Technical Report 651

Validation of Current and Alternative Armed

Services Vocational Aptitude Battery (ASVAB)

Area Composites,

Based on Training and Skill Qualification Test (SOT)

Information on Fiscal Year 1981 and 1982 Enlisted Accessions

00 Donald H. McLaughlin

American Institutes for Research

Paul G. Rossmeissl

Army Research Institute

Lauress L. Wise, David A. Brandt, and Ming-me! Wang

American Institutes for Research

Selection and Classification Technical Area

Manpower and Personnel Research Laboratory

[ __Wf DTIC E1ECTE

CD JUL 2 4 t98E

U.S. Army D

Research Institute for the Behavioral and Social Sciences

October 1984 LA.

ApDroved to, public release; distribution unlimfl$::.

0- t85 7 24 03A
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

L. NEALE COSBY

EDGAR M. JOHNSON Colonel, IN

Technical Director Commander

Researct~ accompiished under . contract.. . . . . . . . . . . . ... 7

for the Department of the Army

Human Resources Research Organization -

Technical review by

John J. Mellinger (Army Research Institute)

Lloyd G. Humphreys (University of Illinois at Urbana-Champaign).

Robert L. Linn (University of Illinois at Urbana-Champaign)

Lonnie D. Valentine, Jr. (Air Force Human Resources Laboratory) I

Lawrence M. Hanser (Army Research Institute)

a

NOT ICES

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NOTE- The findings In this report are not to be construed as an official

Department of the Army position, unless no designated by otLer authorized

documeretS.

C0-
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READ~ iNSTRUC11ONS REPORT D~OCUMEN4TATION PAGE_____ OF-OE COMIPLETING. FOR41

. EIPORT NUMBER -~7V %AC o ION "a A &1G1NTSC rA1ALO. um&EA

ARI Technical Report 65 6 -L n 4 TITLE (WSod Sitia) VALIDATIOJN OF CURRENT AND ALTERNA- S. TYPE Of! REPORT A PERIOD COVERED

TIVE ARMED SERVICES VOCATIONAL APTITUDE BATTERY Interim Report

(ASVAB) AREA COMPOSITES, BASED ON TRAINING AND Oct 1982 - May 1984

SKILL QUALIFICATION TEST (SQT) INFORMATION 014 S. PE~#ORMNG ONG. IIEVOftT NUMBER

IISKALYER 98 ANY) 11482 ENLISTED ACCESSISTN. . O - - -RN .U.) __II 7.AUTI4ORfa .Ci.A)ORGATNUBa.

Donald H. McLaughlin (AIR); Paul G. Rossmeissl

(ARI); Lauress L. Wise, David A. Brandt, A MDA 903-82-C-0531

Ming-mi Wang (AIR)____________

IPERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROjECT. T ASK A1REA It WORKC UIT NUMBERS

Human Resources Research Organization 2Q263731A792

1100 South Washington Street 2312 101

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it. CONTROLLING OFFICE NAME AND ADDRESS 12, REPORT DATE

U.S. Arm v Research Institute for the Behavioral October 1984

and Social Scierces N3 umbER OV PAGES

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IS. SUPPLEMENTARY NOftES

The Army Research Institute technical point of contact is Dr. Paul G.

Rossmeissl.

,,. KEY 1#000S (Co-ti-.-. on O... ld* It nocoasrY wW 1d i-f.0~y by blcRub1

Validation SQT

Predictors Differential validity

Longitudinal data base Training

ASVAB Aptitude area composites

20 A81I'ACi rCmih1O & M O O W*' ld IbI-W A" 1~111'~ 67 block nU"&*#J

This report describes a large scale research effort to validate and improve the ASVAB aptitude area (AA) composites now used by the AMn~ to select

and classify enlisted personnel. Data were collected from existing Army

sources on over 60,000 soldiers and over 60 MOS. The research had three

major components: First, the composites now being used by the Army were

validated. Second, a new set of composites were derived empirically; and

finally, both s-ts were compared on the basis of predictive validity,.Contilued)

%tCURIT CLASSIFICATION OF THIS PAGIE (Uh. DO#* I:I.Io..)

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SlCUMTv C1.A Al VMA"CAt0 OF 1,flS Pi1AGEhm DWe Iptnmw

ARI Techni al Report 651

20. (Continued)

differential validity, and possible prediction bias. Both sets of composites

were found to perform well, with the alternative set of four composites doing

slightly better than the nine now in operational use.

UNCLASS I FI ED

iIECURITY CLASSIICATIOW r)F THIS PAGE(W n Data Enter.d)
Technical Report 651

Validation of Current and Alternative Armed

Services Vocational Aptitude Battery (ASVAB)

Area Composites,

Based on Training and Skill Qualification Test (SQT)

Information on Fiscal Year 1981 and 1982 Enlisted Accessions

Donald H. McLaughlin American Institutes for Research

Paul G. Rossmelssl

Army Research Institute

Lauress L. Wise, David A. Brandt, and Ming-mel Wang

American Institutes for Research

Submitted by

Newell K. Eaton, Chief

Selection and Classification Technical Area

Approved as technically adequate

and submitted for publication by Joyce L Shields, Director

Manpower and Personnel

Research Laboratory

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

6001 Eitenhower Avenue. Alexandria. Virgini3 22333

Offie. Deputy Chief of Staff for Personnel

Department af the Army

October 1984

Army Project Number Manpower wWid Personnel

20263731AT72

Approved for public release; disiribulon unlimited.

iii
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.
FOREWORD

This document presents the results of the first step in AR) s large

scale research effort for improving the selection, classification, and utilization of Army enlisted personnel. The research reported here is unique in

that the very large sample sizes that were employed make it one of the largest investigations of test validity conducted to date. Two important findings emerged from the research. First, it is now clear that the current ASVAB

composites are good predictors of soldier performance. ,>-cond, the predictive

power of two of the composites could be substantially improved with little

change or cost to the current assignment system. With the operational use of

the new composites which started in October 1984, the Army can expect improved

performance in the clerical and surveillance/communications MOS, which can be

attributed directly to this project.

EDGAR M. JOHNSON

Technical Director

v/
ACKNOWI EDGKEWTS

This report was prepared by staff of the American Institutes for Research and the U.S. Army Research Institute for the Behavioral and Social

Sciences, as specified in contract OMDA 903-82-C-0531 between the Army Research Institute and the Human Resources Research Organization, Newell K.

Eaton, COR. The analytical work was carried out under the direction of the

lead author, in close consultation with the second author. The sections on

Historical Background, Purpose and Objectives, and Assessment of Predictive

Bias were written by Paul Rossmeissl; the sections on Development of Alternative Composites, Predictive Validity of Alternative Composites, Differential Validity, and the Summary were written by Don McLaughlin. The section on Alternative Composite Scales and Cutoffs was written by Lauress

Wise; the section on Predictive Validity of the Current Composites was

written by David Brandt; and the Description of Data was written by committee. Valuable inputs were provided by the project's principal investigator,

John Campbell; by the project's Scientific Advisory Comittee, especially

Robert Linn; by the staff of the Army Research Institute,especlally Newell K.

Eaton, Hilda Wing, Larry Hanser, John Mellinger, and Mike Rumsey; and by

reviewers representing the U.S. Air Force.

vi
EXECUTIVE SUPOIARY

Requirements:

(1) To compute validity coefficients for the Army's Armed Services

Vocational Aptitude Battery (ASVA8) Area Composites for prediction of

enlisted personnel performance in the first tour;

(2) To identify and comparatively validate the best alternative set of

ASVAB composites based on the nine subtests of ASVAB Forms 8, 9, and 10.

Procedures:

Data Preparation. Records of soldiers with either training outcome data

or Skill Qualification Test (SQT) scores as performance criteria were

extracted from Army enlisted accessions for FY81 and FY82. The file was

limited to enlistees who took ASVA8 8/9/10 and who had not had prior service. These records were extensively edited, then partitioned into analysis "cellsw with at least 100 cases each. Criteria were standardized

within each cell.

A "training cell" was defined as a set of soldiers who took the same

training course in the same MOS; and an "SQT cell" was defined as a set

of soldiers who took the same form of an SQT test in the same MOS. The

analysis was based on a total of 29,160 soldiers in 92 training cells and

65,193 soldiers' records in 112 SQT cells. A soldier who took two different SQT forms was included in each cell.

Two secondary data sets, consisting of representative samples of 19,027

applicants for enlistment in FY81 and 13,319 in FY82, were extracted for

the purpose of estimating the performance of composites with a set of

typical enlistees.

Two derived data sets were produced in order to carry out and report analyses at the MOS level: (1) covariance matrices of the ASVAB and criterion

scores for each cell aggregated, using sample weights, across cells

within each Military Occupational Specialty (MOS); and (2) a file of

64,907 records containing a single criterion score for each individual.

These combined criteria analyses were carried out for 98 MOS.

Predictive Validitz Estimation. The correlation coefficient between each

of tne ASVAS Area Compcites and the criterion measure was obtained for

each training cell, each SQT cell, and each combined cell (MOS). These

coefficients were adjustcd for range restriction using the multivariate

adjustment based on the assumptio, of homogeneous linear regression

(Lawiey, 1943). Because the composites are designed for use in selection

vii
and classification of applicants for enlistment, the target population for

the adjustment was taken to be the FY81 and FY82 appliCants.

Identification of Alternative Composites. First, the training cells, the

SQT cells, and the MOS were partitioned into clusters, based on similarity

of ASVAB profiles of successful performance. Then, for each cluster, the

unit-weight composite with maximal predictive validity was identified.

The similarity measures were computed separately for training, SQT, and

combined cells. The similarity between each pair of cells was defined as

the correlation of the predicted criterion performances in the two cells,

for the applicant sample. The performance predictions were based on ridge

regression, using the ASVAB subtests as predictors.

The cells were clustered by adapting standard "leaf-to-stem" procedures.

Upon finding that the results of the clustering were unstable, due to the

high intercorrelations of the predicted criterion scores, the clustering

procedure was modified to use as a starting point the Army's current groupings of MOS into the nine sets associated with the nine composites. These

clusters were combined into four larger clusters when it was found that

the overall predictive validity of four unit-weight composites was as high

as nine separate unit-weight composites. The four-cluster solution was

then further aggregated to three-, two-, and one-cluster solutions for

comparative purposes.

Differential Vlidlt4 Estimation. The validity of the composites for ;redicting differences in an applicant's expected performance in different

MOS was assessed, using a variant of Horst's Classification Efficiency

index. Estimates were obtained for the current composites, for each of

the alternative solutions, and for an application of the MAGE composites.

Predictive Bias Estimation. Validity coefficients were computed separately

tor men, women, blacks, and whites; and separate regression f inctions for

each group were compared to identify potential problems of underprediction

of performance of one group compared to another.

Norming of Alternative Composites. A table was prepared so tnat the alternative composites, calculated as simple sums of ASVAB subtest scores, can

be transformed intc the same distribution as current composites. Although

this allows for use of the same cutoff scores as in the past, a table of

alternative cutoffs was prepared which would leave invariant the average

AEQT score among applicants eligible for the MOS.

Findings:

Predictive Validity of Current Composites. The validities for 98 MOS,

based on combinations of training and SQT scores, ranged from .12 to .74,

with a mean of .45. Grouping MOS by the current composite clusters, the

lowest mean validity was .42, for Surveilliance and Communications MOS and

for General Maintenance MOS, and the highest was .54, for Skilled Technical

MOS. There was almost no tendency for the composite assigned to an MOS to

have a higher validity than other current cwnposites for that MOS.

viii ( ,)
Identificatlon of Alternative Composites. Only unit-weiht alternative

composites were considered, after it was found that optimal unit-weight

composites for four clusters possessed a root mean square (RMS) predictive

validity 97% as great as the root mean square validity of ridge regression

vectors computed separately for each of 98 MOS. The combination of losses

due to using only four composites and limiting them to unit weights was

minimal.

The alternative four-composite solution that we identified as maximizing

the aggregate predictive validity across 98 MOS was:

Clerical (ACL): VE + AR + K

Skilled Technical (AST): VE + AR + MK + AS

Operations (AOP): VE + AR + AS + MC

Combat (ACO): VE + MK + AS + MC.

The operations compcsite combines the current SC, OF, and MM clusters; and

the combat composite combines the current CO, FA, GM, and EL clusters.

Validity of Alternative Composites. The RMS validity of the four-composite

set was .486. Tnis compares with an RMS validity of .489 for the best set

of nine unit-weight composites. Variations of validity in the third decimal place are neither statistically significant nor of great practical

importance, and a variety of alternatives to the four-composite solution

were explnred.

Of special interest were the three-, two-, and one-composite solutions.

The loss in validity which would result from using the new *combat* composite (ACO) for both the "combat" and *operations" MOS is negligible (.001),

as is the loss in, further, using the new Clerical composite (ACL) for

both Clerical MOS and Skilled Technical MOS. This two-composite solution

captures 97% of the predictive power of the ASYAB for the performance criteria used in these analyses. Finally, use of the single (ACO) composite

for all MOS resulted in a reduction to 96% of the predictive power of the

ASVA8.

Certain of the composites account for a large part of the difference in

validity between current and alternative composites. When compared to

validities of optimal composites for the same clusters of MOS, the Clerical

composite (CL) appeared to be weak, with a validity of .48 versus a potential of .56. One other composite, Surveillance and Communications (SC),

was mildly weak, with a validity of .45 versus a potentiAl of .50.

The gain in expected performance if these composites were changed can only

be approximated because of the constrained nature of the sele:tlon and

classification process. If, however, the choice were purely bet.ieen

assigrment to an individual P40S and rejection, application of Cronbacn's

formula yields an expected gain of .05 standard deviations per person in

ix
the two clusters of MO from introduction of these two revisions to the

current composites.

Differential Validity-of Current and Alternative Composites. The ability

of current and alternative composites to identify the best MOS for each

enlistee was assessed, using a variant of Horst's Classification Efficiency

index. The current composites and five alternative sets of composites all

possessed between 43% and 68X of the differential validity of the ASVAB as

a battery. There was small positive relationship between the number of

composites in a set and the measure of differential validity. The ninecomposite sets appeared to capture more of the battery's differential predictive value than the one-, two-, three-, or four-composite sets. The

performance of the current composite set (68%) was virtually identical to

the performance of the alternative which merely replaced the CL and SC

composites (66%). In general, the differential validity of the ASVA8 as a

battery was higher for low-frequency MOS, but this effect was less pronounced for the sets of unit-weight composites.

Predictive Bias of the Current Cmosites. The validities of the composites are slightly higher overall for whites (.45) than blacks (.38), but

there is, if anything, a tendency to underpredict performance of whites

more than blacks. The validities of the EL and SC composites are greater

for nales than for females, but overall the average difference in validity

only slightly favors males (.47) over females (.43). Underpredictions of

performance were split between males and females, with the most noticeable

underprediction being roughly .06 standard deviations for women using the

SC composite. In general, the over- and underpredictions were small,

aspecially in the region near the cutoffs.

Predictive Bias of Alternative Composites. In general, the patterns of

alfferential validity and underprediction observed for the current composites also were found for the four alternative composites, ACL, AST, AOP,

and ACO. The overall average validity for whites (.47) was somewhat higner

than for blacks (.40), but the underpredictions of performance were suffered primarily by whites. An exception to tris was the underprediction

of blacks' performance by the alternative skilled technical composite

(AST). Blacks' criterion scores in the OF cluster were unoerpredicted by

both the current and alternative composites, and the degree of underprediction was sligntly greater using the proposed comporite.

The alternative composites had a slightly smaller difference in validities

between men and women (.48 vs. .42) than the current composites, but again

the most noticeable differences were the greater validities for men in the

EL and SC clusters. There were also somewhat greater underpredictions of

women's performance in the CL, OF. and SC clusters using the alternative

composites, although in general the differences were small.

Xj
Conclusions:

Selection of a Composite Set. First, the results on predictive validity

favor the alternative four-composite solution over the current nine composites in terms of overall absolute predictive validity and differential

validity for MOS classification. The results for predictive bias are

mixed, but the effects are not large in either direction. The average

validity of the alternative composites is .48, vs. .45 for the current

composites. The differential validitius of the three-, two- and onecomposite sets were progressively worse, but the magnitude of the differences were smail.

The major source of the relative deficiency of the current composites lay

in two of the composites, CL and SC. Because of the costs of implementation of different levels of change in composites, an interim proposal is

to replace these two composites with the ACL and AOP composites, respectively, keeping intact the nine-cumposite structure. The average validity

of the revised nine composites would be rhised from .45 to .47, while the

differential validity as measured by the criterion adapted from Horst

(1954) would be virtually unchanged. This solution would also avoid the

introduction of AST, with its small increase in underprediction of blacks'

performance in skilled technical MOS.

Broade:,ing the Span of Predictors. The current composites, as well as the

best alternative composites, account for only about 20 tT 25 percent of

the variance in the criteria, but they account for over 90% of the variance in the criteria that is predictable from the ASVAB. The ASVAB measures four common factors, but only two eigenvalues are greater than one,

and the first principal component accounts for roughly half the variance.

This level of predictability is clearly not sufficient for accurate identification of the optimal assignments of enlisted personnel to MOS. While

it wis impossible to assess the contributions of limitations of the criteria ani of the ASVAB separately in these analyses, the adjusted validities were modest, with only 14 out of 98 greater than .6. There is a need

for use of a broader set of predictors in the selection and classification

process for enlisted personnel.

Increasing the Sample Size. The present analyses combined the data from

two years, FY81 and FY82, with a substantial increase in the possible

coverage of MOS over the coverage available from one year's accessions.

For many MOS, there are not sufficient numbers in any year to support

needed parameter estimation for the purposes of deriving optimal assignment procedures. However, with a proper control for trends across years,

the data base can be built up over a few years to the point where the

needed two thousand cases in each MOS are available for analysis.

Although the replication of these analyses two years hence was to focus on

the FY83 and FY84 cohorts (with the addition of utility informatior.,, the

data base for those ana!yses will actually be the four-year cohort, FY81

through FY84 accessions. This will provide the basis for exploring both

trends and criterion measures taken later in a solJier's career, as well

as an adequate data base far a larger set of MOS. L. ___ _ _ _ _ _Xi
Utilization:

The major practical result of this investigation was the identification of

suitable replacements for the two relatively weak ASVAB Area Composites

currently in operational use by the Army. Introduction of new composites

for the Clerical & Administrative and Surveillance & Communications MOS

will significantly improve the expected performance of enlisted personnel

entering these MOS, without affecting differential validity of the composites or intL',icing significant predictive bias.

In addition, this effort resulted in the development of systematic procedures for the validation of ASVAB composites, including data editing,

range restriction adjustment, ridge regression estimates of optimal composites, differential validity estimation, predictive bias assessment, and

setting of cutoff scores. At the same time, the results highlighted needs

for additional research and development to build on the foundation of

credibility created by this effort. In particular, there are needs for

criterion validity and reliability information, performance utility estimates, cumulative additions to sample sizes, further work on range restriction adjustment and differential validity measurement, and a broadening of

the coverage of skills required in different MOS. This coverage must be

included in both the criterion measures and the predictors.

Throughout the remainder of this project, work will go forward on the

development of better predictors and better criteria; and future validations of enlisted personnel selection and classification p-ocedures can be

expected to refine and extend the results presented here.

(
Table of Contents

Historical Background. .. ... ..... ....... I

Purpose and Objectives .. .. .. .. .. .. .. .. 5

Description of Data. .. ... ...... ........ 7

Overview. .. ..... ...... ..... .... 7

Samples .. .. .... ...... ...... .... 7

ASVA8 Data....................9

Training Data..................12

SQT Data. ...... ...... ..... .... 16

Trimirming of Outliers. .. ..... ...... ... 17

Combined Criteria. .. ... ..... ........ 17

Predictive Validity of the Current Composites......21

Introduction . . .. .. . . .. . .21

Adjustment for Restrictiion of ange.........21

Validities of AA Composites Using Combined Crierion 22

Training Data .. .. ..... ..... ....... 24

Validities by Training Cell. .. .... ....... 25

SQT Data....................25

Validities by Clusters'of SQT Ceils........26

Validities by SQT Cell. .. ...... ....... 26

Discussion. .. ..... ...... ..... ... 27

Development of Alternative Composites .. .. ........ 29

Identification of MOS Clusters .. ... ........ 29

Similarity Measure . . . .. .. .. .. .. .. .. 30

Initial Cluster Analyses. .. ..... ....... 32

Final Cluster Analyses. .. ..... ......... 33

Predictive Validity of the Alternative Composites .39

Comparisons to Other Sets of Composites. .. ..... 42

Differential Validity. .. ... ..... ........ 45

Case 1: both MOS i and MOS j use

the same composite. .. ..... ..... .... 48

Case Z: MOS i and MOS j use

different composites. .. ..... ..... ... 48

xiii
Table of Contents (cont'd)

Page

Assessment of Predictive Bias ... ............. . 53

Analyses of Differences by Race ............ .... 53

Analyses of Differences by Gender ........... 70

Other Analyses of Subgroup Differences ... ...... 80

Summary ...... ... ...................... 101

Alternative Composite Scales and Cutoffs ........ . .. 103

Composite Scales ..... ................ ... 103

Cutoff Scores ....... ................... 103

Summary ....... ....................... . I. 105

Limitations ....... .. .. ......... 105

Information on Criterion'Quality.... .. .. 105

Information on Utility of Performance ... ...... 105

Sample Size ....... ................... 106

Findings ...... ................... . ... 106

Predictive Validity of Current Composites . . . 1 106

Identification of Optimal Alternative Composites 106

Validity of Alternative Composites ........ . 107 I. 

Differential Validity of Current and

Alternative Composites ... . . ....... 107

Predictive Bias of the Current Composites . . . . 108

Predictive Bias of Alternative Composites . . . . 108

Recommendations .......................... ... 108

Selection of a Composite Set. ... ...... 108

Relative Value of Composites and Cutoffs . . . 109

Broadening of Span of Predictors . . . . . . .. 109

Increase the Sample Size ..... ............ 110

Conclusion ...... .. ................... 110

References .. ... .. .. ..................... Ill

Appendix A Supplemental Tables ... ............ ... A-I

Appendix B Editing of Training Outcome Scores . . . . B-1

Appendix C Conversion of Sum of Subtest Standard

Scores to Army Standard Scores .... C-1

Addendum ,Qj
List of Tables

Table 1: Operational Composites Currently in

Use by the Army ... ............. 3

Table 2: Other Composite Systems ..... .......... 4

Table 3: ASVAB 8/9/10 Subtests . . . . . . . . . .. 9

Table 4: Correlations and Standard Deviations among

the Nine ASVAB Subtests .... ......... 11

Table 5: Distribution of FY81/82 Accessions

oy Cluster ........... .... 12

Table 6: Uescriptive Statistics on the Training

Criterion by Criterion Cell ......... ... 14

Table 7: Descriptive Statistics on the SQT

Criterion by Criterion Cell ......... ... 18

Taole 8: Adjusted Validities of the Current

Composites: Combined Criteria .. ..... 22

Taole 9: Intercorrelations among the Current

Composites: Applicant Population .... 23

Taole 10: Sandle Correlations and Adjusted

Validities: Training Criterion .... 24

Table 11: Average Adjusted Validities:

Training Criterion ..... ........... 25

Table 12: Sample Correlations and Adjusted

Validities: SQT Criterion ......... ... 26

Table 13: Average Adjusted Validities:

SQT Criterion . . . ........... 27

Table 14: Optimal Unit-Weight Composites for

each Current Cluster ..... .......... 35

Table 15: Local Optimal Four-composite Solution . . . 35

Table 16: (Weighted) Average Adjusted Validities

Combined Criterion . .......... 37

Table 17: (Weighted) Average Adjusted Validities:

SQT Criterion ... .............. . . 37

xv
List of Tables (cont'd)

Page

Table 18: (Weighted) Average Adjusted Validities:

Training Criterion ............ 37

Table 19: Average Adjusted Validities of the

Proposed Composites: Combined Criterion. 39

Table 20: Intercorrelations among Proposed

Composites in the Applicant Population . 40

Table 21: Average Adjusted Validities of the

Proposed Composites: SQT Criterion ..... 41

Table 22: Average Adjusted Validities of the

Proposed Composites: Training Criterion . 41

Table 23: Average Adjusted Validities of the MAGE

Composites: Combined Criterion ..... 43

Table 24: Average Adjusted Validities for the High

School Composites: Combined Criterion . 43

Table 25: Intercorrelations among the MAGE Composites

in the Applicant Population ... ....... 44

Taole 26: Intercorrelations among the High School

Composites in the Anplicant Population . 44

Table 27; Differential Validity Estimates for

Alternative Sets of Composites

(Unweighted) .... ... .............. 50

Table 28: Differential Validity Estimates for

Alternative Sets of Composites

(Weighted) ... ............... ..... 51

Table 29: Sample and Adjusted Validities for

Blacks (OBO) and Whites ("W"): Current

Operational Composites, SQT and Training

Criteria Combined ............. ..... 54

Table 30: Sample and Adjusted Validities for

Blacks ("B") and Whites ("W"): Four

Alternative Composites, SQT and Training Criteria Comoined ... ........... . 54

Table 31: Predicted Criterion Scores for Blacks ("BN)

and Whites ("'W"): Current Operational

Composites .... ............... .... 56

xvi
List of Tables (cont'd)

Table 32: Predicted Criterion Scores for Blacks ("B")

and Whites (IW"): Four Alternative

Composite Solutions ...... . . . . . 58

Table 33: Sample and Adjusted Validities for

Males ("MO) and Females ('Fl):

Current Operational Composites SQT

and Training Criteria Combined ..... 71

Table 34: Sample and Adjusted Validities for Males ("M")

and Females ("F"): Four Alternative

Composites Solution SQT and Training

Criteria Combined ............ .. 71

Table 35: Predicted Criterion Scores for Males (OM") and

Females ("F"): Current Composites . . . . . 79

Table 36: Predicted Criterion Scores for Females (OF")

and Males ("M"): Four Alternative

Composites Solutions ... ............ ... 81

Table 37: Means and Standard Deviations of the

Combined Criterion Scores for Black (*B")

and White ("Wu) Subgroups ... ......... 83

Table 38: Means and Standard Deviations of the

Combined Criterion Scores for Male ("M")

and Female (OF") Subgroups ............. 83

Table 39: Predicted Score Differences (Diff.) and

Standard Errors (SE) of the Difference

between Blacks ("B") and Whites ("W") for

Particular MOS Current Operational Composites 99

Table 40: Predicted Score Differences (Diff.) and

Standard Errors (SE) of the Difference

between Males ("MR) and Females (OF") for

Particular MOS Current Operational Composites 100

Table 41: Predicted Score Differences (Diff.) and

Standard Errors (SE) of the Difference

between Blacks (IB") and Whites (W"u) for

Particular MOS Fpur Alternative Composites

Solution ............ ....... . . 101

xvii
List of Tables (cont'd)

