Technical Report 702 -

Towards an Understanding of Army

Enlistment Motivation Patterns

Rebecca M. Pliske, Thnothy W. Elig, & Richard M. Johnson

6-

00

4C*)

Personnel Utilization Technical Area

' Manpower and Personnel Research Laboratory

DTIC

~EL&CTENOV 0 4 M

U t.=.U. S. Army .

La-. Research Institute for the Behavioral and Social Sciences

January 1986

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IN

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Deputy Chief of Staff for Personnel

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EDGAR M. JOHNSON COL, IN

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ARI Technical Report 702

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TOWARDS AN UNDERSTANDING OF ARMY ENLISTMENT Final Report

MOTIVATION PATTERNS February 1984 - November 198E

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Rebecca M. Pliske, Timothy W. Elig, and

Richard M. Johnson .-

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Recruiting

Enlistment motivation .AA

20. A8TrNAcr (cintane aa menmr~ aftb N neawasay mid Identify by block n~inber)

--This report summarizes information from the ARI New Recruit Surveys for

1982 and 1983 related to enlistment motivations of new Army recruits. In addi- "

aI tion to presenting cross-tabulated responses for survey questions, principal

component analyses (PCA) were completed on recruits' responses to questions on ._ their reasons for enlistment. The PCA indicated that recruits enlist for a %

variety of economic and psychological reasons, such as self improvement, economic advancement, military service, time out, travel, and education money. .e.

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Technical Report 702Z

Towards an Understanding of Army

Enlistment Motivation Patterns

Rebecca M. Pliske, Timothy W. Elig, & Richard M. Johnson

Pesne Uiiaio ehiclAe

PalA.Gd, he

MapwrdPersonnel tliaio encl Arabrty

Newell K. Eaton, Acting Director

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

.9 5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

Office, Deputy Chief of Staff for Personnel *.

Department of the Army

January 1986

Army Project Number Manpower, Personnel and Training

20162722A791

* ~Approved for public release; distribution unlimited. '5"
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ARI Research Reports and Technical Reports are intended for sponsors of R&D tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part -

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

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FOREWORD

This report summarizes recent findings from survey efforts undertaken by

ARI in support of the Office of the Deputy Chief of Staff for Personnel and the

U.S. Army Recruiting Command. This study presents information on psychological

variables that influence young peoples' enlistment decisions and argues that

policy makers should use models of the enlistment decision process that include

both psychological and economic variables.

EDIG M. OHNSON

Technical Director

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TOWARDS AN UNDERSTANDING OF ARMY ENLISTMENT MOTIVATION PATTERNS

EXECUTIVE SUMMARY

Requirement:

In order to attract high-quality applicants, the Army spends a good deal

of money on economic incentives such as the Veterans' Educational Assistance Program (VEAP). The military personnel planners who allocate the money for"-" '

these incentives need to consider the important psychological and economic %

factors that underlie enlistment motivation patterns.

Procedure : c%

The data presented in this report were collected as part of an ongoing

survey effort conducted by the U.S. Army Research Institute for the Behavioral

and Social Sciences (ARI). New recruits were surveyed at U.S. Army reception

stations across the country during the spring and summer months of 1982 and

1983. In addition to presenting cross-tabulated responses for survey questions

on recruits' reasons for enlisting, principal components analyses were completed on these data.

Results:

These analyses indicated that six distinct factors underlying recruits'

enlistment motivation can be identified. They are as follows: self improvement, economic advancement, military service, time out, travel, and education

money. The analyses suggest that recruits enlist in the Army for a variety of

economic and psychological reasons.

Utilization of Findings:

The information presented in this report will be used by military personnel planners who allocate money for various recruiting efforts. These data

will also be added to a growing longitudinal data base used for modeling individual decision making and microeconomic forecast modeling.

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TOWARDS AN UNDERSTANDING OF ARMY ENLISTMENT MOTIVATION PATTERNS

CONTENTS

Page

SURVEY PROCEDURES AND SAMPLE ............ ....................... 2

RESULTS ............... .. ................................. 4 -

Forced-Choice Questions ............ ........................ 4

Multinomial Importance Ratings .......... .................... 10

Internal Validity Check .......... ....................... ... 12

Principal Components Analyses ........ ..................... .-. 16

SUMMARY AND CONCLUSIONS ......... ......................... 30

REFERENCES ............. ................................ 31

APPENDIX A ............. ................................ 32

LIST OF TABLES # ?.,

Table 1. Survey and population demographics for non-prior service,

Regular Army recruits, 1982 and 1983 ....... .............. 3

2. Comparison of most important reasons for enlistment

1979/1982/1983 ..................... ........ 6

3. Percent of recruits responding to forced-choice questions on most important reason for enlisting by AFQT category classification ............ ......................... 7

4. Percent of recruits responding to forced-choice questions *, ,

on reason for enlisting by sex........................... 8 "'

5. Percent of recruits responding to forced-choice questions

on most important reason for enlisting by educational

background of recruit ............... ......... 9

6. Percent of recruits responding to multinomial questions

on reasons for enlistment by AFQT category classification . . . 13

7. Percent of recruits responding to multinomial questions on reasons for enlistment by sex . .. .. .. .. .. .. . .. 13

8. Percent of recruits responding to multinomial questions .

on reasons for enlistment by educational background ... ...... 14

ix e

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CONTENTS (Continued) __-_

Page

Table 9. Percent of recruits responding to multinomial and

forced-choice questions on reasons for enlistment ........ 15"-'"

10. Percent of recruits responding to forced-choice question

on most important reason for enlistment by their plans

after enlistment ......... ........................ 17 ,

11. Percent of recruits responding to the forced-choice .%

question on reasons for enlistment by their employment

status when they enlisted ...... ................... 18

12. Rotated factor loadings (oblique solution). . . . . . . . ...... . . 20

13. Factor correlations for rotated factors ... ............ 23

14. Rotated factor loadings for higher order factor analysis . . .. 24 -a.

15. Comparison of factor loadings (oblique rotation) for 1982

and 1983 ........... ............................ ..... 25

16. Mean factor scores by demographic variables .. .......... 27

A-1. Rotated factor loadings from the orthogonal solution ...... . 32.....

A-2. Factor loadings (oblique rotation), number of factors

restricted to three ........ ...................... 33

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.51- WV.'N VXV

TOWARDS AN UNDERSTANDING OF ARMY ENLISTMENT MOTIVATION PATTERNS

Our nation's Armed Services are faced with the continuing challenge of

attracting large numbers of qualified young men and women. Since the

introduction of the all volunteer force, the Armed Services have had to compete ,-%,

with private sector employers and educational institutions for these young

people. This competition promises to become even more intense in the near

future because the number of Service-eligible youth is declining while the

manpower needs of the Services are growing.

In order to attract high quality applicants, the Services spend a good

deal of money on economic incentives such as increased levels of compensation and on special programs such as the Veterans' Educational Assistance Program LP

(VEAP). The military personnel planners who allocate the monies for these

recruiting efforts rely to a large extent on economic models of military

accessions. A common procedure for examining the effects of enlistment

incentives on military assessions is to use some type of econometric modeling.

