recall for the various components, as well as knowledge questions directed to their

specific offers. The analyses of print exposure, recall, and knowledge can therefore be conducted both overall and within and across

segments.

Attitude Formation

Measures in this part of the model include perceptions of Army

attributes, perceptions of the attributes of other services, perceptions of college and civilian jobs, the importance of attributes to

the youth, attitude toward enlistment, and propensity to enlist in the

Army or its components. We noted earlier that the hierarchy of

effects model implies that attitude toward enlistment is a function of

the products of importance and perceptions, summed over relevant

attributes. We further noted that if market segments were specified 5

in terms of importance, this model resolves into an interaction effect

of importance on the relationships between perceptions and attitudes/intentions. This latter formulation may produce more useful

results, as displayed in Figure 20.

%

118
cc4

.2 E.

cI

c4

05

4',

WV

U.I 2,2 a

I- 4KL

11
Figure 20 presents a simplified model involving the prediction of

intention to enlist in the Army and its components and attitude toward%

enlistment, as predicted by perceptions of the Army, of other services, and of civilian jobs, moderated by the importance of

attributes. The direct paths connect variables expected to be associated; interactions are displayed by a path linking a variable to a

path. The model is simplified in several senses. First, we assume

only two perceptions/importances are relevant: money for education

and skill training. In fact, there are likely to be several other

dimensions of interest. Second, we show only those paths associated

with money for education. (For purposes of this example, we ignore

perceptions of college, since it seemed tortuous to argue that college

offers opportunities for college funding.) Third, for purposes of

clarity we do not show importance of attributes moderating the relationships between perceptions and attitude toward enlistment, although

we expect to find this empirically.

In words, the model expresses the following expectations. The

greater the importance of money for education to youth, the more his

or her perception that the Army offers such money will influence his

or her intention to enlist in the Army. However, the same is true for

other services, so that the greater the importance of money for education, the more influence the perception that other services offer such

money will have on the youth's intention not to enlist in the Army. A

similar case can be made for civilian jobs. By implication, for

education-oriented youth, the intention to enlist is the outcome of

the relative weight of perceptions that the Army offers opportunities

for funding college balanced against the belief that other services

and civilian jobs offer the same opportunities.

The dynamics of attitude toward enlistment generally should be

similar with one exception: the attitude toward enlistment is the

outcome of the perceptions that the Army and other services offer the

attribute of interest, weighed against the perception that civilian

jobs offer a similar benefit.

The path-connecting attitude toward enlistment and intention to

enlist in the Army can have two interpretations. It may reflect the

fact that youth who favor enlistment tend to enlist in the Army simply

because the Army enlists more youths than other services. It may also

mean that any attitude to enlist favors the Army by virtue of its %

greater education benefits, j,6b oppoztunities and the like.

While the model as displayed irt Figure 20 emphasizes money for

education, parallel interpretations could be made for skill training.

For example, the model (properly diagramed) would argue that for

skill-oriented youth, the intention to enlist in the Army is a outcome

between the perception that the Army offers skill training weighed

against the perceptions that other servi'ces or civilian employment

offer similar opportunities. Further, the model could be expanded to

include analysis of the perceptions of the various Army components.

The model makes no assumptions regarding the relationship between

the importance of money for education and the importance of skill

120

'ar *Ja 0.~5
training. It may be that they are negatively related (youth desiring

money for education may tend not to want skill training), independent,

or positively related. Similarly it may be that perceptions of

attributes are negatively intercorrelated (indicating a specialized

market position), or positively correlated (indicating a powerful

image which diffuses discrimination among attributes).

Further, the model makes no assumptions regarding the relations

among perceptions across services. It may be that youth who see the

Army as offering money for college are more or less likely to see

other services as offering similar benefits. Similarly, the model

makes no assumptions regarding the relations among perceptions of Army

components. They may be positive, indicating a strong brand image, or

negative, indicating attitudinal niches for the various components.

This model of attitude formation allows us both to utilize the

conceptual and empirical basis of the Fishbein and Azjen (1975) utility model of attitude formation while emphasizing competitive positioning and brand differentiation for vatious classes of offers (among

services and compared with jobs, college, etc.) It further allows us

to analyze the internal structure of perceptions and needs to determine areas of image and offer strength and weakness. This aim is

furthered by linking the models of cognition and attitude formation,

as we discuss in the next section.

Combined Models of Cognition and Attitude Formation

By linking the models in Figures 18 and 19, we can begin to

assess the effects of Army (and other service) advertising on perceptions and intentions. A simplified version of the linked submodel is

depicted in Figure 21. As with previous models, only minimal paths

are specified. According to the model, we expect exposure to and

recall of Army advertisements to affect knowledge of Army offers and

main message comprehension. Further these can be expected independently to affect perceptions of Army attributes. They also may affect

importance of attributes by heightening youth awareness of various

opportunities (for skill training, educational benefits, etc.).

Paralleling this influence process, however, we also have measures of recall of other services' advertising, and indirectly, youth

knowledge of other services' offers. For example, we have measures of

respondent knowledge of whether Army educational benefits are greater

or less than those offered by other services, and which services offer

the GI Bill. By implication, we have information on youth knowledge

of other services' offers relative to those of the Army. Finally, we

have measures of other services' main advertising messages and the 5

perceptions of other services. Thus, we can model the parallel influence process for other services, with recall leading to knowledge and

main message, and both leading to perceptions.

The first step in testing this linked model will be to verify

that the hypothesized paths relating exposure and recall to knowledge

and main message comprehension, and then in turn to importance and

perceptions, do exist. We will look more specifically at which

121 S
-W -WI

N 1.

- 10-

E, -

* .0 V.

0

0.0

c 0

* 0

IN'.

1222
perceptions are affected by advertising recall and exposure. In this

first step, we expect to model the influence process separately for

the Army, its components and for the other services.

The second step will be to compare the coefficients in the model

across services, deriving measures of the relative effectiveness of

Army and other services advertising. Thus, for example, we could say

that main message comprehension of Army advertising affects perceptions of Army attributes a given amount, measured by the path (or

regression) coefficient relating them. A similar coefficient can be

derived linking other service main message comprehension to other

service perceptions. If the coefficient for Army advertisements is

larger than that for other services, we can infer that the Army

message was more effective at influencing respondent perceptions.

A third step will be to assess whether paths not specified in the

model do in fact exist. For example, is recall of Army advertising

associated with perceptions of the other services, net of recall of

the other services' ads? This would be evidence of either misidentification of viewed advertising or a diffusion of advertising influence. Diffusion might occur if viewers took the Army's advertising

message to be applicable to all of the military services.

A Linked Model of Cognition, Attitude Formation, and Conation/Behavior

The fourth submodel to be examined links the measures of intentions, propensity and behavior and produces the model presented in

Figure 22. This model approaches the full model described in the next

section. Here, we would direct our attention specifically to the

effects of advertising and perceptions on intentions and behavior.

These latter measures would be derived from questions regarding the

respondent's attitude toward enlistment, whether he or she is likely

to enlist (and in which service or component), and what behaviors he

or she has undertaken to that end.

This model asks whether attitude toward enlistment and intention

to enlist in the Army and its components is dependent on perceptions

of Army attributes, alone or in combination with the importance of

those attributes. Using the results from Figure 20, we would go on to

examine which perceptions are most important in predicting attitude

and propensity. We can then move forward in the model, asking whether

propensity and attitude are associated with behaviors (and which

behaviors). We can also move backward by asking whether exposure to

and recall of advertising predict attitude and propensity independently of perceptions, or whether the effects of advertising are

filtered through intentions. Assuming that exposure to advertising

results in positive attitudes toward enlistment, a predominantly indirect effect would argue that the Army attributes encompass the predominant buying motives of enlistment-minded youth. A direct effect of

advertising exposure (unmediated by perceptions and/or importance)

would argue for enlistment as an "impulse purchase," or as operating

through mechanisms or attributes unrelated to measured attribute

perceptions.

6

123
cc a

EC.2

. 41.

C *

Ohj

00

CC

124.
U. - f ; 14V )ra. -

Perhaps most importantly, we can observe which of the two- (or

three-) step paths from advertising cognition through perceptions have

the strongest indirect eizects on intentions and behaviors. The

model's assumption that the effects of advertising are mediated by

perceptions and importance has several important implications. Some

perceptions which (in combination with importance) might be expected

to have strong influences on intentions may not be easily influenced

by advertising (e.g., patriotism). Other perceptions which might be

relatively easily influenced by advertising may not be important motivations for enlistment. The analyses of the two-step paths should

uncover those perceptions which both can be influenced and are important for enlistment.

In fact, because the two-step path through perceptions involves

an interaction term, it may be that the most critical advertisements

are those that influence perceptions which interact strongly with

importance for a large population segment. Thus, for example, advertisements emphasizing college funding are effective if, for a reasonably large youth segment, (a) the importance of college funding opportunities (b) interacts strongly with the belief that the Army represents a source of funding (c) on enlistment intention. In other

words, the advertisements are effective if a large segment is motivated to enlist in the Army by college funding, and if advertising can

influence the belief that the Army represents a source of college

funds.

