ED 117-413 AUTHOR TITLE INSITUTION- DOCUMENT RESUME CE' 006) 057 Alley, William E.; Gouldr R, Bruce Feasibility of Estimating Personnel Turnover from Survey Data--A Longitudinal Study. Final Report for Period DePember 1973-DeCember 1974. 4 Personnel Researc4 SPONS,AGENCY----Ai,rP9,--ce--Bitman-Resources-Lab,--Bracks_ A B _Texas. REPORT NO APREL-TR-75-54 PUB DATE Oct 75 NOTE 30p. EDRS PRICE NF -$0.83 BC-52.06 Plus Podtage DESCRIPTORS *Career Choice; Job Tenure; *Longitudinal Studies; Mathematical Models; *Military PerSonnel; Multiple Regression Analysis; Occuphtional Surveys; *Prediction; Predictor Variables; Vocational Interests; *Work Attitudes IDENTIFIERS Air Force; *Reenlistment I - -ei ABSTRACT The report investigates the' validity Of career intent and job attitude statements for predicting teenlistmentS among 54,803 airmen in 101 enlisted specialties. Statements were made while completing Air Force occupational surveys during the period 1966471. Frequency and percentage distributions characterizing item responses °and career decisions of the sample are given. Relationships between career intent/job attitude responses and actual reenlistment decisions were studies using multiple linear regression techniques. Job attitudes were fCund to be substantially related to career decisions but did not provide unique contributions to' predictions of career decisions when used in conjunction with career intent statements. A model-seeking exercise identified a second-degree polynomial model with career intent and time-in-service interaction vectors as the most appropriate prediction model. Separate prediction equations were obtained for a number of selected subsamples. 1/ Practical examples of forecasting reenlistment rates are illustrated. The regression model selected for the demonstration used weights derived from the total sample to predict the percentage'of actual reenlistments for 30 specialties. Ten principal findings of the study- are identified. Appended is a chart of Air Force specialties used in the ,sample. (Author/MS) *********************************************************************** Documents acquired by ERIC include many informal unpublished * * materials not available from other sources. ERIC makes every effort * * to obtain the best copy available. Nevertheless, items of marginal * * reproducibility are often-encountered and this affects the quality * * of the microfiche and hardcopy reproductions ERIC makes available * * via the ERIC Document Reproduction Service (EDRS). EDRS is not * responsible for the quality of the original dodullent. Reproductions * * supplied by EDRS are the best that can be made from the original. * p***********************************************************************
Transcript
ED 117-413
AUTHORTITLE
INSITUTION-
DOCUMENT RESUME
CE' 006) 057
Alley, William E.; Gouldr R, BruceFeasibility of Estimating Personnel Turnover fromSurvey Data--A Longitudinal Study. Final Report forPeriod DePember 1973-DeCember 1974. 4
Personnel Researc4SPONS,AGENCY----Ai,rP9,--ce--Bitman-Resources-Lab,--Bracks_ A B _Texas.REPORT NO APREL-TR-75-54PUB DATE Oct 75NOTE 30p.
ABSTRACTThe report investigates the' validity Of career intent
and job attitude statements for predicting teenlistmentS among 54,803airmen in 101 enlisted specialties. Statements were made whilecompleting Air Force occupational surveys during the period 1966471.Frequency and percentage distributions characterizing item responses°and career decisions of the sample are given. Relationships betweencareer intent/job attitude responses and actual reenlistmentdecisions were studies using multiple linear regression techniques.Job attitudes were fCund to be substantially related to careerdecisions but did not provide unique contributions to' predictions ofcareer decisions when used in conjunction with career intentstatements. A model-seeking exercise identified a second-degreepolynomial model with career intent and time-in-service interactionvectors as the most appropriate prediction model. Separate predictionequations were obtained for a number of selected subsamples. 1/Practical examples of forecasting reenlistment rates are illustrated.The regression model selected for the demonstration used weightsderived from the total sample to predict the percentage'of actualreenlistments for 30 specialties. Ten principal findings of the study-are identified. Appended is a chart of Air Force specialties used inthe ,sample. (Author/MS)
***********************************************************************Documents acquired by ERIC include many informal unpublished *
* materials not available from other sources. ERIC makes every effort ** to obtain the best copy available. Nevertheless, items of marginal ** reproducibility are often-encountered and this affects the quality *
* of the microfiche and hardcopy reproductions ERIC makes available *
* via the ERIC Document Reproduction Service (EDRS). EDRS is not* responsible for the quality of the original dodullent. Reproductions ** supplied by EDRS are the best that can be made from the original. *
FEASIBILITY OF ESTIMATING PERSONNEL TURNOVERFROM SURVEY DATA A LONGITUDINAL STUDY
By. William E. Alley
PERSONNEL RESEABCH DIVISIONLackland Air Force Base, Texas 78236
R. Bruce Gould
OCCUPATIONAL AND MANPOWER RESEARCH DIVISIONLackland Air Force. Base, Texas 78236
4
October 1975Final report for Period December 1973 December 1974
Approved for public release; distribution unlimited.
e-n00O
0
U.S. DEPARTMENT OF HEALTH,EDUCATION IL WELFARENATIONAL INSTITUTE OF
EDUCATION
THIS DOCUMENT HAS BEEN REPRO-DUCED EXACTLY AS RECEIVED FROMTHE PERSON OR ORGANIZATION ORIGIN-ATING IT POINTS OF VIEW OR OPINIONSSTATED DO NOT NECESSARILY REPRE-SENT OFFICIAL NATIONAL INSTITUTE OFEDUCATION POSITION OR POLICY
R
CE
S LABORATORY
2AIR FORCE SYSTEMS COMMAND'
BROOKS AIR FORCE BASE,TEXAS 78235
NOTICE
When US Government drawings, specifications, or other data ar usedfor any purpose other than a definitely related Cover intentprocurement operation, the Government thereby incurs' noresponsibility nor any obligation whatsoever, and-the fact thk the
overrmiorr m ay Itave lorrtm latEd;firriligifeii, OY in AT solipli4dthe said drawings, specifications, or other data is not to b regarded byimplication or otherwise, as in any manner licensing the older'or anyother person or corporation, or conveying any rights permission tomanufacture, use, or sell any patented invc ttion may in any waybe related thereto.
This final report was submitted jointly by the Personnel ResearchDivision and the Occupational and Manpower Research Division, AirForce Human Resources Laboratory, Lackland Air. Force Base, Texas78236 under project 7719, with Hq Air Force Human ResourcesLaboratory (AFSC), Brooks Air Force Base, Texas 78235.
This report has been reviewed and cleared for open publication and/orpublic release by the appropriate Office of Information (01) inaccordance with AFR 190-17 and DoDD 5230.9. There is no objectionto unlimited distribution of this report to the public at large, of byDDC to the National Technical Information Service (NTIS).
This technical report has been reviewed and is approved.
LELAND D. BROKAW, TechnicalPersonnel Research Division
RAYMOND E. CHRISTAL, Chief/R&D DirectorOccupational and Manpower Research Division
Approved for publication.