A crucial assumption of such models is that the equations estimated are

may have a major influence on the outcome of interest. If important variables

are omitted from the equations, then the estimates of the effect of the

variables that are included may be seriously biased.

Economic models of enlistment tend to focus on pecuniary factors such as

pay, benefits, and bonuses that can be directly altered by policy makers and

generally include other "economic" factors such as the unemployment rate, .. 4

minimum-wage levels, and recruiting resourses. Recently, some economists have

begun to include "non-economic" variables in their models. For example, Dale

and Gilroy (1984) have shown that a non-economic variable measuring recruiter

effort had a significant effect on the number of Army enlistments.

Although economic models provide useful information to policy makers, we

agree with Faris (1984) who claims that purely economic models are insufficient

to account for military recruiting patterns. Faris reports data on the

probability of reenlistment intentions of enlisted personnel that indicate two

non-economic variables are significant factors in the reenlistment decision.

One factor reflected the individual's relative satisfaction with the "more

immediate features of the military work role" and the other factor reflected

attachment to the broader role of the military." Faris presents data that

indicate that non-economic factors are also important for the retention of

junior office's.

The purpose of this report is to summarize recent findings from a survey

administered to new recruits entering the US Army that provides information L' o

about Army enlistment motivation patterns. We hypothesized that today's youth

are attracted to the military service for both economic and non-economic

reasons and our results generally support this hypothesis. in this report, we

%*. J.

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present information on psychological variables that influence young peoples'

enlistment decisions and we argue that policy makers should use models of the

enristment decision process that include both psychological and economic

variables. r

The data presented in this report were collected as part of an ongoing .

survey effort conducted by the US Army Research Institute for the Behavioral

and Social Sciences (ARI). In response to a request from the Department of the

Army, ARI developed a Survey of Personnel Entering the Army to answer questions

concerning the demographics and enlistment motivation of new recruits. The

structure of the current survey is based in part on the 1979 Department of

Defense Survey of Personnel Entering Military Service (Doering, Grissmer, and

Morse, 1980a, 1980b).

The ARI survey was first administered in the spring and summer of 1982. A

revised form of the ARI survey was administered in the spring and summer of A

1983. The focus of this report will be on the 1983 survey data that address

the issue of why young people decide to enlist in the Army. Some comparisons

to relevant data collected in the 1982 survey will also be included. Elig

(1983) summarizes the survey design and sampling procedure, provides general

technical information about the questionnaires, and describes the data bases in

detail. Only a brief summary of this information is provided below.

.1o. SURVEY PROCEDURES AND SAMPLE

New recruits were surveyed at US Army reception stations across the

country during the spring and summer months of 1982 and 1983. An effort was

made to minimize sampling bias by sampling all recruits without prior military

service (NPS recruits). Although data was collected from recruits entering the

Army Reserves and the Army National Guard, this report will only present data

collected from recruits entering the Regular Army (RA recruits). Individual

questionnaires were matched with accession records taken from the Military

Entrance Processin - tation Reporting System (MEPRS) to provide important

demographic information such as Armed Forces Qualification Test (AFQT) scores.

Matching MEPRS records were found for 6,318 NPS RA recruits in the 1982 sample

and 8,605 NPS RA recruits in the 1983 sample. The actual number of cases tor

some of the analyses presented in this report is smaller than the total sample

because some items did not appear in all of the alternate forms of the survey

questionnaire.

Table I presents demographic data on several variables that may influence

recruits' responses to survey questions for the 1982 and 1983 samples of new

recruits; data on the total population of new recruits are included for

comparison purposes. The demographics from the ARI surveys indicate that the .1

samples are fairly representative o1 the population of new Army recruits in

1982 and 1983. However, the 1982 and 1983 samples may be somewhat biased

because they were both administered during the last half of the fiscal year.

.. - . -.

"V."-,

........................................................................
Table I

Survey and population demographics for non-prior service, Regular Arny

recruits, 1982 and 1983.

82 82 83 83 82 82 83 83

Sample Pop. Sample Pop. Sample Pop. Sample Pop.

AFQT Reg i on ,.

l&1 31.0 31.9 36.0 36.5 NE 20.9 22.3 20.1 22.2

IliA 18.9 21.1 27.8 24.9 SE 25.1 23.7 21.0 22.3

IIIB 26.6 27.8 30.4 26.6 Sw 15.2 13.3 16.4 27.3

IV 23.5 19.2 5.8 12.0 MW 26.3 26.1 27.4 13.6

West 12.5 14.b 15.1 14.6

Ethnic Term of

Group Enlistment

White 65.9 71.0 73.8 74.0 2 8.5 6.0 7.7 6.9

Black 26.4 24.6 19.6 21.8 3 51.0 56.9 56.2 57.9

Other 7.7 4.4 6.6 4.2 4 40.5 37.1 36.1 35.2

Education Sex

HSDG 91.9 86.0 83.9 87.6 Male 91.0 87.0 90.4 67.6

NHSG 8.1 14.0 16.1 12.4 Female 9.0 13.0 9.6 12.4

Age at

Contracting

17 33.1 9.8 39.9 8.2

18 25.4 32.4 22.4 32.3

19 13.0 19.4 12.4 20.4

20 8.8 11.3 7.4 11.7

21-23 12.4 16.5 11.0 16.9 '"

24 Or 7.3 10.6 6.9 10.5

More

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This potential seasonal bias is attenuated by the fact that many of the

recruits have signed enlistment contracts throughout the preceeding year under

the Army's Delayed Entry Program (DEP). The results of our accession samples

are best interpreted as indicators of the relative strength of motivations for

enlistment rather than definitive percentages of accessions motivated in

specific ways. .'h

RESULTS

New recruits' reasons for enlisting in the Army were assessed using two .

different types of question formats that we will refer to as the forced-choice

questions and the multinomial questions. The forced-choice questions asked

recruits to pick their most important reason for enlistment from a list of ten

alternative reasons. Although the forced-choice format has been the

traditional way of measuring reasons for enlistment and is useful for

cross-year comparisons, it is psychometrically weak. For example, Boesel and

Richards (1982) noted how sensitive it is to order effects. Furthermore, as

discussed by Elig, Johnson, Gade, and Hertzbach (1984), forced-choice questions

are inflexible because they cannot be changed to include other possible reasons

without destroying comparability. Forced-choice items are also insensitive to

the probable mixed nature of enlistment motives. Most recruits probably have

many reasons for enlistment and are not necessarily clear on exactly why they .

enlisted.

The multinomial questions introduced in the 1982 survey make enlistment

motivation amenable to the most powerful statistical tools. For these

questions, recruits were asked to make importance ratings of 15 different

reasons which may have caused them to enlist. The use of multinomial . .

importance ratings was expanded in the 1983 survey to include up to 28

different reasons in some forms of the survey. Assessing recruits' reasons for

enlistment with alternative formats not only allows for a variety of

statistical analyses to be conducted, it also provides a check for the internal

validity of the information obtained in the questionnaire. -.. ',

Forced-Choice Questions

Tables 2-5 present the data from the forced-choice questions pertaining to

reasons for enlistment. Recruits were given two separate lists of reasons and

were asked "which of these reasons is your MOST IMPORTANT REASON for enlisting"

from each list. The two lists were identical except that in List 2 "chance to

better myself" replaced "I want to travel." The two alternative lists of

reasons were included for comparison purposes with similar forced-choice

questions used in previous surveys (e.g., Doering et al. 1980a, 1980b).