Further analyses will be directed at the paths not specified in

the model in Figure 22. As noted above, it may be that recall

measures do not mediate the effects of exposure on knowledge and

subsequent variables. This would mean that the paths between exposure ',

and the dependent attitudes and perceptions are direct.

Finally, the model will be replicated among segments and components and over time. As outlined above, the method of mean substitution and analysis of covariance will be utilized for these replications. '.

A Path Analytic Model for the Effects of Advertising

One of the major analytic activities in modeling the effects of

Army advertising will be the testing of the full Fishbein and Azjen

hierarchy of effects model. The testing of the full model represents

an advance beyond the previous discussion of sub-models in a number of

respects.

First, the discussion of submodels has emphasized the top row of

constructs in the model, those pertaining to youth processes of cognition, attitude formation and conation. However, the hierarchy of

effects model includes second and third rows of constructs specifying

the social and parental influences on the youth's decision-making.

Thus, testing the full model will allow the estimation of the effects

of perceived norms (emanating from parents and others) regarding

125

' %4~~,,\ -v ' '. ja -~.°-- (' ~~ 
enlistment, and the inclusion of the parallel processes of parental

cognition, attitude formations, and influence behavior related to Army

enlistment.

Variables incorporated in the social influence process include

the numbers of friends and/or relatives of the youth who have served

or are serving and what their branch is (questionnaire item SI-2 and

following), the youth's assessment of what the attitudes of peers and

authority figures (e.g., parents, friends, counselors, teachers,

fellow students, employers and co-workers; derived from SI-1), the

youth's assessment of his or her parent's influence (PL-2) and the

youth's assessment of whether people like him/her are joining the Army

(PE-15). While considerations of interview length did not allow a

complete test of the Fishbein and Azjen elaboration of social influence processes, we have included several constructs of theoretical

interest. We can, for example, correlate perceived attitude of peers

and authority figures with the youth's perception of norms regarding

enlistment (measured by PE-15). The differences in size among these

correlations will give some indication of differential congruence

between perceived attitudes and perceived norms. Further, correlations between norms and peer/authority attitudes on one hand, and

attitudes toward enlistment on the other, will indicate the relative

importance of social influences on youth attitudes. Finally, the

correlations between service experience of peers and relatives and the

youth's perceived attitudes, norms and intention to enlist will indicate the importance of other's actual military experience in the

social influence process.

As noted in Chapter 5 (Baxter, 1988) on the analysis of the

parental influence process, we have extensive information on the media

habits, exposure, recall, perceptions, importance and behaviors of

parental influencers for the parental-linked youth sample. These

variables and the results of the analyses described in Chapter 5 can

be integrated in the full model as time and resources permit.

A second advance of the full path model testing over the submodels described earlier is that the full model allows systematic

tests of the correlational adequacy and statistical implications of

the model in an integrated framework. Correlational adequacy will be

assessed several ways. First, using the first two quarters of data

collected, path coefficients can be calculated for the hypothesized

relations in the model. Since the model as specified is comparatively

parsimonious (i.e., many potential paths are not included), the model

will not fit the matrix of zero order correlations exactly. Thus, one

early test of the model will be to assess what additional paths need

to be included (or existing paths deleted) for the path model to be

adequately descriptive and still parsimonious.

A second test of the correlational adequacy of the model will be

to perform the mean substitutions among segments described above, as

well as analyses of covariance as required. The purpose here will be

to determine whether the model is appropriate for all of the various

segments or whether different models will be required for different

segments. A parallel procedure can be conducted for the various

126
components to determine whether the overall model is descriptive of

the decision-process for the Army Reserve, Army National Guard and

Reserve Officers' Training Corps (ROTC) as well as the active Army.

Again, the model will then be re-specified in as many versions as

necessary.

A third test of the correlational adequacy of the model is

permitted (and in fact required) by the scheduling of the first year.

Preliminary analyses will have to be conducted well in advance of the

annual report--we have proposed to conduct these analyses on the first h

two quarters of data. At the same time, the annual report should

include data from the full year of data collection (in this case,

through June 30, 1987). Thus, the scheduling allows a split-half

design to test the stability of the observed correlations and the

overall adequacy of the model. This activity will begin July 1.

Beyond the tests of correlational adequacy of the model, we can

assess the statistical implications of the path analysis for advertising decision-making. As is well-known (Land, 1969) in the path

analytic context, the zero order correlation between two variables can

in non-recursive models be decomposed into three components:

(a) direct; (b) indirect; and (c) spurious (i.e., noncausal). Direct

components are represented by the path connecting the variables,

indirect componerts by the products of the n-step paths through intervening variables, and spurious components by the products of paths

through causally prior variables, with some exceptions (see Duncan,

1975). Computationally, the direct effects component is the beta from

the full regression involving all the predictors. The indirect effect

is the partial beta between the predictor and the dependent variable %

controlling for all causally prior variables minus the direct effect.

The spurious component represents the zero-order correlation minus the

partial beta controlling for causally antecedent variables. For an

example, see Gaertner (1983).

This decomposition has fairly direct implications for advertising

policy. For example, it may be that measures of awareness of advertising are correlated with attitudes toward enlistment or intentions,

but that the decomposition shows the effect mainly to be spurious due

to background factors. This result suggests that at a certain life

stage, youth begin to pay attention to Army advertisements and begin

to form attitudes toward enlistment or make enlistment plans. It

would be hard to argue, given this set of results, that advertising

has created the awareness. As another example, we might expect recall c.

of advertising to have comparatively weak direct effects if it has

little direct causal force. Further, where and how parental influence

enters into the overall decision process is as yet an open question,

as is whether advertising to parents has much effect either on

parental attitudes or indirectly on youth attitudes.

A final step in the path analysis will be to assess the relative

sizes of the various paths in order to draw policy implications. As

noted above, it is possible (or even likely) that some perceptions may

have greater overall effects on attitudes than other perceptions.

Comparisons of these effects, net of prior variables in the model,

127
j ~1~ ~WIW~ VlWXV W lipop VLW2 17 W- V W W

1 ).- 17 -2 1 7 7w V 17:~ ~~

should suggest directions for future advertising executions. (The

statistically minded reader may note that it is difficult to incorporate interactions in a path analysis, but that much of the previous

exposition was directed to the interaction effects of importance on

perceptions. The simplest way to handle these difficulties is by

developing different models for different segments, but other alternatives will be considered.)

These analyses should encompass the modeling effects activities

during the first year of data collection. In fact, it is likely that

not all of the above analyses can or will be conducted. In-progress

briefings and interim papers and reports should allow the Special

Advisory Group (SAG) and the U.S. Army Research Institute (ARI) opportunities to give Westat guidance on directions for the research. We

discuss scheduling for these interim work products in Chapter 8

(Gaertner, 1988).

Analysis Steps (Year 2)

We will treat proposed activities for the second and following

years in more cursory fashion for several reasons. First, much of the

analytical activities of the second year will depend on what is

discovered in the first year of data collection. Second, the second

year's activities can and should be shaped by the reactions of the SAG

to content and methodological strategies in the research, as well as

collaborative research efforts with Army analysts. Finally, as new

contexts for the communications objectives (and perhaps new objectives

or markets) are developed, the research plan should be altered to

accommodate them. Nonetheless, some activities for the second year

seem likely to have high priority.

The second year of data will be used to stabilize the tracking

components and to replicate the basic model arrived at in year 1. In

this sense, some activity will be devoted to consolidating the

analytic results of the first year. Using a split-half method, second

year data can be used to retest the path model. Further, the data (if

no changes in correlations over time can be detected) can be pooled to

produce increased precision in estimates overall and by segment and

component.

We can include new segmentation and brand differentiation strategies developed in year 1 in the basic model. These new segmentation

strategies can also be used to modify the quarterly reports, as

discussed in Chapter 2 (Gaertner et al., 1988). Further, with the

second year of data collection, gross estimates of seasonality can be

made and trend analyses can be extended for tracked measures and

scales.

Perhaps most importantly, the second year of data collection

begins the longitudinal component of the analysis. As described in

Chapter 1 (Gaertner, 1988), one quarter of the Primary Male Sample

(PMS) and Primary Female Sample (PFS) youth will be reinterviewed

annually, for a total of more than 2500 reinterviews in the first

128
year. This longitudinal component provides a critical element in the

overall ACOMS measurement and analysis system in several respects.

First, while the path analytical results can show what sets of

causes and events are plausible and consistent with the crosssectional results, the determination of whether advertising exposure

actually leads to enlistment-oriented perceptions, attitudes and

behaviors can only be assessed over time. Longitudinal analyses can

examine how exposure in earlier times leads to "top-of-mind" awareness

of Army offers and perceptions favorable to enlistment as youth

prepare to make commitments to work, college, and/or service. We

propose to use cross-lagged panel correlation analysis (Cook &

Campbell, 1979) to assess the causal efficacy of relationships drawn

from the longitudinal data.