Table 42: Predicted Score Differences (Diff.) and

Standard Errors (SE) of the Difference

between Males ("M") and Females ("F") for

Particular MOS Four Alternative Composites

Solution ................ . . . . . . 102

Table A-l: List of M0S in the Army ........... . . A - I

Table A-2: MOS with Multiple Tracks for SQT Testing . A - 5

Table A-3: Sample Validities for Current Composites:

Combined Criterion ..... ........... A - 8

Table A-4: Adjusted Validities for Current Composites:

Combined Criterion ...... ............ A - 10

Table A-5: Sample Correlations for the Current

Composites: Training Criterion A - 12

Taole A-6: Adjusted Validities for the Current

Composites: Training Criterion . . . . A - 14

Table A-7: Sample Validities for Current Composites:

SQT Criterion ...... ............. A - 16

Table A-3: Adjusted Validities for Current Composites:

SQT Criterion .. ............ .... A - 18

Table A-9: Ridge Regression Coefficients for

Training Criterion ..... .......... A - 20

Table A-10: Ridge Regression Coefficients for

SQT Criterion ............... ..... A - 23

Table A-1b Ridge Regression Coefficients for

Combined Criteria ............. ..... A - 26

Table A-12: Matrix of Correlations of Expected

Outcome Functions for MOS, Combined

Training and SQT Criteria ....... ... A - 29

Table A-13: Adjusted Validities for Current and

Alternative Composites, by Current

Cluster and MOS .. ............ .... A - 31

Table A-14: Sample Valid;ties for the Proposed

Composites: Combined Criteria ..... A - 33

Table A-15: Adjusted Validities for the Proposed

Composites: Combined Criteria .... A - 35

Xviii
List of Tables (cont'd)

Table A-16: Sample Validities for the MAGE Composites:

Combined Criterion . . . . . . . ... . A - 37

Table A-17: Adjusted Validities for the MAGE Composites:

Combined Criterion . . ........... A - 39

Table A-18: Sample Validities for High School

Composites: Combined Criterion . ... A - 41

Table A-19: Adjusted Validities for High School

Composites: Combined Criterion .... A - 43

Table A-20: Cumulative Distributions for the Current

Composites Based on a 2% Sample of all

FY81/82 Applicants ..... ........... A - 45

Table A-21: Conversions from Sum Scores to

Composite Scores SQT Criteria ..... A - 47

Table A-22: New and Current Composite Scores

Maintaining Constant AFQT Means for

Eligbles Based on a 2% Sample of

FY81/82 Applicants ...... ......... A - 49

Table A-23: Aptitude Area Composite Cutoffs by MOS . . A - 57

Table A-24: ASVAB Subtest Means by Training Criterion

Cell ..... .................. .... A - 58

Table A-25: ASVAB Subtest Means by SQT Criterion Cell A - 60

xix0
List of Figures

Figure 1: Frequency of Correlations of SOS

Similarity Profiles . . . . . . . . . . . .. 33

Figure 2: Regression Lines for Current and Alternative

Composites for CL MOS, by Race . . . . . .. 61

Figure 3: Regression Lines for Current and Alternative

Composites for CO MOS, by Race . . . . . . . 62

Figure 4: Regression lines for Current and Alternative

Composites for EL MOS, by Race ... ....... 63

Figure 5: Regression lincb for Current and Alternative

Composites for FA MOS, by Race ... ....... 64

Figure 6: Regression lines for Current and Alternative

Composites for MM MOS, by Race ... ....... 65

Figure 7: Regression lines for Current and Alternative

Composites for OF MOS, by Race ....... 66

Figure 8: Regression lines for Current and Alternative

Composites for GM MOS, by Race ... ....... 67

Figure 9: Regression lines for Current and Alternative

Composites for SC MOS, by Race ....... 68

Figure 10: Regression lines for Current and Alternative

Composites for ST MOS, by Race ....... 69

Figure 11: Regression Lines for Current and Alternative

Composites for CL MOS, by Gender ...... ... 73

Figure 12: Regression Lines for Current and Alternative

Composites for EL MOS, by Gender ...... ... 74

Figure 13: Regression Lines for Current and Alternative

Composites for MM MOS, by Gender ...... ... 75

Figure 14: Regression Lines for Current and Alternative

Composites for OF MOS, by Gender ...... ... 76

xx
List of Figures (cont'd)

P age

Figure 15: Regression Lines for Current and Alternative

Composites for SC MOS, by Gender . . . ... 77

Figure 16: Regression Lines for Current and Alternative

Composites for ST MOS, by Gender . . . . . 78

Figure 17: Regression Lines for Current and Alternative

Composites for MOS 118, by Race . . . . . . . 85

Figure 18; Regression Lines for Current and Alternative

Composites for MOS 138, by Race . . . . . . . 86

Figure 19: Regression Lines for Current and Alternative

Composite 'r MOS 36C, by Race ....... . 87

Figure 20: Regression Lines for Current and Alternative

Composites for MOS 64C, by Race . . . . . . 88

Figure 21: Regression Lines for Current and Alternative

Composites for OS 1750, by Race ....... 89

Figure 22; Regression Lines for Current and Alternative

Composites for KOS 13F, by Race ... ....... 90

Figure 23: Regression Lines for Current and Alternative

Composites for MOS 11H, by Race ....... 91

Figure 24: Regression Lines for Current and Alternative

Composites for MOS 31M, by Race ....... 92

Figure 25: Regression Lines for Current and Alternative

Composites for MOS 05C, by Gender ...... 93

Figure 26: Regression Lines for Current and Alternative

Composites for KOS 75C, by Gender ....... ... 94

Figure 27: Regression Lines for Current and Alternative

Composites for P10S 72E, by Gender ...... 95

Figure 28: Regression Lines for Current and Alternative

Composites for MOS 76Y, by Gender . . . o . . 96

Figure 29: Regression Lines for Current and Alternative

ComposiLs for MOS 91E, by Gender ...... 97

Figure 30: Regression Lines for Current and Alternative

Composites for MOS 958, by Gender . . . . . . 98

xxi3
Historical Background

The Armed Services Vocational Aptitude Battery (ASVAB) is a test battery for

assessing cognitive abilities and is used oy the military services as their

primary instrument for selecting and classifying enlisted personnel. The

current operational version of ASVAd (Forms 8/9/10) and all previous versions

of military selection and classification tests have been referenced and normalized to the scale of the Army General Classification Test (AGCT), as used

during World War II. In the case of the Army, the development and history

of ASVAB 8/9/10 as a classification instrument can be more directly traced

back through ASVAB 6/7 to tne Army Classification Battery.

For many years prior to the operational implementation of ASVAB in 1976,

each of the services maintained its own test batteries. Within tne Army,

the selection and classification decisions were based upon the Army Classification Battery (ACB). The ACB was first used in 1949 when the aptitude

area system was introduced operationally by the Army. Under this system, an

indiviaual recruit needed to achieve a minimum score in a particular aptitude

area before being assigned to an occupation. The original ACB was substantially revised and improved by Maier and Fuchs (1973) based on an empirical

validation in seven joo areas, to produce ACB-73. The ACB-73 in its final

form consistea of twelve subtests and an interest inventory from which four

interest scales were derived.

In 1974 the Department of tefense (oD) recommened tfte use of a single

interservice test battery for military selection and classification, and in

January 1976, Az,.P.3 6/7 became operational as the DoD-wide selection and

classification instrument. ASVAB 6/7 contained parallel forms of all of the

subtests that had been a part of ACB-73 plus an additional speeded test

(numerical operations). For a number of reasons, not yet completely known

(Maier & Truss, 1983), the original scale scores of ASVAB forms 6/7 were

miscalioratea. The result of this miscalibration was that examinees in the

lower range of the ability distribution were given higher scores than they

would nave received if tne battery had been correctly calibrated. DoD supported three research efforts to identify and then correct this calibration

error. These efforts resulted in a revised set of norms for the battery.

In addition, a panel of experts was formed to review the recalibration

research and to insure that calibration efforts for other operational oitteries did not result in a similar error (Jaeger, Linn, & Novick, 1980).

ASVA8 8/9/10 replaced ASVAB 617 as the operational tast battery in October

1980. Forms 8/9/10 differed substantially from the former battery. Some of

the old subtests were dropped or combined into single suotests, and two new

subtests were added. Finally the complete interest inventory was deleted.

These changes resulted in a battery of ten subtests: General Science (GS),

Arithmetic Reasoning (AR), Word Knowiedge (WK), Paragraph Comprehension (PC),

Nunerical Operations (Ni), Coding Speed (CS), Auto/Shop information (AS),

Mathematics Knowledge (MK), Mechanical Comprehension (MC), and Electronics

Information (EI). ASVAB 8/9/10 is the battery currently in use by the armed

services.
Scores from the ten subtests of the current ASvAB are comoined into Composite

scores in several different ways. 0fne comoination, the Armed Forces Qualification Test (AFQT' is used by all the services for the init..l selection

of personnel. The other composites serve as the basis for assignment of

personnel to particular jobs or training slots. A minimum qualifying score

on one of the aptitude area composites is required for admissiwn to the Army initial level training courses. For example, the combat (CO) composite is

used to classify recruits into the infatitry and armor specialties.

The nine aptitude area (AA) composites now being used to classify Army personnel have oeen in place for over ten years (Maier & Fuchs, 1973). The

composites were developed empirically, first by clustering Army jobs or

Military Occupational Specialties (MOS) based upon their content, and then

by using forward stepwise regression, with success in training as tne criterion, to select the variables or subtests to be included in the composite

score. These subtests were then given unit weights for operational use.

When the ACB-73 was replaced by ASVAB 6/7, the Army decided to retain the AA

composites that had been used with ACB-73. This decision resulted from the

pressures to implement the service-wide test battery as soon as possible,

and the decision was considered practical because of the high similarity

among the subtests of the two batteries.

when ASVAB 8/9/10 became operational, there were substantial changes in the

battery, particularly tne deletion of two of the subtests and the interest

inventory. The direct transfer of computational formulas for the composites

was not possitle. Trhe situation #ad been furtner complicated by a change in

Army training testing procedures. In the mid-1970's, the Army adopted a

criterion-referenced model for its training proficiency tests, and also convtrted many courses to a self-paced mode. While such procedures are appro- priate as measures of training success, they do not generally produce data

tnat are well-suited for validation research. Specifically, the criterionreferenced model typically oroduces test scores that are either pass/fail or

have a very limited range of values, all of which denote acceptable performance. Adaitional information on the time taken to completion, or the numoer

of attempts prior to success, would be far more useful for validation

analyses.

For these reasons Maier and Grafton (1981) used Skill Qualification Test

(SQT) scores in developing the new composite formulas for ASVAB 8/9/10,

Dased on an empirical validation in 19 MOS. The SQT had been implemented

operationally in 1976 to measure job proficiency in a large numoer of Army jobs. Unlike the criterion-referenced training tests, the SQT yields continuous scores as well as pass/fail information and, tnerefore, provides a

more acceptable criterion measure for validation research. Maier and Grafton

found that when SQTs were used as criterion measures, it was possible to

compute a set of scoring formulas for the nine AA composites yielding validities that were adequate to justify their operational use for the selection

and classification of recruits. The set of AA composites developed by Maier

and Grafton are given below:

- ~ -- -
Table I

OperationaT Composites Currently in Use by the Army

Composite Subtests

Clerical/Administrative CL (Vr NO+CS)

Combat CO (AR+CS+AS+MC)

Electronics Repair EL (GS+ Mk+W+El)

Field Artillery FA (AR CSMK+MC)

ueneral Mainternance GM1 (GS+AS+MK EI)

Mechanical Maintenance MM (NO+AS+MC+EI)

Operators/Food OF (VE+NO+AS+MC)

Surveillance/Communicatiors SC (VE+NO CS+A5)

Skilled Technical ST (GS+VE+MK+MC)

VE (verbal ability) is a combination of the word knowledge (WK) and paragrapn

comprehension (PC) suotests. These composites are now being used by tne

P7iruy on an operational basis. 'Iaier and Grafton dio not InveStIgate alternative groupings or clusters of the MOS to be predicted by these composites.

As new MOS have been created, they have been assigned area composites basea

on rational judgments. Otherwise, the assignment of area composites to MOS

have remained the same since the development of A6B-73.

There are two otner sets of composites not now used operationally by the

Army that are routinely computed from ASVAB. The first of these composite

sets is used by the Air Force to select and classify potential enlisted personnel. The Air Force currently uses four ASVAB composites. They are

Mechanical (M), Administrative (A), General (G), and Electronic (E). Collectively they are referred to as tne MAGE composites.

Anotner set of ASVAB composites nas been developed for use when ASVAB is

administered o high school students as a career guidance tool (US Military

Enlistment Processing Command, in press). This set includes a composite for

Mechanical Trades, for Office and Supply, for Electronics/Electrical, for

Skilled Services, and for Academic Ability. Maier and Truss (1983) have

recommended that the first four of these high school composites be used by

tne Marine Corps as the basis for enlisted personnel selection and classification. The composition of botn the MAGE ard tne High School composites is

presented in Taole 2. Since these Composites are already in operational use

within DoD, it is important that they be considered by the Army as it considers a change to its personnel allocation system.
Table 2

Other Composite Systems

MAGE Composites

Mechanical M MC + AS + GS

Administrative A VE + NO -t CS

General G AR + VE

Electronic E AR + MK + GS + El

High School Composites

Mecianical Trades HSMT AR + MC + AS El

Office anC Supply HSOS VE + CS + MK

Electronics/Electrical HSEE AR + El + MK + GS

SKilled Services HSSS AR + VE + MC

Acaaemic Ability HSAA AR + VE

A comparison of these tnree tables reveals tne following relationships. The

CL composite in operational use by the Army is known within the MAGE system

as Administrative (A). Also, the EL composite (Army) is tne Electronic composite in MAGE and also the Electronics/Electrical composite in the High

Scnool system. In adoition, ootn MAGE and the High Scnool systems use the

(AR + VE) combination. In MAGE, it is known as the General composite ana it

also appears as tne Academic Ability composite in the High School set.

4
Purpose and Objectives

The Army Research Institute (ARI) is currently in the midst of a la-ge-scale

research effort to improve the methods the Army uses to select and classify

enlisted personnel. This rusearch includes the development of both iew predictor variables and new criterion measures. As part of that effort, the

research reported here assessed the validity of the present predictor Cattery

as measured by the currently available performance criteria. The validilies

of the operational ASVAB 8/9/10 composites were computed and compared to

alternative sets of empirically developed composites. In addition, the differential validity of the composites was assessed; and finally, the possibility of predictive bias was investigated.

The major steps in this effort are described below.

First, the validities of the current operational AA composites were carefully

examined using both training grades and SQT scores as criteria. Next, an

alternative set of AA composites was empirically derived. The possibility

of a reqrouping of the MOS to be predicted by single composites was considered, as well as the selection of different subtests to form that composite.

Tne alternative set of composites was compared to the operational set in

terms of overall predictive validity, differential validity, nd predictive

Dias.

This work proviles the basis for possible ARI recommendations 'or changes to

the current set of composites, potentially implemented beginning with ASVAB

11/12/13. Finally, on the basis of these validation results, a clearer

unoerstanding emerges as to the limitations of ASVAB and the current criterion measures in the development of improved selection and classification

proceaures for the Army. The areas that show the most promise and need for

further research are identified.

In order to accomplish these goals, the following objectives were set:

(1) To collect predictor and criterion data from existing Army

and DoD sources, and to edit and check these data;

(2) To adjust the data for range restriction and violations of

model assumptions;

(3) To assess the predictive validity of the current operational A composites, using training grades and SQT scores as

criteria;

(4) To construct a set of homogeneous M0S groups on the basis

of predictor-criterion relationships;

(5) To derive the best set or ASVAB subtests to predict performance for each MOS group using multiple regression;

(6) To cross-validate the procedures used in steps 4 and 5;
(7) To investigate the effects of moderator variables for both

sets of composites in order to determine the appropriateness of

using a common composite for all applicant groups;

(8) To estimate the value to the Army of adopting the alternative set of composites in terms of classification efficiency,

increased validity, and cost savings; and

(9) To generalize the results obtained from the MOS with sufficient data to be included in these analyses to all MOS.

6, , \
Description of Data

Overview

The primary use of ASVAB and the composite measures it provides is in the

initial selection and classification of enlisted personnel in the armed

forces. As such, the value of ASVAB is based on its ability to predict (1)

which recruits will perform well and (2) in which occupational specialties

these recruits will perform best relative to other recruits. Therefore,

rational validation of current area composites and identification of alternative area composites relies on information about the relations between

performance on subtests of the ASVAB and later criterion performance. This,

in turn, requires a data base containing both ASVAB scores and valid and

reliable criterion measures for a large, representative sample of Army

enlisted personnel.

Samples

The present validation is based on 29,160 soldiers with training performance

scores and 65,193 SQT records. The sample consisted of all appropriately

screened enlisted accessions in the period fr.m 1 October 1980 to

30 Septemoer 1982, referred to as the FY81/82 cohort. (Prior service

recruits and delayed entry accessions who had not taken ASVAB 8/9/10 were

eliminated from consideration.) This represents a sizeable portion of the

274,220 accessions in tnat time period and covers all major skill areas in

the Army. Analyses based on training outcomes drew upon data from 81 Military Occupational Specialties (MOS); analyses based on SQT scores covered 68

MOS. Of these MOS, 46 were in common to the two sets of analyses. Appenaix

Table A-1 contains a complete list of MOS in the Army.

Information on race and gender was included in the analysis file in order to

assess possiole predictive bias that would result from using composites based

on analyses ignoring these variables. This information was extracted from

Applicant Data Files and Enlisted Manpower Files and edited and recoded to

ensure accuracy. Analyses investigating the possibility of predictive Dias

by race were limited to a comparison of the procedures for black and white

applicants because other minority groups were not present in sufficient numoers in enough MOS to support analyses. For example, there were only ten

MOS in which there were sufficient numbers of Hispanics to support analyses.

Such a sample of MOS was ceemed insufficient to generalize to all MOS in the

Army.

From the file of accessions in the FY81/82 cohort, 77,520 were selected for

analysis. These soldiers possessed criterion data, took ASVAB 8/9/10, and

had no prior service. Of these, 70,829 were in "criterion cells" with at

least 100 soldiers each. A soldier with both training and SQT scores, or

SOT scores in different cells, was used for estimation of statiitics in each

of those cells.

For the training data, a criterion cell was defined as an MOS, school, and

course combination. This level of differentiacion was used because tests

used and scores reported in differ2nt courses within an MOS possessed

7(, )
significantly different characteristics. For the SQT data, a criterkon cell

was defined as an KOS, track, and SQT-year combination. Soldiers in different tracks in an MOS performed different tasks, and SQT forms differed by

year. The data for the criterion measures are described in greater detail

below.

In addition to the primary analysis file, the FY81/82 applicant file was

constructed for use in some of the analyses. This file of applicants was

extracted for the FY81/82 cohort, which because of delays between application and acceptance, differed slightly from the accessions cohort. A total

of 28,981 of the 214,220 accessions (10.6%) during FY8l/FY82 had applied and

signed a contract prior to October 1980. Virtually all of these recruits

took an earlier form of the ASVAB and were therefore excluded from the analysis sample on that basis. Thus, the slight difference in cohort definition

was considered inconsequential for purposes of these analyses.

A sample of 19,027 FY81/82 applicants was drawn and used as the basic population to which the validities would be adjusted and as the sample on which

alternative composites would be validated for selection. The sample was

drawn by first concatenating the FY81 and FY82 applicant files and then systematically sampling within cohort-years. A separate sample of 13,319 applicants was later used for norming the alternative composites and setting tentative cutoffs.

Because separate validations were required for each MOS included in the analyses, a minimum requirement was imposed on tne size of sample in each MOS.

This minimum sample size would determine the minimum difference between

validities which could be reliably assessed. Two types of comparison are

critical: comparison between different composites for the same individuals,

and comparison of tne same composite for different cultural groups. For tne

former, the estimation of a minimum sample size was further complicated by

tne expectation of correlations between the composites. Finally, the need

to generalize to the entire population of MOS was recognized, and this necessitated setting the minimum sufficiently small to include a representative

sample of MOS.

The selection of a minimum sample size of 100 served as a compromise.

First, assuming a correlation of .90 between two composites, each with a

validity of approximately .50, this allows for reliable identification of

differences of .08 between their validities. Second, between two groups for

which the validity of a composite is roughly .5, one with a sample size of

100 and the other four times larger, a difference in validities of about .15

can be treated as significant. The estimation of the sample size needed for

reliable cluster analysis was deferred until cluster analyses were run, at

which time the stability of analyses between half-samples could be examined.

The selected sample size permitted the inclusion of 98 MOS in the final

comoined-criteria validations and 35 and 19 MOS in the investigation of

predictive bias for race and gender, respectively.

In the following sections, we briefly describe the ASVAB measures and the

two criteria used. Finally, we describe the method by which the two criterion measures were combined.

8 3i
ASVAB Data

As described above, the Armed Services Vocational Aptitude Battery has

evolved over several decades. The version currently in use, Forms 8, 9, and

10, consists of ten subtests. The subtests, testing time, and reliabilities

are given in Table 3. The XR-2O reliabilities for the power subtests are

from Ree, Mullins, Mathews, ano Massey (1982). They were obtained from a

sample of 19,359 applicants who were tested at twenty AFEES. Alternate forms

reliabilities for the speeded tests, NO and CS, obtained from Sims and Hyatt

(1981), are also presented.

Table 3

ASVAB 8/9/10 Subtests

Subtest Testing Time (min) Reliability

GS General Science 11 .86

AR Arithmetic Reasoning 36 .91

PC Paragraph Comprehension 13 .81

WK Word Knowledge 11 .92

NO Numerical Operations I .78

CS Coding Speed 7 .85

AS Auto Shop Information 11 .87

MK Mathematical Knowledge 24 .87

MC Mechanical Comprehension 19 .85

El Electronics Information 9 .82

Two subtests, PC and WK, are ordinarily combined to form VE, a more general

verbal ability subtest.

In the fall of 1980, form 8 of the ASVAB was administered to a national

sample of American youth that was weighted to be a representative sample of

American 18-23 year old males and females. In 1983 the Department of Defense

adopted this population as the reference population for constructing the

ASVAB score scale and determining the intercorrelations and standard deviations of ASVAB subtests. This population is referred to as the "1980 Reference Population.0 It is noted, however, that this population has not yet

been used to norm the ASVAB. ASVAB 8/9/10 was normed using the AGCT.

The correlations among the subtests for the 1980 Reference Population, for

the FY8l/82 applicants, and for the accessions on the analysis file used for

the validation (pooled within "criterion cell"), are shown in Table 4.

f~
Standard deviations are shown on the diagonal. While the intercorrelations

in the 1980 Reference Population and the FY81/82 applicant sample show good

agreement, the pooled-within group correlations are substantially lower.

This is no doubt due to the restriction of range that results from the the

selection and classification process.

The subtests of the ASVAB have relatively high intercorrelations in the

Reference Population and in the FY81/82 cohort populations. As a result,

composites based on sums of tne subtest scores tend to have increased reliability as selection instruments. For the purposes of classification into

separate MOS, however, this intercorrelation may be detrimental. If we

assume that different skills come into play in achieving proficiency in

different MOS, then efficient classification requires that these be measured

by the instrument used for assignment to MOS. Although four factors have

been identified in the ASVAB by Kass, Mitchell, Grafton, and Wing (1983),

(Quantitative [AR and MK], Verbal [VE and GS], Speed (NO and CS], and Technical [AS, MC, and El]), the first principal component accounts for 60% of

the variance, and the second for another 15%. Thus, the number of dimensions

effectively measured by ASVAB may not be ideal for predicting soldier effectiveness in a substantial number of MOS.

The use made of the ASVAB for selection and classification is through comparison of composites to cutoffs. Ten composites are currently in use by

the Army, the Armed Forces Qualification Test (AFQT) and nine vocational

aptitude area composites. The latter nine are the focus of this validation

effort: to assess their validity as predictors of performance in the Army

and to identify a set of alternative composites.

Applicants must score above the cutoff on the AFQT to be considered for

entry into the Army, but they must also score above preset cutoffs on the

area composites to be eligible for corresponding MOS. Applicants who desire

an MOS ana are otherwise qualified for the Army and who score above the cutoff for the MOS are accepted, if there is an opening in the MOS. If there

is no current opening, they are frequently accepted for "delayed entry."

(It should be noted that there are other reasons for delayed entry.)

Each of the area composites is equated to a mean of 100 and a standard deviation of 20 in the 1944 reference population using an equipercentile procedure. Oitn a few exceptions, each M0S is associated with one of these composites. A few MOS, such as members of the Army band, are evaluated in terms

other than ASVAB 8/9/10, and in 22 cases, two composites are recognized as

requirements. A single composite was used for the five of these 22 MOS

included in the analysis. The relative proportions of FY81/82 accessions

entering M0S associated with each of these composites is shown in Table 5.

The cutoffs are normally set at multiples of five, ranging from 80 to 120,

and they are subject to revision in response to changing demands of the Army

for different occupational specialties.
Table 4

Correlations and Standard Deviations among the Nine ASVAB Subtests

S AR WE to CS AS %KMc E

GS 10.01 7.7

AR

.722 .45 3 IC.0 7.4

VE .82 .71 10.2

.80 .70 .73 .46 10.3 6.8

.43 .!S .51 10.2

.52 .02 .63 .ZB A .6 ?.0 10.0 7. 1

CS .37 .46 .48 .65 9.4

.41 .A24 .5! *!15 .!? .^.I . .45 4"1 7.:

AS .64 .!5 .60 .28 .23 10.0

.64 .So .53 .35 .S2 .45 .30 -.06 .22 -.02 10.0 7.9

.53 .77 .65 .54 .44 .43 9.0

.69 .41 .83 .66 .70 .41 .62 .31 .52 .21 .41 .23 10.0 I.s

LW .68 .66 .64 .36 .32 .71 .59 9.2

.70 .51 .69 .47 .60 .45 .40 .00 .34 .05 .74 .55 .60 .41 10.0 7.3

: 70 .60 .67 .34 .29 .73 .54 .71 9.2

.76 .!6 .66 .38 .57 .52 .41 .. 0? .34 .01 .7S .59 .S9 .:2 .74 .!] 10.0 7.3

1 Correlations and standard deviations for the FY81/82 Applicant Sample.

2 Correlations and standard deviations taken from 1980 reference

population.

3 Correlationb and standard deviations for the Analysis File used for the

Validation (pooled within Criterion Cell).
Table 5

Distribution of FY81/82 Accessions by Cluster

Current Aptitude

Area Composite Percent

CL 14.7

CO 20.2

EL 9.5

FA 5.7

GM 5.2

MM 10.0

OF 9.3

SC 5.1

ST 14.0

BlanK 6.0

TOTAL 100.0

Training Data

The Army Research Institute gathered end-of-course test scores on soldiers

in 172 MuS at 23 schools during 1981 (Dept. of Army, 1981). The type of

test score varied qualitatively between courses, and for the purposes of

tnis validation, they were extensively edited. A memorandum describing the

editing of training data for this task has been prepared (Wang, 1983) and is

reproduced in Appendix B. In some cases, two scores were available for a

single individual, based on different courses; and in some cases, a meaningful criterion score had to be computed as the ratio of two numbers on a

record (e.g., number of tries divided by number of tests). For these cases,

wang (1983) also gives the procedures we used in determining a training criterion scere for the analyses.