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Results presented in Table 2 show how reasons for enlisUnent have changed

since 1979. As can be seen in columns one and two of Table 2, the biggest

changes in self-reports of motivation from 1979 to 1982 are decreases in

motivation for a "chance to better myself" and "skill training" and increases

in motivation for "money to attend college" and for "escape from unemployment."

"Chance to better myself" and "skill training" also decreased from 1982 to 1983

(columns two and three), while the only notable increase from 1982 to 1983 is

in motivation to earn more money.

Differences in recruits' responses to the forced-choice questions were

observed for several different demographic variables. Tables 3-5 present theP% -q

data according to AFQT category classification, sex, and educational background

of the recruits. The data in Table 3 indicate that although there is a

statistically signifcant difference in self-reports of motivation for _,_.

enlistment for recruits of different AFQT categories (p<.01), there is a great

deal of similarity among the recruits. Not surprisingly, recruits with higher --.. '

AFQT scores (CAT Is and lls) report that the most important reason for their

enlistment was to obtain money to attend college more often than recruits in

the lower AFQT categories. Recruits from the lowest AFQT categories (CAT

IVA/IVB) were more likely to report that the most important reason for their ". ._

enlistment was that they were unemployed as compared to recruits from the

higher AFQT categories.

Differences in recruits' responses according to sex are shown in Table 4.

It is important to note that the differences between the sexes shown in Table 4

may be confounded somewhat with other demographic factors because enlistment

standards are more strict for females (no CAT IV females or females without a -

high school education were admitted in 1983). However, log linear analyses

including both sex and AFQT as categorical variables indicated that the 3-way

interaction between sex, AFQT and response to the test question was not

statistically significant (p<.01), but the sex differences are statistically

significant (p>.01). Females are more likely to report "chance to better

myself" and "money for college education" as their most important reason for

enlisting; whereas males are more likely to report "service to country" and

unemployment" as their most important reasons.

Table 5 presents the data from the forced-choice questions according to

educational background of the recruits. The differences shown in Table 5 are

statistically significant (p<.01). Recruits with some post-high school

education report "money for college" as their most important reason for

enlisting more frequently than recruits with high school educations or non-high

school graduates. The data in Table 5 also indicate that recruits with some

post-high school education are less likely to report "service to cUontry" as

the most important reason for enlistment as compared to recruits who do not

have any post-high school education. IAS

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Comparison of most important reasons for enlistment 1979/1982/1983.

Which one of these 1979 DoD ARI Survey of New Recruits*

reasons is your most Survey of 4%

important reason for April List I List 2

enlisting? Contracts 1982 1983 1982 1983

Chance to better myself 39 30 25 -- '-

(not measured in July-Aug 82)

To get trained in a skill 25 22 19 35 30

Money for a college education 7 15 16 20 17

To serve my country 10 9 9 10 12

I was unemployed 4 10 9 10 10

To prove that I can make it 3 6 7 9 10

To be away from home on my own 5 4 5 5 7 "

Earn more money12746

Travel (not measured in 4 .4 4

• May-J une 82),.

To get away from a 1 1 2 2 2

personal problem

Family tradition to serve I I 1 1 2

100% 100% 100% 100% 100%

*Regular Army, non-prior service enlistments only

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Table 3

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Percent of recruits responding to forced-choice questions on most important

reason for enlisting by AFQT category classification.

Reason for enlistment AFQT Category %

List I I & i liA IIIB IVA/IVB

(N=1426) (N=945) (N=1315) (N=292)

To get trained in a skill 23.5 27.2 34.4 28.8

Money for college education 30.8 21.1 8.7 10.6

To serve my country 11.6 12.4 10.7 9.6

I was unemployed 9.5 9.6 12.5 22.3

To prove that I can make it 7.4 8.4 10.5 9.2

To be away from home on my own 5.3 8.1 7.4 8.2

Earn more money 5.3 5.4 7.7 5.8

Travel 4.1 3.7 5.2 3.4

To get away from personel problem 1.6 2.3 1.5 2.1

Family tradition to serve 1.0 1.8 1.4 1.0

I00% 100% 100% 100%

List 2 (N=1432) (N=950) (N-1327) (N=294)

Chance to better myself 21.6 18.9 25.5 24.8

To get trained in a skill 16.8 19.5 21.8 19.4

Money for college education 27.3 19.6 11.1 9.5

To serve my country 9.6 9.1 9.3 6.8

I was unemployed 6.1 8.3 9.0 11.9

To prove that I can make it 5.7 6.6 7.1 10.2

To be away from home on my own 4.7 6.1 5.0 6.8

Earn more money 5.7 8.4 7.3 7.1

To get away from personal problem 1.7 2.0 1.8 2.4

Family tradition to serve .8 1.5 2.0 1.0

100% 100% 100% 100% . ..

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Table 4

Percent of recruits responding to torced-choice questions on most important ...%

reason for enlisting by sex.

Sex

Male Female

(N=4857) (N=522)

Reasons for Enlistment .1-

List I

To get trained in a skill 29.2 30.5

Money for college education 17.1 24.9

To serve my country 11.5 7.5

I was unemployed 12.2 5.4

To prove that I can make it 9.5 11.7

To be away from home on my own 6.6 7.5

Earn more money 6.0 3.8 .'. k

Travel 4.4 4.2

To get away from personal problem 1.9 4.2

Family tradition to serve 1.5 0.4

100% 100%

(N=4876) (N=524)

List 2 .71

Chance to better myself 23.6 30.5

To get trained in a skill 19.5 19.8

Money for college education 16.6 20.4

To serve my country 9.3 5.9 %

I was unemployed 8.3 4.0

To prove that I can make it 7.2 6.9

To be away from home on my own 5.3 4.0

Earn more money 6.9 4.2 ".

To get away from personal problem 1.9 3.6

Family tradition to serve 1.4 0.b

100% 100 .

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Table 5

Percent of recruits responding to torced-choice questions on most important

reason for enlisting by educational background ot recruit. ' %" °

%-

Educational Background % %

Post High School Non-High __

High School Diploma Grad School Grad

(N=1060) (N=3505) N=885)

Reason for enlistment

List I

To get trained in a skill 24.2 29.8 32.8

Money for college education 27.5 17.9 5.9 .

To serve my country 9.1 11.7 12.2 "1

I was unemployed 11.7 10.3 15.6

To prove that I can make it 7.4 9.8 12.8

To be away from home on my own 5.5 7.2 5.6

Earn more money 6.3 5.6 5.5

Travel 4.3 4.4 5.0

To get away from personal problem 3.1 2.0 2.4

Family tradition to serve 1.0 1.3 2.3

100% 100% 100% ".0

0,. %

(N=1061) (N=3533) (N=881) "•

List 2

Chance to better myself 24.8 22.8 28.8

To get trained in a skill 16.9 20.0 20.5

Money for college education 23.2 17.7 6.7

To serve my country 7.1 9.4 9.6

I was unemployed 8.6 7.4 9.6

To prove that I can make it 5.9 7.0 9.4

To be away from home on my own 3.7 5.7 5.2 .