'HAROLD E. FISCHER, Colonel, USAFCommander
Uriclassified
SECuRITy CLASSIFICATION OF THIS PAGE (When Data Entered)
2:,,GOMT ACCESSION NO. 3. RECIPIENT'S CATALOG NUMBER
i
4. TITLE (and Subtitle)FEASIBILITY OF ESTIMATING PERSONAL TURNOVERFROM SURVEY DATA A LONGITUDINAL STUDY .
,
5. TYPE OF REPORT 6 PERIOD COVEREDFinalDecember 1973 Decembe(1974
6. PERFORMING ORG. REPORT NUMBER
7. AUTHOR(a) fl. CONTRACT OR GRANT NUMBER(*)
William E. AlleyR. Bruce Gould
9. PERFORMING ORGANIZATION NAME AND ADDRESSPersonnel Research DivisionOccupational and Manpower. Research DivisionAir Force Human Resources LaboratoryLackland Air Force Base, Texas 78236
10. PROGRAM ELEMENT, PROJECT, TASKAREA 6 WORK UNIT NUMBERS
62703F77190239
11. CONTROLLING OFFICE NAME AND ADDRESS
Hq Air Force Human Resources Laboratory (AFSC)Brooks Air Force Base, Texas 78235
/
12. REPORT DATE
October 197513. NUMBER OF PAGES
30
14. MONITORING AGENCY NAME a Aoc:TE;ss(Ir different from Controlling Office) 15. SECURITY CLASS. (o/ this report)
Unclassified
15a DECLASSIFICATION, DOWNGRADINGSCHEDULE
16.. DISTRIBUTION STATEMENT (of this Report).
Approved for public release; distribution unlimited.
17. DISTRIBUTION STATEMENT (of mh abatract entered in Block 20, II different from Report)
16. SUPPLEMENTARY NOTES
This report also documents efforts conducted under Work Unit 77340501.
19. KEY WORDS (Continue on rave:iem milde If necessary and identify by block number)
20. ABSTRACT (Continuo on ravara aide if necessary and Identify by block number)This report investigates the validity of career intent and job attitude statements for predicting reenlistments
among 54,803 airmen in 101 enlisted specialties. Statements were made while completing Air Force occupationalsurveys during the period 1966 to 1971. Frequency and percentage distributions characterizing item responses andcareer decisions of the sample are given. Relationships between career intent/job attitude response's and actualreenlistment decisions were studied using multiple linear regression techniques. Job attitudes were found to besubstantially related to career decisions but did not provide unique contributions to predictions of career decisionswhen used in conjunction with career intent statements. A model-seeking exercise identified a second-degreepolynomial model with career intent and time -i(1- service interaction vectors as the most appropriate prediction
. .
DD I FJOANRM73 1473 EDITION OF I NOV 65 IS OBSOLETE Unclassified
4 SECURITY CLASSIFICATION OF THIS PAGE (When Data Entered"
a
UnclassifiedSECURITY CLASSIFICATION OF THIS PAGE(When Dat Entered)
Item 20 (Continued),
model. Separate prediction equations were obtained for a number of selected subsamples representing two, three,and five-digit Air Force specialty code and aptitude requirement groupings. Homogeneous grouping exercises wereperformed to illustrate a procedure for reducing the number of subsample prediction equations to a minimumsubset.
Practical examples of forecasting reenlistment rates are illustrated. The regression model selected for thedemonstration used weights derived from the total sample- to predict' the percentage of actual reenlistments forthirty specialties. Overall, approximately 17 percent of the sample remained in service. The average differencebetween actual vs predicted reenlistment rates across specialties ranged from 9 percent for airmen sulveyed in theirfirst year to 8 percent, 6 percent and 2 percent for airmen in their second through fourth year of service,respectively.
tr
o
Unclassified
SECURITY CLASSIFICATION OF THIS PAGE(Whon Data Entered)
PREFACE
This research wais conducted under Project 7719, Air Force. Personnel SystemDevelopment on Selection, Assignment, Evaluation, Quality Control, Retention,Promotion, and Utilization; Task 771908, Exploration of Policies and Procedures toImprove Procurement and Project 7734, Development of Methods for Describing,Evaluating, and Structuring Air Force Occupations; Task 773405, Derivation of Methodsto Provichflareatv.ar-Prograstorranct-Dcvelactmentufritirforce-Personne1:4 preciation-------,is expressed to Sgt Mike Hutchinson of the Computer Sciences' Division; is expertprogramming assistance and to Dr. Joe T. Hazel of the Occupational 'an ManpoterResearch Division for his guidance during the early course of this work. 0..
st
This research was performed in partial response to RPR 73-41; Relation ofCareer IntentlJob Satisfaction Variables to Actual Career Decisions in the Cross-Sectionof Airman Specialties. Major Gerald Crewe (AFMPC /DPMMBP) served as requirementsmanager.
.,
TABLE OF CONTENTS
PawIn ir-oduc lion 5
II. Method 5
III. Results 6
Career Intent and Job Attitudes 6
Relationships between Career Intent /Jib Attitudes and Career DecisionsRegression Analyses 9
AFSC Differences 15
Update Requirements 17
iNt. Implications for Reenlistment Forecasting 21
V. Summary 24
Reference's 25
Appendix A: Air Force Specialties 27
LIST OF ILLUSTRATIONS
Figure Page,*I Relationships between career intent and career decision by TAFMS year group 16
2 Squared multiple correlations obtained during hierarchical groupingexercise five digit /0:SC's 18
3 hierarchical grouping for five - digit /11:SC's 19
4 Relationships between career intent and career decision for subgroup!and II by TAFMS year group 20
5 Relationship between career intent and career decision by survey date
LIST OF TABLES
TableI Statements of Career Intent and Job Attitudes
r Page
6
2 Means and Standard Deviations for Career Intent/Job Attitude. Responses,and Percent Retained for Selected AFSCs 7
3 Frequency and Percentage Distribution of Career Intent Responses by TAFMS Year Croup 8
4 Frequency and Percentage Distribution of Job Interest Responses by TAFMS Year Croup 8
5 Frequency and Percentage Distribution of Talent and Training Utilization m
Responses by TAFMS Year Group 8
6i Intercorrelations Between Career Intent and Job Attitude Statements 9
:..1)
Frequency and Percentage of Reenlistments and Separations by TAFMSYear Group and Career Intent Response Category 10
8 Frequency and Percentage of Reenlistments and Separations by TAFMSYear Group and Job Interest Response Category 11
7
3
List of Tables (Continued)
Page
9 Frequency and Percentage of Reenlistments and Separations by TAFMSYear Group and Talent and Training Utilization Response Category 12
10 Regressiet Models Defining Hypothesized Relations laps Between Career---Intent-turd-Career-Decision- 14
11 Multiple Correlations Bttween Career Intent Response and CareerDecision for TAFMS Year Groups 1-4 14
12 Summary of F-Tests to Determine the Significance of Selected FunctionalRelationships Between Career Intent and Career Decision 15
13 Summary of F-Tests Comparing lin Aggregate Model with Models Using Separate Equations 15
14 Results of Timel-Time 2 Analysis 21
15 Illustration of Reenlistment Forecasting by Specialty 23
Al Air Force Specialties Included in the Total Sample 27
It+
8
4
FEASIBILITY OF ESTIMATING VERSONNEL TURNOVERFROM SURVEY DATA A LONTUDINAL STUDY
I. INT13.0DUCTION
The problems associated with excessive personnel turnover are costly in terms of the time and moneynecessarytotrainreplacements aml--occasionally-inthc,71oss--oforganiza tio nal-effectiven -the-AirForce, turnover in first-term enlisted personnel is .typically between 75 and 80 percent and, while theoverall occupational structure is designed to operate within these constraints, periodic shortages occur incertain career specialties while in others overages are more commonplaCe. In managing personnel retentionunder the Total Objective Plan for Career Airman Personnel (TOPCAP) system (USAF Personnel Plan,1971), the Military Personnel Collier has experienced some difficulty in anticipating surges and ebbs in thenumber of reenlistmentsby occupational specialty. One proposed solution is to survey personnel abouttheir job attitudes and intentions to reenlist at strategic points prior to expected separation dates.7If thesemeasures have any validity in forecasting actual career decisions, appropriate remedial actions could then betaken in advance to minimize the expected shortages. The purpose of this study was to determine the basicfeasibility of such a system from the standpoint of statistidal accuracy.