Unfortunately, we were unable to obtain data on the reliability or validity

of the end-of-course training scores. These tests were made up at the

Schools for the purpose of testing whether the students had learned wnat had

been taught, and in most cases they were criterion-referenced. That is,

passing students were expected to perform nearly perfectly. Thus, the high

frequency of scores close to 100 is not surprising. In a few cases, the

scores were the number of ti:2s the student had to take and retake a test in

order to pass.

12
In addition to the scores, qualitative information on the outcome of training

was available, and this was used in screening the data. For attritions, a

reason for attrition was given, and scores for non-academic attritions were

highly suspect and therefore deleted. Validations were limited to (1) graduates, including graduates going on to further training, and (2) non-graduates for academic reasons.

For many soldiers, multiple training records were available, based on separate training events. Because of the unknown effects of one training course

on another and because of unknown differences in the types of learners who

take multiple training courses, a single training event was selected for

analysis for each student. This was the earliest one for which (1) a training score was present and (2) the event was eligible for analysis (i.e., not

a non-academic failure).

There were a few records for which the score was missing anci a few more that

were set to missing after identification as outliers. The missing scores

were imputed according to the following rules: (1) for graduates, the mean

of graduates in that cell was imputed; and (2) for (academic) non-graduates,

the value imputed was equal to the first quartile score of the graduates

minus one interquartile range 1(Q3 - Ql)/2] among graduates. The first

quartile was used in lieu of the minimum score on the file in order to preve.t erroneous data entries from influencing the imputed value. Inspection

of tne univariate plots by training cell strongly suggested that some of the

low scores for graduates may actually be data entry errors. For example,

several of the large MOS in the CO cluster contained values of 9 for graduates. It is very likely that the correct scores for these soldiers are in

the 90's, since this is where the great mass of the data for these MOS is

found.

Only seven scores for graduates were imputed; the great bulk of the imputation was for non-graduates. The rule as stated above was carried out for

1,604 cases. In two Training cells, corresponding to MOS 15D and 15E, only

four and five distinct values were used, and were equally spaced in the range

0 - 100. Therefore, the lowest non-zero score was imputed for these two

cells. Ten and four scores were imputed in these two cells, respectively.

Our attention was especially focused on the cells with at least 100 observations, because these were cells for which reasonably stable estimates of

relations between ASVAB subtests and the criteria could be generated. There

were 92 such "training cells," or combinations of MOS. school, and course.

The raw scores ranged from 0 to 100 except for a few cells in which the sign

was reversed. Table 6 presents summary descriptive statistics by Training

cell. In 33 of the 92 cells, the first quartile score was greater than 85,

although the average minimum for these 33 cells was 61. In some other cells,

the scale from 0 to 100 aas merely a crude standardization of a short test

(e.g., scores of 0, 25, 50, 75, and 100 for a four-item test). Finally, the

scores were transformed to a mean of 100 and standard deviation of 20 in

each training cell.

13 ( .
Taole 6

Descriptive Statistics on the Training Criterion

by Criterion Cell

WELL N ROME ST QRA)GZ MIN 01 MEDIAN 6XX Q3 

0582A113 519 91.9S 100.00 9.2S s.SO s6.oo 86.00 95.0 99.00 100.00

osC2D13 613 51.19 90.00 s.90 3.S0 63.00 85.00 8s.0 92.00 100.00

LIBN09 974 94.26 97.00 4.98 3.SO 73.00 90.00 93.0 97.00 100.00

11CIN8o9 s73 93.Y4 97.00 s.S 3.SO 70.00 90.00 93.0 97.00 100.00

11HN809 444 93.89 97.00 S.33 3.SO 70.00 90.00 93.0 97.00 100.00

1101m309 324 94.S 97.00 4.4S 3.00 77.00 93.00 97.0 97.00 100.00

121AS507 143 80.80 9S.00 1S.24 12.00 35.00 71.00 34.0 9S.00 100.00

12761307 224 87.73 94.00 10.64 4.s0 50.00 81.00 55.0 94.00 100.00 1333B310 1030 73.64 53.00 20.09 5.00 17.00 67.00 33.0 83.00 100.00

13E3mo10 483 96.32 100.00 7.01 0.00 70.00 100.00 100.0 100.00 100.00

1373i10 679 83.64 13.00 5.29 4.00 69.00 79.00 33.0 87.00 98.00

ISDSDI1O 29S 73.97 30.00 22.56 20.00 20.00 60.00 $0.0 100.00 100.00

ISEStSIO 253 92.16 100.00 18.32 12.S0 25.00 ?S.00 75.0 100.00 100.00

162BB&11 165 92.S0 92.00 3.41 2.SO 7S.00 90.00 92.0 9S.00 100.00

1628C313 131 91.71 97.00 3.01 2.SO 56.00 93.00 97.0 96.00 100.00

14CCA411 1%* 03.70 92.oa 4.40 2.63 76.00 91.75 95.0 97.00 99.00

IGDDB811 112 95.19 96.00 3.24 2.38 35.00 93.00 96.0 97.75 100.00

14E28311 137 99.81 100.00 1.58 0.00 65.00 100.00 100.0 100.00 100.00

16HHB45811 105 51.49 35.00 5.78 3.50 70.00 63.00 86.0 90.00 95.00

16.33511 119 91.42 93.00 4.01 2.00 78.00 90.00 92.0 94.00 98.00

14PPA811 115 75.15 88.00 19.00 15.00 24.00 S8.00 82.0 S6.00 97.00

16RRA11 407 90.30 37.00 3.06 4.00 40.00 87.00 92.0 9S.00 100.00

14SSA611 596 77.59 6S.00 9.21 6.50 43.00 72.00 75.0 55.00 93.00

17C7CO41 18 92.23 100.00 10.93 3.SO SO.00 53.00 100.0 100.00 100.00

17KG6)01 136 77.92 75.00 17.S0 9.75 2S.00 63.SO 7S.0 33.00 100.00

19D9D904 21S 39.16 100,00 10.14 7.S S4.00 8S.00 92.0 100.00 100.00

19EVE804 171 80.85 8S.00 10.49 9.00 SO.00 72.00 33.0 90.00 100.00

1919F804 126 S.SO 90.00 8.36 s.00 54.00 80.00 85.0 90.00 100.00

27E7E093 184 56.77 0.00 C.IS S.38 60.00 61.25 8.0 92.00 98.00

31M4D113 604 91.50 56.50 5.23 4.75 74.00 04.10 92.0 96.00 100.00

3114C1L3 193 97.63 100.00 2.7S 2.00 54.00 96.00 99.0 100.00 100.00

31VIV061 4S7 39.67 100.00 6.0 9.7S 40.00 10.50 93.0 100.00 100.00

31CAA113 376 97.32 100.00 3.29 2.00 82.00 96.00 91.0 100.00 100.00

34KAC113 660 94.42 96.00 4.13 2.S0 78.00 92.00 9S.0 97.00 100.00

41CG7091 101 -1.20 -1.14 0.18 0.09 -2.25 -1.27 -1.1 -1.09 -1.00

443J1091 137 -1.5s -1.71 0.17 0.13 -1.93 -1.65 -1.5 -1.43 -1.00

4SXK1091 101 -1.43 -1.33 0.25 0.17 -2.33 -1.40 -1.4 -1.25 -1.00

4SKKO91 129 -1.63 -1.43 0.32 0.19 -3.00 -1.58 -1.6 -L.5c -1.00

SIKBKI07 167 91.40 6$.so S.99 3.7S 66.00 66.S0 92.0 96.00 100.00

S4CSSO31 13 92.66 96.00 6.25 S.4S 47.S8 84.20 94.0 97.50 100.00

5486A031 272 75.09 63.09 12.46 13.37 53.45 63.09 12.0 09.64 98.48

SS5093 234 S.SS 60.00 4.34 4.00 71.00 51.00 35.0 $9.00 98.00

S73P9101 123 97.73 99.00 2.00 1.00 91.00 97.00 98.0 99.00 100.00

S7HQISS1 224 96.6s 34.16 3.3S 2.03 74.57 04.84 36.9 19.00 94.26

10 6SS 13t 79.39 96.00 10.60 .50 43.00 72.00 32.0 69.00 97.0

41CHISS 136 81.1S 87.00 7.67 S.SO 59.00 76.00 32.0 37.00 95.00

(1o4t'4)

14 /
Table 6

Descriptive Statistics on the Training Criterion

by Criterion Cell (Continued)

TCBLL m MEAN NODE STD QRANGE MIN 01 MEDIAN Q3 MAX

628CS807 244 0.61 93.00 6.67 S.SO 70.00 36.00 91.1 97.00 100.00

62ECE107 133 84.17 77.00 S.74 S.2S 74.00 78.S0 84.0 19.00 97.00

62FCFi07 149 1S.14 77.SO 7.32 4.7S 41.00 77.S0 6.0 91.00 96.00

6303103 SS9 47.1S 12.00 S.76 4.00 64.00 3.00 $8.0 91.00 100.00

6383101 331 9S.19 96.00 3.41 2.SO 62.00 93.00 96.0 91.00 100.00

63DSA171 342 91.93 100.00 2.11 0.00 90.00 100.00 100.0 100.00 100.00

63097091 161 -1.14 -1.00 0.13 0.07 -1.S7 -1.21 -1.0 -1.07 -1.00

63HH1091 706 -1.07 -1.00 0.06 0.04 -1.31 -1.10 -1.0 -1.02 -1.00

63NTS171 S09 99.04 100.00 1.64 1.00 92.00 96.00 100.0 100.00 100.00

63T7F1171 172 97.03 100.00 S.OS 1.S0 73.00 97.00 100.0 100.00 100.00

63WM1011 481 -1.13 -1.00 0.12 0.10 -1.40 -1.20 -1.1 -1.00 -1.00

63YTV171 17S 96.03 100.00 2.39 2.00 90.00 96.00 100.0 100.00 100.00

64CECO07 202 61.23 IS.00 4.34 2.SO 70.00 80.00 83.0 1S.00 1S.00

44C4C603 161 91.61 94.00 S.90 4.10 74.00 84.00 91.0 97.00 100.00

67N64011 163 90.7S 91.00 4.3S 3.00 30.00 39,00 91.0 94.00 96.00

67TL6SSL 124 37.43 92.00 1S.0 4.S0 72.00 63.00 B3.s 92.00 99.00

67UP11 210 90.89 96.00 7.61 4.00 60.00 35.00 92.0 96.00 100.00

67V1S011 194 90.3S 91.00 S.08 3.50 71.00 8.00 92.0 9S.00 99.00

67YS1111 144 12.74 76.00 6.20 S.SO 63.00 77.00 33.0 36.00 96.00

468TS1S1 121 84.39 S1.00 S.26 4.00 70.00 31.00 85.0 69.00 97.00

69JW6651 120 15.79 36.00 6.32 3.88 66.00 82.25 $6.0 90.00 97.00

68mWS11 134 1.S2 90.00 3.32 3.7S S.00 82.7S 93.0 90.21 93.00

71NLISSI 17S 13.09 69.00 6.93 1.00 SO.00 79.00 84.0 89.00 98.00

72V30113 143 93.42 100.00 2.31 1.00 82.00 96.00 99.0 100.00 100.00

73CSR121 202 93.44 99.00 6.43 3.SO 69.00 91.00 96.0 93.00 100.00

755121 494 i.30 11.00 11.71 7.13 3S.00 71.75 88.0 94.00 100.00

710SD0S 233 12.S7 71.00 9.40 9.SO 70.00 71.00 64.0 90.00 99.00

7Z13810 276 81.12 74.00 7.17 .SO 70.00 74.00 60.0 37.00 97.00

76CEC101 1142 3S.99 90.00 6.63 S.00 40.00 31.00 37.0 91.00 99.00

76PSF101 560 16.92 33.00 S.09 4.00 71.00 33.00 37.0 91.00 93.00

76VEV101 401 0.98 1.00 0.03 0.01 0.18 0.97 1.0 1.00 1.00

76kD3o1o 131 92.26 91.00 S.14 3.10 75.00 69.00 94.0 96.00 100.00

76WPWIO 344 92.26 93.00 4.17 Z.SO 73.00 90.00 93.0 9S.00 99.00

761SX10 1s3 93.91 94.00 2.94 2.00 32.00 92.00 94.0 96.00 100.00

76YRY101 331 0.92 1.00 0.07 0.06 0.70 0.39 0.9 1.00 1.00

7SYS0101 293 90.04 37.00 4.94 3.SO 77.00 87.00 89.0 94.00 100.00

76YGYI0S 470 90.93 100.00 3.S 3.SO 69.00 03.00 92.0 100.00 100.00

12C2rq10 390 61.76 40.00 17.S1 13.63 20.00 47.75 61.b 7S.00 96.00

91301929 76) 94.63 100.00 6.69 4.00 34.00 92.00 97.0 100.00 100.00

91CO2929 233 39.92 94.00 7.69 4.00 11.00 97.00 92.0 9S.00 100.00

91301929 162 33.62 93.00 6.33 3.63 42.00 1.715 90.0 93.00 100.00

922S929 133 63.39 64.00 3.12 3.7S S2.00 I1.10 1S.0 89.00 97.00

943KA101 627 16.37 86.00 1.63 1.00 31.00 3S.00 46.0 17.00 91.00

94343503 237 36.63 90.00 6.91 S.00 70.00 33.00 87.0 93.00 100.00

943431 414 93.S6 94.00 1.32 1.00 69.00 93.00 94.0 9S.00 97.00

9535313 721 81.33 62 67 S.13 3.06 60.00 73.SO 32.0 34.67 91.33
SgT Data

Since 197, The Army has administered the Skill Qualification Test (SQT to

enlisted soldiers. The purpose of these tests was to assess individual

qualifications for promotion and to evaluate the overall effectiveness of

Army training programs. Each year, a separate SQT is constructed for each

MOS and skill level (with the exception of a very few exempted MOS). In

many cases, alternative forms are constructed for the same MOS and skill

level corresponding to different *tracks" within that KOS. An SQT "track"

corresponds to a specialized job within an MOS. Most commonly, separate

tracks correspond to different types of equipment to which soldiers in the

MOS might be assigned. In some other instances, the different test tracks

correspond to additional responsibilities for selected individuals within

the MOS such as Special Forces. Soldiers in different tracks within the

same MOS, in general, take different SQT tests. A list of MOS with multiple

SQT tracks in FY82 is contained in Appendix Table A-2.

Eacn test assesses a soldier's ability to perform tasks specified in the

Soldier's Manual for the co;responding MOS and skill level. Anywhere from a

dozen to several dozen tasks are selected each year for testing. Announcements are prepared and distributed identifying the specific tasks to be

tested, allowing soldiers opportunity for preparation. In the past, both

hands-on and written assessment procedures were used in determining a passfail score for each soldier on each of the tested tasks. For the period of

data covered in t#is analysis, only written measures were used. A number of

multiple choice items were constructed for each task. The nunoer of items

varied from as few as 2 or 3 to up to 9 or 10. A passing score was set

requiring the soldier to correctly answer all or nearly all of tne multiple

choice questions in order to pass the task. The soldier's total score is

tnen the percentage of items passed, averaged across tasks.

Major changes in the SQT program were introduced in 1983. At tnis time, the

SQT oecame one component in the Army Individual Training Evaluation Program

(iTEP). The other components include a new Common Task Test (CTT) 3nd the

Commander's Evaluation. The SQT was changed to include only MOS-specific

tasks, because tne common soldier's tasks were to be tested separately in

the CTT. in addition, most of the tracking of tests within MOS and skill

level was dropped.

The different SQT forms assess different tasks and are not precisely equated

with respect to difficulty. Procedure,; for more precisely equating alternative forms are being studied for 1984. As a consequence, it is essential

that each form be standardized separately.

For many soldiers, multiple SQT test results were available, either for different MOS, different tracks, or different SQT-years. Because taking an SOT

test was not considered to have a major influence on future skills, multiple

SQT scores for an individual appearing in different cells were treated as

separate events, each in its own MOS/track/SQT-year 'cell.* There were 112

cells with at least 100 records, and these covered 68 MOS.

The rationale for including the multiple records for an individual when they

were in different cells is as follows. The criteria are regarded as proxies

16
for an underlying and unobserved indicator of performance throughout a soldier's career. Under this assumption, multiple records give a more complete

sampling of performance over the entire career. It was not within Lhe scope

of the present effort, however, to take the further step of combining the

measures into a single time-weighted average of performance for each individual. Such a refinement would be appropriate when utility of performance data

are availaole.

Although the SQT forms were professionally developed, reliability and validity information on these forms could not be obtained. !'.em statistics sufficient for the derivation of inter. al estimates of consistency were not

availaole.

The SQT scores, like the training scores, were transformed to a mean of 100

and standard deviation of 20 for analyses. Prior to the standardization,

tie SQT scores ranged theoretically from 0 to 100, and 60 was considered a

nominal passing score. However, the majority of the scores were greater

than 85, and many were 100. Table 7 presents summary descriptive statistics

for the SQT criterion score.

Trimming of Outliers

As noted in the discussion of the training data, several very implausible

values remained on the file following the imputation. This was also observed

for the SQT data. Therefore, a method of trimming these outliers was

employed.

After completing the editing and imputing or the Training and SQT scores,

OLS regressions of each criterion on the ASVAB subtests were run within

Training and SQT criterion cells to identify outliers. Residuals from the

regression lines as well as several so-called "influence diagnostics" were

examined. On the oasis of this examination it was decided to delete all

cases from the file for which te regression residual was greater than four

standard deviations. For the training criterion cells, the standard deviation of the graduates was used. For the Training data, 272 data onints were

deleted; for SQT, 247 values were removed. The major effect of this trimming was to eliminate implausible scores that likely were the result of data

entry errors.

Comoined Criteria

The main validation analyses were undertaken separately for the two sets of

criteria with the understanding that if the results were to differ substantially, a method for combining the results would -e needed. Three major

alternatives were considered:

(1) Select one of the criteria, if evidence indicates that it

is clearly better than the other, either on psychometric

grounds, on reasonableness of the results of the validation, or on so-iple size and coverage of the space of MOS;

17
Table 7

Descriptive Statistics on the SQT Critericn

oy Criterion Cell

PCELL N m?" MOE STD Q(ANGE MINIMUM g MEDIAN Q3 MAXIMUM

os01132 6L3 77.1S 64.00 11.20 3.59 31.58 70.0! 80.00 34.00 0oo.o

0SCOe2 1197 76.05 30.77 9.65 6.37 36.64 70s9 76.92 13.33 96.15

05C0183 11s6 73.17 75.00 9.36 6.61 42.56 66.#9 73.46 00.00 100.00

0500133 119 75.96 72.72 9.06 6.56 40.35 69.68 76.41 32.61 92.39

05H1183 110 i6.94 91.96 7.61 4.S6 SS.30 62.27 68.36 91.93 96.75

113 1e1 606 60.40 08.00 3.16 6.00 57.00 34o00 89.00 96.00 CO.00 1180163 423 55.48 40.30 11.20 7.04 17.25 46.06 54.44 62.14 94.42

11BIL32 3896 S6.74 92.36 8.32 5.36 SO.00 62.14 39.20 92.86 100.00

11B 2 611 07.11 39.29 7.6o S.36 57.14 32.14 39.79 92.66 100.00 1136162 419 37.74 92.36 7.56 S.36 57.14 82.14 39.29 92.66 100.00

11B7182 226 66.97 92.66 7.90 S.36 64.29 32.14 39.29 92-86 100.00

lic lot 171 8.04 96.00 3.61 7.00 s2.CO 32.00 39.00 9L.00 100.00

11Co013 112 59-:7 57.42 14.56 9.43 10.00 s1.10 60.09 69.9s 94.44

11c1112 sss 9. 10 92.5r T .04 s.s5 51.85 8S.19 92.31 96.30 100.00

IC2i#2 186 33.49 92.59 3.33 S.55 ss.56 85.19 63.89 96.30 100.00 11C4182 246 88.0? 86.89 9.41 5.55 52.17 85.19 88.8 96.30 100.00

11C5182 217 39.67 38.89 7.60 S.SS 64.71 25.&9 63.J9 96.30 100.00

11H 161 124 85.25 38.00 6.56 5.88 57.00 80.25 36.00 92.00 100.00

11HI142 442 07.24 92.59 8.36 5.56 5.94 81.40 88.89 92.59 100.00 11H2182 321 39.14 92.59 5.92 3.70 69.57 85.19 88.89 92.59 100.00

1250193 1976 92.35 95.83 5.79 4.16 68.75 87.50 91.67 95.83 100.00

1231132 1103 37.04 90.00 7.77 S.00 51.00 63.00 39.00 93.00 100.CD

12F0102 175 36.69 88.39 7.48 5.56 59.26 81.48 88.89 92.59 100.00

12COJ83 271 91.94 41.67 6.01 4.16 70.83 87.50 91.67 26.83 100.00

135 131 130 35.13 92.00 9.00 3.00 6C.00 80.00 S4.50 92.00 100.00 13BO183 3902 72.41 72.62 11.83 8.26 25.24 64.48 7".62 80.99 100.00

1381132 109 87.33 88.00 9.26 5.91 52.00 34.00 36.00 95.83 100.00

13H2182 263 90.26 92.00 6.78 4.00 68.00 80.00 92.00 96.00 100.00

1383182 1s 89.00 92.00 7.48 6.00 58.33 $4.00 90.06 96.00 100.00 1384162 1184 86.87 92.00 8.85 4.34 50.00 33.33 83.00 92.00 100.00

1335182 627 36.93 83.00 0.33 4.70 56.00 32.61 88.00 92.00 100.00

1310182 415 84.48 81.00 11.74 7.77 38.89 76.47 88.00 92.00 100.00

13EOI183 194 60.76 63.69 14.45 10.35 7.14 50.94 62.10 71.63 90.28

130182 593 94.59 100.00 5.63 3.55 70.83 92.31 96.15 100.00 100.00

15D0182 259 78.86 1.2s5 13.20 9.30 43.75 68,75 81.25 67.50 100.00 17K2132 110 39.02 92.59 7.50 5.55 41.S4 35.19 92.15 96.30 100.00

9D 181 104 $3.14 88.00 9.34 S.84 51.00 77.00 85.00 88.7S 100.00

19DO183 334 69.00 30.63 12.00 7.82 24.54 61.48 69.53 77.12 100.00

19D1182 742 91.30 93.33 6.24 5.00 66.67 36.67 93.33 96.67 100.00

19E1162 676 10.05 93.33 7.54 5.23 60.00 86.21 90.32 9 .67 100.00

19Z3143 1010 15.35 8C.s1 9.43 6.46 39.49 69.54 76.79 82.46 96.92

1992183 355 74.17 84.36 9.39 6.69 31.72 67.83 74.16 B1.26 99.08

1913132 611 $8.90 93.33 6.10 3.33 63.33 36.67 9(,.00 93.33 100.00

2690103 142 72.85 76.92 11.53 6.57 32.56 .6.67 75.13 80.41 96.15

27EC12 132 89.94 33.46 7.40 3.77 63.16 33.46 92.00 96.00 100.C-0

2710183 225 08.70 89.74 6.43 3.29 57.89 86.05 39.74 92.63 98.68

31J013 130 35.11 81.76 7.07 4.93 61.75 80.78 35.16 90.64 100.00

31N1183 1449 75.41 69.87 10.56 7.08 36.41 60.72 7S.64 32.68 100.00

31I162 573 87.01 100.00 9.28 6.05 S0.00 $1.63 i8.46 9 .7S 100.00

31K2132 404 87.45 100.00 9.25 6.36 SO.00 32.01 Is.9 94.74 100.00

31V0123 151 77 09 81.33 3.76 6.17 43.33 71.47 79.00 64.00 95.67

31V1112 356 5.04 90.00 1.2 S.00 56.00 30.00 36.67 90.00 100.00

35XL 112 31.54 95.6 12.89 6.19 18.33 74.39 35.33 10.75 100.00

36C2162 390 74.49 7S.00 3.85 5.69 46.67 65.57 75.00 10.95 95.63

j6KO182 939 33.27 90.00 3.51 5.35 47.37 73.95 34.21 39.66 100.00

(cont'd)
Table 7

Descriptive Statistics on the SQT Criterion

by Criterion Cell (Continued)

PCELL N MEAN 1ODE STD ORANGE MIN MU4 Q1 MEDIAN Q3 MAXIMKUM

36K0113 147 67.45 63.73 9.09 6.38 31.38 60.98 47.SS 73.73 98.82

43113 100 7S.62 100.00 20.06 12.06 20.00 64.29 o.oo 90.00 100.00

s51o82 196 3S.01 67.So 0.48 6.2S 54.17 79.17 37.50 01.67 100.00

520132 176 77.66 84.62 11.S4 6.S9 2 .s7 71.43 73.76 84.62 96.1S

S51122 230 30.34 83.46 0.45 6.73 0.00 75.00 31.29 8.46 96.1S

57o183 194 63.2. 66.67 11.40 7.1S 26.32 60.71 67.36 7S.00 100.00

6280182 220 30.69 34.00 9.27 6.SO 52.36 7S.00 q2.21 39.00 100.00

629102 172 03.62 35.71 8.43 1.76 57.69 77.78 9,.45 39.29 100.00

62roiS2 117 R3.66 39.29 9.58 S.76 5s.s6 77.78 35.71 39.29 100.00 6330182 1471 7S.98 76.67 7.66 2.34 47.37 73.33 76.67 30.00 93.33 63Ho162 335 77.99 77.76 3.24 5.77 SO.00 73.08 77.76 34.62 94.74

63N 82 236 7S.7S 79.31 7.99 S.27 so.00 72.22 75.36 82.76 93.10

63wMI82 180 33.44 30.89 3.06 s.C5 58.32 77.78 35.19 80.89 100.00

63To02 I09 33.5o 33.39 S.44 3.70 74.07 35.19 33.39 92.59 100.00

64CO182 1S73 79.98 79.31 9.43 S.17 35.5s 7S.86 ?1.31 tt.21 10.00

64 o13 2043 o.8. 3S.71 3.28 S.3s 44.44 71.00 32.14 3s.71 100.00

67H 13 123 91.51 2.00 6.61 4.00 72.00 88.00 92.00 96.00 100.00

67oN102 336 90.03 93.75 6.10 3.33 64.S2 37.10 90.63 93.75 100.00 67U0182 207 17.77 39.46 7.i6 5.17 65.52 82.76 39.66 93.10 100.00 67V0182 232 01.36 92.31 5.76 3.77 69.23 88.46 92.31 96.00 100.00 67Y0182 194 89.13 92.59 6.52 3.99 52.96 84.62 88.89 92.59 100.00 63G0182 121 06.06 92.31 7.4' 4.15 61.54 84.00 33.46 92.31 100.00

6300162 119 92.20 100.00 7.28 3.35 64.71 86.46 92.31 96.15 100.00 71DO152 115 90.37 96.00 9.96 5.15 50.00 95.71 92.00 96.00 100.00

71L113 7623 55.93 52.77 12.32 8.24 19.90 47.55 S5.38 64.04 100.0

71L2183 167 59.32 64.16 14.72 9.92 17.64 50.22 61.34 70.06 89.23

71MO1S2 132 84.50 39.29 9.24 5.74 SO.00 75.57 85.71 90.04 100.00 72E0183 564 74.93 76.28 9.94 6.65 39.49 68.75 75.77 82.05 98.08 73C0132 268 70.02 76.00 15230 10.00 20.83 60.00 72.00 80.00 100.00 73C0183 415 77.09 82.69 14.38 10.57 21.1S 61.54 75.00 42.69 100.00

74D3283 132 7o.84 92.00 11.53 8.15 47.40 71.57 78.41 87.88 100.00 '50102 423 68.94 63.00 17.10 10.00 6.25 60.00 69.S! 60.00 100.00

750133 631 S5.63 41.08 14.16 11.00 0.00 44.91 55.38 66.90 94.10 7SC1182 113 66.88 60.00 15.04 10.00 13.75 S.00 68.00 76.00 100.00

7SC0163 289 60,79 S1 41 11.96 7.02 30.13 S3.08 61.15 61.11 100.00

75D0112 370 67.36 68.00 14.69 1.22 12.50 60.00 63.00 70.43 100.00 75D0183 650 31.$7 44.23 13.60 9.47 7.69 42.21 50.77 61.15 96.54 750132 17S 68.26 68.00 1S.61 10.00 12.50 60.00 68.00 30.00 100.00 7SE0183 2-9 56.43 54.71 13.06 3.33 15.38 47.22 56.19 64.39 98.46

7SF1183 144 44.04 57.69 12.05 8.79 37.37 56.00 62.68 73.59 69.49 76C0183 320 63.71 61.43 10.93 7.50 32.14 S4.67 62.92 71.67 90.71

7fV0163 716 69.44 67.44 10.46 7.27 40.39 62.45 69.S2 76.98 94.00 76W0182 .95 70.93 10.00 14.02 &.i8 2S.00 63.64 72.00 30.00 100.00 76H0113 .21 63.94 73.19 11.32 7.22 19.14 62.22 70.00 76.67 97.7e

62C1162 209 92.76 1U.00 7.14 S.77 68.15 33.46 6.00 100.00 100.00 12C2182 133 92.38 100.00 7.16 4.07 66.00 86.00 93.75 96.15 100.00

9130132 203 88.61 92.00 6.86 4.00 63.00 14.00 91.30 92.00 100.00

91PO132 159 79.22 13.23 8.84 6.53 53.33 73.91 79.17 36.96 100.00 9110i$2 145 69.0 96.00 9.45 6.00 14.1' 84.00 11.67 96.00 100.00

9230132 310 91.35 100.00 7.02 3.92 64.71 38.46 92.59 96.30 XCO.00

92H0132 114 36.11 35.71 6.4S 6.04 61.54 30.77 8S.71 12.86 100.00

9480132 1543 37.91 90.00 9.07 5.00 53.33 83.33 90.00 93.33 100.0C

9430133 2306 7S.b4 79.33 9.40 6.16 32.09 70.00 76.67 12.33 100.0(

9 301S2 1353 8.03 38.69 7.90 3.99 SS.00 34.62 33.89 92.59 100.00

9530133 V80 6.29 0.93 S.42 3.S2 64.94 35.06 *2.7$ 92.09 100.0 96B01!3 172 76.30 34.62 15.51 7.70 30.?7 69.23 76.92 34.62 100.0)

91CU162 186 66.45 70.00 16.69 12.50 15.00 SS.00 70.00 80.00 95.0)

19 i
(2) Rely primarily on one of the criteria, but where the

results using the other criterion would make more sense,

use the other; or

(3) Combine tne data prior to analysis.