Second, the longitudinal data should allow the charting of

enlistment histories and milestones for enlistment-related attitudes

and behaviors. The questionnaire contains measures of behaviors

undertaken as youth explore their career options. These data,

assessed longitudinally, can show how youth progress toward enlistment

(and/or college/ career choice) and what the major barriers appear to

be. The generic form of this analytic approach has been described as

event history analysis. Event history analysis is a generic label for

analyses of longitudinal data having as their principal characteristic, the occurrence or nonoccurrence of some event (e.g., death, marriage, arrest, enlistment, etc.). Among the wide variety of regression models suitable for event history analysis are logistic regression, parametric models such as the exponential, Weibull and Gompertz

regression models, and semiparametric models such as the proportional

hAzards model. Event history analysis has been utilized in the

description and prediction of employment histories (Rossi, Berk, &

Lenihan, 1980), the effectiveness of various treatment regimens

(Crowley & Hu, 1977), juvenile delinquency (Wilson, 1981), and the

development of political attitudes (Tuma, Hannan, & Groeneveld, 1979).

Westat researchers have utilized event history analysis to describe

the process of Army Delayed Entry Program loss and accession under

contract with ARI. The analytic technique has been discussed at

length in our original proposal, and subsequent instrument design has

been guided in part by an interest in pursuing this analysis in the

second year.

It should be noted that annual reinterviews may not be ideal

either for event history analysis or for establishing causal

sequences. The critical events in postadolescent career choice are

not necessarily annual, or even uniformly spaced over the period from

16- to 24-years of age. Rather, it seems likely that junior and

senior year of high school will be particularly eventful for sampled

youth, particularly with respect to enlistment decision-making. For

this reason, it may be sensible to reinterview some or all of these

youth more frequently than annually, and to reinterview younger and

older respondents less frequently. These issues will become part of

the design of the longitudinal component of ACOMS in the upcoming

months.

129
REFERENCES

Baxter, S. J. (1988). Analysis of parental influence. In G. H. Gaertner & T.

W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and

analysis issues for field settings. Chicago: Rand McNally.

Crowley, J., & Hu, M. (1977). Covariance analysis of heart transplant

survival data. Journal of the American Statistical Association, 72,

27-36.

Duncan, 0. D. (1975). Introduction to structural equation models. New York:

Academic Press.

Duncan, 0. D., Featherman, D. L., & Duncan, B. (1972). Socioeconomic

background and achievement. New York: Seminar Press.

Fishbein, M., & Azjen, I. (1975). Belief, attitude, intention and behavior:

An introduction to theory and research. Reading, MA: Addison-Wesley.

Gaertner, G. H. (1983). Structure, environment, and organizational governance

in manufacturing. Decision Sciences, 14, 546-563.

Gaertner, G. H. Introduction. In G. H. Gaertner, & T. W. Elig (Eds.).

(1988). The Army Communications Objcctives Measurement System (ACOMS):

Survey analysis plan (ARI Technical Report 786). Alexandria, VA: U.S.

Army Research Institute for the Behavioral and Social Sciences.

Gaertner, G. H. (1988). Work products, deliverables and schedules. In G. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement System (ACOMS): Survey analysis plan (ARI Technical Report

786). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

Gaertner, G. H., Keil, L. J., & Gay, N. L. (1988). Tracking audience

response. In C. H. Gaertner & T. W. Elig (Eds.), The Army Communications

Objectives Measurement System (ACOMS): Survey analysis plan (ARI

Technical Report 786). Alexandria, VA: U.S. Army Research Institute for

the Behavioral and Social Sciences.

Land, K. C. (1969). Principles of path analysis. In E. F. Borgetta (Ed.),

Sociological methodology 1969, 3-37. San Francisco, CA: Jossey-Bass.

Rossi, P. H., Berk, R. A., & Lenihan, K. J. (1980). Money, work and crime:

Some experimental results. New York: Academic Press.

Tuma, N. B., Hannan, M. T., & CroeneveId, L. P. (1979). Dynamic analysis of

event histories. American Sociological Review, 84, 820-854.

Wilson, M. J (1981). In-program reoffense rates: A comparison of

experimental and control group performance in Ventura, California

(Research Report No. 22). Eugene, OR: Institute of Policy Analysis.

130
I.

Wilson, M. J. (1988). Market segmentation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Wilson, M. J. (1988). Brand differentiation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

1,3

[13

.. 131ra

I.-. ~ *%W ~ -4 I ' A. Ak. . %
7. PLAN FOR THE INTEGRATED ANALYSIS OF FIT, EXPOSURE, AND CHANGE

Gregory H. Gaertner and Sandra J. Baxter

Introduction

The Statement of Work for the Army Communications Objectives

Measurement Survey (ACOMS), as amended, asks for a comprehensive

analysis of:

(1) how the Army's existing advertisements embodied the Army's

communications objectives;

(2) how the Army's advertising placements reflected the

communications objectives; and

(3) how changes in the Army's advertising content and placement

were reflected in the knowledge, attitudes and behaviors of

the Army's target markets.

The first analyses can be referred to as examining the "Fit" of

advertisements to communications objectives; the second as examining

the "Exposure" of Army advertising; and the final segment as referring

to the examination of "Change" in youth attitudes based on exposure to

the themes embedded in Army advertising.

Chapter 6 (Gaertner, 1988) outlined a course of analytical activities relying only on the main ACOMS interview data. In this chapter,

we extend this proposed analysis plan to include data on Fit and Exposure and its relations to Change in attitudes. Since the current

chapter also includes descriptions of proposed data collection for the

Fit and Exposure data, we provide only an outline of the proposed

overall analysis of Fit, Exposure and Change. A more detailed analysis plan is anticipated during Phase IV, as issues in the acquisition

of necessary data are clarified and resolved.

We begin this chapter with a proposed strategy for the acquisition of data to assess the message content of Army advertising. We

then discuss issues in the measurement of exposure to Army advertising

and propose a strategy for the acquisition of syndicated data measuring exposure to Army advertising. Finally, we provide an outline of

the plan for the integrated analysis of Fit, Exposure and Change.

Message Content Analysis

The message analysis will be based on intercept interviews conducted with youth in shopping malls in five cities. While several '

respondent selection criteria will be used (as described below), there

is no intention for the youth to constitute a representative sample

producing nationally generalizable results.

Unlike copytesting, used by the Army's advertising agency as a

developmental tool, the message analysis will employ finished

advertisements. Fifteen 30-second television ads and 13 print ads

133

%W N,-
II

(see Figure 23) will be tested at each of the five sites. Each youth

will be shown one Army ad buried in the middle of four "clutter" ads '

for audio equipment, a car, financial services, and insurance. The

clutter ads were selected as typical of advertising shown during the

same television programs or appearing in the same magazines as the

Army ads. After several questions assessing unaided and aided recall

of ad sponsors, the Army ad will be shown again and the respondent

asked to indicate how much the ad carries each of 14 possible messages

regarding Army service (listed in Figure 24).

S ampl1e

Interviews will be conducted with 4,200 youth 16-to 24-years-old

who have no more than two years of college education and are without

military experience. Three questions in the screening portion of the

questionnaire in the appendix insure that the respondents meet these

age, education, and military experience criteria. Interview supervisors will be instructed to select respondents to approximate the

following desired sampling proportions:

(1) 35% are 16- to 18-years-old, and 65% 19- to 24-years-old,

(2) 75% are men and 25% are women (except for ads directed to

women, for which the gender percentages will be reversed), P

and

(3) 75% are White, 15% Black, and 10% of other ethnicity.

Sites

The shopping malls will be located in five cities--New York,

Chicago, Atlanta, Dallas, and Los Angeles--chosen to represent the

major geographical regions of the country. At each site 30 youths

will be interviewed for each of the 28 advertisements, generating 840

respondents per site, for totals of 150 respondents per ad and 4,200

respondents in the study. The interviews will be conducted by subcontractors who routinely perform mall-intercept surveys.

Stimulus Packages

Each Army television and print ad will be packaged in the same

set of clutter ads in the identical order. For the television ads,

respondents will be shown a video tape (using a 3/4" tape VCR and

color television set) with the following content:

(1) Title board showing sequence number of ad (5 sec.)

(pause: 5 sec.)

(2) Clutter ad #1 for a car (30 sec.)

(pause: 5 sec.)

(3) Clutter ad #2 for a financial service (30 sec.)

(pause: 5 sec.)

(4) Army ad (30 sec.)

(pause: 5 sec.)

134
Television Ads Print Ads

1. The Letter (similar) 1. The Letter

2. MLRS (similar) 2. MLRS

3. TACSAT (similar) 3. TACSAT %

4. Promises Kept (similar) 4. Promises Kept2 yr.

(similar) 5. Promises Kept- g

4 yr.