Earn more money 5.6 7.1 6.5

To get away from personal problem 3.3 1.7 1.9 ..

Family tradition to serve 1.0 1.3 1.6

100% 100% 100%-

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In general, the data from the forced-choice questions on the most

important reason for enlistment indicate that recruits frequently report that

the most important reason for their enlistment was "a chance to better myself."

Because "skill training" has declined with "chance" over the years and because

"skill training" gets the biggest increase when "chance" is not asked (See .

Tables 2-5), "chance to better myself" is often interpreted as economic

self-improvement. Support for this interpretation comes from order-effect-e

research that found that "skill training" Is the most frequently selected item

when it is asked before "chance" while "chance to better myself" is the most

frequently selected item when asked before "skill" (Boesel and Richards, 1982).

An alternative explanation is that "chance to better myself" is just a nebulous e

phrase that sounds good and is all things to all people. However, we

hypothesized a third alternative; we believe "chance to better myself" does

have an exact, non-economic meaning. By using the powerful analyses available

for our multinomial measures we feel we are on the track of finding that

meaning. However, before discussing these analyses, we will briefly summarize

the cross tabulations of responses to the multinomial questions and some

additional cross tabulations used to check the internal validity of our survey

data.

Multinomial Importance Ratings

Recruits were asked to rate the importance of 28 reasons for enlisting on

a four-point scale. For each reason they indicated whether this reason was -

not at all important," somewhat important," "very important," or "I would

not have enlisted except for this reason." The data for the reasons that

received the highest importance ratings are shown in Tables 6--8. The numbers

in the tables were obtained by combining the percent of respondents who

indicated that reason was "very important" with the percent who indicated "I

would not have enlisted except for this reason." As with the forced-choice

questions, differences in responses were found for different demographic %

breakdowns of the data. There are so many subtle differences in the data that %

discussion of all of them is impractical; some of the larger differences will __

be summarized below.

Table 6 shows the percent of respondents according to AFQT category.

Although there appears to be a good deal of similarity in the responses ot the

recruits from the different AFQT categories, chi square tests indicated that

there are statistically significant differences for most of the reasons

(p<.01). "Chance to better myself" was rated as being very important by all

categories of recruits, but there is a slight tendency for the recruits from

the lower AFQT categories to give higher importance ratings to this factor as

compared to recruits from the higher AFQT categories. Importance ratings of

"skill training opportunities" was also moderated by AFQT category; recruits

from the lower AFQT categories are more likely to rate skill training as being

very important than are recruits from the higher AFQT categories. The recruits

from AFQT categories I and II were more likely to indicate that money for

college education was very important than recruits from lower AFQT categories.

:.\.

10

• . .. % -. * ]
dP .*

'Table 6

Percent of recruits responding to multinomial questions on reasons for enlistment r Uby AFQT category classification.

AFQT Category

I & LI IlIA IlIB LVA/IVB

Reasons for enlistment '.

*Chance to better myself 67.5 (2226)+ 69.6 (1461) 70.7 (2003) 71.0 (456)

*Skill traini g opportunity 48.0 (2226) 56.2 (1453) 62.1 (2000) 62.3 (453)

*Money for college-" ".

education 62.1 (2224) 55.4 (1458) 36.7 (1998) 37.3 (451)"-..'.

*Learn to be responsible.'.. ma ture 37.7 (1457) 43.2 (968) 49.5 (1367) 50.8 (303) "% '

*Serve my country 44.3 (2224) 45.7 (1466) 45.5 (2005) 4Y.6 (454) " .. °

Become more self reliant 39.7 (1456) 42.1 (966) 44.4 (1372 47.4 (304)

Physical training 41.0 (2221) 41.0 (1467) 39.5 (2001) 43.5 (457)

*Prove I can make it 28.8 (1455) 33.9 (966) 36.0 (1359) 38.0 (303)

*Money for votech/business

education 34.9 (2225) 36.6 (1464) 27.4 (1996) 32.3 (455)

*Earn more money 20.5 (2218) 25.3 (1460) 28.0 (2001) 29.1 (450)

+ p(.Ol.

+Number in parentheses represents sample size.

IN

.-. ,\
The percent of recruits who indicated these reasons were very important ."

are presented by sex in Table 7. As with the forced-choice questions, the %

reader should be cautioned that other factors may contribute to the sex

differences presented in Table 7 because female recruits must have a high .. p

school education and be classified as Cat 3B or above on AFQT to be eligible ,..-

for enlistment. (However, separate log linear analyses, one including both sex

and AFQT as categorical variables, and another including sex and educational

background, indicated no statistically significant 3-way interactions.) In Y-;"

general, although female recruits rated all the reasons presented in Table 7

as being more important than male recruits, the relative importance of the

reasons is similar for both males and females. That is, "chance to better

myself" had the highest percent of recruits indicating this was a very

important reason for both males and females, and "skill training" has the next

highest percent. Chi square tests indicated that there are statistically

significant differences between male and female recruits for all of the reasons

listed in Table 7 except "physical training". "-.

Table 8 presents the percent of recruits responding to these questions

based on educational background. Chi square tests indicated that there are %

significant differences for all of the reasons listed in Table 8. "Chance to

better myself" was rated as being very imp( rwant by all the recruits, but this

was especially true: fur high school t;raduates. "Skill training appears to be

less important for recruits with post high bchoul education and "college money" S....

is less important for non-high school graduates.

Internal Validity Check

By asking essentially the same questions in alternative formats, it is

possible to assess the internal validity of our survey data by doing some

simple cross tabulations. The data in Table 9 represent the percent of

recruits who responded "very important" or "would not have enlisted except for

this reason" when they were asked to make importance ratings of these reasons .

tabulated according to their responses to the torced-choice question on reasons I'

for enlistment (List 2). For example, column one presents data from those

recruits who chose "'chance to better myself ' from the forced-choice list; 83.3%

indicated "chance to better myself' was "very important" or "I would not have

enlisted except fur this reason" when asked to rate the importance of this

factor when it was presented in the multLinomial tormat; 73.7% of the these .

individuals rated :,kill training as "very important" or "I would not have

enlisted except fur this reason", and so on, The data along the diagonal in

Table 9 indicate there is a great deal of consistency in recruits' responses to the two types ut question format. The other data presented in the table

illustrate that there are indeed multiple reasons underlying recruits'

motivation to enlist.

V "

Z .

.1 %
L _I

Table 7

Percent of recruits responding to multinomial questions on reasons for enlistment ;,

by sex.

Sex %

Reasons for enlistment Male Female %.6

Chance to better myself 70.0 (7462)' 76.0 (799)

* Skill training opportunities 55.7 k7445) 62.5 (798)

* Money for college education 46.6 (7444) 63.3 (797)

* Learn to be responsible 44.7 (4980) 55.7 (529)

* Serve my country 45.9 (7454) 40.4 (799)

* Become more self reliant 43.4 (4987) 56.0 (527)

Physical training 41.3 (7468) 42.4 (800)

* Prove I can make it 33.0 (4969) 38.2 (529)

* Money for votech/business education 31.2 (7453) 38.8 (798)

* Earn more money 24.3 (7436) 26.9 (800)

* p<.O

+ Number in parentheses represents sample size .