In fact, tne results based on the two different types of criteria were not

similar, and combination was judged necessary. The lack of convergence is

indicated by the correlation between training and SQT criteria, for individuals with both variables. We computed the sample correlation between training outcome and SQT score for soldiers in analysis cells that had 50 or more

complete observations. Eighty-one such cells, containing 10,615 records,

were found. The correlations ranged from -.12 to .56 in different MOS, with

an unweighted mean of .22 (Weighting by the number of soldiers in each analysis cell yielded a correlation of .21). While the low correlations may be

due in part to restriction of range, unreliability, and methods-contamination

of one or tne other of the criteria, they may also indicate valid differences

in the aspects of soldier performance covered by the two types of measures.

The third method of criterion combination was used. No overriding cause was

uncovered for favoring one criterion over tne other, and to ignore one part

of tne criterion base for an MOS would blind the validation to a possibly

very important aspect of a soldier's success in an MOS. The only way to

maximize the representativeness of the analyses for entire MOS was to combine the criteria.

For the purpose of identification of clusters and predictive validation of

current and alternative composites, the combination was at the MOS level.

ASVAB-criterion covariance matrices were computed for each MOS as the

weignted average of "cell" covariance matrices, using the sample sizes for

weignts. Pooled covariance matrices were generated for the 98 MOS. Other

analyses in the research required scores at the individual level (e.g.,

regression residuals). Such a fiie was generated using either the standardized SQT or Training criterion if only one was available, or the higher of

the scores if both existed. This file contained 64,907 observations. The

frequencies by MOS ann AA cluster are presented in the following section in

Taole 8 and Appendix Table A-3.

23 4
Predictive Validity of Current Composites

Introduction

This section describes our procedures for adjusting for restriction of range

and discusses the validities of the current ASVAB composites after adjustment. Our discussion focuses on several interrelated issues:

* The levels of predictive validity, after adjustment;

* Differences among composites in ability to predict

performance;

* Differences among MOS clusters of predictability of

performance.

The predictive validity coefficients indicate the extent to which the composites cover the skills necessary to obtain proficiency in the corresponding MOS, as measured by training outcomes and SQT scores. Although the

primary interest is in the composite associated with each particular MIOS,

there is also some interest in the entire matrix of composites by MOS, in

order to adoress the question of whether a lower than average validity in a

particular MOS is due to the nature of the composite or to the relation of

tne criterion in that MOS to the ASVAB in general. Differences among MOS in

tne overall relation of the ASVAB criterion can be interpreted as indicators

of either (1) needs for greater criterion reliability or (2) areas in which

the skills ccvered by the ASVAB need to be broadened.

Because tne major conclusions of these analyses are baseu on comparisons of

validity coefficients, it is essential that these be adjusted, insofar as

possible, for Known artifactual sources of variation. In particular, we

know tna! some MOS are much more selective than others in ASVAB skills. It

is known that observed validities are reduced as a furction of selectivity.

Therefore, the observed validities were adjusted for selectivity, or range

restriction, prior to comparison.

Adjustment for Restrirtion of Range

Classical methods of adjustment thdt have been used in previous ASVAB validations were used here. We chose to adjust to the FY81/82 applicant population rather than the 1980 Reference Population on the assumption that this

population was more representative of the applicant pool presently available

to the Army.

For the purposes of ASVAB validation, the multivariate adjustment due to

Lawley (1943) and described by Lord and Novick (1968, pp. 146ff) was used.

We assumed that explicit selection was being made on all ASVAB subtests. In

the follc :ing sections we will first discuss the validities for the combined

criteria and then present results for Training and SQT data separately.

" .
Validities of AA Composites Using the Combined Criterion

Table 8 gives tne adjusted validities for the nine current composites fqr

each of the AA clusters and Table 9 gives the intercorrelations among the

composites in the FY81/82 applicant population. The validities were obtained

by averaging the validities for the individual MOS within each AA cluster

and weighting by the number of soldiers in each MOS in the FY81/82 cohort.

The main diagonal of Table 8 gives the validities of the composites associated with each cluster of MOS.

Tables of sample correlations and adjusted validities for individual MOS are

given in Appendix Tables A-3 and A-4. These tables also contain estimates

of the standard errors of the validity coefficients. The SE's paired with

each validity estimate were obtained by the so-called "bootstrap" method of

generating repeated replications (Efron, 1979; Diaconis & Efron, 1983). A

method of obtaining sampling errors by repeated replication was used because

it was believed that the classical formula for the standard error of an

unadjusted correlation coefficient would not be appropriate for the adjusted

validities.

A "bootstrap" replicate is defined as a sample of size N drawn with replacement from a sample of size N. To compute the estimates found i--{tis report,

100 bootstrap samples were drawn from each MOS in the combined criterion

file. For each oootstrap sample (N=64,907), a matrix of adjusted validities

was computed. These hundred matrices were then merged, and the standard

errors were computed from the distributions of

Table 8

Adjusted Validities of the Current Composites:

Combined Criteria

Cluster Composite

of MOS N CL CO EL FA GM MM OF SC ST Average

CL 10368 48 51 53 54 49 46 50 50 53 50

CO 14266 'N 44 43 43 43 42 44 40 44 42

EL 5533 38 r7 47 46 47 46 47 44 47 45

FA 5602 39 49 W 48 49 49 49 45 44 47

GM 2571 39 48 46 T6 47 48 48 45 47 46

MM 7073 36 48 46 45 -9 48 48 43 46 45

OF: 8704 38 48 47 45 48 -47 48 44 48 46

SC 3729 39 49 48 47 48 47 WR 45 49 47

ST 7061 51 56 57 57 55 54 56 3_4 58 55

Average A( 49 48 48 48 47 49 46 48 47

22
Table 9

Intercorrelations among the Current Composites:

Applicant Population

Composite Composite

CL CL CO EL FA GM MM OF SC ST

CL 100

CO 80 100

EL 73 89 100

FA 84 94 91 100

GM 67 9U 96 84 100

MM 75 93 88 84 93 100

OF 83 94 88 88 91 97 100

SC 96 91 82 87 82 88 94 100

ST 76 89 96 90 94 87 92 84 100

the hundred replicates. For comparison purposes, the process was also carried out for the unadjusted validities.

Tables A-3 and A-4 present these validities and bootstrap estimates oi standard errors. For comparison, the approximate large-sample standard error of

a correlation based on the formula, one divided by the square root of N, is

included as the last column. For unadjusted coefficients, the bootstrap and

large-sample estimates of standard errors should and do show good agreement.

For adjusted validities, the bootstrap estimates are consistently higher.

For tables that present validites at the cluster level, standard errors are

not included because the number of observations at tnis level of aggregation

is so large that the standard errors are all .02 or less.

The most striking feature of the data in Table 8 is the uniformity of the

validities. All of the entries are between .36 and .58, and the mean of the

validities for the set of operational composites is .47. Except for the CL

composite, whose validites range from .36 to .51, the composites all perform

about the same. In every instance, a given MOS cluster is predicted about

as well from its own composite as from several of the others. One MOS

cluster, ST, appears to be slightly more predictable than the others; and

another cluster, CO, appears to be slightly less p-edictable. The remaining

OS clusters show very little variance.

Of the current composites, only CL consistently shows validities in the 30's.

We will discuss the possible weaknesses oT the CL composite and the speeded tests in our more detailed analysis of the validities by individual MOS cells using Training and SQT criteria separately.

23
Training Data

The sample correlations of the operational composites and the adjusted validities for each of the MOS clusters based on Training outcomes scores is presented in Table 10. These statistics are weighted by the number of FY81/82

accessions in each MOS. For cases in which there is more than one training

criterion cell per MOS, the weight was allocated on the basis of the proportion of observations for that MOS in each criterion cell.

Table 10

Sample Correlations and Adjusted Validities: Training Criterion

MOS Cluster Sample r Adjusted Validity

CL 19 4

CO 25 36

EL 22 40

FA 25 35

GM 29 52

MM 28 44

OF 20 35

SC 18 34

ST 32 54

Table 10 shows the effects of the restriction of range adjustment on the

validity. Due to differences in selection ratios and differences in the

distributions of the criterion, the adjustment cannot be expected to have

the same effect on all correlations. While the table does show that some

clusters are better predicted by their composite than others, it is not at

all clear from these data whether certain composites are "better" than others

or whether, in general, some MOS clusters are better predicted than others,

regardless of composite.

Table 11 presents the average adjusted validities for all the composites in

the same form as Table 8. It is apparent from Table 11 that there is no

great variation in the average effectiveness of the composites using the

training criterion. Except for the clerical composite, which is slightly

less predictive tnan the remaining composites, the average validities across

all MOS clusters in the Army are within two points of each other.

Performance in some MOS clusters is appreciably less well predicted than in

others. The CO, OF, FA, and SC clusters are well below the overall mean.

Each of these MOS clusters is composed of MOS that involve a substantial

24 C> 4/
amount of physical and psychomotor skill. It may be that the reason that

the ASVAB does relatively poorly for these MOS is that especially important

predictors are absent from the battery. Regrettably, in each instance there

is no composite that predicts that cluster relatively well.

Table 11

Average Adjusted Validities: Training Criterion

MOS Composite

Cluster N CL CO EL FA GM MM OF SC ST Average

CL 5272 40 43 45 46 42 39 42 42 45 43

CO 2879 "U 36 33 35 33 34 35 34 34 34

EL 2610 35 U~ 40 41 39 40 41 39 40 40

FA 1759 27 37 IT 35 35 3? 36 32 33 34

GM 1944 42 52 51 SU 52 52 52 49 50 50

MM 5426 33 44 42 41 T4_ 44 44 40 42 42

OF 4526 28 35 34 33 35 4~ 35 33 3i 34

SC 1463 33 35 35 36 33 32 'N 34 35 34

ST 3181 46 52 53 51 52 50 53 3T 54 51

Average 35 41 40 43 40 39 40 38 40 40

Validities by Training Cell. The sample and adjusted validities for the

ASVAS composites using training outcome as the criterion are presented in

the Taoles A-5 and A-6 of the Appendix.

On the oasis of Table A-6, some general comments can be made about the performance of some of the composites. Clearly, the weakest composite is CL.

it was not the best composite for any cell in this sample. In fact, FA was

better than CL for all the training cells in which CL is the operational

composite. The other weak composite, SC, was not the best composite for any

of the cells for which it is operational and was the best composite for only

five cells--and three of the five were cells with validities in the .30's.

SQT Data

Table 12 presents the averaged sample and adjusted validities for the nine

MOS clusters using the SQT score as the criterion. These data suggest that

SQT may be slightly better predicted by the ASVAB than thp training criterion. This may, in part, be due to the more favorable distributions of SQT

25
scores and that these SQT scores were generated entirely from paper-andpencil tests. We examine the SQT validities more closely in the sections

below by following the same procedures used for the training criterion.

Table 12

Sample Correlations and Adjusted Validities: SQT Criterion

MOS Cluster Sample r Validity

CL 29 49

CO 33 44

EL 28 45

FA 34 45

GM 23 40

MM 28 45

OF 33 50

SC 29 47

ST 32 55

Validities by Clusters of SQT Cells. Table 13 displays the weighted average

adjusted validities of the clusters of MOS in the sample using the SQT as

the criterion. As was the case with the training data, there is little

variability among composites within a cluster. Except for the CL and SC

composites, the average validities of the composites are within a couple of

points of one another when collapsed across AA clusters. There is greater

variability in the predictability of clusters, and the pattern is slightly

different than for training data. GM, MM, and CO are most poorly predicted

by the ASVAB, while CL and ST are best predicted. As before, the CL composite predicts performance in the CL cluster better than would be expected,

but it must be remembered that the CL composite performs worst overall.

Generally, composites that include GS, MK, AS, and MC tend to have higher

validities than other composites.

Validities by SQT Cell. Tables A-7 and A-8 in the Appendix present the

sample and adjusted validities for the SQT Cells included in this analysis.

They show that the performance of the two weak ccmposites identified in the

analysis of the training data is similar. The CL composice had the highest

validity for only one SQT cell--and that was not a cell in which CL was toe

operational composite. SC had the highest validity for only three cells--

again none were for the cells that use SC as their composite. The comparison

of the validities of the composites in comparison to the vaiiuity of the

AFQT shows the same pattern. The AFQT was better than the CL composite for

all nineteen cells in the sample that used CL and was better than four of

26
the five SC cells in the sample. By Contrast, all other composites were

better than AFQT for most of their cells. Because the weak performance of

CL and SC replicates across criteria, it appears that t:here is room for

improvement in the absolute prediction of performance in these MOS.

Table 13

Average Adjusted Validities: SQT Criterion

MOS Composite

Cluster N CL CO EL FA GM WM OF SC ST Average

CL 8006 49 52 55 55 51 48 52 51 55 52

CO 15970 - 44 44 43 43 43 44 40 44 42

EL 5960 35 " 45 43 45 44 45 41 45 43

FA 6964 36 46 ' 45 46 46 46 42 46 44

GM 1304 33 41 40 "Z 40 40 41 38 41 39

MM 4309 32 44 43 41 T 45 44 39 43 42

OF 4724 40 51 51 48 51 50 46 51 49

SC 3649 40 52 51 49 52 51 s 3T 47 52 49

ST 6915 48 54 55 55 53 51 54 '57 55 53

Average 39 48 48 47 47 46 47 44 48 46

Discussion

From these results a few general trends emerge. Among the composites, CL

appears to be tne least adequate. Alternative composites that included a

quantitative component consistently did better for the MOS in which CL is

operational. The FA composite, which includes both AR and MK, was consistently better than the CL composite for the Clerical MOS. Maier (1982) presents data that show that adding more mathematical content to CL does

increase its validity. Our data are consistent with his findings.

The relatively weak performance of the CL composite observed here is also

consistent with the findings of Sims and Hiatt (1981). Their adjusted validity coefficients for ASVAB 6/7 show the same pattern. Sims and Hiatt

recommended groupings of 140S using a combination of empirical evidence and

face validity. They recommended that CL be used in nine 1405 included in

their sample. In every instance, both the FA and the ST composites had the

same or higher validities than CL. Thus, it seems clear, on the basis of

training data, that some composite that includes a quantitative component

will predict training success in a clerical MOS better than CL.

27
Our findings regarding the pattern of validities for the Clerical cluster

are also consistent with the results of the the investigation of CL iomposite carried out by Weltin and Popelka (1983). They obtained ASVAB and Vndof-course grades for 3,984 new trainees entering the Army for clerical

training in FY81 in twelve MO$. They evaluated the current CL composite

(CL-VE+CS+NO) by comparing its adjusted validity with the adjusted validity

of a revised composite suggested by a multiple regression of the ASVAB subtests on the training criterion. Results for the twelve MOS were quite similar in that all suggested that a quantitative subtest (either MK or AR)

consistently accounted for the most variance in the criterion. Also, a

revised composite consisting of unit weighted AR and VE predicted as well or

better than the current composite in all twelve MOS. They reported that

this composite correlated significantly higher than the operational composite. Thus, a clear message in both our assessment and the Weltin-Popelka

analysis is that substituting a math subtest for the speeded subtests appreciably increases the validity of the CL composite.

Several authors have speculated on the poor performance of the CL composite.

The major factors singled out by other workers are:

e The failure to adhere to uniform testing conditions has a

greater effect on the speeded tests than the other tests in

the ASVAB. When the timing of the test is not rigidly

enforced, extra items can be marked by examinees. Weltin

and PopelKa also reported that examinees tested under military conditions have lower scores than those tested under

civilian conditions.

a Scores on the speeoed tests can be improved appreciably by

practice. McCormick, Dunlap, Kennedy, and Jones (1982)

found that when applicants were permitted to take the ASVAB

repeatedly, scores of the speeded tests showed the greatest

improvement. Thus, if these skills are relevant to job performance in MOS, it may be that they are sufficiently trainable that variance is removed by the time that criterion

data are collected.

In general, the results of these analyses indicate that the current ASVAB

area composites provide information relevant to the prediction of performance

in training and on the job. However, they fall short of the ideal of targeting specific jobs for individuals. There is little evidence that these composites capture skills specific to the MOS with which they are associated.

/ 1'~~~"
Development of Alternative Composites

The second major goal of this investigation is to determine whether there

are alternatives to the operational composites that would significantly

improve the usefulness of the ASVAB as a selection and classification

instrument. The data used to validate the operational composites also

provides the basis for creating and evaluating alternative combinations of

ASVAB subtests.

Theoretically, the ideal composites for the classification of enlistees into

MOS would oe the combinations of ASVAB subtests that most accurately predict

the success of the enlistees in each of the separate MOS. Within the licmitations of available criteria, it is possible to identify the best prediction

function for each MOS, and each function could be evaluated to determine the

differential expectations for success following from alternative assignments.

The present investigation, however, aimed at the development of composites

that could be used in the context of the current operational selection and

classification system. This precludes the evaluation of a large number of

complex functions at the time of enlistee selection and classification. The

available database also precluded the estimation of stable regression functions in a sizeable number of MOS.

Instead, the goal in this effort was the identification of a small number of

"area" composites, each of which would be relevant for a large number of MOS

and could be calculated from a simple sum of ASVAB subtest scores. The

practical limitation to a small number of composites does not, in fact,

greatly limit the predictive validity of the resulting composites. There is

little practical difference between using a single composite for a set of

MOS and using the empirically determined noptimal" composites for each MOS

in the set.

The development of alternative area composites thus involved two steps. Tne

first step was to determine the clusters of MOS for which a common composite

would be appropriate. The second step was to find the sum of ASVAB subtest

scores for each cluster that obest" predicts the expected performance of all

soldiers assigned to MOS in the cluster. The main issue in the first step

concerns the choice of a criterion for deciding which MOS to group togetner

for purposes of assigning a common composite. The two main issues in the

second step concern (1) the choice of trade-offs between simplicity of use

and predictive validity and (2) the choice of trade-offs between overall

predictive validity, discriminant validity for MOS classification, and cultural fairness in the evaluation of alternative composites. Finally, an

overall issue in the development of composites concerns the combination of

results based on different criterion measures.

Identification of KOS Clusters

In the pa:t, the clusters of MOS that use a common composite have been

developed primarily on judgmental rather than empirical grounds. MOS which

experts judge to require similar cognitive skills in training and subsequent

job performance nave been grouped together. As new 40S have emerged, they

29 I~
have been assigned to an existing composite function (and hence to the cluster of MOS that use that same function) on the basis of rational judgments,

since no empirical data were initially available for these MOS.

In the current investigation, clusters of NOS that require similar combinations of the skills measured by the ASVAB were determined empirically. A

prediction function relating ASVAB subtest scores to available criterion

measures was identified fo, each MOS in the analysis sample, and the MOS

were clustered on the basis of the similarity of these prediction functions.

The stability of the resulting clusters of 14)S was cross-validated using

half-sample analyses.

Similarity Measure. The similarity between two MOS is based on the similarity between the prediction functions relating ASVAB and criteria in tne

individual MOS. To the extent that they are similar, the same prediction

function can be assigned to both without loss of predictive validity. The

prediction functions that were considered in these analyses were limited to

linear relations. An examination of scatter plots indicated neither nonmonotonic functions nor step-functions, and the available criterion measures

did not support the selection among more complex models.

The "optimal" composite for any MO5 was operationally defined as the weighted

sum of ASVAB subtest scores most highly correlated with the criterion measure. Any alternative linear composite can be divided into two parts - its

"projection" onto the optimal composite and an orthogonal residual. Only

tne "projection" part contributes to the validity of the alternative composite, and tne relative size of that projection is given by the correlation

between the two composites. Thus, the correlation between the "optimal"

composites for any pair of MOS offers an ideal measure of similarity because

it indicates the validity of the composite of one MOS for predicting the

criterion in the other MOS. The correlation was estimated in the applicant

population, because this best represents the population for which the ASVAB

composites would be used for selection and classification.

Focus on correlations among the optimal composites for predicting performance

embodied a decision to ignore variation in the overall predictability of

criteria in different MOS. Two sums of ASVAB subtest scores with weights in

the same relative proportions are perfectly correlated, even if the weights

for one of the sums are uniformly smaller due to lower predictability of the

criterion. Such lower predictability inoicates either lower criterion reliability or greiter criterion variance in skills not assessed by the ASVAB.

The latter case reminds us that two MOS can be quite similar in terms of

ASVAB predictions, even though they are quite different in terms of skills

not assessed by the ASVAB.

The validity of the procedure for developing alternative composites is limited by the need to rely on sample estimates of the "optimal" weighted sum

for each MOS. Normal practice has been to use ordinary least squares (OLS)

regression coefficients as the weights for the optimal composite. OLS estimates provide the maximui correlation between the ASVAB and the criterion in

the sample used to estimate these coefficients. There is concern, particularly for MOS with relatively few soldiers, that OLS coefficients capitalize

on chance variation so that the rest:lting multiple correlations are not replicable. Especially witn predictors as highly correlated as the ASVAB sub30
tests, OLS regression coefficients based on small samples may not crossvalidate. In order to improve the reliability of the results, we decided to

use ridge regression to estimate the optimal weighted sums of ASVAB subtest

scores (see Draper 1 Van Nostrand, 1979, formula 3.11). In the language of

matrix algebra, the ridge regression estimates for each MOS, BqMCS, are

computed by the matrix multiplication:

-I1

cOv s(Y ) * covS(Xx + (k)

where y is the criterion, X is a vector of p predictors, covMoS (Z,X) is

the vector of p covariances of the predictor with the criterion in the MOS,

covM0S (X,X') is the p x p matrix of covariances among the predictors in

the pari~ular MOS, and k4OS is defined by:

kM0S= p varMOS(y)(l - r 5S)/[(nM1 S - p - 1)(B0os q0S)J

where the B#OS and rpoS are based on OLS regression.

In essence, the ridge regression procedure adds an emprically determined

value, k, to the diagonal elements of the matrix of cross-products among the

predictors. If K is taken to be zero, ridge regression specializes to O1.S

regression. The effect of positive values of k is to shrink the estimated

values of the regression coefficients toward zero (in comparision to OLS

regression coefficients).

Our ridge regression coefficients were somewhat smaller and somewhat more

stable than the OLS regression coefficients. Ridge regression vectors were

computed for each SQT and training cell with at least 100 observations. The

ridge regression vectors for 92 training cells, 112 SQT cells, and 98 comDined cells are given in Appendix Tables A-9, A-10, and A-ll.

Using the ridge regression coefficients, similarities between MOS cells were

computed as the correlations of the predicted performance scores based cn

our sample of 19,027 FY81/82 applicants. The 98 by 98 similarity matrix for

the combined criterion cells is shown in Appendix Table A-12. Similar

matrices for the SQT and Training criteria were computed. The analyses for

the development of alternative composites were carried out separately on the

three criteria.

The correlations among the optimal weighted sums were quite high. Roughly

three-fourths were between .90 and 1.00. This reflected both the high

intercorrelations among the ASVA8 subtests and the fact that the same subtests tended to be tne best individual predictors for most MOS. The high

correlations created problems for the empirical approach to clustering.