Several recent surveys of the literature have found job attitudes to be consistently related toemployee tenure (Porter & Steers, 1973; Tuttle & Hazel, 1974; Walters & Roach, 1973). Shenk andWilboum (1971) reporting on studies with Air Force officer personnel, found that responses toquestionnates, particularly career intent statements,_provided indications of actual career decisions as muchas four years in advance. Bruner-( -F97T) noted similar relationships with enlisted personnel surveyed a yearbefore actual separation .dates. To establish more definitive guidelines about the approach, the Air ForceHuman Resources Laboratory (AFHRL) was asked to review the problem, determine the overall validity ofcareer intent and job attitude statements for predicting turnover among enlisted personnel and to provideempirical evidence which might reflect differences, if any, in forecasting accuracy as a function of (a) thetime interval between survey date and date of decision, and (b) membership in specific occupationalspecialties
II. MIETIIOD
Survey responses from more than 50,000 Air Force enlisted personnel surveyed as part of theOccupational Research Program (Christal, 1974) were selected for analysis. Surveys used were conductedduring the period September 1966 to November 1971. Respondents in the sample ranged in age from 17 to25 years, were predominatly male and were serving in their first four-year enlistment at the time of thesurvey. In the questionnaire, respondents were asked to indicate whether or not they intended to reenlist atthe end of their current obligated service commitment. In addition, they were to rate their present job interms of the interest and perceived utilization of training and talent associated with it. The format of theseitems is shown in Table 1. Also recorded on the questionnaire were the 'total months of active Federalmilitary service (TAMS) completed to date and the occupational specialty to which they were currentlyassigned. Occupations in the Air Force are identified by five-digit occupational specialty codes (ABCs)which distinguish between career fields and between various skill levels within fields. For purposes of thisanalysis, airmen with I 12 months of service were considered to be in the first TAWS year group; thosewith 13 24 months were included in the second TAFMS year group, etc.
To determine reenlistment status at the completion of four years of active duty, individual recordswere transferred to computer files and matched with the historical airmen reenlistment and loss file (ARIL,current as of 30 September 1973) maintained by the Computational Sciences Division, AFI IRI LacklandAFB, Texas. Excluding records for persons with missing or invalid data or who had not yet reached thereenlistment decision point, the final sample contained 54,803 cases from 101 different career specialties.
Frequency and percentage distributions were used to describe characteristics of the sample and' toindicate attitudinal differences between TAFMS year groups (one to four) and Eclected ABCs.Relationships between career intent/job attitude responses and actual career decisions were investigated by
1. 95
I
Table 1. Statements of Career Intent and Job Attitudes
Career intent (CI) Job interest (41) Utilization of Talent andTraining (TT)
I Plan to Reenlist:'
D 1
I Find my Job:
Extremely
My Job Utilizes my Talentsand Training:
-Hull Nat-at- Ail
No, I Plan to Separatewithout Retirement
Very Dull 2 Very Little 0 2
Benefits 2 Fairly Dull 3 Fairly Well Ll 3
Uncertain,Probably No El 3
So-So 4 Quite Well 0 4
Fairly Interesting 5 Very Well 05Uncertain,Probably Yes 0 4 Very Interesting 6 Excellently D6
Yes 5 Extremely Interesting 7 Perfectly Cl 7
'Inc first alternative (No, I Plan to Retire) wan not considered a valid response\optinn for fu.st-term personnel. Thesmall number of rmpondents who marked this alternative were deleted from the analysis.
application of multiple linear regression techniques as of by Bottenberg and Ward (1963). In theseanalyses, predictions were based on item responses and group membership in one of the four TAFMS yeargroups. The criterion variable in each case was reenlistment status; coded one if reenlisted, zero otherwise.The relative contributions of the job attitude questions in predicting reenlistment were first evaluated todetermine if they contributed uniquely to the overall prediction system. On the basis of these analyses.models to predict reenlistment on the basis of career intent were identified using the total sample ofrespondents while separate equations were obtained for a number of selected subsamples representing two.three, and five-digit AFSCs, aptitude areas, and minimum aptitude requirements within areas.Homogeneous grouping exercises were performed to explore possibilities of grouping separate AFSCequations on the basis of empirical similarities. Throughout the analyses, F-statistics were used whereapplicable to test for statistical significance.
III. RESULTS
A distribu on of respondenti across major AFSCs included in the survey, means and standarddeviations for res nses to the career intent/job attitude statements and percentages actually retained in theservice is shown in Table 2. For AFSCs included in the table, mean values on each of the statements andpercentages retained show wide disparities between specialties. Tabled values for mean career intent rangefrom a low of 2.55 in the Aiito Flight Control Systems Specialty (325X0) to 3.10 for Jet Engine Mechanics(432X0). Average job interest was highest for the Avionic Navigational Systems Specialists (328X1) andlowest for Aerospace Control and Warning System Operators (276X0). The extent to which the job wasreported to utilize training and talents was highest for Ctunputer Operators and lowest again for AutoFlight Control Systems Specialists. Percentages retained beyond minimum service requirements averaged ,16.7 percent for the total sample and ranged from a low of 10 percent (Air Passenger Specialists; 605X0) toa high of 31 percent (Air° Traffic Control Specialists: 272X0). For a complete listing of AFSCs included inthe analysis. see Appendix A.
Career Intent and Job Attitudes
'Although the career intent and job attitude data are cross.,sectional in the sense that responses fromdifferent TAFMS groups were obtained in the same survey, it is interesting to note the pattern of
. differences among respondents in their first through fourth year of service' (Tables 3 through 5). In Table 3._
--
O
Tab
k 2.
Mei
n an
d St
anda
rd D
evia
tions
ftir
Car
eer
Inte
nt/J
ob A
ttitu
de,
Res
ponS
es, a
nd P
erce
nt R
etai
ned
for
Sele
cted
AFS
C's
Gro
up ID
AF
SC
Ca'
reer
Spe
cial
ty
Car
ter,
Inte
ntJo
bin
tere
stU
tiliz
atio
nof
Tal
ent
Per
cent
Ret
aine
dN
Mea
nS
DM
ean
Sd
Mea
nS
D
125
2X1
Wea
ther
Obs
erve
r67
92.
72.8
44.
451.
4933
31.
3924
.30
2 1
272X
0A
ir T
raff
ic C
ontr
ol37
32.
88.7
8'4
.76
.58
3.80
1.19
31.3
7
3 4A
eros
pace
ron
trl &
War
ning
Sys
.