13 - - .

I .o . ,",

.. ".,

: 7,.,

.% V.,"°

13°" ,

I . . - . . - . . . - - . . . . . % % ° - - , - . , ' . . . . , - . - - . . - . . " . . - • . . . . . ' , " . ° ' . . " " . . - . . . ' . - -. "I .""- . '" ." "" " " " "" . ''"""" " . - - ."" ' -% " '

' % ,

."" " .'"r ", " " . ,- " ' -" " ' " " " ' -- ' %

" , -' - ' - %
-able 8

11ercent of recruits responding to multinomial questions on reasons tor enlistment

by educational background.

Educational Background

Post High School Non-High

High School Diploma Grad School Grad.

Reasons for Enlistment

* Chance to better myself 68.2 (1655)+ 70.3 (5398) 63.6 (1322)

* Skill training opportunities 50.2 (1655) 57.9 (5388) 55.7 (1319)

* Money for college education 61.1 (1658) 49.0 (5381) 28.5 (1319)

* Learn to be responsible mature 38.9 (1081) 47.0 (3613) 48.2 (891)

* Serve my country 39.0 (1660) 46.4 (5401) 48.4 (1311) Li

* Become more self reliant 41.9 (1083) 44.2 (3613) 48.0 (895)

* Physical training 42.0 (1655) 40.7 (5403) 43.6 (1328)

" Prove I can make it 28.8 (1082) 34.7 (3601) 32.7 (892)

* Money for votech/business

education 36.3 (1658) 32.5 (5389) 124.3 (1319) ,

* Earn more money 23.6 (1649) 25.5 (5390) 22.3 (1313)

* p<.Ol + Number in parentheses represents sample size

.. %

.4.%

Uq

•- .

'o",. "

• iS ,.--.' - - - ' - ..' " . . . ..--"-.. .. ...... .
.. ,... ,

%.%

Table 9 ' ."

Percent of recruits responding to multinotnial and torced-choice questions on

reasons for enlistment.

Forced-choice format -'.-

Better Skill College Serve My

Myself training Money Country Unemployed .

Multinomial format

Better Myself 83.3* 53.7 40.0 46.2 20.2

Skill Training 73.7 78.7 45.0 40.5 24.3 -

College Money 64.2 53.7 88.1 35.1 33.3

Serve My Country 70.9 34.7 34.6 83.4 12.3

i

Unemployed 50.4 54.9 34.5 30.6 72.8

*Numbers in table reflect percent of recruits responding "I would not have

enlisted except tor this reason" combined with the percent of recruits

responding "very important."

I.-...-

L2~

. , .
%''

The survey included many other questions in addition to the questions oil

reasons for enlistment. In a further attempt to validate our data, we

tabulated recruits' responses to the forced-choice question on reasons for

enlistment with a question that asked them about their plans after enlistment. N

These results, which are shown in Table 10, indicate that recruits'

self-reports of enlistment motivation are consistent with their self-report of ..

plans after enlistment. For example, 45.7 percent of the recruits who plan to

go on to college after their enlistment, chose "money for college" as their

most important reason for enlistment. Interestingly, 17.1 percent of the

recruits who plan a career in the army, chose "service to country" as the most

important reason for enlisting. It is important to note that 37 percent of the

respondents (n=2,038) indicated they "did not know" what their plans alter

enlistment would be.

We also cross-tabulated the responses to the forced-choice question on

reasons for enlistment with a question that asked recruits to report their

employment status when they enlisted. These data are shown in Table 11. As

expected, recruits who indicated they were unemployed at the time of

enlistment, were much more likely to choose "I was unemployed" as the most

important reason for enlistment as compared to recruits who were employed or

attending school at the time of enlistment. Interestingly, the pattern of

responses to the forced-choice question on reasons for enlistment for recruits

reporting they were employed full time is very similar to the pattern of

responses for recruits reporting they were attending school.

Principal Components Analyses

As discussed previously, recruits have multiple reasons for wanting to

enlist in the Army and in order to assess the relative importance of these

multiple reasons we asked the recruits to rate the importance of 28 different

reasons. Many of these reasons were similar in nature. For example, recruits

were asked to rate "I enlisted to become a better individual," and "I enlisted

to learn to be a responsible, mature person." Obtaining responses on sets of

similar questions permits the use of sophisticated statistical techniques such

as principal components analysis (PCA) that can reveal a great deal about the

underlying processes that may have generated the observed responses to this set

of questions.

Recruits' importance ratings of the 28 reasons for enlistment were

analysed using PCA to reduce the 28 reasons to a smaller set. Principal

components analysis groups similar reasons together into "factors" (or 7

components) according to the degree of correlation between the reasons. After

the factors are "extracted" from the correlations between the separate reasons, " ,'-.,

the factors are "rotated" to improve the interpretability of the factors,

L.6

.

L-b

----9---i
°--

lable 10

Percent of recruits responding to forced-choice question on most imipor tait

reason for enlistmeut by their plans after enlistment.

Plans After Enlistment aer o

Civ ilian Career Dun' t ..-

Employment College Vo/tech Reenlist Army Know

(686)* (1027) (243) (530) (977) (2038)

Reason for enlistment

Chance to better myself 17.8 18.3 14.4 29.2 30.2 26.0

To get trained in a skill 27.6 11.4 18.5 18.5 18.9 21.2

Money for college education 6.7 45.7 21.0 8.9 6.9 12.0

To serve my country 6.6 4.5 7.8 10.9 17.1 7.8

I was unemployed 15.9 3.9 9.5 7.0 6.8 8.2

To prove that I can make it 6.9 4.1 7.4 9.1 7.1 8.4

To be away from home

on my own 5.5 5.0 6.6 6.2 3.5 5.8

Earn more money 8.9 4.9 9.5 6.4 5.7 7.3

To get away from personal

problem 3.1 1.6 4.5 2.3 1.2 1.9

Family tradition to serve 1.2 .8 .8 1.5 2.7 1.0

100% 100% 100% 100% 100% 100%

%-

*Sample Size.

... ..

,,. .e :.

of-.? .
IN

Table 11

Percent of recruits responding to the lorced-choice questioil on reasons tur

enlistment by their employment status when they enlisted.

Employment Status

Full Part Laid Looking Attending

Time Time Off Fired Quit 1st Job School

(518)* (575) (286) (69) (296) (129) (b39)

Reason for enlistment

Chance to better myself 24 25 24 29 26 21 22

To get trained in a skill 19 21 19 17 22 18 18

Money for college

education 18 17 15 12 14 12 21

*To serve my country 11 8 7 4 7 5 11

I was unemployed 4 4 16 13 11 23 7

- ~ To prove that I can

make it 8 7 7 7 7 10 8

To be away from home

on my own 7 8 3 1 4 3 4

Earn more money 6 7 7 6 5 6 7

To get away form personal

4'problems 2 1 1 7 3 2 2 p

*Family tradition to serve 1 1 1 3 2 0 2

.4 *Sample size.

*11

.4 4..
We were particularly interested in exploring the interpretability of the '.-"

- "chance to better myself" reason. As indicated in the discussion of the

forced-choice and multinomial questions, recruits tend to pick this reason as

their most important reason for enlisting. It is not clear, however, whether %

this reason refers to economic improvement such as "earn more money" and "get

trained in a skill" or self improvement such as "become a better individual."