Clusters would necessarily be based on relatively small differences in these

correlations. Quite different configurations of clusters could yield very

similar aggregate composite validities. lievertheless, we proceeded initially

to evaluate the results of purely empirical clustering of MOS.

31

-. - - ~ . .-.-. . . . . . . . . . . . .
Initial Cluster Analyses. As the primary clustering method, we used a leafto-stem clustering a gorithm developed by this project. In this approach,

the cells (i.e., MOS) are initially assigned to separate clusters. They are

then joined in a series of iterations that reduced the number of cluster' by

one at each step. At each step, those cells were combined that would minimize the loss in predictive power of the resulting composites. In effect,

at each step the combined regression functions were computed for all potential cell combinations, and the combination which caused the smallest decrement in the average validity of the resulting set of composites was selected.

Although this did not ensure that the final clustering would be optimal, it

was stepwise optimal.

A variation of this algorithm was also developed that forced the clusters to

matcn a pre-specified set of clusters up to a given point. In particular,

it was set to match the nine current area composite clusters in the initial

series of combinations. A threshold number of clusters was set, and until

the number of clusters had been reduced to that threshold, no combinings

were allowed to cross the boundaries of the nine current clusters.

In addition to the primary clustering method, cluster algorithms in three

stardard packages were used. Unlike the primary method, SAS PROC VARCLUS is

a stem-to-leaf procedure. That is, it started with a single cluster and

broke it successively into smaller clusters. The other two packaged procedures were also stem-to-leaf methods. They were the BIMED BMDPIM variable

clustering program and the IMSL subroutine, OCLINK.

The results produced by the different clustering algorithms were compared

using a series of programs that examined clusters and identified optimal

composites for them. While there was some convergence of results between

tne cluster algorithms, there was divergence of results across data sources.

Tne clusters identified by the primary clustering algorithm from SQT data

and from training data differed from each other. Both solutions differed

substantially from the current area composite clusters. These findings demonstrated very little agreement among the clusters from the three sources.

The lack of convergence between training and SQT data, in particular,

strengthened our focus on the combined criterion data file, with its broader

coverage of each MOS.

A critical issue in this phase of the research is the dependability of the

MOS clusters formed by these procedures. To evaluate this, the results of

the clustering were cross-validated by dividing each cell in the analysis

file into half-samples and performing the analysis using each half-sample.

The results of the cross-validation did not support the further investigation of purely empirically derived clusters. In particular, the comparisons

of the similarity matrices based on the two half-samples were disappointing.

These two similarity matrices were compared by correlating each row of one

with the corresponding row of the other. Because a row of the similarity

matrix represents a profile of the similarities of the corresponding MOS to

all th- other MOS, the resulting correlations indicated the stability of the

profi'es of MOS similarity across random replications. As can be seen in

Figure 3, there was very little stability in the profile of similarity measures derived from the two half-samples. Overall, the distribution of correlations of the similarity profiles was centered at about .20.

32
t6-

1~4

50.

S.J

I.|

0.0o 0.04 Pa $.Is 240 0.26 0.30

Figure 1. Frequency of Correlations of MOS Similarity Profiles.

One possible explanation for the instability of the similarity matrices would

be the presence of a few outliers -ith nigh influence. If this is the case,

additional trimming of the data wojld improve stability. As a procedure for

both trimming anO removing c2iling effects, we transformed the criterion

data to normal scores and carried out the same half-sanple !7ross-validation.

Regrettably, the results of this new half-sample cross-vilidati,. n were

essentially unchanged: the average correlation of .15 did not indicate

sufficient stability to warrant proceeding further on purely empirical clustering. We could not reasonably recommend formation of clusters based purely

on empirical data with this level of instability.

Final Cluster Analyses

The results of the cross-validation showed that an empirical approach to

clustering was contraindicated. We therefore decided to modify existinU

clusters based on the data and to identify optimal composites for tne 'e

alternatives. The leaf-to-stem algorithm was used with the constraint that

k 5 33
until the number of clusters was reduced to a threshold of 20, no combinations of MOS across different current composite clusters were permitted.

For the final 19 steps, however, the algorithm was freed to form corfbinations that minimized the loss function. Except for the CL cluster, there

was little correspondence between th6 current clusters and the empirical

cl.stering.

From this work, iL was clear that there was no strong natural clustering in

the data. Predictive validity would not be highly sensitive to small variations in the structure of the composites. Therefore, we chose to restrict

our search to a set of alternatives that would be more practical than other

alternatives. In particulai', alternative clusters were evaluated directly in terms of the predictive validity of their oest unweighted sum of subtest

scores involving four or fewer subtests. Sufficient justification for this

was provided by the identification (described below) of a set of four unitweight composites with a root mean square validity of .485, or 97% of the

aggregate validity obtainable by use of separate ridge regression vectors

for each MOS. The best unit-weight composites for each of tne nine current

clusters are shown in Table 14. The mean squared validities are shown for

composites involving three, four, and five subtests, compared with the maximum possible mean squared validity for the particular set of MOS. Because

these estimates are based on ridge regressions, rather than OLS regressions, and because they are based on unit-weight composites, they are not likely to

oe inflated estimates of true validities.

we restricted our se&.'ch to clusters thiL would not break up any of the current composite clusters. An initial four-cluster summary was selected in

wnicn all current composite clusters were left intact. From that starting

point, the clusters were successively recomtived in order to identify a

final local maximum aggregate r2 , for unit-weight composites with four or

fewer suotests. The results are shown in Table 15.

For the proposed set of composites, the aggregate root mean square absolute

validity was .486. These composites are strikingly more similar to each

Other than to the current composites as well as the MAGE and High School

composites. All of the proposed composites use VE and either of trne quanti- tative subests as a nucleus. The major difference is the weight given to

the Technical factor (AS and MC). For the third and fourth composiLes, the

only difference is in the choice of which quantitative test is included.

This similarity is a consequence of using predictability as our criterion:

the most valid suttests were selected for inclusion in all four composites.

Taoles 16, 17, and 16 present comparisons of the validities of the four

alternate composites with the current composites using the combined, SQT,

and training criteria. These estimates of validities were computed using the ridge regression vectors, and therefore are generally smaller than the

corresponding OLS estimates. These tables show appreciable gains in validity for the CL and SC clusters. For CL, the gains appear for both training and SQT criteria; in the case of SC, the effect is confined to the SQT

criterion.

From this four-composite solution, the most effective combination to create

a tnree-composite solutton was to comoine the CO, FA, GM, EL cluster witn

345
Table 14

Optimal Unit-Weight Composites for each Current Cluster

Wtd Mn. SQR. Validity Unit Weight Composite

CLUS Max 3 4 5 3-subtest 4-subtest 5-subtest

CL .305 .288 .287 .288 VE AR R VE AR MK CS VE GS AR MK CS

ST .328 .299 .305 .312 VE AR MC VE AR MK AS VE AR MK AS CS

CO .200 .187 .191 .193 VE AR MC VE MK AS MC VE AR MK AS MC

EL .240 .217 .222 .226 VE MK AS VE MK AS MC VE AR MK AS MC

FA .259 .242 .249 .252 MK AS HC AR M AS MC VE AR MK AS MC

GM .214 .184 .194 .196 MK AS MC NO MK AS MC VE NO MK AS MC

SC .235 .218 .223 .226 VE AR MC VE AR AS MC VE AR MK AS MC

OF .232 .221 .223 .223 VE AR AS VE AR AS MC VE AR El AS MC

MM .238 .215 .217 .220 AR AS MC VE AR AS MC El AR MK AS MC

Table 15

Local Optimal Four-composite Solution

Composite Subtests

Clerical/Administrative ACL (VE + AR + MK)

Skilled Technical AST (VE + AR + MK + AS)

Operations AOP (VE + AR + MC + AS)

Combat ACO (VE + MK + MC + AS)

35
the SC, MM, OF cluster, using the composite for the former. The result,'ng

aggregate absolute validity was .485. An examination of Appendix Table A-l3,

in which validities are shown for the four alternative composites as well as

the nine current composites for each MOS, corroborates this combination.

There are very few MOS for which the composite VE+AR+AS+MC is significantly

superior to VE+MK+AS+MC.

Also, all two-cluster solutions were examined, and the optimum was found to

be to assign the CL composite to the ST as well as CL cluster. The resulting

aggregate absolute validity was .484. Finally, the best single composite,

VE+MK-'S+MC, was found to have a weighted average predictive validity of

.480, for the entire set of 98 MOS.

The issue of predictive bias was not raised within this process of composite

identification but assessed after the choice was narrowed to a single alternative to the current composites. In future evaluations of composites, it

snoulo be possible to include predictive bias assessment as a part of the

search process, similar to the way in which overall predictive validity was

inicluded.

If tne search procedure is to include a combination of criteria: predictive

validity, differential validity, and predictive bias; a mechanism is needed

to evaluate the trace-offs among these different aspects of validity.

36
TABLE 16

(Weoq1ced) AVERAGE ADJUSTED VALIDITIE:! + COMBINED CRITERION

ALTERNATE CONPOS:TES CURRENT COMPOSITES

9A VAKo1,.m- 4S sca VA.L

AL. 64097 -- 0.46 0.47 0.47 0.47 0.39 0.47 0.43 0.45 0.46 0.46 0.47 0.46 0.46

C. 103b8 .07 0.53' 0.52 0.49 0.49 0.46' 0.51 0.48 0.45 0.48 0.49 0.52 0.48 0.si

7 3h .0. 0.54 O.SS. 0.53 0.54 0.48 O.S4' 0.52 0.51 0.53 O.53 0.54 0.52 O.54

3 14:.i .01 0.41 0.43 0.43 0.43' 0.35 0.43 0.40 0.41 0.43 0.431 0.42 0.42 0.42 _ .0. 0.41 0.45 0.45 0.45' 0.36 0.45 0.41 0.44 0.44 0.45 0.44 0.45 0.45.

5kO.' .02 0.46 0.47 0.49 0.49' 0.39 0.48 0.45 0.48 0.48 0.49 0.47 t 0.48 0.48

z m i .01 0.41 0.43 0.42 0.43' 0.36 0.42 0.40 0.42 0.43 0.42 0.41 0.42- 0.42

3Z 2-: .05 0.44 0.46 0.47' 0.47 0.36 0.46 0.42' 0.44 0.45 0.45 0.44 0,46 0.46

'Z 8>4 .02 0.44 0.46 0.47' 0.46 0.36 0.45 0.42 0.44 0.44' 0.45 0.43 0.45 0.45

7:-2 .01 0.41 0.44 0.4s, 0.4s 0.33 0.43 0.40 0.441 0.44 0.4S 0.42 0.44 0.43

TABLE 17

(Weigbted) AVERAGE ADJUSTED VALIDITIES: SQT CRITERION

ALTEFNATE COKPOSITES CLRRENT COMPOSITES

F7 VA.

C-~ .-- - -X GA:N A~a As- AO? -A,20 C' ST c P'M OF cm !LG

A-- C: :: -- 0.4f C.48 0.48 0.48 0.38 0.47 0.43 0 .40 0.46 0.46 0.45 0.46 0.47

?::t .C9 0.1 " O.S6 0.53 0.53 0.49' 0.55 O.51 0.49 0.52 0.52 0.55 0.51 C.55

T 6.5 .01 0.5 0.s" 0.1 0.51 0.45 0.51' 0.49 0.48 0.50 0.51 O.S1 0.50 0.52

- .0. 0.42 0.44 0.44 0.44' 0.35 0.44 0.40 0.42 0.43 0.43' 0.42 0.43 0.43

S r . .01 0.44 0.47 0.43 0.42, 0.35 0.47 0.42 0.46 0.46 0.45 0.44 0.47 C.45"

* t "-- .03 0.43 0.45 0.4t, 0.4b 0.35 0.45 0.41 0.44 0.45 0.45 0.43- 0.45 0.45

4 .02 0.34 0.36 0.36 0.36' 0.29 0.35 0.33 0.35 0.35 0.35 0.35 0.34* C.35

-" !.. .07 0.4' 0.50 050'* 0.53 0.37 0.49 0.43" 0.49 0.46 0.49 0.46 0.41 0.49

.2 0.4q 0.51 0.51* 0.51 0.39 0.49 0.45 0.48 0.49' C.41 C.47 0 5 C.49 -

'. " .Cl 0.37 0.40 0.41' 0.41 0.29 0.39 0.35 0.40' 0.39 0.40 0.27 0.40 C.39

TABLE 1B

(Weiq?-ted) AVERACE ADJUSTED VALIDITIES: TRAININ! CRITEPICN

ALTEP?;ATE C0MPOS:TE0 CJP.ENT CC :POZ!TSE ........................................................................................................

"D5';AL.

.1 G N 14 : _ .5 AS A'? A: CL ST sC OF CO FA M _-.

A.L zr". -- 0.38 0.39 0.39 0.39 0.33 0.35 0.26 0.37 0.38 0.39 0.38 0.38 0.38

:L 5" -z .07 0.45" 0.44 0.42 0.42 0.38' 0.43 0.40 0.39 0.40 0.41 0.44 0.41 C.44

T 3,.1 .01 0.45 0.46* 0.45 0.4s 0.39 0.4S' 0.43 0.43 0.44 0.45 0.44 0.44 0.45

: 2!' .00 0.33 0.34 0.i4 0.34' 0.29 0.34 0.33 0.33 0.34 0.34 ° 0.33 0.33 0.33

!L t..0 .01 0.36 0.38 0.37 0.37* 0.33 0.36 0.36 0.37 0.37 0.38 0.37 0.36 3.3L'

'A J' ! .00 0.41 0.43 0.44 0.44' 0.37 0.43 0.41 0.44 0.44 0.46 0,44- 0.43 0.43

'4 1'4 .01 0.38 0.40 0.40 0.40' 0.35 0.40 0.39 0.41 0.41 0.41 0.40 0.40" 0.40

I . l 0.32 0.31 0.30* 0.30 0.28 0.31 0.29' O.28 0.20 0.30 0.31 0.29 0.31

' -.-. .01 0.31 0.'3 C.33' 0.33 0.26 0.32 0.30 0.32 0.32' 0.32 0.31 0.33 C.32

. 01 0.36 0.39 0.401 0.40 0.30 0.38 0.36 0.35* 0.39 0.40 0.37 0.39 0.18

37 ( .
38\6
Predictive Validity of Alternative Composites

The selection of alternative composites was based on comparisons of

adjusted validity coefficients. In evaluating a potential composite, the

validities of all potential unit-weight composites were generated and the

optimum composites identified. The set of alternate tompostes presented

in the previous chapter s built on the VE subtest, two of the technical

subtests, and the two subtests measuring quantitative ability. That is,

the most predictive subtests appear in all four composites.

The result of focusing on the most valid portion of the ASVAB is that these

composites are more highly intercorrelated than other sets. Also, they

achieve the primary aim of the effort: they have higher validities. These

two facts are apparent in Tables 19 and 20, which present the adjusted

validities and the intercorrelations for the four alternative composites.

The adjusted validities were obtained by weighting KOS validities by the

number of soldiers in the MOS. The correlations were obtained from the

sample of FY81/82 applicants. Adjusted validities of the alternative composites based on SQT and Training data separately are shown in Tables 21

and 22.

Table 19

Average Adjusted Vdlidities of the Proposed Composites:

Combined Criterion

MOS Composite

Cluster N ACL AST ACO AOP Average

CL/ACL 10368 56 54 52 51 53

CO/ACO 14266 T7 44 44 44 43

EL/ACO 5533 46 48 T 48 47

FA/ACO 5602 47 49 5U0 49

GM/ACO 2571 45 48 4 48 47

MMV/AOP 7073 44 48 9 49 47

OF/AOP 8704 46 49 49 1F 48

SC/AOP 3729 47 49 50 49

ST/AST 7061 58 58 57 37 57

Average 48 50 50 50 49

39
Table 20

Intercorrelations among Proposed Composites

in the Applicant Population

Composite ACL AST ACO AOP

ACL 1.00

AST 97 1.00

ACO 91 97 1.00

AOP 91 97 98 1.00

The corresponding sample and adjusted validities by MOS are contained in the

Appendix Tables A-14 and A-IS together with their standard errors.

The aggregate root mean square predictive validity for the alternative composites for their assigned MOS is .486. The root mean square validity for

the current composites, by comparison, is .454. There were two major findings: first, there are very few MOS for which the current composite has

greater validity than the proposed alternative, even though the number of

composites is reduced from nine to four. Second, the current composites for

Clerical/Administrative and Surveillance/Communications MOS were significantly weaker than the proposed alternative composites for these MOS. The

average validity for MOS in the CL cluster could be increased from .48 to

.56, based on the combined criteria, and the average validity for MOS in the

SC cluster could be increased from .45 to .50. The difference for CL was

apparent using both training and SQT criteria, but the the difference for SC

was based almost entirely on SQT results.

Based on the combined criteria, the optimal four-composite solution had at

least as high absolute validity as the current composite in every cluster.

Moreover, for only one of the 98 MOS on which the combined criteria analyses

were based (26Q), was the validity of the current composite as much as .02

greater than the validity of the proposed composite, and that case could be

eliminated by reclassification of 26Q to the Skilled Technical cluster.

Thus, if savings can be realized by reducing the number of composites from

nine to four, there is no indication that this will reduce the absolute predictive validity of the composites for any cluster of 140S. The one negative

comparison at the cluster level was for the prediction of training outcomes

for the Combat cluster, where the difference of .002 in validity favored the

current composite. This difference is not significant.

The four composite solution can be reduced to a three-, and then a twocomposite solution with virtually no loss of absolute validity. First, the

third composite, VE+AR+AS+MC, can be eliminated and its MOS "reassigned" to

the fourth composite, VE4K+/AS+MC. The average loss of validity for the

reassigned MOS is merely .002, and the overall mean validity for the threecomposite solution is .485, compared to .486 for the four-composite solution.

40 ((A
Table 21

Average Adjusted Validities of the Proposed Composites:

SQT Criterion

MOS Composite

Cluster N ACL AST ACO AOP Average

CL/ACL 8006 58 56 53 53 55

CO/ACO 15970 U 44 45 44 44

EL/ACO 5960 43 45 46 45

FA/ACO 6964 44 47 47 46

GM/ACO 1304 39 41 41 41

MM/AOP 4309 41 45 46 44

OF/AOP 7724 49 52 52 n 52

SC/AOP 3649 50 53 53 37 52

ST/AST 6915 56 56 55 SO 56

Average 48 49 49 49 49

Table 22

Average Adjusted Validities of the Proposed Composites:

Training Criterion

MOS Composite

Cluster N ACL AST ACO AOP Average

CL/ACL 5272 47 46 44 43 45

CO/ACO 2879 M 34 35 35 34

EL/ACO 2610 40 41 4 41 41

FA/ACO 1759 31 34 z 36 34

GM/ACO 1944 48 51 37 52 51

MM/AOP 5426 39 43 T 44 42

OF/AOP 4626 34 36 36 1 36

SC/AOP 1463 37 36 34 7 36

ST/AST 3181 52 54 53 '0 53

Average 40 42 42 42 42

41
The only noticeable difference between the four- and three-composite solutions is that the best three-composite solution is somewhat unbalanced, with

70% of the assignments based on just one of the three composites. Of 205,000

FY81/82 accessions in the 98 MOS used for the combined criteria analyses,

36,000 were assigned to Clerical/Administrative MOS, 24,000 were assigned to

Skilled Technical MOS, and 145,000 were assigned to other MOS.

The optimal two-composite solution further eliminates the second (AST) composite, "reassigning" its MOS to the first (ACL) composite. This has the

effect of reducing the overall average validity from .485 to .484. However,

the average validity loss for the MOS in the ST cluster is .006, which is

statistically significant.

In general, these results do not differentiate between the two-, three-, and

four-composite solutions, although they do demonstrate improvements in comparison to the current composites in all cases. The differential validity

of the composite sets for classification purposes must be examined in order

to make a selection among these alternative solutions.

Comparisons to Other Sets of Composites

It is of interest to compare the performance of these composites to the two

other sets of currently used composites mentioned in the introduction. Both

sets consist of fewer than nine composites, but differ from the set developed

here in that there is less overlap among the composites. The statistics for

the MAGE composites and the High School composites appear in Tables 23 and

24, respectively. Corresponding tables of sample and adjusted validities

for individual MOS appear as Taoles A-16, A-17, A-18, and A-19 in the

Appendix.

The MAGE composites do not offer a solution to the single greatest weakness

of the Army's Current operational composites--the low validity of the CL

composite. The Administrative composite in MAGE, in fact, is the CL composite. Other than this, however, the MAGE composites perform nearly as well

as the proposed set and are comparable to the current set. Differences in

tne average validity of a composite across all AA clusters are in the rdnge

of .02 to .03.

The High Schuol composites perform about as well as the MAGE composites with

the exception of their Office and Supply composite. Probably because it

does not include both speeded subtests, its validity is appreciably higher

than CL.

The comparisons of the current, alternative, MAGE, and High School composites

reinforce the claim that there are no great differences in validity among

different composites within a given AA cluster. The most valid composites

are composed of the most valid subtests.

42

4Z "(I
Tabtle 23

Average Adjusted Validities of the MAGE Composites:

Combined Criterion

MUS Composite

Cluster N M A G E Average

CL 10368 45 48 54 53 50

CO 14266 42 36 42 43 41

EL 5533 45 38 46 47 44

FA 5602 48 39 46 48 45

GM 2571 46 39 44 46 44

M 7073 48 36 44 46 43

OF 8704 47 38 47 47 45

SC 3729 47 39 47 48 45

ST 7061 52 51 57 57 54

Average 47 40 47 48 46

Table 24

Average Adjusted Validities for the High School Composites:

Combined Criterion

MOS Composite

Cluster N HSAA HSMT HSOS HSSS HSEE Average

CL 10368 54 47 54 53 53 52

CO 14266 42 43 40 44 43 42

EL 5533 46 47 43 47 47 46

FA 5602 46 49 44 49 48 47

GM 2571 44 47 43 47 46 45

M9 7073 44 49 41 47 46 46

OF 8704 47 48 43 48 47 47

SC 3729 47 48 44 49 48 47

ST 7061 57 54 56 58 57 56

Average 47 48 45 49 48 47

43 /
Table 25

Intercorrelations among the MAGE Composites

in the Applicant Population

Composite M A G E

M=GS+-MC+AS 1.0') Mechanical

A=NO+CS+VE 61 1.00 Administrative

G=AR+VE 81 81 1.00 General

E=AR+MKI+GS+EI 88 73 93 1.00 Electronic

Table 26

Intercorrelations among the High School Composites

in the Applicant Population

Composite HSAA HSMT HSOS HSSS HSEE

HSAA-AR+VE 1.00 Academic Aptitude

HSMT-AR.AS+MC+EI 85 1.00 Mechanical Trades

HSOS=VE+CS+1K 89 73 1.00 Office and Supply

HSSSZAR+VE+MC 97 93 87 1.00 Skilled Services

HSEE-AR+EI+MK+GS 93 92 86 94 1.00 Electronics

44m
Difft rential validity

The overall performance of the Army depends on how well the skills of

recruits can be matched to the requirements of the MOS they enter. Therefore, a sot of composites must be evaluated in terms of its differential

validity.

In theory, the differential validity of a set of composites is based on the

correlation of the best predictor of differences between MOS with the actual

differences that one would observe. The practical problem is that, in general, it is infeasible to collect criterion data from the same individual in

all jobs. That is, one cannot ordinarily observe the criterion needed for

estimating differential validity. Fortunately, Horst (1954) developed a

method for measuring the crucial part of the differential validity of a test

battery witnout the necessity of these observations.

Actual use of a set of composites for classification of recruits into MOS is

a complex process, however, and an abstract measure of differential validity

can only approximate the relative value of one set of composites, compared

to another. A more accurate comparison of composites would involve simulation of the assignment process. Work is progressing on the development of

the appropriate simulation algorithm. Nevertheless, for the present evaluation. differential validity was estimated using the procedure outlined by

Horst (1954).

The results presented here must therefore be interpreted with caution. One

set of composites might be measured as possessing greater differential validity than another, even though the otter set would lead to a more valuable

increase in overall performance of enlisted personnel. Four aspects of the

practical application of composites for classification of Army recruits are

particularly important to consider in interpreting the results.

(1) The constraints on numbers of recruits needed in each MOS severely

restrict the assignment process, so that many recruits must be

assigned to MOS for which they are not optimally matched.

(2) Recruits are free to maze choices and cannot be summarily assigned

to the MOS that the composites identify as optimal.

(3) The appropriate criteria are not expected performance differences

but the relative utility of those differences; however, the utility

scales are not yet available.

(4) Current practice mixes selection and classification, yet we are

addressing the questions of validity for selection and classification separately.

Each of these factors would affect the measurement of overall performance of

any set of composites, and to th; extent that the effects are the same for

all composites, Zhe general results can be meaningfully interpreted. Factors

that would affect one set of composites more than another, however, will

require further investigation. For example, if the source of diffeiential

validity in one set of composites lies primarily in comparisons between "high

45 "7
payoff" MUS, the real value of that set of composites would be relativel.

higher than its measured validity.

As noted above, the starting point for this measurement is the work of, Hc:.-

(1954). Horst demonstrated that one could compute the ordinary least Squares

(OLS) linear predictor of the difference between two criteria for an individual without actually having measurements of both criteria for any single

individual. Using this result, he proposed a Classification Efficiency index

equal to the average (over all pairs) of the variances of the predictors of

the differences. In addition, he showed that this index could be elegantly

representeo in terms of the variances and covariances of the predictions of

single criteria.

The formula for Horst's Classification Efficiency index which we used is:

(1) H2 Average(yik - Yjk )Z/2,

where the yiK and Yjk are the OLS estimates of standardized

criteria i and j for individual k, and the average is over all i,

j, and K, such that i does not equal j.

Horst pointed out tne proolem in using h as a Oirect measure of differential

valicity; namely, that the maximum value it can take on, if the predictors

are perfectly accurate, is not unity. Tne maximum value is I minus the average intercorrelations among the criteria. Unfortunately, these intercorrelations cannot be measured witnout ooserving multiple criteria for single

individuals. However, because the intercorrelatios of cr'teria will be tne

same no matter what the predictors, the values of H for different sets of

predictors of tne same criteria can be compared.

Brogden (;959) proposed a measure of differential validity similar to the

measure aerived by Horst. Brogden's .,easure, wnich we shall call 0, is tne

product of (a) tne average absolute predictive val.idity of the predictors

ano (c) tne square root of one minus the average of the intercorrelations of

the preoictors. when these intercor elations are equal, it is related to

Horst's neasure.oy tne following equ tion:

(2) H2 + (1 + g/(p-l)) x Variance'valiaity coefficients),

where the varianct is oetwee, criteria,

g is tne int.rcorelatic of -ne predictors, and

p is the number of pred 'or

That is, w;.e, all the criteria are equally I predicted by the composites,

h is equal to 0, and in any other case, H ii ludes a component of differential validity due to tne variation in predictaoility of the criteria.