5. Father & Son (similar) 6. Father &

Daughter

6. Computer Expert 7. Get Technical

7. Theme 8. Hi-tech

Aviation

8. Visibility Poor Rev. 9. Black WOFT

9. Choices Rev. 10. Hispanic WOFT

10. 9 AM Rev. 11. We Were There

11. Ranger Pride 12. 2+2+2

12. Apache 13. College

Preparation

(Black GI Bill)

13. Light Fighter

14. Alpha Team

15. Flight School

Figure 23. List of Army ads by medium and sequence

showing those with similar content.

0.,

135

W.V.Ir- -f.Ir. 1, - 0C
SHORTHAND LABEL

SORT BOARD CARDS FOR ANALYSIS PURPOSES

[The Army offers:]

A. A wide variety of opportunities

to find a job you can enjoy. "job can enjoy"

B. A physically challenging

environment. "physical challenge"

C. An experience you can be proud of. "experience proud of"

D. An advantage over going right from

high school to college. "pre-college advantage"

E. An opportunity to develop

leadership skills. "leadership skills" ,

F. The chance to work with the

latest high tech equipment. "high tech equipment"

C. A great value in your civilian

career development. "civilian career"

H. An excellent opportunity to

develop self-confidence. "self-confidence"

I. The opportunity to develop

your potential. "develop potential"

J. A mentally challenging

experience. "mental challenge"

K. An opportunity for you to become

more mature and responsible. "mature/responsible"

L. Many opportunities for training p

in useful skill areas. "useful skill"

M. Many chances to work with highly

trained people. "trained people"

N. An excellent opportunity to obtain

money for a college or vocational

education. "education money"

Fizure 24. Fourteen attr'ibutes of Army ads for Sort Board cards,

and shorthand labels.

13 6

'41.
(5) Clutter ad #3 for audio equipment (30 sec.)

(pause: 5 sec.)

(6) Clutter ad #4 for insurance (30 sec.)

(7) Title board reading "End of Ads" (5 sec.)

(pause: 15 sec.)

(8) Army ad reshown (30 sec.)

(9) Title board reading "End of Sequence" (5 sec.)

The Army television ads will be edited into 15 stimulus packages

(numbered 1 to 15) and then put in sequence on one master video tape.

Each site will receive copies of the master tape, thereby standardizing the order of presentation of the video stimulus packages.

The 13 Army print ads will also be positioned after print ads

for a car and financial services, and before ads for audio equipment

and insurance. Another copy of the same Army ad will then follow.

Each of the original 13 stimulus packages will be reproduced on a

color photocopying machine, placed in plastic page protectors and compiled in a three-ring binder. Printed pages will be used in lieu of

the beginning and middle title boards to indicate the sequence number

(1 to 13) of the stimulus package and the end of the five ads initially shown. Interviewers will be given copies of the binders and

instructed to use them in the sequential order of the ads, standardizing presentation order as with the video stimulus packages.

Materials

In addition to the questionnaires, the survey will require prepaO0 ration of a screening card to be handed to potential respondents (see

page 10 of the questionnaire), and sort boards and cards listing the

14 attributes of Army service plus another attribute ("The Army offers

a good opportunity to show one's patriotism.") to demonstrate for

respondents how the sort board is used. We will also produce five

signs (one per site) and numerous 5" x 8" cards to be handed out, all

containing the Privacy Act Notice (shown in Figure 25). Training

materials for interviewers and their supervisors will also be needed.

Finally, we will have to prepare appropriate numbers of copies of

the two stimulus packages, the video tape and binders containing the

print ads. The mall-intercept survey firms differ in the number of

3/4" VCR machines they use. The New York firm has three, the Los

Angeles firm has two, and the Atlanta, Dallas and Chicago firms have

only one. Each site will be sent twice as many tapes as they have

VCRs and twice as many binders as they expect to use on a daily basis

to allow for damage to the stimulus packages.

Pilot Test

To test the full set of survey procedures, including the training

materials, we will conduct a very small pilot test. The pilot test

will be conducted at Springfield Mall in Virginia, using the supervisors and interviewers hired by The Opinion Centers, a mall-intercept

survey firm based in northern Virginia. It is important to conduct

the pilot test in an actual mall setting for two reasons: (1) it

137

%m
PRIVACY ACT NOTIFICATION

(NAME OF FIRM)

" You are being requested to participate in research

on advertisements aimed at young adults.

" Your answers are voluntary and confidential, and

you may choose not to answer any question.

" The information you give us will only be used in

combination with the answers from many other young

adults.

" This research is authorized by law (10 USC 503 and

10 USC 2358) and the information you give us is

protected by an Act of Congress called the Privacy

Act of 1974.

Figure 25. Privacy Act notification to be used on

signs and hand-out cards.

p

13-

138

%t.
provides the opportunity to observe a data collection method new to

most of us, and (2) it allows us access to training materials used by

a mall-intercept firm which can serve as models for our own.

Eight respondents will be given the video tape stimulus packages,

and eight the print ad packages. The video tape respondents will be

given only seven of the fourteen cards listing Army attributes to sort

following the second exposure to the Army ad. The print ad respondents will be given the remaining seven cards.

Analysis

The analyses outlined in this chapter are based solely on the

message survey data; the analyses which integrate the message data

with those from the ACOMS survey and syndicated exposure data sources

are outlined next.

The stand-alone message analysis will be conducted in three

phases, each focused on different issues. During each phase, the

initial analyses will answer a number of research questions focused on

the Army ads and the messages and attributes ascribed to them. Subsequent analyses will look for patterns in the first analyses, such as

long-running versus newer ads and ads with similar versions in both

media versus those without (see Figure 23). The final set of analyses

will explore response differences by demographic characteristics of

respondents (gender, age, education, and ethnicity), by geographical

area (the five sites), and by unaided (questionnaire item 6) and aided

(items 15 through 18) career intention, i.e., work, school, military

service, or active Army duty.

In Phase I, the ads will be analyzed in terms of recall to answer

a series of research questions:

(1) What percent of the respondents mentioned the Army ad first

on the unaided recall question (item 7 in the questionnaire)? What percent mentioned the Army ad anytime with

unaided recall, and what percent required aided recall

(item 9)?

(2) Which Army ads had the most frequent mention with unaided

recall? Which the least?

(3) What percent of the respondents did not recall seeing the

Army ad at all?

In Phase II, the ads will be assessed according to the messages

they conveyed to the respondents, both main message (item 10) and

secondary messages (item 11):

(1) What is the main message communicated by each ad according

to the respondents who, unaided or aided, recalled it? What

is the full set of messages they derived from each ad?

139
(2) Summarizing across the television and print ads in turn, h

what are the major messages conveyed by Army advertisements

in each medium?

(3) Do the ads fall into distinctive groups within media? Do

these groups coincide with actual campaigns?

i The Phase III analyses will focus on the 14 attributes (queried

in questionnaire item 12 and shown in Figure 24) of Army service and

the respondents' descriptions of each ad as having more or less of a

given attribute. The questions addressed in this phase include the

following:

(1) What is the mean score of each attribute across all television ads? Across all print ads? Which individual ads

have scores above the mean for each attribute in turn?

(2) What are the primary attributes ascribed to each ad?

(3) How do the ads cluster on each attribute?

(4) When clustered according to main attribute(s) (e.g., code

value seven to ten), do the ads fall into distinctive groups

within media? Do these groups coincide with actual advertising campaigns? Are the groupings similar to those based

on main message analyzed in Phase II?

Schedule

The schedule for completion of the message analysis, shown in

Figure 26, is premised on receipt of OMB clearance in mid-April.

Arrangements and materials for the pilot test will be readied in early

February for actual piloting the third week of the month. Following

revision and reproduction of the materials, they will be distributed

to the five sites in mid-March. Training of supervisors and interviewers will be held at the sites over a two-week period during the end of

March and beginning of April.

Interviewing will begin immediately after OMB clearance is

received thereafter and extend for nine weeks. The length of this

period is due to the relatively low incidence of potential respondents

in shopping malls and the fact that most firms conduct interviews only

Thursday through Saturday, days when the most shoppers are in malls.

Data preparation will follow the interviewing period by one week

but will continue for four weeks after the end of the fielding. Analysis will begin in mid-July, 1987, for six'weeks. Drafting of the

report will begin three weeks into the analysis and end, with analysis, in late August. The report will be put into final form during

September and early October.

140

L% , a / '?d K **'* I ~ ~ '
tJ451',

C0,9 ___________________

'.4

2 Cul

-1 13 

P9

vi >

4-j

4-4

aa.