By using PCA we can determine whether "chance to better myself" combines with

economic reasons such as "earn more money" or with self-improvement reasons

such as "become a responsible, mature individual." We predicted that the

"chance to better myself" reason would combine with other non-economic reasons %*..

to form a "self improvement" factor. Confirmation of this hypothesis would

support our hypothesis that recruits are motivated to join the Army for both

economic and non-economic reasons. Although we recognize that "economic"

factors such as "earn more money" or "get trained in a skill" could also be

considered as ways to improve one's self, we believe these motivational factors

can be distinguished from factors that are more directly related to personal

growth and maturity, such as "become a responsible, mature individual."

The principal components analysis indicated that there are six distinct

factors underlying the 28 reasons for enlistment rated in the survey. [We

restricted the eigenvalues to 1.0 or more to ensure the stability of the

factors.] Table 12 shows the results of rotating the factors and allowing the

factors to be correlated (a direct quartimin oblique solution). The numbers

presented under the factor columns in Table 12, called "factor loadings,"

indicate the strength of the relationship between the individual reasons listed

in the left most column and the factors. Reasons loading positively on the

same factor tend to be important to the same people; the larger the factor

loading, the stronger the relationship between the individual reason and that

factor. The individual reasons in Table 12 have been ordered according to the

size of their factor loadings. Reasons that load on more than one factor 9

appear towards the bottom of the table. Factor loadings smaller than .25 are

generally not interpreted and have been removed. The right most column of the

table, labeled "shared variance" indicates how well all of the factors

considered together account for the variability for that individual reason.

These numbers provide an indication of how well the PCA "fits" the data. A . ,-

general rule of thumb is that the individual variables (reasons in our

analysis) should have a shared variance estimate of at least .30.

The results of the factor analysis are very interesting. We have labeled

the first factor in the solution "Self improvement". It includes "chance to

better myself" and several other reasons which are related to self improvement

such as "learn to be a responsible, mature individual," "become more self

reliant," "become a better individual," "need for discipline," "leadership

training," and "physical training."

The second factor in the solution is an economic factor, which we labeled ..

"Economic advancement." It includes reasons such as "obtain a better job when ...

* I get out, "I was unemployed," "earn more money," and "obtain skill training."

1 q

., • - .",- •• •, S. .-. - •-,.j. - .% .• , S. -_ ,% . % , ' , . ,, . ..
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j............. co ca z* 0 A - -j -L4( /

PO.'~ .
The third factor, which we have labeled "Military service," consists of

reasons that generally deal with the desirablity of military lite in general.

For example, it includes "retirement benefits," "fringe benefits,". "be a

soldier," and "serve my country."

Factor IV was the most difficult factor to name because several different

types of reasons loaded on this factor. We have labeled it "Time out" because

this is consistent with most of the reasons that loaded on this factor that

include the "take time out to decide future plans" reason. Other reasons that

had high loadings include "join old friends," "escape personal problems," and

"family tradition to serve." Interestingly, "chance to better myself" has a

fairly high negative loading on this factor.

The last two factors were readily interpretable. The fifth factor has

been labeled "Travel." It includes "chance to travel," and "get away from

home on my own." The sixth factor has been labeled "education money;" it

includes "money for college education," and "money for votech or business

education."

The stability of the PCA solution was tested by splitting the total sample

of recruits who had made importance ratings of all 28 reasons into two samples

according to the last digit of their social security number (odd versus even)

and then conducting separate PCAs. These solutions were almost identical to

the solution presented in Table 12. Another analysis was also done on the

total sample in which the factors were rotated such that they remained

uncorrelated (an orthogonal, varimax solution) and the results of this analysis F.C

appear in Table A-1 in Appendix A. Very similar results were obtained using

the two different rotation methods. The similarity of these different analyses

suggests that the factor pattern shown in Table 12 is quite stable.

Principal components analysis (PCA) was used to reduce the 28 separate reasons for enlistment to a smaller set of more general reasons. The PCA used

to produce the pattern of results shown in Table 12 allowed the factors to be

correlated with each other. Because several of these factors are rather narrow

in scope, such as "Education money" and "Travel," it is possible that further

reduction of these factors into an even smaller number of more general factors

would be meaningful. To explore this possibility, the correlations between

these factors, called "first-order factors," were used as input into another

PCA to identify "higher-order" factors that are broader in scope. If our

hypothesis that recruits enlist for both economic and non-economic reasons is

correct, then then the "higher-order" solution should contain separate factors

44

.. ~that reflect the economic and self improvement motivations for enlistment. .

-I

.44
The correlations between the first-order factors are shown in Table 13 and '.e.

the results of the "higher-order" factor analysis are shown in Table 14. The .

factors in this PCA were rotated such that they could be correlated (i.e., an

oblique rotation was used). These results indicate that there are three broad %-

factors which underlie the importance ratings of the 28 reasons and the

first-order factor analysis. The first higher-order factor includes both the

"Self improvement" factor and the "Military service" factor identified in the V.

first-order PCA. The second higher-order factor is an "Economic" factor and it

includes the "Economic improvement" factor and the "Education money" factor

identified in the first-order PCA . The third higher-order factor includes the

"Time out" factor and the "Travel" factor from the first-order PCA. Note,

however, that the first-order "Travel" factor also loads on the higher-order

factor we have labeled "Self improvement."

Thus the results of both the first-order and the higher-order principal

component analyses confirm our hypothesis that there are both economic and

non-economic reasons underlying recruits' decisions to enlist. In the

first-order PCA, six separate factors are formed that reflect a variety of both

economic and self-improvement reasons. Furthermore, even in the higher-order

factor analysis when we attempt to form very broad factors, "Self improvement"

does not combine with "Economic advancement" which suggests that these are very

distinct reasons influencing the enlistment decision.

The results of the higher-order factor analysis are particularly

interesting when we compare the results of PCAs conducted on comparable sets of

questions from the 1982 and 1983 surveys. The 1983 survey only included 15

multinomial questions about reasons for enlistment, in contrast to the 28

questions included in the 1983 survey. To compare the results from the % 'I

different years additional PCAs were conducted for the 15 reasons that appeared .p. .

in both surveys. Oblique and orthogonal rotations produced similar solutions.

." The factor loadings for these analyses that appear in Table 15 are from the

oblique solutions. The pattern of results for the two years are quite similar;

four factors were identified for both the 1982 and 1983 data, and three of

these factors correspond fairly well with the three higher-order factors

identified when the entire set of 28 reasons was used in analyzing the 1983

data.

.4'?

The first factor presented in Table 15, for both the 1983 and 1982

samples, is the "Self-improvement" factor which includes "Be a soldier,"

"Service to country," "Physical training," "Prove I can make it," and "Want

respect," in addition to "Chance to better myself." The second factor for both

years is the "Time Out" factor which includes "Travel" and "Get away from

home," as well as "Take time out to decide life plans." The third factor, for

both the 1983 and 1982 samples, is the "Economic" factor which includes "Skill

training," "Earn more money," and "Unemployment." The fourth factor for the P of

1983 data is labeled "College Money;" whereas, the fourth factor for the 1982 ..

data is labeled "Escape."