Cen
ter
Spec
226X
029
1 X
0C
omm
672
657
2.71
2.62
.82
.70
3.03
4.13
136
1.55
1.98
3.05
1.09
l'32
11.9
0
195.
3271
'5
293X
3R
adio
Ope
rato
r55
72.
65.7
33.
0613
91.
991
1.12
630
4X4
Gro
und
Rad
io C
omm
Eq
Rep
rtin
537
2.58
.77
3.55
131
2.38
1.22
17.5
0
732
5X0
Aut
o Fl
t Con
t Sys
Spe
c44
42.
55.7
73.
251.
271.
97i
1.04
15.7
7
832
81(0
Avi
onic
Com
m S
pec
666
2.88
.82
5.13
1.19
3.83
1.26
15.6
2
932
8X1,
,A
vion
ic N
av S
ys S
pec
716
2.90
.81
5.22
1.15
3.90
1.32
-16
.06
1032
8X4
Avi
onic
In/
Rad
ar N
av S
ys S
pec
1,24
22.
81.8
34.
971.
413.
671.
4117
.15
1142
1 X
2A
cft P
neud
raul
ic R
ep=
879
2.77
- .7
84.
901.
403.
9013
717
:97
1242
1X3
Aer
ospa
ce G
nd E
qp R
prna
r89
92.
68.7
93.
871.
582.
911.
24'
22.5
8
1343
1X1A
Acf
t Mah
at s
pec
(Rec
Eng
)62
82.
87.7
84.
8013
93.
5313
627
39l-
A14
431X
1CA
cft M
aim
Spe
c (J
etg1
-2 E
ng)
1.61
72.
94.7
74.
9713
63.
831.
4126
:47
11.4
1543
1X1E
Acf
t pla
int S
pec
(Jet
frdu
l Eng
)1,
185
2.95
.80
4.78
1.44
3.61
- '
1.42
24.8
1
1643
2X0
Jet E
ngin
e M
echa
nic
738
3.10
.93
4.41
.87
3.46
1.22
16.1
2
1744
3X0G
Mis
sile
.Mec
hani
c40
52.
98.9
83.
0413
52.
011.
2028
40
1846
1X0
Mun
ition
s pl
aint
Spe
c1,
497
2.81
...8
343
61.
523.
1113
619
462X
0W
eapo
ns M
echa
nic
1,70
12.
78.8
34:
2.0
1.61
3.15
139
114.
4009
2051
1XO
Com
pute
r O
pera
tor
1.43
02.
79.8
25.
2513
84.
041.
4811
.26
2153
4X0
Air
Fra
me
Rep
air
Spec
840
2.75
.76
4,90
1.33
3.80
1.35
12.7
4
2255
1X0
Pave
men
ts M
aim
Spe
c'
766
2.78
.78
335
1.71
2.45
:13
010
39
2357
1X0
Fire
Pro
tect
itin
Spec
1,94
53.
04.8
64.
731.
543.
531.
5016
.14
2460
5X0
Air
Pas
seng
er S
pec
.51
82.
80.7
93:
6613
023
713
89.
65
2563
1X0
Fuel
Spe
ci;d
ist
1.21
72.
7.7
74.
041.
613.
041.
4717
.58
2670
2X0
Adm
in S
pec
6.48
..9
43.
1513
92.
101.
1824
.23
2781
1X0
Secu
rity
Spe
c2.
....9
3.8
34.
151.
863.
131.
6210
.23
2890
2X0
Med
ical
Svc
Spe
c1.
022.
59.7
64.
781.
6533
81.
5418
.28
2992
2X0
Air
crew
Lif
Sup
Spe
c6
2.65
.81
3.78
1.66
2.65
' 1.3
820
.65
981X
0D
enta
l Spe
c"
..92.
61:7
24.
841.
503.
461.
5614
31
Tot
al S
ampl
e54
.803
'2.
81.8
44.
651.
573.
461.
4816
.73
alnc
IA.e
s ca
sts
not n
zpre
sent
ed ie
s ss
Ea-
cted
AFS
C's
sho
wn
in ta
Ne.
C
Table 3. Fiequency and Percentage Distribution of-CareerIntent Responses by TAFMS Year Group'
aTable excludes pries with niirzing or invalid response data (N c 2.508).
the 'proportion with definite plans to reenliii increases slightly over time from three to seven percent. Themost noticeable change occurs in the uncertain categories which constitutes 67 percent c?Ltilie first-yearsample yet only 34 percent of the. fourth-year group. The percentage of airmen who finitely plan tdseparate increases from 29 percent in the first year to 59 percent in the fointh. These data indicate thatonly about a third of enlistees- in their first year of service have definite preconceived notions aboutsubsequent reenlistment. The uncertainty which characterizes the first and second years is not as evident inthe third and fourth years as increasing exposure to service life cryStalizes career intent attitudes.
Job InterestCategory
Table 4. Frequency and Percentage Distribution of Job InterestResponses by TAFMS Year Group'
a:I able rxt hides t sacs with missing or invalid resporra data IN 1 ,',4
128
,; .',; ''
Just as career intent stafements change from the first through the fourth years, both the job attitudemeasures show differences which v4rx with time in service (Tables 4 and 5). Average job attitudes becomeincreasingly more negative as time-in-service increases. Of first -year airmen, 15 percent report that their jobsare dull, and 28 percent repOrt that their talents and training are utilized very little or not at all. For thefourth-year airmen, 24 percent find their jobs dull and 32 percent feel under-utilized. At the time that jobsshould be becoming more demanding with increased technical and supervisory responsibility, job interestand perceived utilization of talents are actually decreasing. Gould (1972, 1974) and Christal (1974), foundthat the negative trend may be much more pronounced when specific specialties are considered.
Table 6, which shows the intercorrelations between responses to the job attitude and career intentstatements, indicates a high degree of relationship between measures of job interest and reportedutilizationof trVing and talents (R v= .63). The relationship between these statements and career intent was not aspronounced (R = .31 and .27, respectively).
,
Table 6. Interrcorrelations Between Career Intent.and Job Attitude Statements
Survey ItemsCareerIntent
JobInterest
Utilizationof Talents
_
Career Intent 1.0000 .3065 .2719Job Interest .3064 .1.0000 .6282Utilization ofTraining and Talents .2719 .6282 1.0000
Note. In samples of this size, a correlation of .08 or geater is significantbeyond the .01 level.
Relationships between Carder Intent /Job Attitudes and Career Decisions
The relationship between career intent statements' and .career decisions is shown in Table 7 whichcontains theoercentage of reenlisiments and losses at -each level of career intent for four time intervalsbeten survey date and time of reenlistment decision. As,,,might be expected, there is an increasingtendency to reenlist as career intent responses become more positive. For respondents surveyed in, their firstyear of active duty, 36 percent of those expressing definite plans to,, reenlist actually do while only 13percent reenlist of those expressing definite negative intent. Comparisons across TAFMS intervals indicatethat the relationships between intent and actual decision are more pronounced as the time between survey
rydate and reenlistment action. become shorter. Of those surveyed during the fourth year, 76 percent of the"definitely yes" category reenlisted versus four percent of airmen responding "definitely no."