41 4

>~ 4*

141~

VN 1oel e e

zbzf*
Exposure to Army Advertising

The message content analyses will tell us what messages are seen

by youth in Army television and print executions, but not how frequently youth are exposed to these messages. Ideally, of course, we

could ask youth surveyed in the main ACOMS interviews how many Army

advertisements they had seen, but they are unlikely to be able to say

with any accuracy. We could, alternatively, ask them when they had

watched television in some previous period and match up their reports

against advertising airings to see whether they had been exposed to

Army advertisements, but this inquiry would take up too much of the

interview. Instead, what we propose is to learn how much exposure to

Army advertising was received by other youth who are very similar to

the interviewed youth, during the same period as the interviews. Then

we will assume that both groups of youth were exposed to about the |

same amount of advertising. In fact, we will append these exposure

estimates to the ACOMS respondents' data files. We outline these

technical procedures next.

The more closely the group providing the exposure estimate

matches the survey respondent, the more accurate this estimate will be

for the survey respondent. Unfortunately, as might be expected, the

closer the match, the more expensive the data.

Day-part estimates of television audience published by Arbitron

or Nielsen give numbers of people viewing a given station in a small

geographic area in quarter-hour intervals broken out by sex for ages 2

to 17 and 18 to 24 (and older, which does not concern us here). Local

geographic areas for Arbitron are defined as Areas of Dominant Influence (ADIs); for Nielsen, these are called Dominant Marketing Areas

(DMAs). While these sources are comparatively inexpensive they have

two drawbacks. First, we would need to match the advertisements aired

(by ADI or by DMA) to the day-part schedule to see whether the sex/age

group of interest was likely to be watching. Second, the published

groupings are fairly imprecise, allowing only very approximate imputation of likely exposure. In practical terms, we would have to assume

that all males 18 to 24 in a given ADI (or DMA) would be equally

likely to have seen a particular Army advertisement aired at a given

time on a given station.

We would like, therefore, to be able to draw more directly on the

data from which the published reports are derived. Unfortunately,

these data are proprietary. Further, even if we could get them, they

are so extensive (100,000 diaries of viewing by the quarter hour collected four times annually) that the analysis of them would be very

expensive.

There is, however, a compromise which partly circumvents the two

noted drawbacks of published reports at a reasonable expense. Arbitron offers a customized report, Arbitron Information on Demand (AID)

which operates in this manner: we send them the affidavits produced

by television stations stating that a particular advertisement was

aired at a particular time, and they furnish, for the sweep period,

the diary estimates of audience per ADI broken out in terms of six

142
categories. The categories can be genders, races and age breaks. We

have proposed cuts which prodcrae estimates for 1) Males, 16- to 17, 2)

Males, 18- to 19, 3) Males, 20- to 24, 4) Females, 16- to 24, 5)

Blacks, 16- to 24, and 6) Total, 16- to 24.

These data would also provide data useful to the U. S. Army

Recruiting Command (USAREC) in evaluating advertising targeting,

coverage and effectiveness. They provide at low geographic levels,

the market segments of interest in evaluating reach and frequency for

Army advertising.

Figure 27 shows the form of the data to be purchased for a sample

schedule. (The data will be purchased from Arbitron in machinereadable form.) The rows of the figure (labelled with a "7") describe

the schedule of spots for a particular ADI (one table will be obtained

for each ADI, yielding 214 in all for a given sweep period). Each row

is a spot (i.e., channel and time combination which contains an Army

advertisement in this ADI). The columns of the figure (labelled with

a "5") are the demographic cuts ordered -- in our case, the

age/sex/ethnic groups delineated above. Looking at the first row and

the first column (labeled with an "8") we see that this spot delivered

85,000 of the total of 1,523,000 of this kind of household, or 5.6%,

identical to the rating shown.

These exposure data (the rating in particular) will be appended

to the ACOMS data base by matching the respondent's demographic

characteristics to those of the aggregate exposure data (age, sex,

race, and ADI). For example, an ACOMS interview respondent interviewed in May who is a white male 17-years of age in Wilkes-Barre, PA,

will receive the ratings and message weights of the white male 17-

year-old column of the Wilkes-Barre ADI for May. The exact procedures

for this matching operation are described below.

Figure 28 portrays the steps involved in acquiring and appending exposure data to the ACOMS data base. The first step will be Young and

Rubicam advertising agency (Y & R) providing affidavit banks indicating date, time (within the quarter hour), and ADI (geographic location) of aired commercials. Next, Westat or Arbitron will match

commercial by ADI to day-part schedule (i.e., date and within a quarter hour) (see far left-hand column in Figure 27). The third step is

for Arbitron to provide Westat with the population characteristics of

viewer/listening audiences exposed to the commercials within the quarter hour during which the Army commercials were aired. Population

characteristics are reported by specific strata, i.e., by ADI

(geographic region), sex, age category, and race. Next, Westat will

categorize aired commercials into the 15 general commercials (e.g.,

College steps, Light infantry, etc.), each having specific message

attributes (e.g., stepping stone, challenge, variety of experiences).

Finally, the aggregated exposure data (by ADI, age, sex, and race)

will be appended to the ACOMS Computer Assisted Telephone Interviewing

(CATI) data base by matching respondent characteristics in ACOMS data

base with those characteristics in the exposure data.

143

-
DIARY RAF REPORT

A'

.43 -43 r. C1MN,617.01. l. A -43 41 1fT my .6 1

to". ft MNT:N*O.cc. T. AM9 U! 

ZO.a -1

Is, 10 COO CMC Mo 1::: T10

.06 l:.: 00:3 3 1.0 4 : Z

.5 .1.9.' .101107 .-

.00.0 ... 7. , To :01Plz 7.1 S.. :4% 

to..

HOW0 .0 R13-AD,0 YOUR DIR RAP RWR1T7

Spot-y*Spt Amys41

e~ir-arc~r u-~.~' rx~ cO wne ~ 54 8 Thefrs jar~4~ flolAl

2Sd~teb-SPe A navm. eis/r

is- jo he nae e~nc-a-dw 41 5X~eY M a- MW954S The coM ma-r-r for Wtn A

av ar AW JOBNAN 5w Decgi eed; a rtc. Th cost ce 8.00 roewoor wa The Af IcS are ale ttig 51.18a O 56

10w Th cst fce ti-e rxay~ ~ nP 3w~ MEer= ~ ~ r~~7 Th wT or~w bae oesn 5:5As nWE a 10

T'h~e A. A mseaxm oof 60 isT 

S1.18

Figure~~~ 27le Samplen daafraohrceistcflitnn ~7 

audiences.ui Sdx~* 6 S= A.A marnjnof 

144

'9...
Y&R

provides Match

indicating date. time A1A Mh*fe to day-part

(within the 1/4 hour), schedule (i.e., date and

and ADI (geographic location within the 1/4 hour)

of aired commercials. (see '74.. Table 1).

ARB TrM CN -"sta

provides Westat the Westati•

population characteristics of categorizes aired

viewers exposed to the commercials into the 12

1/4-hour airing time general commercials(e.g., during which the Army College fund. Light infantry, commercials were run. etc..) each having specific

Pop~ulation characteristics are msaeatiue eg,

reported by specific strata. i.e., stepping stone, challenge,,

by ADI (geographic region), variety of experiences).

sex. age category, and race.

Aggregated . exposure data (by ADI,

age, sex, and race) are

appended to the ACOMS

data base with those

aggregate characteristics

in the exposure data.

f eU . Flow of activities t~o acquire and append exposure

data to che Army Communications Objectives Measurement Survey %

(ACOMS) data base. =

145

7U
Data in the ACOMS CATI data base are collected continuously;

therefore ideally, data on exposure also should be acquired continuously. However, exposure data are collected in all ADIs four times a

year (February, May, July, and November), with continuous data collection in a limited number of ADIs. Because of cost constraints, we

propose to acquire exposure data for the sweep periods and to assign

the exposure estimates to the interview data collected one month

before and after the sweep period in order to provide comprehensive

data for the quarter.

The dates, times, channels, and ADIs of aired Army commercials

will be needed from Y & R. In addition, the form of which such data

will take is important to know. For example, data on tape will facilitate the ease with which data are merged with the ACOMS data base.

Two general assumptions underlie our proposed exposure analysis.

First, Arbitron audience data are accurate estimates of those who see

aired programs. For example, the diary method as opposed to meter

method provides only household demographic data, whereas the meter

method requires specification of actual viewer characteristics. The

second assumption is that aggregate data can be used to reflect the

probability that an individual respondent in the ACOMS CATI data base

has seen a commercial. This assumes that all persons in a given subpopulation stratum had an equal probability of viewing a television

commercial.

While we recommend sweep exposure data, we may wish to request

exposure data over continuous time periods to more accurately reflect

television viewing. This, however, would entail greater costs.

Another issue is the appropriate demographic breaks for the exposure data. Arbitron allows only six demographic stratification criteria to be requested. We recommend the following criteria for stratification: Males, 16- to 17-years of age; males, 18- to 19-years;

males, 20- to 24-years, females 16- to 24-years; blacks, 16- to 24

years; and Hispanics 16- to 24-years of age. These categories are

useful allow calculating the sum of the viewing audience, and the

difference between categories allow further specification of subpopulations, e.g., the difference between females 16- to 24- and males 16-

to 17-years of age gives the number of females 18- to 24-years of age

as the viewing audience, etc.