A coroilary of equation (2) is that a battery can possess differential valiity, as measurea by m, even though the predictors are all perfectly correltea witn eacn other. In that case, D is equal to zero, out H can oe

greater tnan zero, if some criteria are more predictable than otners. Thus,

4 6 a
as pointed out by Maier (1982), examination of the intercorrelations of predictors is insufficient for estimation of the differential validity of a

battery.

Although it seems counter-intuitive at first that a set of perfectly correlated composites could possess differential validity, the following example

niakes clear that they can. Consider the case of a single composite. Suppose

that the composite measures a set of skills that account for much of the

variation in performance in MOS A but very little of the variation in performance in M0S B. (Perhaps some unmeasured skill accounts for most of the

variance in M0S B.) Then it makes sense to assign individuals with higher

values of the composite to MOS A and individuals with lower scores to MOS

B. Although the skill measured by the composite is related to performance

in both MOS, its relation is much stronger in MOS A. Thus, a single composite has differential validity for classification among MOS.

The present problem is somewhat different from that addressed by Horst. His

objective was to measure the performance of an entire battery in predicting

differences between criteria, while the objective of the present analyses is

to compare the performance of different sets of composites based on the same

(ASVAS) battery. As noted by Maier (1982), Horst's derivation is based on

the assumption that the predictors are based on the full battery; i.e., that

they are the multiple regression vectors for predicting the criteria from

the ASVAB. Thus, while computation of H for the 98 separate MOS regression

vectors provides the maximum achievable differential validity of the ASVAB,

the computation of the differential validity of a particular set of composites involves more than merely applying Horst's formula to the covariance

matrix of the composites.

Each MOS is associated with a single composite, so the comparison of expected

performance between two M0S is associated with a pair of composites (although

in many cases, they are the same composite). To assess the differential

validity of alternative sets of composites, then, we applied the formula in

equation (1), where the predictors for each pair were limited to the one or

two composites associated with the pair. Specifically, we obtained the least

squares predictor of tiie difference in criteria between each pair of MOS and

then averaged the squares of these over all pairs of MOS.

The ,measure of composite differential validity we used was:

(3) M2 z Average(B(ij)Cijk)2/2,

where Cijk is the pair of composite values associated with

MOS i and j for individual k,

B() is the regression vector for predicting the

ofference Yik-Yjk based on the two composites, and

the average is over all i, j, and k, with i and j not equal.

As Horst had noted, o,, can estimate the required regression coefficients,

even though no individual case has more than a single criterion score.

Because the estimation for different pairs is based on different sets of

composites, however, the elegant solution which orst discovered is not

available. Nevertheless, the computations were straightforward, though

somewhat expensive in computer time.

47 0
The computations were carried out for two separate cases: (1) pairs of MOS

associated with the same composite, and (2) pairs of MOS whose composites

were not perfectly correlated. In each case, the critical assumption, which

also underlies Horst's derivation, is that the regression of criterion on

composite is the same in both the selected and unselected groups. To simplify the derivation, we assumed that all variables were standardized for

the group for which they are available.

Case 1: both MOS i and MOS j use the same composite

The objective is to select bij to minimize

(4) Average( Yik-Yjk - b(ij)ck )2,

where the average is over all accessions, and

c is the common composite for both MOS i and MOS4.

The solution can be shown to be

(5) b(ij) = bi - bj.

That is, the result is simply the difference between the regression coefficients for predicting the criteria in the two MOS separately.

Case 2: MOS i and MOS j use different composites

The objective is to minimize

(6) Average( Yik-Yjk - (b(ij)icik+b(ij)jcjk) )2,

where the average is over all accessions,

cik and cik are the two composite values associated

with MOS i and j, for individual k, and

btij)i and b(ij are the associated regression

coefficients for predicting the difference.

The joint solution for (ij)' = (b(ij)i. b(ij)j) turns out to be

(7) 1(ij) = C(i) -

where B(.) and Bti) are the regression vectors for

prI ting E available criteria in MOS i and j each using

the pair of composites. Note that the values of B(i) and

j(j) depend on the particular pairing of i and j.

Thus, in both Case I and Case 2 we obtain computable estimates of the

regression coefficients; and from the e it is straightforward to obtain the

measure defined in equation (3). The maximum value for this statistic, for

any set of linear composites based on the ASVAB, is the value of H' in

equation (1).

48
The feilure of a set of composites to possess differential validity, therefore, can be divided into two parts: (1) failure of the ASVAB as a battery

to measure skill components that differ between MOS, and (2) failure of the

particular set of composites to capture the potential differential validity

of the ASVAB. We can assess the extent to which the composites capture the

differential validity possessed by the ASVAB as the ratio of M to H.

The measures H and M weiglht each of the MOS equally in the estimation of

differential validity. Although all of the MOS differences are important

for some decisions, it is plausible to assign greater weight to the valid

estimation of differences that are involved in the most frequent decisions.

Therefore, as a further refinement, we also computed a weighted version of

H and M, weighting the entry for edch pair by the product of the numbers of

the accessions in the two MOS.

The results are contained in Tables 27 and 28. For comparison purposes,

these taoles also include provisional estimates of the differential validity

of the MAGE composites. We used G for the CL and OF clusters, E for the EL,

SC, and ST clusters, and M for CC, FA, GM, and MM. After dropping the

Administrative!CL composite-and reordering, we renamed this composite set

"GEM."

The loss of one third of the differential validity of the ASVAB through

using only 9 unit-weignt composites instead of 98 separate OLS regression

vectors is to be expected. The 98 OLS regression vectors not only captured

true variance between each pair of MOS but also capitalized on any chance

variation between pairs of MOS. The 9-composite solution, on the other

hand, assigned the same composite to many pairs of MOS; and for these pairs

there was no opportunity to capitalize on chance variation.

Generally, tne unit-weight composites yield differential validity estimates

from 43% to 68% of the potential differential validity in the ASVAB. The

solutions with fewer composites, as expected, yielded slightly lower estimates of differential validity, although there was virtually no difference

between the proposed 2, 3, and 4 composite or GEM alternatives. Use of a

single composite resulted in noticeably lower differential validity. The

weignted differential validity estimates were generally lower than tne

unweighted estimates, reflecting the general phenomenon of smaller differences between tne larger MOS. Tnis may be due either to greater uniqueness

of sKills in small MOS or lower stability of the estimates in those MOS, or

both.

The comparison between the operational composites and the alternative set of

nine composites, in which the Clerical & Administrative (CL) and Surveillance

& Communications (SC) composites were replaced, yielded no noticeable difference. Thus, the significant increase in overall predictive validity

achieved by introduction of these two changes is not at the cost of decrease

in differential validity.

49
Table 27

Differential Validity Estimates for Alternative Sets of Composites

(Unweighted)

Average Squared Root Mean Square Relative

Differene Difference Efficiency

Composites (H2 or M2) (H or M) (H or M)/H

Full linear model

(98 composites) .099 .314 (100%)

Current 9 composites .041 .202 64%

Revised 9 composites .036 .190 60%

(CL and SC changed)

Alternative 4 composites .026 .160 51%

Alternative 3 composites .024 .154 49%

Alternative 2 composites .023 .150 48%

Alternative 1 composite .019 .136 43%

GEM ** .025 .159 51%

* Note: the measure of differential validity is H for the full 98-

composite alternative and M for the other alternatives.

** Three of the four "MAGE" composites. G is used for CL and OF clusters

of MOS; E is used for EL, SC, and ST clusters of MOS; and M is used for

CO, FA, GM, and MM clusters of MOS.

50
Table 28

Differential Validity Estimates for Alternative Sets of Composites

(Weighted)

Average Squared Root Mean Square Relative

Difference Difference Efficiency

Composites (H2 or M2)* (H or M) (H or M)/H

Full linear model

(98 composites) .046 .214 (100%)

Current 9 composites .021 .146 68%

Revised 9 composites .020 .142 66%

(CL and SC changed)

Alternative 4 composites .016 .125 59%

Alternative 3 composites .014 .120 56%

Alternative 2 composites .016 .125 58%

Alternative 1 composite .011 .106 50%

GEM** .014 .117 55%

Note: the measure of differential validity is H for the full 98-

composite alternative and M for the other alternatives.

** Three of the four wMAGEN composites. G is used for CL and OF clusters

of MOS; E is used for EL, SC, and ST clusters of MOS; and M is used for

CO, FA, GM, and MIM clusters of MOS.

51 Molt
Assessment of Predictive Bias

An important scientific and policy issue is the question of predictive bias

of the selection and classification procedures. The primary concern here is

whether the use of an alternative set of ASVAB composites would lead to bias

in the selection and classification of Army enlisted personnel. This question was addressed in three ways: First, the adjusted validities of the subgroups wer- calculated and compared. The subgroup validities were adjusted

to the total applicant population rather than the separate subgroup populations. Second, the differences between the predicted scores for each subgroup were compared in the range of composite score values that contain the

operational cutoff points. Third, the common and subgroup regression lines

were plotted over this region. The sample regression lines were used as the

basis for the latter two sets of comparisons. Unadjusted lines were used

because the classical adjustment for restriction of range makes the assumption that the regression line in the selected group is the same as the

regression line in the unselected population.

As noted in the earlier description of the data available for this research,

we were limited in the analyses of subgroup differences to comparisons

Detween race (blacks and whites) and between gender. We performed subgroup

analyses only on those MOS that contained a sample of at least 100 soldiers

of each subgroup. For race, this sample included 35 MOS and for gender it

included 19 MOS. After the analyses had been obtained for each MOS, the

results were aggregated to the cluster level, we will first discuss tne

analyses based upon comparisons between black and white soldiers ano tnen

turn to a discussion of analyses investigating differences as a function of

gender.

Analyses of Differences by Race

The sample and adjusted validities of tne current operational composites

based upon the combined criterion as a function of race are presented in

Taole 29. (Tables for all subgroup analyses based upon each of the criteria

treated separately can be found in the appendices). Similar data based upon

the proposed four alternative composites are given in Table 30.

Inspection of these two tables shows that, in general, both sets of composites predict performance in each of the subgroups well. The smallest adjusted validity in either table is a respectable .25, while the average adjusted

validities are sizeable at .41 and .43 for the current and alternative composites respectively. While the validities in both tables are high, the

validities obtained from the three alternative composites were consistently

nigher for both subgroups across all of the clusters.

Both tables show small differences between the validities obtained by whites

in comparison to blacks. Thest differences are quite stable across the two

different sets of composites. The average difference in adjusted validities

between blacks and whites among tne current composites was .08, while in the

case of the alternative composites this value is slightly smaller at .07.

Tne only sizeable cnanges in the black- white validity differences were found

in GM and MM clusters, where the subgroup differences were .04 and .03

smaller for the alternative composites. The stability of these differences,

53(75
Table 29

Sample and Adjusted Validities for Blacks ("B") and Whites ("W"):

Current Operational Composites

SQT and Training Criteria Combined

Sample Sample Adjusted

Cluster/ Size Validities Validities Difference

Composite MW" "B" "W" "B" "W" "B" (Adjusted)

CL 4780 6985 .30 .13 .51 .42 .09

CO 14523 3570 .30 .19 .44 .41 .03

EL 4527 3111 .26 .10 .43 .29 .14

FA 4936 3234 .36 .19 .56 .42 .14

GM 474 624 .20 .11 .41 .55 -.14

MM 2729 103S .25 .12 .40 .34 .06

OF 6941 3316 .29 .14 .47 .39 .08

SC 3207 1708 .25 .11 .44 .30 .14

ST 6682 956 .27 .14 .41 .25 .16

Table 30

Sample and Adjusted Validities for Blacks ("B") and Whites ("W"):

Four Alternative Composites

SQT and Training Criteria Combined

Sample Sample Adjusted

Cluster/ Size Validities Validities Difference

Composite "w" "B" NW" MBU "W "B" (Adjusted)

CL/ACL 4780 6985 .41 .26 .57 .49 .08

CO/ACO 14523 3570 .31 .22 .45 .43 .02

EL/ACO 4527 3111 .27 .l .44 .29 .15

FA/ACO 4936 3234 .37 .19 .57 .42 .15

GM/ACO 474 624 .29 .08 .46 .56 -.10

I*/AOP 2725 1939 .26 .18 .40 .37 .03

OF/AOP 6941 3316 .31 .22 .49 .42 .07

SC/AOP 3207 1708 .34 .22 .47 .33 .14

ST/AST 6682 956 .27 .18 .42 .26 .16

541
IN

despite the radical changes in the makeup of the composites between the

operational and the alternative sets, suggests that the small differences

observed in ASVAB composite validities as a function of race are most likely

attributable to the ASVAB subtests themselves or to the criterion measures,

rather than to the way they are combined into composites.

Differences between subgroup validities such as those observed in Tables 29

and 30 above do not necessarily mean that either set of composites is culturally biased. Cronbacn (1976) makes the distinction between equality of

test validities and fairness in selection policies. The relationship of the

subgroup regression lines to each other is the key issue in the analysis of

predictive bias.

Clearly, predictive bias would not be an issue if both groups shared the

same regression line. If this were true, each recruit would have the same

predicted value on the criterion regardless of subgroup membership. Therefore, a natural way of investigating predictive bias is to identify values

of the AA composite for which a significant difference in predicted criterion scores exists.

To compare the black and white regression lines, we calculated the predicted

criterion scores for the two subgroups for composite scores ranging from 80

to 110 points. This range of values was selected because it contains all of

the cutoff scores now in operational use by the Army. The two sets of predicted scores were then subtracted to obtain the difference score, and standard error of the difference was estimated using the formula for the variance

of the difference given in Rogosa (1980). The differences between the two

regression lines are given in Table 31 for the current operationil composites

and Table 32 for the proposed four alternative composites.

Inspection of these two tables shows that, in general, for both sets of composites the two subgroup regression lines tend to be close over this range

of composite scores. The average differences between the two lines for the

current composites are: 3.80 for the CL cluster, 2.76 for the CO cluster,

2.38 for the EL cluster, 4.88 for the FA cluster, 4.23 for the GM cluster,

3.40 for the MM cluster, .88 for the OF cluster, 5.93 for the SC cluster,

and 2.56 for the ST cluster. The average differences for the proposed

alternative composites were 1.57 (CL), 2.10 (CO), .89 (EL), 2.77 (FA), 4.70

(GM), 1.10 (WM), -.90 (OF), .90 (SC) and .87 (ST). While some of these differences and those given in the tables are statistically significant, they

tend to be relatively small in comparison to the standard deviation of the

combined SQT and training criterion, which had been standardized to a value

of 20 for accessions into each MOS. Only for fairly high values of the composite scores (around 110) did the differences in predicted scores for the

two subgroups become large. These findings are typical of the comparisons

of black and white regression lines found in other educational, employment,

and military research (i.e., Hanser & Grafton, 1983).

Tables 31 and 32 show that the relationships between the black and white

regression lines are similar for both sets of composites. In both tables

the differences most often have positive values, indicating that the white

regression lines lie above the black regression lines. In other words, the

black criterion scores are overpredicted by the regression line based upon

the white subgroup. This relationship of average overpredi.tion of the black

55
Table 31

Predicted Criterion Scores for Blacks ("B") and Whites (UW"):

Current Operational Composites

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined B W) (W - B) of the Difference

CL Cluster

80 88.44 90.03 90.14 .10 2.54

85 91.07 91.69 93.03 1.34 2.17

90 93.70 93.34 95.92 2.58 1.84

95 96.33 95.00 98.81 3.80* 1.57

100 98.95 96.66 101.70 5.04w 1.39

105 101.58 98.32 104.59 6.27* 1.35

110 104.21 99.97 107.48 7.50* 1.44

CO Cluster

80 90.66 89.37 91.59 2.22 1.19

85 93.14 91.54 93.94 2.40* 1.14

90 95.61 93.71 96.29 2.58* 1.11

95 98.09 95.88 98.64 2.76* 1.11

100 100.57 98.05 100.99 2.94* 1.14

105 103.04 100.22 103.34 3.12* 1.19

110 105.52 102.39 105.69 3.30* 1.26

EL Cluster

80 90.18 91.50 90.86 -.63 2.23

85 93.24 93.60 93.97 .37 1.88

90 96.31 95.70 97.07 1.37 1.60

95 99.37 97.80 100.18 2.38 1.43

100 102.44 99.90 103.28 3.38* 1.42

105 105.50 102.00 106.39 4.39' 1.56

110 108.57 104.10 109.49 5.39" 1.81

FA Cluster

80 90.07 90.33 92.54 2.21 1.14

85 93.12 92.30 95.38 3.08* 1.01

90 96.18 94.26 98.22 3.95* .91

95 99.23 96.23 101.06 4.82' .83

100 102.29 98.20 103.90 5.69* .80

105 105.34 100.17 106.74 6.56* .81

110 108.40 102.14 109.58 7.44w .87

(cont'd)

56
Table 31

Predicted Criterion Scores for Blacks (*8") and Whites ("w"):

Current Operational Composites (Continued)

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined B W) (W - B) of the Difference

GM Cluster

80 95.09 97.24 95.84 -1.40 2.50

85 98.53 98.78 99.26 .48 2.27

90 101.97 100.32 102.68 2.36 2.35

95 105.41 101.86 106.09 4.23 2.69

100 108.85 103.40 109.51 6.12 3.22

105 112.29 104.93 112.93 8.00* 3.86

110 115.73 106.47 116.35 9.88* 4.56

MM Cluster

80 91.50 89.84 93.74 3.90* 1.60

85 94.1 92.29 96.02 3.73* 1.48

90 96.74 94.75 98.31 3.56* 1.42

95 99.36 97.20 100.60 3.40* 1.41

100 101.98 99.65 102.88 3.23* 1.45

105 104.60 102.10 105.17 3.06. 1.56

110 107.22 104.56 107.46 2.90 1.70

OF Cluster

8 93.14 90.73 94.87 4.14* 1.17

85 96.03 94.38 97.43 3.05* 1.11

90 98.91 98.03 100.00 1.97 1.10

95 101.80 101.68 102.56 .88 1.45

100 104.68 105.32 105.12 -.20 1.25

105 107.57 108.97 107.69 -1.29 1.39

110 110.45 112.62 110.25 -2.37 1.56

SC Cluster

80 89.64 86.83 93.82 6.99* 2.09

85 92.25 89.32 95.96 6.64* 1.82

90 94.87 91.81 98.09 6.28* 1.59

95 97.48 94.30 100.22 5.92* 1.41

100 100.09 96.79 102.36 5.57* 1.29

105 102.71 99.28 104.49 5.22* 1.27

110 105.32 101.76 106.62 4.86* 1.34

ST Cluster

80 85.52 85.78 86.02 .24 1.30

85 88.54 87.98 89.00 1.02 1.25

90 91.56 90.19 91.98 1.79 1.24

95 94.58 92.40 94.96 2.56* 1.25

100 97.60 94.61 97.95 3.34* 1.29

105 100.62 96.82 100.93 4.12* 1.35

110 103.64 92.02 103.91 4.89* 1.44

*p ( .05

57 -7
Table 32

Predicted Criterion Scores for Blacks ("B") and Whites ("W"):

Four Alternitive Composite Solutions

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined B W) (W - 8) of the Difference

CL Cluster

80 93.10 92.90 92.36 -.54 2.43

85 96.08 95.47 95.63 .16 2.08

90 99.06 98.04 98.91 .87 1.76

95 102.04 100.61 102.18 1.57 1.50

100 105.02 103.18 105.45 2.27 1.33

105 107.99 105.75 108.72 2.98* 1.29

110 110.97 108.32 112.00 3.68* 1.37

CO Cluster

80 92.17 91.22 92.35 1.13 1.18

85 94.46 93,17 94.62 1.45 1.13

90 96.74 " .11 2.9 1.78 1.11

95 99.03 97.06 99.16 2.10 1.11

lo 101.32 99.00 101.43 2.42* 1.13

105 103.60 100.95 103.69 2.74* 1.18

110 105.89 102.90 105.96 3.07* 1.25

EL Cluster

80 95.35 95.58 94.87 -.71 2.23

85 97.71 97.52 97.34 -.17 1.88

90 100.06 99.46 99.82 .36 1.60

95 102.42 101.39 102.29 .90 1.43

100 104.77 103.33 104.76 1.43 1.42

105 107.12 105.27 107.23 1.96 1.56

110 109.48 107.21 109.70 2.50 1.81

FA Cluster

80 95.02 94.92 95.08 .16 1.14

85 97.42 96.50 97.53 1.03 1.01

90 99.81 98.08 99.99 1.93* .90

95 102.20 99.67 102.44 2.77* .83

100 104.59 101.25 104.89 3.64* .80

105 106.99 102.83 107.35 4.52' .81

110 109.38 104.41 109.80 5.39* .87

(cont'd)

58
Table 32

Predicted Criterion Scores for 8lacks (4S") and Whites (*Wo):

Four Aiternative Composite Solution (Continued)

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined B W) (W - 8) of the Difference

G 1 Cluster

80 97.92 98.69 97.73 -.95 2.49

85 100.62 99.78 100.71 .93 2.27

90 103.32 100.86 103.68 2.82 2.34

95 106.02 101.96 106.66 4.70 2.68

100 108.72 103.05 109.64 6.59* 3.21

105 111.41 104.14 112.61 8.48* 3.85

110 114.11 105.23 115.59 10.36* 4.55

MM Cluster

80 93.55 91.71 94.80 3.09 1.59

85 95.88 94.49 96.92 2.43 1.48

90 98.22 97.28 99.04 1.76 1.41

95 100.55 100.06 101.16 1.10 1.40

100 102.88 102.84 103.27 .44 1.45

105 105.21 105.62 105.39 -.23 1.55

110 107.54 108.40 107.51 -.-9 1.70

OF Cluster

80 92.84 91.40 93.82 2.42* 1.16

85 95.76 95.19 96.50 1.31 1.09

90 98.68 93.98 99.19 .21 1.08

95 101.60 102.78 101.88 -.90 1.13

100 104.52 106.57 134.56 -2.00 1.23

105 107.44 110.36 107.25 -3.11" 1.37

110 110.35 114.15 109.94 -4.21- 1.54

SC Cluster

80 92.59 91.30 93.45 2.16 2.00

85 95.17 94.13 95.87 1.74 1.75

90 97.74 96.96 98.28 1.32 1.52

95 100.31 99.79 100.69 .90 1.35

100 102.89 102.62 103.10 .49 1.24

105 105.46 105.44 105.51 .07 1.22

110 108.03 108.28 107.93 -.35 1.28

ST Cluster

80 85.20 88.38 85.19 -3.19' 1.29

85 88.37 90.22 88.38 -1.84 1.25

90 91.54 92.06 91.58 -.48 1.23

95 94.70 93.90 94.77 .87 1.24

100 97.87 95.74 97.96 2.22 1.28

105 101.04 97.58 101.16 3.58* 1.34

110 104.21 99.42 104.35 4.93' 1.43

* pc.05

-59 ---.-
regression line oy the white regression line across this range of composite

scores was true for all of the current composites and all but one (OF) of

the proposed alternative composites.

The alternative composites differ from the current operational set in two

ways. Overall, the differences in predicted criterion scores observed in

the alternative composites are smaller Lhan the differences found with the

operational composites. The average of the absolute values of differences

from the current composites is 3.42, while the proposed alternative composites show an average absolute value of the differences of 1.76. Again, both

of these values are fairly small when compared to a criterion standard deviation of 20. Th- other noticeable aspect in which the two sets of composites differ nas already been noted above. When the alternative OF (AUP)

composite is used to predict performance for the OF cluster of MOS, the

white regression line tends to sligntly underpredict rather than overpredict

tne DlaCK regression line. Tables 31 and 32 show that the basic pattern of

general overprediction of tne DlacK regression line by the wnite regressicn

witn some underprediction for low composite scores is the case for botn tne

operational ano tne alternative composites.

Given tnat the Army does not use separate black and white regression lines

to select ind classify enlisted personnel, the relationship of each subgroup

line to tne conwnon regression line becomes important when significant differences between the subgroup lines exist. If the criterion scores for a suogroup are suostantially underpredicted by the common regression line (e.g..

tne Sabgroup line falls aoove the com~on lim), use of t"- cemen line to

select and classify potential personnel would be unfair to that subgroup

since its "true" predicted criterion would be higher than the value preaicteo uy tne common selection/classification instrument.

Underpreaiction of any subgroup is a serious proolem only wnen the underprediction is for values of the composite near the cutoff point for tnat MOS.

Tnis is true because an individual is aole to enlist in his or ner MOS of

cnoice as long as nis or her composite score is above the appropriate cutoff.

CLomposite scores well aoove tne cutof" do not lave any real meaning to tne

system. For example, if two individuals with composite scores of 95 and 106,

respectively, wished to enter an M0S with a cutoff score of 9U, both would

oe allowed to enlist in tne MOS. The ten-point difference in their composite

scores would not affect eitner person's selection or classification.

To investigate tne relation.nips between the subgroup regressions and the

common regression lines, we Plotted tne black, white, and comgnon lines in the

region tnat contains the cutoff scores for the Army M05. These plots are

presented in Figures 2 through 10. These plots show that the predicted

values of all three lines tend to have higher slopes for the alternative

composites than for tne current composites. This finding is in agreement

witn the earlier validity data which snowed somewnat higher criterion predictability witn the use of the alternative composites. In each of the

figures, the plots based upon the alternative composites tend to show the

three lines being closer togetner than they are in the plots obtained from

tne current composites. This is consistent with Table 32, which showed that

the alternative composites have the smaller differences among the predicted

criterion scores from the two subgroups.

60
CLUSTER=CL VERSION=OLO

120-- -- - _ _ _

ClicI -

-~ ~ ~ ---- -C 1 -

45--

so--

so as so as 100 IDS 110

C.OMPOSITE SCO0RE

LEDtHOt SUDDROUP a-- - C -- W

CLUSTER=CL VERSIBN=NEW

fC -_----- _10__ 

C

go

so9 o sLD D 1

COPSTSCR

LEEC UOOP --

Fiue2 ersinlnsfrCrrn n lentv odadnw _ Copsie _ _ _ _ . y _O ac
CLU5TEft=CB VERSIONz0L a 125-

ts95-

* C

so 86 so 55 100 105 110

COMPOSITE SCORE

LEDENOt SUBGROUP -- ---S - C - - W

CLUBTERWC0 VERS ION=NEWi

S1105

7 IDS

N

f5t

C

so ts o ti oo 0Il

COMPOIITE SCORE

LCOCND SUSI0OU' ---- a - C --

Figure .s. Regression lines for Current and Alternative (old and new)

Composites for CO MOSI by Race

62
12CLUSTER-EL VEftSIONzINEW

a'a

II

T 106-_ ___

T 100.