Tables 8 and 9 dernopstrate that measures of job interest and utilization of talent and training werealso related to career decisns although to a lesser extent than was the career intent statement. Again it willbe noted that the magnitude of relationship between these two measures and the criterion increases as theinterval between survey and reenlistment decision decreases. Of those surveyed during their first yearof service, 14 percent of those reporting very dull jobs reenlisted versus 22 percent of those reporting veryinteresting jobs. For fourth-year airmen, only six percent reporting very dull jobs (response categories 1-2)reenlisted while 26' percent of those reporting very interesting jobs (response categories 6-7) reenlisted.Similar findings were noted for the utilization measure as shown in Table 9.
Regression Analyses., .
, S"e*eral regression analyses were performed to quantify the relationships between expressed careertointen 'ob attitudes and career decisions and to provide prediction systems by which future reenlistment
actio could be estimated. The goals of the analyses were to simplify the final form of the predictionmodel, to group subpopulations into homogeneous categories based on similarity of regression equations,and finally to establish the efficiency of the group prediction systems. The first analysis was performed todetermine if information from both the career intent and job attitude statements.were necessary to forecastreenlistrnents. To address this question, the accuracy of prediction using a composite equation containinginformation from the career intent statement, jOb interest and utilization of talent and training wascompared to the accuracy obtained using the best
1si le piedictor of the three; that of the career intent
A.
Table 7. Frequency and Percentage of Reenlistments and Separations by TAFMSYear Group and Career Intent Response Category',
TAFMS
Career Intent Response
Reenlistment, 'Probably ProbablyCategory Yes Yes NQ No Total
arable excludes cases with missing or invalid response data (N = 1,543).bResponse categories 1 and 2 combined.
cResponse categories 6 and 7 combined.
1612
statement. The difference between the two multiple correlations (R1 = .3589, Ri = .3580) was notconsidered to be of practical significance and the job attitude measures were dropped from furtherconsideration for the remainder of the study. These findings would tend to support the view that the careerintent statement functions as a composite indicator of other underlying causative influences; some weremeasured in this study (i.e., job interest, utilization of talent and training) and some were not (i.e., externaleconomic conditions).
In the remaining exercises the four TAFMS categories were treated as separate subsamples because ofrecognized differences in the accuracy of prediction at each level. For respondents within each of the fourTAFMS categories, a starting model of the form shown in Table 10 was assumed. Model 1 required thatfour separate parameter values be estimated for each TAFMS year group. The accuracy of predictionobtained with this model, as evidenced by the multiple correlation between predicted and actual criterionvalues, is shown in Table 11. Correlations ranged from .14 for statements obtained during the first year ofservice to .17,-.37, and .53 for statements obtained during the second, third, and fourth years, respectively.
For practical application to smaller samples of data, this model has certain disadvantages. In selectedAFSCs, some of the career intent categories may contain too few cases to provide reliable estimates of theparameters. Moreover, the increased power of a model requiring fewer estimated parameters would likelyoffset any loss in predictive accuracy (i.e., by permitting interpolation of estimates for which there are noobservations). To determine if simpler, more parsimonious models could, be used as effective substitutes,several sets of restrictions were imposed n the starting model. The initial set resulted in the second modelshown in Table 10, in which the obs red relationships were assumed to be curvilinear in their mostcomplex form. of
Another more restrictive set of assumptions resulted in a linear model 3 where differences inprobability of reenlistment between adjacent career intent categories were assumed to be constant.
The fourth and final model to be considered made use of only a subset of the four possible careerintent response categories. Rather than distinguishing between four levels of intent, those indicatingdefinite or probable reenlistment intentions were contrasted with those expressing some doubt aboutreenlistment combined with those expressing negative intentions to reenlist. This equation, termed theSimplex model, is shown as model 4. This model most nearly corresponds to the procedure in whichreenlistment forecasts are based solely on the number of respondents expressing positive reenlistmentintentions versus those which do not. O
The accuracy of prediction obtained with models 2 through 4 are also shown in Table 11. Results ofF-tests comparing the various models separately for each TAFMS year group 'are summarized in Table 12.These data indicated no significant differences between Models 1 and 2 in three of the four TAFMS groupcomparisons. In the fourth year comparison, the differences between the two models were found only inthe third decimal of the squared multiple correlation. Model 3, which assumed a linear relationship, failedto replicate the more complex model in all the comparisons within year groups as did the simplexmodel 4.On the basis of these results and the a priori reservations about the starting model, it was concluded thatthe curvilinear model 2 was sufficiently accurate in most cases to serve as a basis for estimating reenlistmentdecisions. Figure 1 shows the model 2 equations, designated 2.1 through 2.4, to correspond with each ofthe four TAFMS year groups, and the resulting plots for the total sample. The most striking characteristicof these data is the obvious discontinuity in the accuracy of the statements as a function of when they wereobtained. The first and second year equations are quite similar, both yielding fairly low correlations withthe criteria. It appears that at some time between the second and third year of service, the ,validity of thecareer intent statement shows a marked improvement as evidenced by the increase in correlation from .17to .37 in the third year and .53 in the fourth. In terms of probability statements, the chances ofreenlistment, given a positive career intent response, increase from approximately .37 for responsesobtained in the first two years to .66 and .74 for those obtained in the third and fourth years of service.'
'These values were obtained by substituting the appropriate CI value (2, 3, 4, or 5) into the model 2 equations forTAFMS groups 1 through 4 in turn. For example, to estimate the reenlistment probability of q.person indicating positiveintention in his fourth year of enlistment, the values "5" and "25" were substituted for C and C in the formula for model2 (4th year group):
= .0813 .1279(C) f .0519(C2) = 74.
137
01.
Table 10. Regression Models Defining Hypothesized RelationshipsBetween Career Intent and Career Decision
Model FunctionalNumber Form Equation Definition of Terms
1 Categorical Y = a X( ) + a2
X(2 ) a3
X(3) + a4
x(4) Y = dichotomous criterion vectorcoded 1 if reenlisted ;0 otherwisea
Ia
4= unknown parameter estimates
X(I) X(4) = mutually exclusivecategorical predictor vectors basedon career intent statements. X( I )was coded 1 if the correspondingelement in Y was observed on a personresponding with definite intentionsto reenlist; 0 otherwise. X( 2 ) X(4)were similarly coded for the threeremaining career intent responses.
Curvilinear Y = boll + biC + b2C2 be b2
= unknown parameter estimatesU = unit vectorC = predictor vector containingcareer intent responses aided 2through S (i.e.; 2 = definitely will notreenlist:5 = definitely will reenlist)C2 = predictor vector containingsquared career intent respons'es
3 linear 1=c0U+ciC
.. 4 Simplex Y = do
U + d1.X"
c0cl = unknown parameter estimates
= unit vector(' = predictorvector containing careerintent responses coded 2 through 5
de dl = unknown parameter estimatesU = unit vectorX" = dichotomous predictor vectorcoded 1 if the corresponding elementin Y was observed on a person
expressing definite or probableintentions to reenlist; 0 otherwise
Table 11 Multiple Correlation? Between Career Intent Response andCareer Decision for TAFMS Year Groups 1
41)ue to the largc N's on whit h these estintatcs are ',ascii. all of the correlation,. shown are significant at the .01 level.1; listitnates for the linear models eorrcspoild to simple pearain prociut t moment correlations between caret. intent
anti tarter tic& ision (point hi orrclation).r.