Plan for the Analysis of Fit, Exposure and Change

In this final section of this chapter, we specify how data from

these various sources will be drawn together in support of an integrated analysis of the effects of advertising. Drawing from the

previous discussion of the exposure data, we can give the following

overview of the proposed procedures:

The ratings for time periods for a demographic group represent a

likelihood that a youth in that group was exposed to a particular Army

advertisement at a particular time. Summed over all of the exposures

of that execution in that ADI, we have the duplicated reach of that

146
execution for that demographic group in that sweep period and ADI.

From the message content analysis, we will know the content profiles

of the various Army advertisements. When the distinctive profile of a S

particular advertisement is associated with its rating for that demographic group, we will have the expected message content of that spot

in terms of the 14 active Army attributes for that ADI/demographic

group. Summed over all the spots in that ADI, we would have the

message emphases and likely exposures (expected message content) of

all Army television advertising for that time period, ADI and demographic combination, again in terms of the 14 Army attributes. These

14 expected message content variables can then be associated with the

respondent matching that ADI/demographic category.

A more detailed discussion of procedures for assembling the integrated data set is organized around the production of several matrices, displayed in Figures 29 through 32.

The first set of matrices, labeled (1), Attribute by Site Matrix

(Figure 29), will be produced from the message analyses. The cell

entries are the mean ratings of advertising attribute content

(responses to question 12 in the message analysis questionnaire) for

each message analysis site for one advertisement (college steps). We

expect to produce one version of this matrix for each advertisement.

Our goal in the production of these matrices will be to produce a

single mean weight for each attribute for each advertisement, indicating the extent to which that advertisement emphasizes each of the

attributes. Thus, we hope that the sites do not differ in terms of

the mean weights per attribute. The column variable can also be

ethnicity, gender, propensity, etc. Again, our hope is the same, but

we expect to have to run analyses of variance to show that no significant differences among sexes, ethnicities, propensities or sites need

to be considered in producing matrix (2).

Matrix (2) (Figure 30) is the Attribute by Advertisement matrix,

assembled from the left hand columns of the 15 versions of matrix (1),

and representing the mean message weights of the advertisements. This

matrix is of some use by itself, since it displays a profile of the

message content of the Army's advertisements. Its main use for ACOMS,

however, is in combination with matrix (3).

Matrix (3) (Figure 31) will be constructed from the syndicated

data. For each advertisement, we will have exposure in terms of

ratings for 6 demographic breaks within each ADI, quarterly. Since

there are 214 ADI's (at last count), the matrix has 6 X 214 or 1284

columns, and 15 rows. This matrix has independent information value,

as it contains the basic information on advertising exposure over the

quarter. Its main use for ACOMS, however, comes when it is postmultiplied with matrix (2) to produce matrix (4).

Matrix (4) (Figure 32) is the matrix multiplication product of

matrices (2) and (3), and will have 14 rows and 1284 columns. It will

present the mean message weights by attribute exposed to each

ADI/demographic combination for the quarter. Thus, the matrix will be

produced four times annually, corresponding to changes in matrix (3).

147

% L P- 7_1 & S
A..j

00

04 g4

us

0a

-41

a

C6 M C-4 C14

0 0

0. 0 0

0 > 

a 6

4 C4 . u 

14
.. . f.) *o ~ 

41.

4).

41.

>I

414

41

0 -4

1.0 0 z

4) -4 '-H

c -1 0 ) UI

0 Li-4 b i 4

-4 ul-w

V CL w 

.to 0 .

4 -4 -4 0

u 0 wm

4

) 4Li

0 0 r

4-.

:1 0 01 >

140
7I

1

- 4)

1-4

14~- ck(Wb

ca 1 r. 0

04

0 4

4)0

04) ,

1.I 0

4104 *41

4) 44

4)J 4)

%0C 0%->

c15

~ 4 . ... ' . ' * ~ *. **~* * '' a.* a .A. ''~ A ')*a~- 'a -~'- ~ * P.-

7~~.''..'..~~~c a a 'a ') 0 ~ N % V'a a ~
a~J 74W~

aI

cli

QF

igf

bI.

-4

Sc 0

44J

0

A-) 1-

4OZ4

bo M

-4 "4 -. 4~

4: Z4

0 -4. .4

oC 0. >% bqjJo 0 .4 0 - .4) Co ji C13

,0 -4 Q3 cc

0 CL '-

0 0 

V) 4 WO.d) -. $4 144

,4- -4d 4"

w. -4 m~ w 0.

CN -4 cc- (.
Like the previous matrices, it has independent information value, as

it represents the contents of messages exposed to various geographic

and demographic gzoups in the quarter. However, its main use in ACOMS

is as a large reference document. We expect, for each respondent in

the main ACOMS sample, to look up which column in matrix (4) s/he

belongs to (given ADI ani demographic characteristics) and to assign

him/her the mean message weights for that quarter corresponding to the

entries in that column. (In fact, the procedure will be a bit less

straightforward. The demographic cuts within ADI in matrix (3) are

not mutually exclusive, and will need to be made so before producing

matrix (4).) This process will yield 14 new variables, quarterly, for

the main ACOMS data file which can be used in any of the analyses for

ACOMS.

Several comments are in order. First, the process will apply I

only to television advertisements, and not to print. There does not

appear to be any reliable source of print exposure available

currently.

Second, the process may not be as straightforward as it appears.

We have assumed that there will be no differences by ethnicity,

propensity, etc., in attribute weights derived from the message analysis. This is probably a reasonable assumption, since the message

analysis task for respondents is assessing advertising content, not

judging artistic merit or personal relevance. If strong differences

are found, however, the resulting computational complexity ripples

through the process. Assume we found that high propensity message

analysis respondents perceived different message emphases than low

propensity respondents in the same advertisement. We would then need

to produce two versions of matrices (1) and (2) for each ad, for high

and low propensity respondents respectively. Therp would then be two

versions of matrix (4), and 2 X 1284-2568 possible slots for an ACOMS

survey respondent to fall into. Matrix (3) would remain the same.

Even if there are good reasons to believe that high and low propensity

respondents have different media habits, Arbitron doesn't ask about

propensity. Questions of media habits are adequately covered in the

main ACOMS interview in any case.

Third, it should be apparent that advertisements for which no

message analysis information are available present difficult, but not

intractable, problems. We can, for example, simply assign them the

mean message weights for all advertisements, taken from the row averages of matrix (2). If however, the untested advertisement presents a

distinctive message profile, its comparative impact would be lost in

subsequent matrix products and in ACOMS. This may argue in favor of

using a similar message analysis strategy in testing new advertising

executions, and further, in favor, of retesting some ads to assess

changes in perceptions of their message contents over time.

Analysis

As notLd above, the exposure data can be analyzed usefully from

several different perspectives. First, the exposure data, by themselves, represent the best available measures of the exposure of

152

V - V -. v .-.- N - %-*- ,- - - * . , . . . . •. .- .- k%, a %. 
target markets to Army advertising, both overall and per advertisement

and message, both for youth as a group and for ethnic, gender and age

subgroups of interest. Second, as variables in the ACOMS data file,

the message weights can be correlated with attitudes toward the Army,

toward recall of advertising, knowledge of offers, and behaviors with

respect to enlistment. In the most straight-forward sense, the

message weights represent additional layers of analytical power in the

initial stages of the models presented in Chapter 6 (Gaertner, 1988).

However, given the uncertainty of acquisition, we will defer more

detailed analysis plans for these data until Phase IV.

Summary and Conclusions

This chapter has reviewed sources of Fit and Exposure data available in the ACOMS system, and how these can be integrated with the 0

main survey responses to provide an overall assessment of the content

and impact of Army advertising. In combination, the data sources of

the ACOMS system provide a tracking and modeling capability of

formidable power.

.j

6

151S

153
ip

REFERENCE

Caertner, G. H. (1988). Modeling the effects of Army advertising. In G. H.

Gaertner & T. W. Elig (Eds.), The Army Communications Objectives

Measurement Systems (ACOMS): Survey analysis plan (ARI Technical Report

786). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

15 F

I.i

mm

i 154
8. WORK PRODUCTS, DELIVERABLES AND SCHEDULES

Gregory H. Gaertner

As originally conceived and subsequently modified, Army Communications Objectives Measurement System (ACOMS) is intended to offer a

variety of different kinds of support to the Army's communications

programs, involving different work products for a diverse audience in

a mix of short and long time frames. In tracking the Army's communications objectives, for example, timeliness is crucial, requiring

focused reporting in a succinct format. In assessments of advertising

strategy, deeper analyses are necessary to produce segmentations,

analyses of the dynamics of advertising influences, and models of fit,

change, and exposure. These analyses will inform the tracking reports

and provide basic insights to Army advertising managers and to the

professional community. In all cases, briefings and documentation are

required to fully communicate the methods and results.