C

a90

C

so so as * 100 IN5 tic

COMIPOSITE SCORE

LEGENDs SUDG413LW ---- a - C --

CLUSTER=EL VEASIOM:OLO

1205

C II I

C

CW*8OITE 1ICONE

LEDENDz RMJOROUP - -- -I - C --

Figure 4. Regression lines for Current and Alternative (old and new)

Composites for EL MOS, by Race
CLUSTERaFA VER510N=VO

110

R

1 0

C

0

Be a5 90 15 too I0S Ito

COMlPOSITE SCORE

LCENE~ds SUBGROUF -- - - - C -

CLUSTERFR VER310ONEW(

1"

5

C

COMPOSITE 5CNE

LZOEM0i SIOUSP ---- a - C -

Figure 5. Regression lines for Current and Alternative (old and new)

Composites for FA MOS. by Race

64 c2
CLUSTER=KM VERI IONzOLO

125-

N 1006-

so- 30 8 0015

COMPOSI~tT1Y E $CORaEb

125-

120- -

tsC

to

COMMOIT[ "COE

LtKOC iuot 5=---s___ - C --

Figure 6. Regression lines for Current and Alternative (old and new)

Composites for Wt MOS, by Race

65
CLUSIERaOF VERS ION01LO

125-

120 - _ _ ____

It

00

it

SO so105 1;0 10 110li

CStIPOSITE $CONE

LEDIMO: 5UI0*OUP - -- -- 6 - c 

CLU3TER=OF YEMSICN=NEW

1251

C 110-

It 100.

0

W

CMVMOITE KOftE

LEGNO. Si*OW ---- B - C -

Figure 7. Regression lines for Current and Alteriative (old and new)

Co~nosites for OF WOS, by Race

66 ggOV
CLUSTEftzOI VERtSIO0L

Its

SIl

C

Ila

C~hPOSITE -SCORE_

CLUSTER:OE VERI ION=NE14

C

sogR0

NWST CR

LEED5BRO - -

Fiur 8 Rersio lie fo Curn an Alentv (ld nd ew

Copsie fo M ,byRc

f67
CLUSTERnIC 351 IWd=0LD

125-

IM

Itsa i

N05

a t

so IBS 30o 100 105 110

LEDENDI SUUO3O4JP I - C --

CLUSICRIC VERSIONaMN

it

C 110 ___

I I

E

LE NO MUGW --- a

Figure 9. Regression lines for Current and Alternative (old and new)

Composites for SC MOS, by Race

68
CLUSTERa3T YERSIDOauLD~

156

C

TIs

ftOIE cf

SU to

It

C

soI~ Is ~ so wu 100.c - 105 Ito

Figure 10. Regression lines f,3r Current and Alternative (old and new)

Composites for ST P4)5, by Race

69 9 )
Figures 2 through 10 also Show that tne pattern of the relationships between

the three regression lines near the possible cutoff values is quite similar

for the two sets of composites for most of the nine clusters. Eignt of the

nine composites Show overprediction of performance by blacks rather than the

more serious underprediction of performance. The one exception was the OF

cluster, which revealed overprediction for the lower composite scores and

underprediction for the higher values. This pattern among the regression

lines of the OF cluster was observed for both the operational and the proposed alternative composites.

To summarize the findings of the investigation of black versus white predictive bias, it appears that there are small differences in the predictive

validities and the regression lines for the two groups for all composites in

ootn their operational and alternative versions. The subgroup regression

lines are also not perfectly approximated by a single conmon regression

line, altnougn for both sets this difference results in overprediction

rather than underprediction of blacks in the region of the lines where selection and classification takes place. However, since the validity and

regression line differences were not very large and the use of the common

regression line does not, in general, result in underprediction of performance by blacks, either set of composites could be used without unfairly

impacting the enlistment of black soldiers.

Analyses of Differences by Gender

The sample and adjusted validities for gender subgroups of the current AA

operational composites based upon the combined SQT and training criterion

are presented in Taole 33. Similar data but based upon the four proposed

alternative composites are found in Table 34. The CO, FA, and GM clusters

are not included in either of these tables because no MOS in these clusters

met the criterion of at least 100 female soldiers (CO and FA do not contain

MOS tnat are currently open to enlistment for women).

AS was the case in tne analysis of racial subgroups, both sets of composites

tend to be accurate predictors of performance in each subgroup. Here the

mean overall validities were .42 and .45 respectively for the current and

alternative composites. The tables also show that the adjusted validities

for the alternative composites tended to be higher than the values obtaineo

by the current composites for both subgroups and across all clusters. Tne

one exception to this rule was the validity of the ACO composite when used

to predict the performance of men in the EL cluster. In this case the

adjusted valioities were equal for the current and alternative composite.

Another similarity between the data presented in Tables 33 and 34 and the

validity differences discussed earlier for the black and white subgroups is

that there was little change in the adjusted validity differences between

the groups as a function of the two composite sets. The mean difference

zctween male and female adjusted validities was .06 for the operational composites and .05 for the alternative composites. The change in validity offferences between the two tables is only .01 and for two of the clusters jCL and SC) tne difference in subgroup validities was consistent across compos- ite sets. This finding further suggests that differences in the predictive

70
Table 33

Sample and Adjusted Validities for Males ("M") and Females ("F")

Current Operational Composites

SQT and Training Criteria Combined

Sample Sample Adjusted

Cluster/ Size Validities Validities Difference

Composite "M" "F" "M" "F "MN" "F" (Adjusted)

CL 9035 4352 .30 .19 .48 .45 .03

EL 3110 852 .25 .10 .41 .16 .25

M 238 195 .30 .33 .43 .51 -.08

OF 8142 1536 .31 .23 .47 .43 .03

SC 4113 1097 .29 .13 .47 .28 .19

ST 5912 1195 .27 .31 .46 .50 -.04

Table 34

Sample and Adjustea Valioities for Males ("M") and Females ("F")

Four Alternative Composites

SQT and Training Criteria Combined

Sample Sample Adjusted

Cluster/ Size Validities Valdities Difference

Composite "M" "F" "M" "F" "M" "Fu (Adjusted)

CL / ACL 9035 4352 .42 .32 .56 .53 .03

EL / ACO 3110 852 .26 .14 .41 .19 .22

WM / AOP 2238 195 .31 .34 .43 .52 -.09

OF / AOP 8142 1536 .35 .27 .50 .46 .04

SC / AOP 4113 1097 .37 .25 .51 .32 .19

ST / AST 5912 1195 .27 .35 .46 .52 -.06

validity of ASVAB composites between gender or recial subgroups is primarily

a function of tne the ASVAB subtests and not the manner those subtests are

comDine@ into composites.
iotn Tables 33 and 34 snow that in two clusters (EL and SC), there were

fairly large differences in predictive validity between males and females.

For the EL cluster this difference was .25 for the current composite and .22

for the alternative composite. The difference for the SC cluster was consistent at .19 for both composites. Whether these validity differences

impact upon the selection and classification of women into these MOS clusters will oe further discussed in the analysis of the differences between

the regression lines and the discussion of the plots of the common and subgroup regression lines.

Table 35 presents tne comparisons between the female and male regression

lines for the current operational composites, while similar data are given

for the four alternative composites in Table 36. The data in these tables

were ootained in the manner that has been previously described in the analyses of racial subgroups.

Tables 35 and 36 shOw that, despite the higher predictive validity of the

alternative AA composites for both subgroups, tne subgroup regression lines

based upon tne operational composites tend to be closer together than the

female - male regression lines oased upon the alternative composites. The

mean aosclute value of the differences in predicted criterion scores between

the two groups was 1.69 for the operational composites in comparison to 2.79 for the alternative composites. Four clusters (CL. W, OF, and SC) showed

sizeable increases in the aosolute value of the differences, but of these

the change for W should not present an issue for the assignment of person- nel to MOS. It represents an increase in overprediction of the female

regression line by the male regression rather than underprediction. A raore

serious concern is the apparent underprediction of female performance by tne

proposed alternative AA composites in the CL, OF, and SC clusters. It should

De noted, however, that an observed average of about two ano a half units of

underprediction for these clusters is fairly small in comparison to the comDined criterion standard deviation of 20. The seriousness of these differences in regression lines also depends on where along the common regression line tney are found, and this issue can be best addressee by examining tne

plots of the tnree regression lines for each cluster.

Figures 11 through 17 present the plots of tne female, male, and common

regression lines across for the range of composite scores that contain tne

cutoff values, for both the current operational and the proposed alternative

composites. A comparison of tne figures for these two sets of composites

snows tnat for one cluster (ST) the pattern among the plotted regression

lines is quite similar for the two sets of AA composites. For two otner

clusters (EL and MM) the alternative composites snow more overpreoiction of

female soldier performance than do the current operational composites. Since

in botn of tnese cases the female line is overpredicted by the common line,

a switch to the alternative should not hinder the enlistment of women into

the WJS that comprise the MM and ST clusters. The plots for the remaining

tnree clusters (CL, OF, and SC) all showed an increase in underpreoiction of

female performance with the alternative compositcs. For the CL and SC cluSters, the current composites also Showed underprediction of the female criterion scores, and the new composites produced a small increase in that

underprediction, particularly for high composite scores. In the case of the

72
I too- 1 I

to

CLU IPUST E SCOM*E1

12-

so ;0 -- 110a

Figure 11. Regression lines for Cirrmnt and Alternative (old and new)
c ItoIas

to __ 1; It

12

C t

T6 IcU 0 11

LEO~ SUBGROUP - c ---

Figu,-.- 12.S ~ Rersso ~ ~ ~ lie cLIR~ frCrr nd e rntv odadnw

Copsie fo L O. yGedr

1274
CLUSTEA--M VURSIOW14L

75-3

COMPOSITE SCORE

LEGEND$ SUBGROUP -C - M 

CLUSTERzAMI VEJ1SZO0sIC

C 110

R

I

C

CUMPOSITE SCORE

LZOENM SIUBGROUP - C V -- n

F-igure 13 Regression lines for Current and Alternative (old and new)

Composites for WI MOS, by Gender.

75
Cl.USTftz*Fl YERS!O~aVO

c It

T 10

so 35 90 Is too lag11

COM~POSITE SCORE

LEDEND: SUINOR13UP - C - - -

CL.USTMO:0 YERS:3NzU.H

c IIso~

as___ _ _ goIsIc _ IDS _ It

LCSCWD SUSNOU - - - - - - 10

Figure 14 Regression llines for Current and Alternative (old and new)

Comrposites for OF M40S, by Gender.

76
CLU&TER2C vWEms oOLf

t2

120

11

i tt

10 , '-- -- - " ----"

N 9 ---- ---- ---- ---

C0

I

a i

* go

80 I US 100 O 110

CNOP051Tr SCant

LIEOV(0 SUSOMOP - C .... F --

CLUSTER.SC VER2?ez IdWsW

1293,

its116..

c Ila,-

C

n

i1 -

so 30 3 of 100 110

CONPOSITC Rem*

LSKUS UUS OSUM - . . --

Figure 15 Regression lines for Current and Alternative (old and new)

Composites for SC MOS, by Gender.

77
S.

a tc

Ca"tOSITE WCORE

LEGENDI SUDOROUP - C - - - ~ N

CLUSTER&ST VUS I2K=EW

c 11

c 2-

Cs

CO T CR

LSWsVA w

Fiue1 ersinlnsfrCrrn n lentv odadnw

CopstsfrS Ob edr

C7
4

Table 35

Predicted Criterion Scores for Males (OM*) and Females (UFM):

Current Composites

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined F M) (M - F) of the Difference

CL Cluster

80 88.39 91.26 86.77 -4.49 3.02

85 91.03 93.36 89.68 -3.68 2.50

90 93.66 95.46 92.59 -2.87 2.03

95 96.30 97.56 95.50 -2.06 1.65

100 98.93 99.66 98.41 -1.25 1.42

105 101.56 101.77 101.33 -.44 1.38

110 104.20 103.87 104.24 .37 1.58

EL Cluster

80 84.23 86.20 83.98 -2.22 2.97

85 88.28 89.74 88.09 -1.65 2.39

90 92.32 93.28 92.20 -1.08 1.83

95 96.37 96.83 96.31 -.51 1.35

100 100.42 100.37 100.42 .05 1.03

105 104.46 103.91 104.54 .62 1.04

110 108.51 107.46 108.65 1.19 1.39

MM Cluster

80 90.37 85.24 90.88 5.63 2.98

85 93.16 88.74 93.59 4.85* 2.38

90 95.95 92.25 96.31 4.06* 1.92

95 98.74 95.75 99.02 3.27 1.71

100 101.53 99.25 101.74 2.49 1.86

105 104.32 102.75 104.46 1.70 2.29

110 107.11 106.25 107.17 .92 2.87

Or Cluster

80 93.26 93.93 93.00 -.93 1.65

85 96.22 96.84 95.99 -.85 1.27

90 99.18 99.75 98.97 -.78 1.01

95 102.13 102.6o 101.95 -.70 .97

100 105.09 105.57 104.94 -.63 1.17

105 108.05 108.47 107.92 -.55 1.52

110 111.00 111.38 110.90 -.48 1.95

(cont'd)

79
Predicted Criterion Scores for Males ("1") and Females (*F"):

Current Composites (Continued)

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined F M) (M - F) of the Difference

SC Cluster

80 89.64 94.26 90.44 -3.82 4.05

85 92.25 95.45 93.08 -2.37 3.29

90 94.87 96.63 95.72 -.92 2.57

95 97.48 97.82 98.36 .54 1.94

100 100.09 99.01 101.00 1.99 1.50

105 102.71 100.20 103.64 3.44* 1.46

110 105.32 101.38 106.28 4.90* 1.84

ST Cluster

80 85.21 81.52 86.03 4.51 3.19

85 88.22 84.89 88.92 4.03 2.67

90 91.22 88.26 91.82 3.55 2.18

95 94.23 91.64 94.71 3.07 1.72

O0 97.23 95.01 97.60 2.59 1.33

105 100.24 98.39 100.50 2.11 1.10

110 103.24 101.76 103.39 1.63 1.13

p' .05

OF cluster, the three regression lines of the current composite are essentially equal, while a switch to the alternative composite would result in

some undarprediction along the entire regression line. In general, the

degree of underprediction of female scores shown in these three clusters is

relatively small. The CL cluster is perhaps the most extreme case and here

the common regression line falls only about two points below the female line.

Considering all of the data discussed above, it appears that the alternative

AA composites could replace the composites now being used operationally

without increasing predictive bias on the basis of gender. The differences

in predictive validity of the two sets of composites are quite similar, and

the degree of underprediction of female performance by a common regression

line is much the same for both composite sets.

Other Analyses of Subgroup Differences

One possible explanation of the lower predictive validities for blacks in

Taoles 29 and 30 and for females in some clusters of Tables 33 and 34 is

that these subgroups showed less variability in their criterion scores than

the other two subgroups. The data relevant to this hypothesis can be found

80
Taole 36

Predicted Criterion Scores for Females ("F") and Males ("M"):

Four Alternative Composites

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined F M) (M - F) of the Difference

CL Cluster

80 93.10 94.36 90.98 -3.38 2.86

85 96.08 97.63 94.06 -3.57 2.37

90 99.06 100.91 97.14 -3.77* 1.93

95 102.03 104.18 100.21 -3.97* 1.56

100 105.01 107.45 103.29 -4.16* 1.34

105 107.99 110.73 106.36 -4.36* 1.31

110 110.97 114.00 109.44 -4.56* 1.49

EL Cluster

eO 91.37 92.47 91.02 -1.45 2.95

85 94.49 95.38 94.20 -1.18 2.37

90 97.61 98.30 97.38 -.92 1.82

95 100.74 101.21 100.55 -.66 1.34

100 103.86 104.12 103.73 -.40 1.02

105 106.98 107.04 106.90 -.14 1.04

110 110.11 109.95 110.08 .13 1.38

MM Cluster

80 92.70 84.42 93.28 8.86 2.97

85 95.13 87.82 95.66 7.83 2.36

90 97.57 91.23 98.03 6.80 1.90

95 100.00 94.63 100.40 5.77 1.70

100 102.43 98.04 102.77 4.74 1.85

105 104.86 101.44 105.14 3.70 2.27

110 107.29 104.84 107.52 2.67 2.86

OF Cluster

80 92.75 94.06 92.21 -1.85 1.62

85 95.81 97.24 95.32 -1.91 1.25

90 98.88 100.42 98.44 -1.98* .99

95 101.94 103.59 101.55 -2.04* .95

100 105.01 106.77 104.67 -2.10 1.15

105 108.07 109.95 107.78 -2.17 1.49

110 111.14 113.13 110.90 -2.23 1.91

(cont'd)

8 673
Predicted Criterion Scores for Females (OFf) and Males ("M"):

Four Alternative Composites Solution (Continued)

Subgroup

Composite Predicted Criterion Score Difference Standard Error

Score (Combined F M) (M - F) of the Difference

SC Cluster

80 92.59 95.16 92.26 -2.90 3.88

85 95.17 97.41 94.89 -2.53 3.16

90 97.74 99.66 97.51 -2.15 2.47

95 100.31 101.91 100.14 -1.78 1.86

100 102.89 104.16 102.76 -1.40 1.44

105 105.46 106.41 105.39 -1.02 1.40

l1O 108.03 108.66 108.02 -.65 1.76

ST Cluster

80 85.38 81.69 86.14 4.46 3.18

85 88.46 85.27 89.10 3.83 2.67

90 91.54 88.86 92.07 3.21 2.17

95 94.63 92.44 95.03 2.59 1.71

100 97.71 96.03 97.99 1.96 1.33

105 100.79 99.61 100.95 1.34 1.10

110 103.87 103.20 103.91 .71 1.13

p ( .05

in Table 32 for the comparison of racial subgroups and Table 33 for comparisons based upon gender. It should be noted that all of the standard deviations in these tables are similar, because the criterion measures had been

standardized to have a standard deviation of twenty in each MOS.

Examination of Table 37 shows that the small differences observed between

black and white composite validities are not due to any major restriction in

the variability of the criterion for black soldiers, relative to white soldiers. For seven of the nine clusters the black subgroup showed greater

criterion variability than did the vhite subgroup. The differences in predictive validity between these groups, therefore, cannot be attributed to

differences in criterion variability.

The data in Table 37 do suggest an explanation for the observed overprediction of black soldier performance by the use of a common regression

line. For ail nine clusters in this table, the mean criterion score for

blacks is slightly smaller than the value for whites. Such a relationship

normally leads to common line overprediction of ti._ subgroup with the lower

mean criterion score.

82
Table 37

Means and Standard Deviations Of the Combined Criterion Scores

for Black (B) and White (W) Subgroups

Means Standard Deviations

Cluster 8 W B W

CL 97.63 105.68 19.18 19.63

CO 94.46 103.71 20.05 19.08

EL 99.60 105.75 19.72 18.74

FA 97.23 107.55 19.24 18.25

GM 99.74 104,72 19.28 20.77

MI 95.13 103.99 20.63 18.97

OF 97.51 104.34 20.43 19.00

SC 97.00 105.78 19.93 18.62

ST 96.34 104.27 19.85 18.68

Table 38 shows that lower criterion variances cannot explain the differences

in validities between females and males in Taoles 33 and 34. For the clusters that had shown somewhat lower validities for females than males (CL,

EL, OF, and ST), only in the CL cluster did the criterion scores from female

soldiers have less observed variance than the male criterion scores.

Table 38

Means and Standard Deviations Of the Combined Criterion Scores

for Female (F) and Male (M) Subgroups

Means Standard Deviations

Cluster F M F M

CL 103.63 101.00 18.12 19.89

EL 102.09 105.05 19.16 18.92

MIM 96.09 101.36 20.76 19.92

OF 99.34 101.78 20.23 19.77

SC 98.0 104.23 20.10 19.03

ST 99.86 103.76 20.37 18.74

• o _ i 83
For All of the comparisons among subgroup predictive validities and reg-ession lines discussed above, the reporting of analyses has been at the cluster

rather than the 1405 level. In order to aggregate the information to'this

level, the statistics were first calculated for each 1OS. The resulting

data were then pooled (weighted by sample size) across the appropriate iX)S

to obtain the analyses for each cluster or composite. While this approach

is the most reasonable way to aggregate MOS-level data to the cluster level,

it does not inform about MOS-level relationships. This question is particularly relevant for 140S with different proportions of subgroup populations.

We addressed this question by comparing regression lines for sets of two MOS

within each cluster. The particular MOS for these analyses were selected

according to the following criteria: First, there had to be at least two

MOS within a cluster for which we had data for at least 100 soldiers in each

subgroup. Second the two NOS within each cluster were selected by taking

the two that showed the greatest difference in the ratio of subgroup sample

sizes. For example, in the the analyses of racial differences within the CL

cluster, the two 140S examined were 71L and 75D. In the case of 71L the ratio

of whites to blacks was 1.07, while in 75D the same ratio was .43. This

procedure was followed in order to maximize the probability of uncovering

differences in the regression lines as a function of the distribution of

subgroups within the MOS. The procedure had the side effect of allowing for

the reporting of analyses of OS with relatively small sample sizes in comparison to the other analyses of this report, but the minimum sample of at

least 100 soldiers per subgroup was still large in comparison to past

research.

For the MOS meeting these criteria the differences between subgroup regression lines for both racial and gender comparisons are given in Tables 39 and

40 for the current composites and in Tables 41 and 42 for tne alternative

composites. The comparison of the subgroup regressions to the common regression line are presented in Figures 17 through 30,

Three important findings emerge from these tables and figures. First, these

data indicate that a switch to the alternative composites would not result

in an increase in predictive Dias for either blacks or women. Most (nine

out of fourteen) of the 1405 show quite similar patterns among the subgroup

regression lines drawn from the current and alternative composites. For the

comparisons based upon race, only MOS l1H and 13F showed substantial change

with tne new composites. For 140S llH, the switch to the new composites would

tend to result in overprediction of black soldier performance while the current system produces some underprediction. For MOS 13F. the new composites

produce a subgroup regression line that is closer and no longer nearly parallel to the common regression line. Neither of these changes would negatively impact the enlistment of blacks into these OS.

Likewise, a change to the alternative composites does not present problems

for the enlistment of female soldiers even when the pattern among the regression lines appears to change with tie composites. In the case of M4OS 05C

this change results only in the regression lines being closer together, ano

therefore showing less underprediction of femalt performance by the common

regression line. For MOS 75C the relative degree of underprediction versus

overprediction is fairly constant for the two sets of composites, but J ere

each occurs along the common regression line changes as the composition of

the composites changes. In this MOS, underprediction tends to occur for

84
110- :

C9P1T IN-R

Is.

:0

0

it

110- _

C I ___ __ ___--

so of go 100 IDSIl

LLOCN~i M"SITw SCOME

Figure 17 Regression lines for Current and Alternative (61d and new)

Comnposites for MOS 118, by Race

85
12

C t

to

to

C&LtPOSI TI Scoftt

LIGENo? SURUP I -C -

1103 Of CRITERIYON SCORf'*138 VCRSIONaNtM

120-

-------

soUt o o

ftN TI"f L90 SUNOM-ij -t__

Fiue Rgeso ie o urn n lentv odadnw

CopstsfrMS10 yRc

p8
NOS OF C;ITCRtON SCORfa3SC VERSIONzW.O

120.

110-,

~0 85____

LI2~ SUORU -- -- a - C

Its

SO

C 1

T O 1Is u o05 1

0 1

N

115-

70-

@ a .. 8- .. .. .. 9 IN I " I l

T 10 - - .. . .. .

Fu 10 __li f Cr a A r i ( n e

C

Ift1

0-

iD0 II 10 35 I O105 110l

LCOEPWt.' SUjIOqOUjP .... 3 - - C -- U

Figure 19 Regress~on lines for Currnt and Alternative (old and new)

Composites fcr MCS 36C, by Race

87 / .j
1105 OF CRITERION 5COXEx7IL VER31ONcOLD

110

I

I

0

N

0

1103 OF CRITERION SCOIKE=71L VERSION=NEM

120

115-

C10

ft

T oo E

C

3s so Is too10 101 110

CO"WOITE SCOSE

LEOENOS SPAWOWJ ---- 9 - C -

Figure 20 Regression lines for Current and Alternative (old and new)

Composites for MOS 7lL, by Race
IMSi Of CRTEAJON6 SCOUEs7SO VEPSJOMOL

12

I M-----

- -------

CO8?OUZTE SCONE

L!OCNM: 5SRtI? ---- a - t -

M903 Of CRITERION SCORE=750 VERSIONEW

123

Its

100

Cl

00 to IS l

CGVIIT CR

Kq5U

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~Figure 22 Regression ines for Current and Aternelve (old and new)

Composites for M'OS 13F, by Race

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Figure 23 Regression lines for Current and Alternative (old and new)

Comnposites for I'VS 11H, by Race

91
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4Figure 24 Regression liner, for Current and Alternative (old and new)

Composites for MOS 31M, by Race

92
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Figore 25 Regression lines for Current and Alternative (old and new)

Com~posites for NtiS 05C. by Gender

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Figure 26 Regression lines for Current and Alternative (old and new)

Composites for MOS 75C, by Gender

94
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Figure 28 Regression lines for Current and Alternative (old and new)

Composites for MOS 76Y, by Gender

96
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Figure 29 Regression lines for Current and Alternative (old and new)

Composites for ?40S 91E, by Gender

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Taole 39

Predicted Score Oifferences (Diff.) anc Standard Errors (SE)

of tne Difference between Blacks (8) and Whites (W) for

Particular MOS: Current (iperational Composites

Composite Score

N 85 95 105

Cluster B W Cutoff Diff. SE Diff. SE Diff. SE

CL Cluster

MOS 71L 1229 1322 95 -4.69 1.44 -5.13 1.07 -5.51 .82

MOS 750 481 205 95 -2.47 3.32 2.40 2.29 7.26 1.66

CO Cluster

MOS 11H 122 769 85 2.14 1.89 .46 2.04 -1.21 2.31

MOS 118 1146 4174 85 1.94 .74 2.46 .71 2.97 .77

EL Cluster

MOS 31M 563 1185 95 4.38 1.33 4.18 1.03 3.98 .97

M05 36C 214 132 90 -2.53 3.50 -1.93 2.36 -1.32 2.98

FA Cluster

MOS 13F 125 657 100 7.74 2.00 8.10 1.67 8.46 1.65

MOS 138 1814 2471 85 2.08 .80 4.12 .65 6.16 .63

lower composite scores using the current composite. With the alternative

composite, underprediction is observed for higher scores of the AA composite.

Tne other M0S that snowed a noticeable change among the regression line witn

tne alternative composites was 76Y. In tnis case tne alternative composite

tends to snow somewhat more underprediction of female performance than oes

tne current composite. while the average difference (3.25 points) in underprediction of the alternative versus the current composite for 76Y is small

relative to the criterion standard deviation, the difference does approach

statistical significance. This finding suggests that as new criterion oaza

become available further attention and research be devoted to analyzing tne

differences between male and female soldiers in MOS 76Y. In most cases,

however, as with tne comparisons based upon race, the change to the alternative composite should not result in substantial underprediction of subgroup

performance. The new composites could be used operationally without an

increase in predictive bias in the selection and classification system.

The second finding of these analyses is that, as expected, the large MOS

(eg. l1B, 138, etc.) show patterns of under- and overprediction that are

quite similar to tne summary 'ita presented earlier at the cluster level.