14
18
3 Table 12. Summary of F-Tests lo Determine the Significance ofSelected Functional Relationships Between Career
Intent-and Career Decision
R2
TAFMS Comparisons Fulf Rest df I df 2 F
First Year 1 vs2 .0202 .0199 1 5776 1.77"
1 vs 3 .0202 .0188 2 5176 4.13*
1 vs 4 .0202 .0071 2 5776 38.62**
Second Year I vs .0276 .0276 I 16453 0.0"I vs 3 .0276 .0272 2 16453 3.38*
I vs 4 .0276 .0117 2 16453 134.52**
Third Year I vs 2 I 34'7 .1347 I 17146 0.0"s
I vs 3 .1347 .1211 2 _ 17146 134.65**
I vs 4 .1347 .0635 2 17146 676.93**
Fourth Year I vs 2 .2841 .2809 I 12904 57.68**
I vs 3 .2841 .2694 2 1 2904 132.48**
1 vs 4 .2841 .1989 2 12904 767.84**
41 L. ategorical: 2 L tirvilirtear; 3 linear: 4'Significant heyiind the .05 level.*SignitiLaiit beyond the .01 level.
"'Non significant.
AFSC Differences
As a preliminarj, step in the investigation of career field differences in these relationships. separateregression equations were developed for each of the 30 livedigit ABCs shown in Table 2. Occupations
were selected for separate analysis on the basis of haying sufficient sample site to provide relatively stable
estimates of the parameters involved. To determine whether there were significant differences in theequations, the accuracy of prediction obtained by using 30 separate equations was compared with thatobtained using a single common equation for all ABCs combined. The!. results of this comparison are
presented in Table 13 together with similar comparisons for separate equations based on three and two-digitAI'S(' group membership. Also shown in Table 13 arc the full and restricted model Res obtained when
AFSCs were grouped according to minimum aptitude requirements and general aptitude areas. 1..tests forthese comparisons were not considered appropriate because of a certain degree of overlap between grOups.
For this reason, however, the estiMated differences in R2 values may be taken as conservative.
Table l 3. Summary of F-Tests Coniparing an Aggregate Model with ModelsUsing Separate Equations
Subgroup Typo
R'df 1 d12 FFull Rest*
five-digit AFSC .1511 . 1 009 348 28780 4.90**
Three -digit AFSC .077 .1047 336- 39433 594**
Two-digit AFSC .1444 .1129 300 48971 6.03**
Min Apt Groups .1260 .1123 143 >50000 NC
Aptitude Area .1180 .1150 35 >50000 NC
ant beyond the .01 li vet
N(' Nut Lomputed. ,,everal Air full e Al S( an he caierpr)icul into more than one aptitiiik area. Sim thy.
'created Llorrie overlap billNecti the group'. it W.Ib riot divined appropriate to L (Impute ratim and hued No11.11,11111,
for thew.. Lomparhom.
1915
60 . 40
a E.
.20
Fist
Yea
r (M
odel
2.1
r
Y =
.174
7 -.
0645
C +
.020
9C2
R =
.141
80 -
.60_
40 .20
Def
inite
lyPr
obab
lyPr
obab
lyD
efin
itely
No
No
Yes
Yes
Car
eer
Inte
nt
TIo
rd Y
ear
(Mod
el 2
3)
Def
inite
lyN
o
Y =
237
7 -.
1976
C +
.056
5C2
R =
367
Prob
ably
Prob
ably
Def
inite
lyN
oY
esY
esC
aree
r la
tent
.60
.40 .20
-
Seco
nd Y
ear
(Mod
el 2
.2)
Y =
.075
7 -.
0191
0 +
015
341
R =
.166
Def
inite
lyPr
obib
lyPr
obab
lyD
efin
itely
No
No
Yes
Yes
Car
eer
Inte
nt31
'
.80
-
Four
th Y
ear
(Mod
el 2
.4)
.60
_
.40
.20
_
Y =
.081
3 -.
I279
C +
.05I
9C2
R =
.530
Def
inite
lyPr
obab
lyPr
obab
lyD
efin
itely
No
No
Yes
aY
esC
aree
r In
tent
Figu
re 1
. Rel
atio
nshi
ps b
etw
een
care
er in
tent
and
car
eer
deci
sion
by
TA
FMS
year
gro
up.
In all cases where F-statistics were computed. the difference in predictive accuracy across all TAFMSgroups combined were significant beyond the .01 level. Although the large N's involved in thesecomparisons require that consideration be given to .the practical importance of the results, it would bereasonable to conclude that at least two or more separate equations would be desifable to.. estimate
reenlistment decisions for operational purposes. The. decision about which level of aggregation to choose(five digit vs three digit vs, two digit, etc.) is of curse dependent on a- number of factors such as theavailability and sophistication of computing machinery and differ tial flow-rates through the variousAFSC categories. The basic statistical strategy would he to develop se crate equations for each five digit
AFSC. Since some of these groups would not have sufficient numbers of cases on which to base a separateanalysis, they could be aggregated to the three-digit level and even to the two-digit level if %tropic siteremained a problem. Once defined, the number of filial equations could then be further reduced based (illthe empirical similarities between them. Tli'ose AFSCs having similar equations could be clustered into moreor less homogeneous sets so that the fewest possible distinctions would be made between AFSC categories.
The hierarchical grouping procedure defined by Bottenberg and Christal (1961) would be useful inthis context. The first stage of this procedure requires N separate equations as input: one for each subgroupor interest. During the first iteration, a search is made for the two subgroup equations which, if combined.would result in the minimum loss of predictive accuracy. These subgroups are redefined as a single groupleaving N-I groups for further consideration. The hierarchical grouping is c6ntinued until all subgroupequations have been included into a Single cluster. In reviewing the results of these exercises, considerationis given to that stage at which the amount of loss in predictive accuracy exceeds the marginal benefitsderived from retaining fewer equations. To illustrate the approach. grouping-exercises were performed onthe set of '30 equations developed- in the previous analyses. Figure 2 shows the progressive loss in accuracyat each of the grouping Stages. Figure 3 'shows the results of this analysis4Riginning with the ten-groupsolution. It is interesting to note the composition of the final two subgroups. A ptuhoc comparison of theABCs included revealed that all but one of the subgroup I specialties had aorariable reenlistment bonus(VBR. multiple 3 or 4) associated with it during the. time of the study while none of the subgroup.IIspecialties could be So categorized. The plots for these two groups (shown in Figure 4) indicate that forpersons in the V RR group surveyed in the first three years, there was a much higher likelihood ofreenlistment across all categories of career intent as compared with the non-VRB specialties.