Analytical work products from ACOMS will include:

(1) Design documents (such as this analysis plan) which propose

or summarize decisions for data collection and/or analysis;

(2) Documentation reports summarizing procedures for data

collection or analysis having a limited, technical purpose

and audience;

(3) Quarterly reports, providing tabular and narrative summaries

of results of immediate interest on a quarterly basis; -

(4) Quarterly results briefings, providing the basic information

contained in the quarterly reports in briefing form;

(5) Annual reports on subjects defined by the analysis plan;

(6) Progress briefings on areas covered in the annual reports,

providing opportunities to discuss progress on annual report

content areas and allow the SAG (and others as appropriate)

to assess directions of particular promise; and

(7) Papers and presentations for eventual publication in the

professional community.

This listing reflects the diversity of ACOMS products, audiences

and schedules: (a) relatively narrow technical documents and broadly

interpretive analyses; (b) short succinct reports and lengthier analyses with fundamental insights; (c) summary briefings and extended

narratives; and (d) quarterly and annual schedules.

The decisions of the Special Advisory Group (SAG) and the

Contracting Officer's Representative (COR) have resulted in a flexible

yet closely monitored process for developing and presenting research

products. The initial schedule for this process is depicted in

155
Figure 33, which indicates schedules for analytical activities, draft/

progress deliverables, and final deliverables.

We began tracking audience response trends at the beginning of

interview data collection on October 13, 1986. After two months of

data had been collected, we began measurement development, an anticipated four-month activity. Following this period, major effort will

be directed to the construction of scales for the quarterly reports

and for subsequent analytic products. We anticipate the replication

of existing scales from the Army propensity and quality in the Youth

Attitude Tracking Survey (YATS II) (Research Triangle Institute, 1985)

and the development of new scales (knowledge recall, knowledge awareness, perceptions, importance, behavioral propensity). See Chapter 2

(Gaertner et al. , 1988) for details. Then we begin work on segmentation and brand differentiation in Chapter 3 (Wilson, 1988) and Chapter

4 (Wilson, 1988), on parental influence (Chapter 5) and on modeling

the effects of advertising (Chapter 6). The message content data

collection underlying the Fit analysis will be undertaken in the early

part of 1987, and exposure data described in Chapter 7 (Gaertner &

Baxter, 198R) will be collected at the turn of the year. No studies

on advertising in areas of dominant influence (ADIs) are anticipated

in FY 1987.

The first quarterly tracking report was presented in narrative

and briefing form in mid-February, 1987, covering data from October,

November and December. This report did not include scale summaries,

although the third quarterly report (mid-August) should include scale

score summaries of results. Contents of the quarterly tracking

reports are described in Chapter 2 (Gaertner et al., 1988).

FY 1988 will begin the collection of the longitudinal data,

allowing for more detailed investigation of the causal processes

underlying the model, the cumulation of behaviors relating to enlistment and developing an event history of the enlistment process. Thus,

the new fiscal year will mark the beginning of integrated FitExposure-Change analyses.

1

156 !
B ... ill ...

V) C

_ _ -4-0(1 :

m 0~

21 0

0 > 0

a~ 0

() -C

u - Q)

cC L L - 4- C)J Ud

U, Li

V>0)Z 0 -0 o

ozo

'4

* Z LL Ji2O0< << L

1571
REFERENCES

Gaertner, G. H., & Baxter, S. J. (1988). Plan for the integrated analysis of

fit, exposure, and change model. In G. H. Gaertner, & T. W. Elig (Eds.),

The Army Communications Objectives Measurement System (ACOMS): Survey

analysis plan (ARI Technical Report 786). Alexandria, VA: U.S. Army

Research Institute for the Behavioral and Social Sciences.

Gaertner, G. H., Keil, L. J., & Gay, N. L. (1988). Tracking audience

response. In G. H. Gaertner & T. W. Elig (Eds.), The Army Communications

Objectives Measurement System (ACOMS): Survey analysis plan (ARI

Technical Report 786). Alexandria, VA: U.S. Army Research Institute for

the Behavioral and Social Sciences.

Research Triangle Institute. (1985). Youth Attitude Tracking Study II, Wave

16--Fall 1985 (Contract MDA903-83-C-1072). Arlington, VA: Defense

Manpower Data Center.

Wilson, M. J. (1988). Market segmentation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Wilson, M. J. (1988). Brand differentiation analyses. In G. H. Gaertner and

T. W. Elig (Eds.), The Army Communications Objectives Measurement System

(ACOMS): Survey analysis plan (ARI Technical Report 786). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

158

an A~ -- A w
p

WN BIBLIOGRAPHY

Baxter, S., & Gay, N. L. (1988). The message content of advertisements

for active Army enlistments (ARI Research Report 1473). Alexandria,

VA: U.S. Army Research Institute for the Behavioral and Social

Sciences.

Gaertner, G. H., & Elig, T. W. (Eds.). ( 1 9 8 8 ) . T h e A r my

Communications Objectives Measurement System (ACOMS): Survey analysis

plan (ARI Technical Report 786). Alexandria, VA: U.S. Army Research

Institute for the Behavioral and Social Sciences.

Gaertner, G. H., Nieva, V. F., Elig, T. W., & Benedict, M. E. (Eds.).

(1988). The Army Communications Objectives Measurement System (ACOMS):

Quarterly reports (ARI Research Product 88-04). Alexandria, VA: U.S.

Army Research Institute for the Behavioral and Social Sciences.

Greenlees, J. B. (1988). The Army Communications Objectives

Measurement System (ACOMS): "Tour of duty" viewing patterns (ARI

Research Note 88-17). Alexandria, VA: U.S. Army Research Institute

for the Behavioral and Social Sciences.

Nieva, V. F., & Elig, T. W. (Eds.). (1988). The Army Communications

Objectives Measurement System (ACOMS): Survey design (ARI Technical

Report 785). Alexandria, VA: U.S. Army Research Institute for the

Behavioral and Social Sciences.

Nieva, V. F., Rhoads, M. D., & Elig, T. W. (Eds.). (1988). The

Army Communications Objectives Measurement System (ACOMS): Survey

methods (ARI Technical Report 787). Alexandria, VA: U.S. Army

Research Institute for the Behavioral and Social Sciences.

Nieva, V. F., Gaertner, G. H., Elig, T. W., & Benedict, M. E.

(Eds.). (1988). The Army Communications Objectives Measurement

System (ACOMS): Annual report, school year 86/87 (ARI Technical Report

784). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

Rhoads, M. D., Elig, T. W., McEntire, R. L., & Hoke, E. (1988).

The 1986/87 Army Communications Objectives Measurement System:

Supplementary tabulations of enlisted markets (ARI Research Product 88-

05). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

I'

159
Rhoads, M. D., Elig, T. W., McEntire, R. L., & Hoke, E. (1988).

The 1986/87 Army Communications Objectives Measurement System:

Supplementary tabulations of officer markets (ARI Research Product 88-

06). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Westat, Inc. (1988). The Army Communications Objectives Measurement

System (ACOMS): Codebook for the message content analysis data file

(ARI Research Note 88-18). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Westat, Inc. (1988). The Army Communications Objectives Measurement

System (ACOMS): Parental users' manual (ARI Research Product 88-07).

Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences.

Westat, Inc. (1988). The Army Communications Objectives Measurement

System (ACOMS): Users' manual (ARI Research Product 88-08). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social

Sciences.

IV

160.

LIN-
Appendix

XZB8A(VZ ANALYSIS QUESTIONIRE

ID# (1-5

Mall/Shopping Center: (6)

Date: Sex of respondent: Male 1 (7)

Female 2

INTRODUCTION: Hello. I'm

from Crossley Surveys, a marketing research company, and we are

conducting a survey today regarding advertising. We are

interested in how young adults like certain commercials.

First, I have several questions to see if you fit with the

sample we are interviewing today.

(MND RZUPONDINT CARD #1)

1. Which age category are you in: (8)

15 years and under I.

16 to 18 years 2

19 to 24 years 3

25 years and over 4

T1RXZITZ: Thank you, but you do not fit with

the sample we are interviewing today.

(RZTRIEVE CARD #1)

2. Have you completed more than two years of college? (9)

Yes 1

No 2

TRRXINTZR: Thank you, but you do not fit with

the sample we are interviewing today.

3. Havi you ever held a full-time job? (.0)

Yes 1

No 2

F- ', - - - - -.! U - ~~ ~V ? --

F' ~ A A ~ ~ A~ ~ V
I..

4. Have you ever joined or served in any military service? (11)

Yes 1

No 2

TERMINATE: Thank you, but you do not fit with

the sample we are interviewing today.

We will not use the answers you have

given in any analysis. Thanks again

for your time.

You qualify for our sample. I would like you to

participate in a brief study on (television/magazine) advertising. It will only take about 10 to 15 minutes.