4F~

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00

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Table 14

Rotated factor loadings for higher order factor analysis.

Higher Order Factors

First order Self Time Shared,.

Factors Improvement Economic Out Variance

Military Service .777 .... .608 ' ,

Self improvement .742 .... .619

Economic advance -- .761 .588

Education money -- .745 -- .777

Time out ... .887 .547

Travel .457 -- .512 .623

Variance accounted for: 1.649 1.094 1.019

• .-.

4".I .

,a

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Table 15

Comparison of factor loadings (oblique rotation) fur 1982 and 1983. .. ..

1983 Sample (N=5,381)

Fa c tor

.".-\.'

I ii III IV __

Self Time College Shared %. e Improvement Out Economic Money Variance

Reasons for Enlistment

Be a soldier 0.806 ...... .6412

Service to country 0.751 ...... .5459

Physical training 0.647 ...... .4815

Want respect 0.555 ...-- .4451

Chance to better myself 0.536 -- 0.441 .5205

Time to decide -- 0.686 .-- .4515

Away from home -- 0.623 .-- .4537 ..

Escape personal problem -- 0.526 -- -0.395 .4549

Travel -- 0.502 -- -- .3913

Unemployment .-- 0.735 -- .5628

Earn more money .... 0.680 -- .4868

Skill training .-- 0.519 0.507 .5258

Money for college -- 0.387 -- 0.608 .4949

Family tradition 0.272 -- -0.474 .3504

Prove myself 0.473 0.265 .-- .4253

Variance accounted for: 3.046 1.646 1.371 1.170

1982 Sample (N=2,885)

C tot 5.:-.'.*i

Factar

I II III IV

Self Time Shared

Improvement Out Economic Escape Variance

Reason for enlistment

Be a soldier 0.797 ...... .6334

Service to country 0.785 ...... .6005

Physical training 0.614 ...... .5021

Want respect 0.586 -- .4720

Prove myself 0.542 0.264 .... .4498

Get away from home -- 0.599 .... .4219

Time to decide -- 0.582 .... .3868

travel -- 0.523 -- .3812

Unemployment .-- 0.800 -- .6366

Earn more money .-- 0.699 -- .5051

Escape personal problems -- 0.358 -- 0.621 .5497

Skill training .-- 0.421 -0.570 .5013

Family tradition 0.267 .-- 0.510 .3541

Chance to better myself 0.487 ..-- -0.463 .4950

College money -- 0.481 -- -0.322 .3488

Variance accounted for: 3.071 1.588 1.402 1.179

25 I ...
The similarity of these results with those of the higher-order PCA on the..e

1983 data prompted us to conduct one additional set of PGAs. Using the data on

the 15 reasons that were rated in both the 1982 and 1983 surveys, we ran

another set of PCAs but this time we restricted the number of factors to three.

The results of the oblique rotation solutions are shown in Table A-2 in

Appendix A. The three factors identified in these analyses are very similar to

those identified in the higher-order PCA of the 1983 data.

The results of the PCAs indicate that there are three broad factors

underlying the importance ratings of the reasons for enlistment and, that when

additional reasons are added to the set it is possible to identify six distinct

factors. These six factors include both economic and non-economic reasons that

motivate young people to enlist in the Army. The next question we address is

whether a particular factor is characteristic of a particular subgroup of the

population.

Factor scores were generated for each recruit in order to relate the

factors to various demographic variables. For our data, factor scores indicate the degree to which each individual factor explains the variablity in each

recruits' importance ratings of the reasons for enlistment. The factor scores ...

were generated from the orthogonal PCA (shown in Appendix A) so the factor

scores would be independent. These factor scores were used as the dependent

variables for a series of analyses of variance (ANOVAs) that used various

demographic variables as independent variables. The results of these analyses

are summarized in Table 16. The numbers presented in Table 16 are mean factor

scores that are interpretable in a relative sense. That is, larger positive

numbers indicate that this subgroup of recruits tended to have higher scores on

this factor and larger negative numbers indicate that this subgroup of people

has lower scores on this factor.

The ANOVA on recruits' factors scores on the "Self improvement" factor

indicated that educational background, sex, AFQT category, region of the ."..

country, length of enlistment term, and ethnic group all had a significant

effect (p<.Ool) on recruits' factor scores for the "Self improvement" factor.

Recruits have higher scores on this factor if they have any of the following

characteristics: non-high school graduates, female, AFQT categories IlIA and

below, are from the southern or western regions of the country, enlisted for a

3-year term, and list their ethnicity as "other."

The ANOVA on recruits' factor scores for the "Economic advancement" factor

indicated that AFQT category, region of the country, age at signing the

enlistment contract, and term of enlistmeiit had significant effects on factor

scores for this factor. Recruits have higher scores on this factor if they

have any of the following characteristicb: AFQT category 1IIB/IV, trom the J- -

southeastern or midwestern states, were between 19 and 21 when they signed 3

their enlistment contract, and enlisted for a 3-year term.

%. - .

26°

] -

4o
Table 16

Mean factor scores by demographic variables. b -

Factor

Self Economic Military Time Education

Improvement Advancement Service Out Iravel Money

Education

Non-high school grad 13 8 5 8 -16 -38

High school grad 1 -2 -1 -4 7 1

Post-high school -13 -32 -5 6 -11 28

Sex

Male -1 -1 3 3 -2 -3

Female 18 6 -35 -28 20 29

AFQT

CAT 1 -25 -52 -1 5 -5 38

CAT ii -6 -13 -4 -8 -1 42

CAT IliA 4 0 -2 -6 -3 3

CAT IIIB 7 13 4 8 4 -27

CAT IV 0 30 2 22 1 -28

Region

Northeast -2 -11 -11 -4 3 -3

Southeast 5 5 17 -2 -9 -3

Southwest 9 -12 5 0 -6 1

Midwest -8 13 -8 4 5 -2

West 3 -7 -3 -3 7 13

Age at contracting

17 3 -13 1 -1 8 2

18 2 2 -7 9 10 -10

19 4 13 -10 -2 10 3

20 4 17 -4 3 -12 10

21 -7 13 3 -5 -24 2

24 -14 2 22 -19 -48 8

27 5,

': 27•

. .,.-.-. .o,, -.-. ,.---. -. .,4 . , . .. -. -.-... .. . . . . . . .. .. . . -. - .. . . -.' .',
1.x % o. * VT rr'. -7 .C ._ P 7- -w; 7r -. .V r. ".rw rtr . --.- r " "

:., ..-

'Fable 16 (continued) .:....;

acto"r

Self Economic Military Lime Education , - Improvement Advancement Service Out Travel Money

Enlistment Term

2 years -22 -36 -25 6 17 57 K

3 years 5 7 -4 2 -2 -5

4 years -2 -5 10 -5 0 -4

Ethnic Group

White -3 -3 3 -3 -1 -2

Black 9 9 -17 7 5 9

Hispanic 4 7 4 14 -5 -5

Other 28 -11 -2 11 5 15 • "

Rural/Urban,

Large city 2 -6 -5 3 -5 8

Large city suburb 0 -1 2 3 6 8

Medium city 6 1 -3 -1 4 5

Medium city suburb 3 6 -8 I 6 2

Small city -3 0 3 -6 -4 -.