Update Requirements
As is the case with most behavioral phenomenon, generalizations appropriate at a given time cannothe expected to apply indefinitely. n the present context,- for example, it would be helpful to know theit
extent to which the equations detill g relationships between intent and career decisions would change overtime so that appropriate update requirements could be more firmly established. Unfortunately, there isonly limited data from the present study which addressts this problem directly. In four AFSCs, results of'Time I/Time 2 surveys administered an average.of four years apart were available for analysis. To determineif there were significant changes in the meaning of the career intent statements over time within each of thecareer fieldwomdels using separate -equations for both Time I and "rime 2 groups were compared torestricted Tails using a common equation for both year groups. It was evident in all of the comparisons,as shown iii table 14. that significant changes had occurred in the interval between surveys. This wouldiodic to the nvessity for updating the information about AFSCs more frequently than every four years. Inaddition to the Time I /Time 2 comparisons, it was al. o possible to diaracterize real-time differences inthese relationships. Figure 5 shows the regression plots I ir ABCs ,grouped according to survey date.Although these comparisons are partially confounded by irtue of the tact that occupations were notrandomly sampled within each time period. there did seem to be a significant trend toward increasingaccuracy for the later surveys (F -7' 47.82, (11,-, 48/52299, p < .01). Again it was evident that frequentupdating could be expected to ymprove the accuracy of prediction in an operational setting.
aFull model contains separate equations for Time 1 and Time 2.b Restricted model contains a common equation for both Tim- 1 and
**Significant beyond the .01 level.
line 2.
IV. IMPLICATIONS FOR REENLISTMENT FORECASTING
Throughout this report, the focus of analysis has been on the probability a reenlistment for theindividual jobincumbents. To illustrate the potential application of these techniqueSln forecasting groupreenlistment rates, predictions based on models 2.1 throug,h 2.4 were generated for 'each of the 30 largestAFSCs sampled in the study (Table 15). For every career field the following procedure was applied. First, abivariatc frequency distribution was obtained' showing respondents at each TAFMS x career intent level.Let, X resent elements of this matrix corresponding to the number of persons at the ith career intentreplevel and the jth TAFMS year group where i = 1,4 and j 1,4. The percentage of reenlistments expectedfrom theAth TAFMS year group Rp4 would be obtained by computing:
Air Xis
l00
where AU represents th.e predicted probability of reenlistnt for persons at the ith career intent level andthe.jth TAFMS yyar group (based on model 2 equations):
The expected percentage of reenlistments for persons in the 3rd, 2nd, and 1st TAFMS year groups wouldbe obtained by setting j = 3, 2, and 1 respectively and recomputing. In the 252X1 career field, for example,enlistees in'the 4th TAFMS year group responded to the career intent' question as folloWs:
No W 125 (X14)Probably No = 26 (X24)Probably Yes = 11 (X34)
Yes 14(X44) g
The respective probabilities of reenlistment (Ail) associated with each response based on Model 2.4 are .03,.16, .40 and .74. Multiplying and summing corresponding Ali X ifs yields (.031) (125) + (.16) (26) + (.40)
,(11) + (.74)(14) 23, the number of expected reenlistments for respondents in their 4th year. Dividing bythe total. number Of cases (176) across all categories and convertingtte a percentage value yields 13.1percent.
Also shown in Table 15 for comparison purposes are estimates of the pelOtitage of ,airman° reenlistments based on the Simplex model' and the percent of actual reenlistments for each career
,"21tese estimates are obtained from the number of respondents in the appropriate TAFMS year group who. responded"yes" or "probably yes."
1.4
.4
N,
DefinitelyNo
1968
4(966- 1967
Pro ably Probably DefinitelyNo Yes Yes
Career Intent
Figure .5. Relationship between career intent and career decision by survey date.
22
2 6
O
t
Tab
le 1
5. I
llu O
ratio
n of
Ree
nlis
tmen
t For
ecas
ting
by S
peci
alty
Gro
upID
AF
SC
TA
FM
S a
t Tim
e of
Sur
vey
Firs
t Yea
rS
econ
d Y
ear
Thi
rd Y
ear
Fou
rth
Yea
r
% P
redi
cted
Ree
nlis
tmen
ts%
Act
ual
Ree
nlis
tmen
ts
% P
redi
cted
% A
ctua
lR
eenl
istm
ents
% P
redi
cted
Ree
nlis
tmen
ts.
%A
ctua
lR
eenl
istm
ents
% P
redi
cted
Ree
nlis
tmen
ts
Ree
nlis
tmen
ts%
Act
ual
enlis
tmen
ts
Mod
ell
Sim
plex
Mod
ell
Shr
iple
xM
odel
lS
impl
e x
Mod
ell
Sim
plex
125
2X1
17.4
15.5
35.2
14.7
12.9
25.9
17.5
1.7.
026
.513
.114
.215
.3
227
2X0
18.5
,25
.045
.815
.616
.638
.016
.614
425
.817
.019
.417
.7
327
6X0
17.0
133
13.3
14.7
- 1L
811
.015
.113
.011
.715
.616
.112
.6
429
1X0
16.6
10.0
27.1
14.5
9.9
18.0
12.8
7.1
12.8
10.9
8.9
9.5
529
3X3
16.3
9.5
20.6
14.4
10.1
20.6
14.6
15.2
24.7
11.3
10.8
11.5
630
4X4
15.8
15.4
15,4
-14
.011
.224
.114
.911
.416
.810
.29.
710
.3
732
5X0
'17.
033
.3'0
.015
.01-
7221
.914
.712
.818
.08.
26.
39.
7
832
8X0
t7.7
23.5
-52.
915
.618
.5'
12.5
17.8
20.4
13.4
19.4
24.3
20.3
9.32
8X1
19.9
28.6
42.9
16.4
21.0
414
.816
.816
.215
.718
.021
.2'
17.3
1032
8X4
25.1
50.0
50.0
17.8
31.0
15.6
16.4
16.8
18.6
11.6
9.9
15.1
1142
1X2
17.7
16.4
23.6
15.8
17.0
22.0
16.7
17.2
19.1
12.9
11.2
1?.7
1242
1X3
18.1
,32
.0.
40.0
14.7
14.1
25.2
16.9
,16.
227
!412
.211
.615
.0
1343
1X1A
17..8
21.2
34.9
15.7
17.4
31.0
17.3
-' 19
.523
.717
.317
.624
.21"
:-''
14...
,143
1X1C
18.3
24.7
31.6
16.3
20.0
29.7
16.7
16.5
25.7
18.6
19.7
21.2
1543
1X1E
18.5
22.4
22.4
16.7
24.0
31';4
18.2
19.9
',2
4.6
18.1
19.6
120
.7
16 1743
2X0
443X
0G20
.321
.043
.950
.019
.020
.Q
18.8
16.3
40.2
24.5
11.2
37.7
22.3
23,9
36.1
34.9
15.8 A
16.5
17.6
21.9
21.3
1 §.
0tis
.,' :49.
9
1846
1X0
18.7
25.9
16.1
.15
.519
.412
.618
.022
.614
.012
.313
.415
.3
-19
462X
018
.630
.115
.516
.625
.012
.315
.314
.316
.611
.910
.513
.620
511-
X0
18.2
23.2
17.0
15.6
19.7
9.2
17.3
21.5
10.3
12.2
12.1
10.3
2153
4X0
18.5
24.1
16.8
16.2
20.0
19.2
16.3
13.9
10.7
11.8
8.7
1.0.
2
2255
1X0
17.9
19A
32.3
16.2
' 21.
712
.715
.013
.84.
111
.8.'
10.1
11.3
2357
1X0
18.8
28.4
14.2
17.8
31.4
14.6
.20.
227
.618
.1*
18.8
21.3
16.9
2460
5X0
17.6
18.8
12.8
14.7
-15
.07.
916
.215
.16.