5. Will you come with me to the interviewing station where

we have the ads I want to show you? (12)

Yes 1

No 2

TERMINATE: Thank you for your time. We will

not use the answers you have given

in any analysis. Thanks again.

(TAKM RESPONDENT TO THE INTERVIEWING STATION. DO NOT INTERVIEW

IN TIE MALL.)

Your answers to this survey are voluntary and %

confidential, and you may choose not to answer any question. The

information you give us will only be used in combination with the

answers from many other young adults. This research is

authorized by law, and the information you give us is protected

by an Act of Congress called the Privacy Act of 1974. Our pledge

of confidentiality is posted on the sign over there (POINT TO THE

SIGN).%

(AFTER TIE RESPONDENT HAS READ TIN NOTICE, CONTINUE WITH

TIE NEXT INSTRUCTION.)

?F

Ii

162

%'4

,4 I >, , .. -. .,' .'.'.'. '; ;. '. ,'V;.;,' ',.-.'- ;, , J , ' ,, ,
Before we get started, I'd like to ask you a question about

your future career plans, and the kind of things you expect

to be doing, that will help us interpret your reactions to

the advertisements.

6. What do you think you might be doing for the next few years? S

(PROBE: Anything else?) (RECORD ALL THAT APPLY) (13-.7)

GOING TO SCHOOL ......... 2

WORING.................I

DOING NOTHING. .......... 1

JOINING THE MILITARY/SERVICY. I.

OTHER ..................

REFUSED..................

DON'T KNOW. ... . ....... 8

Please look at this (videotape/booklet). When you are

finished, I'd like to ask you a few questions about it.

(ALLOW SUFFICIENT TIME FOR RESPONDENT TO DIGEST THE MATERIAL.

TZR2I TAKE BACK THE BOOKLET 17 TESTING PINTi MEDIA.) -

7. Can you recall any of the names of the advertisers or

sponsors of the ads you just (saw/heard)?

(CONTINUE ASKING UNTIL RESPONDENT CANNOT RECALL ANY OTHERS.

WRITE IN VERBATIX RESPONSES.*)

FIRST MENTION: (1.8)

SECOND MENTION: ese?)__RE__RDALLT__TAP__)_(3-(19)

THIRD MENTION: JONNGTA/SE . (20)

FOURTH MENTION: (21)

163

e RE-U.SE .. 7 

DON TKNO . ... .. .
We don't want to take too much of your time, so we're going

to focus on just one ad.

8. (INTERVIEWER: WAS THE ARMY RECALLED AtS THE SPONSOR OF THE.%

ARMY AD?) (22)

Yes I

No 2

Skip to Q.10

9. Do you recall seeing an ad sponsored by the Army? (23)

Yes 1 NO 2 .

Skip to Q.12 

"

10. Other than trying to get you to enlist, what was the main

message you got from the Army advertisement? (RECORD

VERBATIM RESPONSE; PROBE ONLY FOR CLARITY AND DETRIL)(24-26)

11. What other messages did the ad communicate to you? (27-35)

1

p...
0

12. Please look at the Army advertisement again.

(SHOW ARMY ADVERTISEMENT. FINISH PLAYING VIDEOTAPE/ I

TAKE BACK PRINT AD BEFORE PROCZDING.)

I am going to give you a deck of 14 cards. Each card

contains a messaqe that might have come across to you from

the Army ad. I want to know what messages did come across

and which did not. I'm only interested in learning your opinion. There aren't any right or wrong answers.

ge are going to use a 0 to 10 scale for this, as shown on I

this sort board. (HAND SORT BOARD TO RESPONDENT) As you , can see, the "0" position at the left side of the board is

labeled "message not carried at all", the "5" position in

the middle is labeled "message carried to a medium extent," %

and the "10" position at the right is labeled "message

carried to very great extent."

What I'd like you to do is sort each card into one of the 11.

piles, based on the how much you think the message on the

card was carried by the ad you saw. After you have sorted

through the entire deck, I'll ask you to look back through

every pile to make sure each card really is in the pile you

want.

Let's use an example to show how this works. Here's a card I

that says "the Army offers a good opportunity to show one's

patriotism." (MAND CARD TO RESPONDENT) If you think the ad

you just saw carried this message a little bit, you might

want to put the card by the "2." If in your opinion the ad

carried the message to a medium extent, you might want to

put the card by the "5", or "6". If you thought the ad

carried the message about patriotism to a great extent, you

might want to put the card by the "9 or "10" pile. Do you

have any questions about the scoring scale?

After sorting all of the cards, yo-i may decide that the . patriotism message was less strong, compared to others, than

you originally thought. You might take the card out of the

"5" pile, say, and put it in the "4" pile.

Are you ready to begin? (SBH1FFLZ THE CARDS) Here are the

cards.

165

A, , .A. 
12. (Continued) 6

(INERVIEWR: A -'1R COMPLETION OF INTERVIEW, ENTER TWODIGIT NUMBER 07 - CARD WAS PLACED IN. CODE 97 IF

RESPONDENT REFlasuD TO SORT A CARD, CODE 98 17 SAID DON'T

KNOW. )

The Army offers:

A. a wide variety of opportunities to find a " job you can enjoy. (36-7)"

B. a physically challenging environment. (38-9)

C. an experience you can be proud of. (40-1)

D. an advantage over going right from

high school to college. (42-3)

E. an opportunity to develop leadership

skills. (44-5)

F. the chance to work with the latest high

tech equipment. (46-7)

G. a great value in your civilian career

development. (48-9)

H. an excellent opportunity to develop

self-confidence. (50-1)

1. the opportunity to develop your

potential. (52-3)

J. a mentally challenging experience. (54-5)

K. an opportunity for you to become more

mature and responsible. (56-7)

L. many opportunities for training ir

useful skill areas. (58-9)

M. many chances to work with highly

trained people. (60-1)

N. an excellent opportunity to obtain money

for a college or vocational education. (62-:)

jp 0.
The last few questions ask for some information about you.

13. How old are you? (64-5)

14. What is the highest grade or year of school or college

that you have completed and received credit for? (66-7)

LESS THAN 8TH GRADE ........ ............... 7

8TH GRADE ........ .................... 8

9TH GRADE ........ .................... 9

10TH GRADE .......... ................... 10

11TH GRADE ....... ................... 11

12TH GRADE ....... ................... 12

1ST YEAR OF A 4 YEAR COLLEGE ... .......... 13

2ND YEAR OF A 4 YEAR COLLEGE ... .......... 14

1ST YEAR OF A JR OR COMMUNITY COLLEGE ...... ... 15

2ND YEAR OF A JR OR COMMUNITY COLLEGE ...... . 16

1ST YEAR OF A VOCATIONAL, BUSINESS OR TRADE SCHOOL 17

2ND YEAR OF A VOCATIONAL, BUSINESS OR TRADE SCHOOL 18

REFUSED ........ ..................... 97

DON'T KNOW ........ ................... 98

15. How likely is it that you will be going to college in the

next few years? Would you say... (68)

Definitely. . . . 1

Probably ....... 2

Probably not, or. 3

Definitely not? 4

REFUSED .. ..... 7

DON'T KNOW. . . . 8

16. How likely is it that you will be working in a civilian job?

Would you say... (69)

Definitely. . . I

Probably ... ..... 2

Probably not, or. 3

Definitely not? . 4

REFUSED .. ..... 7

DON'T KNOW. . . 8

17. How likely is it that you will be serving in the

military? Would you say... (70)

Definitely. . . . I

Probably ... ..... 2

Probably not, or. 3

Definitely not? 4

REFUSED .. ..... 7

DON'T KNOW. . . . 8

167

14
Z.11

S

18. How likely is it that you will be serving on active duty in

the Army? Would you say... (71) Definitely. . . . .

Probably ........ 2

Probably not, or. 3

Definitely not? 4

REFUSED .. ..... 7

DON'T KNOW. . . . 8

19. Please tell me whether you are: (72)

White ...... ............. 1

Black .................. 2

Asian or Pacific Islander, or 3

American Indian or Alaskan Native 4

REFUSED ...... ............ 7

DON'T KNOW ..... .......... 8

20. Are you Hispanic: (73)

Yes 1

No 2

4r.4
i.

I

Finally, my supervisor will be calling some of the

people I talk with today to make sure I asked the

questions correctly. No one else will call. May I

have your name and telephone number?

Name:

Area code: ( ) Telephone number:

That is all the questions I have. Let me remind you that

the information you have given us is confidential. Thank p

you for your participation.

%.

I

.i

5'

169

V ~ ~ Y''' /"Vf%' ' 5 \'' ''.~ %'c' %S.'SsS~S~*%' ~'% '-
X,

CARDS TO BE HANDED TO RESPONDENT

CARD #1

1. Your age category: 15 years and younger

16 to 18 years old

19 to 24 years old

25 years and older

CARD FOR DEMONSTRATION OF SORT BOARD

The Army offers a good opportunity

to show one's patriotism.

170

: OtIP