Rural -3 2 2 -3 -1 -15

Farm -2 -4 7 -3 6 -8

2.

a-o°'. °.

28' " ""
The ANOVA on factor scores for "Military service" indicated that sex,

region of the country, term of enlistment, and ethnic group all had significant

effects on the factor scores for this factor. Recruits have higher scores on

this factor if they have any of the following characteristics: male, from the

southeastern states, enlisted for a 4-year term, and listed their ethnicity as

"white," or "hispanic."

The factor scores for the factor we have labeled "Time out" were ._

significantly affected by the following demographic variables: educational

background, sex, AFQT category and age at signing contract. Male recruits,

non-high school graduates and recruits with some post-high school education,

recruits from the I, IIIB and IV AFQT categories, and 18-year-old recruits

tend to have higher factor scores for this factor. .

The ANOVA on the factor scores for the "Travel" factor indicated that

educational background, sex, region of the country and age at signing contract

all had significant effects. Recruits scored higher on this factor if they had

any of the following characteristics: high school diploma graduates, female,

from the northeastern, midwestern or western states, and age 19 or younger at

the time they signed their enlistment contract.

The ANOVA on the factor scores for the "Education money" factor indicated

that the following demographic variables had significant effects on the factor

scores for this factor: educational background, sex, AFQT category, term of

enlistment, and whether the recruit came from a rural or urban area. Recruits

had higher scores on this factor if they had any of the following characteristics: post-high school education, female, higher AFQT categories

(especially categories I and II), 2-year term of enlistment, and if the

recruit came from a medium or large city or a suburb of a medium or large city.

The information in Table 16 can also be used to assess the relative

importance of the six different factors for a particular category of *.i.'

individuals. Consider, for example, term of enlistment. Recruits who signed

up for a two-year term have large, positive factor scores for the Travel and

Education money factors and large negative scores for the Self improvement,

Economic improvement, and Military service factors. This suggests that

recruits who enlist for two years are motivated to enlist because of travel . -j

opportunities and the opportunity to obtain money for their future education.

The largest factor score for recruits who signed up for a four-year term is for

the Military service factor. This suggests that this group of individuals is

strongly motivated by patriotic reasons and a desire to be part of the military

service.

II

-k. _; .
SUMMARY AND CONCLUSIONS'

The purpose ut this report was to summarize recent tindings trom a survey

administered to new recruits entering the US Army that indicate young people

are joining the Army for both economic and non-economic reasons. Our results % e

indicate that there are a variety of reasons underlying a recruit's enlistment

decision and that different types of individuals (e.g., high school graduates

vs. non-high school graduates, males vs. females, etc.) are motivated to a

certain extent by different reasons.

Future research that attempts to explain enlistment motivation should be

based on models that consider both economic and non-economic variables. New

modeling techniques need to be developed that can directly assess the relative

trade-offs between these two types of factors. For example, would prospective

enlistees be willing to accept jobs that would provide less educational money

for when they leave the service if the jobs offered them challenging

opportunities for personal growth and self improvement while they are in the

"4 service? Will these trade-offs be strongly affected by the longterm career

goals of the enlistee? Although the non-economic factors are less tangible and

S, thus much more difficult to measure than the economic factors, our data

suggests that these non-economic factors can be measured and should be included

in future models to provide a more complete understanding of enlistment

% motivation patterns. ..

. %

..

%

%

¢.,** . .,-.. . . . .

." %. -.- .- . - -- - - .- .-- ..- ",""''."5'", j ., . - . - , . . • , .", -.-... ,-. -, ..-.... ".•,, .-. " "•" .." , .--. '- " .. ".,". " ,• '. %'"
REFERENCES

Boesel, D.P., & Richards, J.A. (1982) Enlistment motivation in the

All-Volunteer force environment: A review of major surveys. Proceedings of

the 24th Annual Conference of the Military Testing Association, 188-193.

Dale, C., & Gilroy, C. (1984) Determinants of enlistments: A microeconomic

time-series view. Armed Forces & Society, 10, 192-210.

Doering, Z.D., Grissmer, D.W., & Morse, J.S. (1980a) 1979 DoD Survey of

Personnel Entering Military Service: Wave I user's manual and codebook

(Rand Note N-1605-MRAL). Santa Monica, CA: The Rand Corporation.

Doering, Z.D., Grissmer, D.W., & Morse, J.S. (1980b) 1979 DoD Survey of

Personnel Entering Military Service: Wave 2 user's manual and codebook

(Rand Note N-1606-MRAL). Santa Monica, CA: The Rand Corporation.

Elig, T.W. (1983) The 1982 DA Survey of Personnel Entering the Army:

Background, user's manual, and codebook (PUTA WP83-3). Alexandria, VA:

Army Research Institute for the Behavioral and Social Sciences. -

Elig, T.W., Johnson, R.M., Gade, P.A. & Hertzbach, A. (1984) The Army

Enlistment Decision: An overview of ARI Recruit Surveys, 1982 and 1983

(Research Product 1371). Alexandria, VA: Army Research Institute for the

Behavioral and Social Sciences.

Faris, J.H. (1984) Economic and non-economic factors of personnel recruitment .4

and retention in the All Volunteer Force. Armed Forces & Society, 10,

251-275.

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Table A-2

Factor loadings (oblique rotatio ,, number of factors restricted to three.

1983 Sample (N=5381) -.e-4

FACTOR j

III III __

Self Time Shared

Reason for enlistment Improvement Out Economic Variance

Be a soldier 0.783 -- - .6207

Serve my country 0.741 ..... 5400

Physical training 0.669 .. .4801

Chance to better myself 0.549 -- 0.438 .5200

Want respect 0.528 0.286 -- .4023

" Escape personal problems 0.649 -0.259 .4482

* . Time to decide -- 0.554 -- .3149

Be away from home 0.547 -- .3944

Skill training .... 0.705 .4918

College money .... 0.503 .2588

Prove myself 0.475 0.335 -- .4189

Unemployment -- 0.387 0.256 .2607

Family tradition -- 0.401 -0.328 .3122

Travel 0.316 0.277 -- .2688

Earn more money -- 0.360 0.416 .3305 -4

Variance accounted for 3.046 1.646 1.371

1982 Sample (N=2885)

FACTOR

III III "' '

Self Time Shared

Reason for enlistment Improvement Out Economic Variance

Be a soldier 0.753 -- -0.270 .6029

Serve my country 0.727 .... .5336

Physical training 0.680 ..... 4925

Prove myself 0.579 .... .4498

Want respect 0.568 0.273 -- .4456

Chance to better myself 0.565 -- 0.366 .4924

Escape personal problem -- 0.743 .5449

Skill training-- -- 0.681 .5013

Earn more money -- 0.274 0.544 .3904

Family tradition-- 0.449 -0.285 .3170

Money for college .... 0.264 .0715

Unemployment -- 0.264 0.489 .3358

Travel 0.381 .... .2405

Be away from home 0.494 -- .3375

Time to decide 0.498 0.000 .3009

Variance accounted for: 3.071 1.588 1.403

Sc3,*.- 33 '

., - x..X.-