517
.721
.419
.0
' 25
631X
0'17
.721
.6-
23.1
15.2
14.0
29.2
14.9
13.1
9.9
16.2
17.0
17.4
2670
2X0k
,20
.536
.41&
215
.3.2
0.5
23.2
18.2
19.8
26.0
18.1
25.3
23.2
2781
1X0'
18.5
25.1
15.5
16.8
25.5
9.2
17.8
21.6
6.4
15.4
18.6
11.7
2890
2X0
15.5
8.3
17.2
14.9
15.4
25.0
14.4
11.8
20.0
10.8
10.2
'12
.0
2992
2X0
15.7
6.3
21.9
14.1
.11
.619
.015
.416
.322
.714
.417
.119
.4
3098
1X0
16.6
13.0
13.0
13.9
8.7
16.1
13.2
10.4
14.2
12.5
9.8
9.8
Mea
n18
.223
.823
315
.718
.8/1
0.0
16.7
17.5
\7.7
14.4
1'5.
3y5
.4S.
D.
1.9
1.1
1.3
1.2
7.1
''8.
42.
46.
47.
13.
25.
4'
/ 4.3
Avg
% E
rror
9.44
12.0
5-.
7.89
10.0
244
:
5.69
6.94
2.12
2,46
.1
specialty. The average error of predictions based on Model 2 equations range from 9.4 percent for airmen intheir first year to 2.1 percent for airmen in their fourth year of service. These error rates were consistentlylower than for the corresponding Simplex estimates which differed from actual rates an average of 12percent in the first year and 2.5 percent in the fourth year. It would be reasonable to assume that evengreater accuracy in group reenlistment predictions would be obtained if more specific models had been used(i.e., those distinguishing between VRB vs- non -VRB. career fields, real -time survey date or ultimately,models developed scientifically for 2,3- or 5-digit career fields).'
V. SUMMARY
The principal research findings from this study can be summarized as follows:
1. Cross-sectional comparisons of career intent responses by time in service indicated a relativelyhigh degree of uncertainty about reenlistment during the first and second years. It was concluded thatrelatively few respondents had formulated definite career intentions either prior to enlistment or in thesubsequent two years.. Beginning in the third and continuing into the fourth year of enlistment, careerintent becomes increasingly polarized toward the positive and negative extremes. Across all four years,there was a progressive downward trend in positive attitudes toward reenlistment. The negative trend withincreased job tenure was noted for both the career intent and job attitude measures.
2. Measures of expressed job interest and utilization' of talent and training appeared to be morerelated to each other than to the career intent item.
3. Each of the career intent and job attitude items-was shown to be significantly related to eventual*carecr decisions. The magnitude of the relationships between career intent and career decision, however,was somewhat larger than for either of the job attiiude statements. Moreover, it was noted that the jobattitude measures made little unique contribution to the prediction of career decjsions with- priorknowledge of.career intent.
4. The accuracy of the career intent statement in predicting career decisions was found to be afunction of the time interval/between survey administration and time of decision. Statements, obtainedduring the last two years of enlistment were considerably more accurate than statement§ obtained duringthe first two years. Validities ac oss the first four years of service were .14, .17, .37 and .53, respectively.
5. The functional form of the relationship between career intent and decision appeared to becurvilinear, particularly for pers ns in their third and fourth years of service.
6. Career intent statements were found to have different validities and functional relationships withcareer decisions when, specific AFSCs were compared. Between-specialty differences, persisted whenspecialties were grouped by their first two or first three digits or according to aptitude requirements.
7, Homogeneous grouping exercises performed on equations for 30 separate 5-digit AFSCs indicatedthe feasibility of reducing the number of final equations retained to a more manageable set. The last stageof the exercise identified two groups of AFSCs which appeared to differ primarily on the basis of thosewith an associated VRB (multiple 3 or 4) versus all others. It was concluded that two or more equationswould be required to obtain maximum predictive efficiency.
8. Based on limited data from Time 1-Time 2 surveys in the same career fields and observedreal-time differences between AFSCs grouped according to year of survey administration, it was concludedthat periodic updating the prediction system at least every 2 to 3 years could be expected to improveforecasting accuracy substantially.
9. The use, ofreCtal-sample equations to forecast the percentage of airmen reenlisting in 30 selectedspIalties yielded average errors of prediction ranging from 2 percent for airmen surveyed in their fourthyear of service to 9 percent for first-year airmen. Thege estimated reenlistment rates were consistently moreaccurate for all year groups than similar estimates based on the simplex model, although differences inaccuracy appeared to be quite small in comparisons at the fourth year.
10. The results of these analyses support the basic statistical feasibility of using career intentstatements obtained during the first-term (particularly years 3 and 4) as advanced indicators of careerdecisions at the individual or group level. The evidence suggests that statements of intent might also serve asuseful interim criteria in evaluating personnel selection methods or modifications in policies and proceduresdesigned to increase career retainability among the enlisted force.
24 28
"z
'REFERENCES.
Bottenberg, R.A., & Christal, R.E. An iterative technique for clustering criteria which retains optimumpredictive efficiency. WADD-TN-61-30, AD-261 615.. Lack land AFB, TX.: Personnel Laboratory,Wright Air. Development Division; March 1961.
Bottenberg, R.A., & Ward, LH., Jr. Applied multiple linear regression. PRL-TDR-63-6, AD-413 128.Lackland AFB, TX.: Personnel Research Laboratory, Aerospace Medical Division, March 1963.
Bruner, G.L. The importance of volunteer- status: Ah analysis aid reliability test of survey data. R-717-PR.Santa Monica, CA.: Rand Corporation, December 1971.
Christal, R.E. The United States Air Force occupational research project. AFHRL-TR-73-75, AD-774 574.Lackland AFB, TX.: Occupational Research Division, Air Force Human Resources LaboratoryiJanuary 1974.
Gould, R.B. Reported job interest and perceived utilization of talents and training by airmen in 97 careerladders. AFHRL-TR-72-7, AD-745 099. Lackland AFB, TX.: Personnel Research Division, Air ForceHuman Resources, Laboratory, January 1972.
Gould, R.B. ' Review of Air Force job satisfaction research. Paper presented to the 82nd Annual Conventionof American Psychological Association, New Orleans,, August 1974.
Porter, L.W., & Steers, R.M. Organizational, work, and personal factors to employee turnover andabsenteeism. Psychological Bulletin, 1973, 80(2), 151-176.
Shenk, F., & Wilbourn, J.M. Officer attitudes related to career decisions. AFHRL-TR-7145, AD-744 038.Lackland AFB, TX.: Personnel Research Division, Air Force Human Resources Laboratory,December 1971.
Tuttle, R.C., & Haze14.T.'Review and implications of job satisfaction and work motivation theories for AirForce Research: AFHRL-TR-73-56, AD-782 443. Lackland AFB, TX.: Occupational ResearchDivision, Air Force Human Resources Meoratory, Jpuary 1974.
USAF Personnel Plan. Volume three, airman structure (TOPCApi. Washington: Department of the AirForce, 1 June 1971.
*alters, L.K., & Roach, D. Job attitudes as predictors of termination and absenteeism: Consistency overtime and across organizational units. Journal of Applied Psychology, 1973, 57(3), 341-342.
2 9
25
APPENDIX-A: AIRFORCE SPECIALTIESs,
Table Al. Air Fdrce Specialties Included in the Total Sample