For example, the large MOS in Tables 39 and 41 all snow overprediction of

99
01ack performance for botn sets of composites. Such results are also presented in Tables 31 and 32 in the subsection discussing differences in the

regression lines for the two races 4t the cluster or composite level. This

result is not surprising since the MOS statistics were weighted by sample

size wnen they were pooled to ootain the cluster data.

Table 40

Predicted Score Differences (01ff.) and Standard Errors (SE)

of the Difference between Females (F) and Males (M) for

Particular NOS: Current Operational Composites

Composite Score

N 85 95 105

Cluster F M Cutoff Diff. SE Diff. SE Diff. SE

CL Cluster

MOS 76Y 248 888 95 -.97 3.11 -1.92 i.9d -2.86 1.41

AOS 75C 143 1b8 95 -2.59 2.88 .34 1.73 2.67 7.40

SC Cluster

M05 05C 260 1711 95 -5.44 3.05 -1.14 1.78 3.16 1.26

SOS 72E 237 325 90 13.53 2.72 11.80 1.78 10.07 1.60

ST Cluster

MUS 958 426 3269 100 3.84 2.45 3.28 1.55 2.71 .95

MOS 91E 117 184 95 2.71 4.89 -1.02 3.00 -4.74 2.17

The tnira finding from these analyses is tnat within a cluster it appears

tnat differences in the subgroup proportions can result in major cnanges in

tne pattern anong the regression lines. For example, within tne SC cluster,

use of tne alternative cuwnposites would result in underpredicting female

criterion scores in MOS 05C where tne ratio of males to females is 6.6.

nowever, in MOS 72E where this ratio is only 1.4, use of tne same compos,.e

would result in overprediction of female performance.

This finding suggests that it may be necessary to evaluate predictive oias

at the MUS level. Each MOS in the sample coula be analyzed using the

Johnson-Neyman technique (See Rogosa, 1980) to determine whether a significant difference between tne subgroLp regression lines exists for any value

of the composite. If a regicr of significance exists and includes the cutoff

score for that 14S, further investigation of that 1OS would be warranted.

The aggregation of results to the cluster level mignt best oe cone qualitativcly. For example, the proportion of MOS within tne cluster that snow

significant differences around the cutoff score coulo De reported.

100 ,
Table 41

Predicted Score Differences (Diff.) and Standard Errors (SE)

of the Difference between Blacks (B) and Whites (W) for

Particular MOS: Four Alternative Composites

Composite Score

N 85 95 105

Cluster B W Cutoff 0iff. SE Diff. SE Diff. SE

CL Cluster

MOS 71L 1229 1322 95 -1.47 1.35 -1.00 1.01 -.55 .77

MOS 75D 481 205 95 -1.44 3.14 1.07 2.17 3.57 1.57

CO Cluster

MOS 11H 122 769 85 -.83 1.89 .99 2.03 2.82 2.30

MUS 115 1146 4174 85 .79 .73 2.08 .71 3.38 .76

EL Cluster

MUS 31M 563 1185 95 1.59 1.32 2.03 1.03 2.47 .97

MOS 3bC 214 132 9U -2.35 3.51 -1.63 2.37 .90 2.99

FA Cluster

MOS 13F 126 557 100 45 1.99 3.38 1.67 6.31 1.65

MOS 133 1814 2471 85 1.16 .79 2.64 .65 4.13 .63

Summiary

The current and proposed alternative AA composites were investigated for

possible suogroup Dias in a number of ways, including analyses of predictive

valid ities, comparisons of subgroup regression lines, an plotting the relationsnip of tne subgroup regressions and the common regression line. All

suogroups were found to be well predicted by the composites. Botn sets of

composites were found to show some small differences in predictive validity

as a function of racial background and genader. The comparisons of regression lines indicate that tne use of eitner set of composite- to select and

classify enlisted personnel for the Army should not result in unfair practice; against blacks or women.

101(/
Table 42

Predicted Score Differences (Diff.) and Standard Errors (SE)

of the Difference between Females (F) and Males (M) for

Particular MOS: Four Alternative Composites

Composite Score

N 85 95 105

Cluster F M Cutoff Diff. SE Diff. SE Diff. SE

CL Cluster

MOS 76Y 248 888 95 -2.86 3.02 -5.17 1.92 -7.49 1.37

MOS 75C 149 168 95 3.27 3.76 .61 2.52 -2.06 1.93

SC Cluster

MOS 05C 260 1711 95 -3.63 2.92 -2.35 1.70 -1.07 1.20

MOS 72E 237 325 90 8.18 2.57 4.47 1.68 .76 1.51

ST Cluster

MOS 958 426 3269 100 3.90 2.43 2.9. '.54 1.95 .95

MOS 91E 117 184 95 .61 5.00 -2.0 07 -4.68 2.22

102
Alternative Composite Scales and Cutoffs

Composite Scales

For the current composites, the subtest sum scores are converted to scale

scores with a mean of 100 and standard deviation of 20 in the 1944 reference

population, using a table that gives a somewhat nonlinear mapping. In general, the current conversions compress scores somewhat in the middle range

while expanding the more extreme scores. In addition, the current conversions compress a number of different sum scores that are well below guessing

rates onto the minimum composite score of 40.

In order for some or all of the new composites to be adopted for operational

use, similar conversion tables are needed. Some consideration was given to

proposing alternative scalings, in particular a linear adjustment, so as to

simplify the calculation of the final composites. The final recommendation,

however, is to maintain the current procedures insofar as possible. This

choice leaves open the Army's option of adopting only some of the new composites while leaving some of the existing composites intact, and it also

keeps relatively constant the implicit conversions between composite and

percentile scores.

The conversion tables for the alternative composites were designed to match

the distributions of the current composites on a representative sample of

13,319 FY8l/82 applicants who were first-time test takers and who completed

form 8, 9, or 10. The overall objective in defining conversion tables for

tne new composites was to make the distribution of scores on the new composites resemble the distribution of scores on the old composites as closely as

possible for any group of ASVAB test-takers. To achieve this end, cumulative frequency distributions were computed for each of the existing 9 composites. Table A-20 in the Appendix shows the resulting distributions.

These distributions were then averaged to give a target distribution for

equipercentile equating of the alternative composites. Thus, each subtest

sum score was mapped onto the target composite score with the same percentile. Table A-21 in the Appendix shows these conversions.

The sample size of 13,319 is sufficient to yield maximum sampling errors of

less than . in percentages estimated from the sample. As shown in Table

A-22, the current composite scalings differ from each other by as much as 5

percent in tne percentages at or below given score levels. Therefore, the

estimated maximum sampling error of .5 percent was judged negligible in comparison.

Cutoff Scores

The cutoff levels reflect two competing concerns. The first is that soldiers scoring above the cutoff level on a particular composite must have a

"reasonable" level of expected performance for the MOS in question. The

second is that the cutoff criteria should screei out the right proportion of

applicants relative to the number of available training slots. It mus. not

be so high that a significant number of slots go unfilled, ye" nign enough

103 * /
so that, where there are more applicants than slots, only the "best" applicants fill these slots. Because previous information on expected performance

levels has been relatively weak, supply and demand considerations have predominated in the setting of composite score cutoff levels.

The setting of a cutoff level for a composite does not presume that soldiers

can be-sorted into clearly acceptable or unacceptable categories through use

of paper-and-pencil tests. In general, the relationship between composite

score and subsequent performance level is roughly linear, so that differences at different points on the composite scale reflect similar differences

in expected performance. However, Project A has not yet gathered the critical information on the variation in payoff for performance in different MOS.

Lacking clear-cut standards for criterion referenced cutoff points, we felt

it most appropriate to identify cutoff points that are as consistent as possible with those currently in use, in order to maintain the current balance

between MOS in the distribution of available applicants.

The simplest measure of consistency between new cutoffs and the existing

cutoffs would be that the percentage of applicants "passing" the cutoff criterion snould be the same. Since we have rescaled the new composites so

that the percentage of applicants above or below a given score is the same

for each new composite as for the existing composites, this means that the

existing cutoff points could be used "as is". The percentage passing for

any given applicant group would be the same as it currently is.

We are concerned, however, that since the new composites are more highly

correlated with the overall selection criterion, AFQT, and also with each

other, some problems might arise. In particul'r, the greater correlation

among composites implies that the different MOS would be more likely to

judge the same applicants as eligible, increasing the competitiun among the

MOS. If cutoffs remained the same, more slots might go unfilled as there

woula be fewer different individuals to draw on. However, because the new

composites are more highly correlated with AFQT, those above a given criterion cutoff will tend to have higher AFQT scores on average with the new

composites. This means that the cutoff levels could be lowered slightly and

still leave the same distribution of mental categories among the eligibles

as is currently the case. In a final set of analyses, we set out to determine the cutoff scores that would leave constant the average AFQT scores for

those applicants passing the cutoff.

An "average AFQT" score was assigned for each possible composite score for

each old and new composite. This average AFQT score was defined as the

average of the AFQT percentile scores for all applicants in the sample of

13,3t9 who scored at cr above the given composite score. Then, for each

possible score on each of the new composites, we identified the score on

each of the existing composites that had the same "average AFQT" score.

Table A-22 in the Appendix shows the results of this approach.

Using this table, one ran identify the cutoff on any alternative composite

that corresponds to a specified cutoff on a current composite. A complete

listing of cutoffs for current composites is given in Table A-23 in the

Appendix. For example, if the mean AFQT score of eligible applicants is to

remain constant, a cutoff of 90 on the current CO compcsite translates to a

cutoff of 89. The major differences betwen tne cutoffs for current and proposed compsites is between CL and ACL: the cutoffs for ACL could be roughly

5 points lower than the current cutoffs for CL.
Summary

The purpose of this research was to assess (1) the effectiveness of current ASVAB area composites for personnel selection and classification and

(2) the potential for establishing improved enlisted personnel selection

and classification rules based on the ASVAB. Current composites were

compared to empirically identified favorable alternatives in terms of

predictive validity, differential validity between MOS, and predictive

bias for different population subgroups.

Validation analyses were performed using training and SQT scores for the

FY81/82 cohort of accessions. The results of the analyses are limited by

the data base and by the accuracy of necessary, yet untestable assumptions

made in carrying out the analyses. Data to be collected in Project A will

ultimately allow testing of these assumptions, and despite the limitations,

substantive results were obtained which lead to particular recommendations.

Limitations

Information on Criterion Quality. The analyses were carried out as if the

measured criteria were the sate, and error-free, criteria to be maximized

in selecting and classifying enlisted personnel. To the extent that

unmeasured criteria are also important, the validation of composites is

incomplete. In the worst case, a combination of ASVAB tests might be

highly correlated with unmeasured criteria in an MOS but uncorrelated with

the criteria used in this set of analyses.

Neither reliability nor validity figures were available for the criterion

measures. However, there were many MOS for which both training and SQT

measures were available on the same soldiers; and if the correlations

between these measures were high, it would indicate the likelihood of reasonably high criterion reliability and validity. Unfortunately, the correlations were low, ranging from -.12 to .56, over 81 MOS, with a mean of

.22. Although it is possible that this merely indicates that trainino

scores and SQT scores measure different aspects of overall performance in

the MOS, the need for (1) more systematic coverage of the performance space

for an MOS and (2) criterion reliability and validity information is clear.

Empirical results obtained without this information are likely to have

major inaccuracies.

Information on Utility of Performance. Related to the problem of criterion

reliability and validity is the problem of translating results to gains in

measurable costs and benefits. The analyses were carried out based on the

assumption that the value of a one-standard deviation increase in the performance of a soldier was constant, both across different levels of performance in a single MOS and across .ifferent MOS. To the extent that the

value of a performance increment varies between MOS, the derived precedures

will be inaccurate, and to the extent that utility is a non-linear function

of performance, results of linear regression will Le inaccurate. Of these

two problems, the variation between MOS is of greatest concern, because

linear approximations to non-linear relations are frequently quite good.

105
No information was available on the utility of performance increments.

Subsequent analyses will evaluate the sensitivity of the validity of composites to deviations from the equi-utility assumption. During 1985, the

project will collect utility-of-performance-increments information to provide the basis for subsequent validations to be carried out in the project.

Sa ple Size. Although these analyses were based on the largest database

yet ava ble for ASVAB validation, with criterion data on more than 100

soldiers in each of 98 MOS, and on more than 500 soldiers in 35 of these

MOS, the sample was still not sufficiently large for some of the planned

analyses. In particular, the patterns of correlations of expected performance across MOS were not replicable in half-samples. As a consequence

completely empirical determination of clusters of MOS for the purpose of

assigning composites was impossible, and assessment of predictive bias was

limited.

Samples on the order of 2000 per cell are needed for analyses such as

these, and these were attainable when we focused on current clusters of

MOS rather than individual MOS. While such samples for individual MOS are

extremely costly, they can be accumulated over years if there is sufficient

stability of predictors and criteria. Moreover. deviations from that stability can be estimated from a cumulative data base.

Findins

Predictive ValiditX of Current Composites. The validities for 98 MOS,

based on comoinations of training and SQT scores, and adjusted to apply to

the FY81 and FY82 Army applicants, ranged from .12 to .74, with a mean of

.45. Grouping MOS by the current composite clusters, the lowest mean

validity was .42, for Surveillance and Communications MOS and for General

Maintenance MOS, and the highest was .54, for Skilled Technical MOS. In

general, there was almost no tendency among MOS for the currently assigned

composite to have a higher validity than other current composites.

Identification of Optimal Alternative Composites. Although the data base

did not support purely empirical identification of clusters of MOS for

which the same composite could be used, it was possible to evaluate alternative combinations of the nine clusters of MOS associated with the current composites. Only unit-weight alternative composites were considered,

after it was found that optimal unit-weight composites for four clusters

possessed a root mean square validity 97% as great as the root mean square

validity of ridge regression vectors computed separately for each of 98

MOS. The loss due to using only four unit-weighted composites was minimal.

Roughly 700 different sets of composites were evaluated in terms of predictive validity, with a focus on four composite soljtions, since four

nominal factors of the ASVAB have been identified. The best alternative

four-composite solution that we identified, a locel optimum that is very

likely the actual four-composite optimum, was

106
Clerical (ACL): VE + AR + MK

Skilled and Technical (AST): VE + AR + MK + AS

Operations (AOP): VE + AR + AS + MC

Combat (ACO): VE + K + AS + MC.

The operations composite combines the current SC, OF, and MM clusters; and

the comoat composite combines the current CO, FA, GM, and EL clusters.

Validity of Alternative Composites. The RMS validity of the four-composite

set was .486. This compares with an RMS validity of .489 for the best set

of nine unit-weight composites. Variations of validity in the third decimal place are neither statistically significant nor of great practical

importance, and a variety of alternatives to the four-composite solution

were explored.

Of special interest were the three- and two-composite solutions. The loss

in validity which would result from using the new "combat" composite (ACO)

for both the Ocombat" and *operations" KOS is negligible (.001), as is the

loss in, further, using the new Clerical composite (ACL) for both clerical

MOS and skilled and technical MOS. This two-composite solution captures

97% of the predictive power of the ASVAB for the performance criteria used

in these analyses.

Certain of the current composites account for a large part of the difference in validity between current and alternative composites. When compared to validities of optimal composites for the same clusters of MOS,

the Clerical composite (CL) appeared to be weak, with a validity of .48

versus a potential of .56. One other composite, Surveillance and Communications (SC), was mildly weak, with a validity of .45 versus a potential

of .5U.

The gain in expected performance if these composites were changed can only

be approximated because of the constrained nature of the selection and

classification process. If, however, the choice were purely between

assignment to an individual MOS and rejection, application of Cronbach's

formula yields an expected gain of .05 standard deviations per person in

the two clusters of MOS from introduction of these two revisions to the

current composites.

Differential Validity of Current and Alternative Composites

The ability of current and alternative composites to identify the best MOS

for each enlistee was assessed, using a variant of Horst's Classification

Efficiency index. The current composites and five alternative sets of

composites all possessed between 45% and 67% of the differential validity

of the ASVAB as a battery. There was essentially no difference between

the composite sets, with the exception that the "single composite" solution did not perform as well as others. In particular, the performance of

107
the current composite set was virtually identical to the performance of

the alternative which merely replaced the CL and SC composites. In general, the differential validity of the ASVAB as a battery was higher for

low-frequency MOS, but this effect was less pronounced for the sets of

unit-weight composites.

Predictive Bias of the Current Co!osites. The validities of the composites are slightly higher overall for whites (.45) than blacks (.38), but

there is, if anything, a tendency to underpredict performance of whites

more than blacks. The validities of the EL and SC composites are greater

for males than for females, but overall the average difference in validity

only slightly favors males (.47) over females (.43). Underpredictions of

performance were split between males and females, with the most noticeable

underpredi tion being roughly .06 staneard deviations for women using the

SC composite. In general, the over- and underpredictions were small,

especially in the region near the cutoffs.

Predictive Bias of Alternative Composites. In general, the patterns of

differential validity and underprediction observed for the current composites also were found for the four alternative composites, ACL, AST, AOP,

and ACO. The overall average validity for whites (.47) was somewhat higher

than for blacks (.40), but the underpredictions of performance were suffered primarily by whites. An exception to this was the underprediction

of blacks' performance by the alternative skilled technical composite

(AST). Blacks' criterion scores in the OF cluster were underpredicted by

both the current and alternative composites, and the degree of underprediction was slightly greater using the proposed composite.

The alternative composites had a slightly smaller difference in validities

between men and women (.48 vs. .42) than the current composites, but again

the most noticeable differences were the greater validities for men in the

EL and SC clusters. There were also somewhat greater underpredictions of

women's performance in the CL, OF, and SC clusters using the alternative

composites, although in general the differences were small.

Recommendations

Selection of a Composite Set. First, the statistical results tend to favor

the alternative four-composite solution over the current nine composites

in terms of overall absolute predictive validity and differential validity

for MOS classification. The results for predictive bias are mixed, but

the effects are not large in either direction. The average validity of

the alternative composites is .48, vs. .45 for the current composites, and

there was virtually no difference in differential validity between the

alternative sets of composites.

The major source of tie relative deficiency of the current composites lay

in two of the composites, CL and SC. Depending on the relative costs cf

implementation of different levels of change in composites, a more favorable proposal might be merely to replace these two composites with the ACL

and AOP composites, respectively, keeping intact the nine-composite structure. The average validity of the revised nine composites would be .47,
1!

while the differential validity as measured by the criterion adapted from

Horst (1954) would be virtually unchanged. This solution would also avoid

the ititroduction of AST, with its small increase in underprediction of

blacks' performance in skilled technical MOS.

Relative Value of Composites and Cutoffs. The validation analyses, and

particularly the differential validity analyses, indicated that the procedure of assignment to a cluster of MOS on the basis of the highest composite has limited expected payoff. The various composites are highly corre.

lated, and therefore yield little unique predictive variance.

The choice of "cutoffs" is a far more potent procedure for increasing the

overall average expected performance than is the choice among composites.

Variation in the predictive value of even perfectly correlated composites

can yield gains in classification with appropriate cutoffs, when compared

to random assignment.

The basic rule for assignment is to assign individuals with a great deal

of a particular ability to MOS with the greatest payoff for that ability

(e.g., MOS with the highest measure of association) and to assign individuals lacking the particular ability to MOS not requiring that ability

(e.g., MOS with minimal measures of association).

If there were clearly valid measures of association between ASVAB scores

and payoffs for assigning enlistees to MOS, one would be tempted to recommend the use of these measures of association in the determination of cutoffs. This is not wise at the present time, however, because of the

incompleteness of the coverage of the criterion space ny available measures and the lack of information on the relations between performance and

payoff to the Army (i.e., utility information). The availability of comprehensive, reliable, utility-related criterion measures would make this

approach to assigning MOS attractive.

Broadening the Span of Predictors. The current composites, as well as the

best alternative composites, account for only about 20 to 25 percent of

the variance in the criteria, but they account for over 90% of the variance in the criteria that is predictable from the ASVAB. The ASVAB measures four identifiable common factors, but only two eigenvalues are

greater than one, and the first principal component accounts for roughly

half the variance. Four of the nine subtests, GS, EY, NO, and CS, played

no role in the compositihn of the proposed unit-weight composites.

This level of predictability is clearly not sufficient for accurate identification of the optimal assignments of enlisted personnel to MOS. While

it was impossible to assess the contributions of limitations of the criteria and of the ASVAB separately in these analyses, the adjusted validities were uniformly modest, with only 14 out of 98 greater than .6. There

is a need for use of a broader set of predictors in the selection and

classification process for enlisted personnel.

109
Increase the Sample Size. The present analyses combined the data from two

years, FY81 and FY82, with a substantial increase in the possible coverage

of MOS over the coverage available from one year's accessions. For many

MOS, there are not sufficient numbers in any year to support needed parameter estimation for the purposes of deriving optimal assignment procedures. However, with a proper control for trends across years, the data

base can be built up over a few years to the point where the needed two

thousand cases in each MOS are available for analysis.

Although the replication of these analyses two years hence was to focus on

the FY83 and FY84 cohorts (with the addition of utility information), the

data base for those analyses will actually be the four-year cohort, FY81

through FY84 accessions. This will provide not only the basis for exploring trends but also an adequate data base for a larger set of MOS.

Conclusion

The major practical result of this investigation was the identification of

suitable replacements for the two relatively weak ASVAB Ared Composites

currently in operational use by the Army. Introduction of new composites

for the Clerical & Administrative and Surveillance & Communications MOS

will significantly increase the expected performance levels of enlisted

personnel entering these MOS, without affecting differential validity of

the composites or introducing significant predictive bias.

In addition, this effort resulted in the development of systematic procedures for the validation of ASVAB composites, including data editing, range

restriction adjustment, ridge regression estimates of optimal composites,

differential validity estimation, predictive bias assessment, and setting

of cutoff scores. At the same time, the research effort highlighted needs

for additional research and development that would solve several methodological problems.. In particular, there are needs for criterion validity

and reliability information, performance utility estimates, cumulative

additions to sample sizes, further work on range restriction adjustment

and differential validity measurement, and a broadening of the coverage of

sKills required in different MOS. This coverage must be included in both

the criterion measures and the predictors.

Throughout the remainder of this project, work will go forward on the

development of better predictors and better criteria; and future validations of enlisted personnel selection and classification procedures can de

expected to refine and extend the results presented here.

110
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111 .
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112

75
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113 (3
APPEN DIX A
Table A-I

List of MOS in the Army

Current Current

MOS Name Composite MOS Name Composite

71C. SECRETARY CL 21L; PERSHING CLCT REP EL

710: ;EGAL CLERK CL 22L: NIKE TEST EQUIP REP EL

71iG: PATIENT ADMIN SP CL 22N: NIKE-HERC MAL-LNCH REP EL

71L: ADMINISTRATIVE SPECIALIST CL 23N: N'IKE TRACE ROR REP EL

71M: CHAPEL ACTIVITIES SP CL 23U: NIKE HP ROR SIM REP EL

71N: TRAFFIC MANAGEMENT COORD CL 24C: IH FIRING SEC MECH EL

73C: FINANCE SPECIALIST CL 24E: In FIRE CON MECH EL

748: CARD & TAPE WRITER CL 24G: IH COORD CEN MECH EL

758: PERSONNEL ADMIN SP CL 24H: IM FIRE CON REP EL

75C: PERSONNEL MGT SP CL 24K: IH CW ROR REP EL

750: PERSONNEL REC SP CL 24L: IH .NCh/MECH SYS REP EL

75E: PERSONNEL ACT SP CL 24M4: VULCAP SYS MECH EL

7/F:"PERS INFO SYS MGT SPEC CL 24N: CHAPARRAL SiS MECH EL

76C: EQ REC & PTS SP CL 24P: DEF ACQ RAAR MECri EL

i60: MEDICAL SUPPLY SP CL 24Q: NIKE/IHERC FC MECH EL

7*P: MAT CTL ACTG SP CL .24U: HERCULES ELCT MECH EL

76V: MAT STOR & HOLG SP CL Z5J: OP CENTRAL REP EL

26W: PETROLEUM SUPPLY SP CL 25L: AN/TSQ-73 OP/REP EL

76X: SUBSISTENCE SUPPLY SP CL 266: wEaPONS SPT RDR REP EL

76Y; UNIT SUPPLY SP CL 26C: CST AREA SVL ROR REP

2o0: GCA RADAR REPAIRER EL

11S: INFANTRYMAN CO 6E: AERIAL SURVL SEN REP

11C: INDIRECT FIRE INFHN CO 26H: AIR DEFENSE ROR REP EL

1Nm: HV ANTI-ARMOR WPN INFM) CO 26K; EL WARNING/DEF EQ REP E'.

N1M: FV INFANTRYMAN CU 26L: TAC MWAVE SYS REP L

11X: ATTRITED 110 CO 26M: AERIAL SURVL RD REP

128: COMBAT ENGINEER CO 26N: AERIAL PHOTO SEN REP EL

I2E: ANI4 SPECIALIST CO 26Q: TAC SAT/MW SYS OP EL

12F: ENGR TRVEd CRMN CO 26R: STRA MW SYS UP E;

190: CAVALRY SCOUT CO 26T: RDO/TV SYS SP EL

19E: M48-M60 ARMOR CREWMAN CO 26V: STRAT MWAVE SYS RP EL

19F; 948-M60 TANK DRIVER CO 26Y: SATCOM EQUIP REP EL

19G: AR MOR RECON. VEH CREWMAN CO 27B: LCSS TEST SF/LANCE REP EL

19H: ARMOR RECON. VEH DRIVER CO 27E: TOW/DRAGON REP EL

19j: M60A2 ARMOR CREWMAN CO 27F: VULCAN REPAIRER EL

19K: Ml ABRAMS ARMOR CRMN CO 27G: CHAPARRAL/REDEYE RE? EL

27H: SHILLELAGH REP EL

IlK: GND SURVI ROR CRMN EL 27N: FhAR REP E

17M; REMOTE SFNSOR SP EL 31E; FIELD RADIO REP EL

21G: PERSHING ELCT MAT SP E6 31.J: TEL:TYPEWRTER RE?

continued or, nexT page
List of MOS in the Army (cont'd)

Current Current

MOS Name Composite MOS Name Composite

31F. MCRAN.COMM EQ OP EL 41J: OFFICE MACHINE REP GM

31m: TACTICAL CKT CON EL 42C: ORTHOTIC SPECIALIST GM

31S. FIELD GEN COMSEC REP EL 42D: DENTAL LAB SP GM

31T: FIELD SYS COMSEC REP EL 43E: PARACHUTE RIGGER GM

31V: TAC COMM SYSOP/MECH EL 43M: FAaRIC REPAIRER SP GM

320: STA TECH CONTROLLER EL 448: METAL WORKER GM

32F: FIXED CIPHONY REP EL 44E: MACHINIST cm

32G: FIXED CRYPTO EQ REP EL 458: SMALL ARMS REPAIRER GM

32H: FIXED STA RDO REP EL 450: SP FA TRT MECH GM

348: PCM REPAIRER EL 45G: FC SYSTEMS REP cm

34E: NCR 500 COMPUTER REP EL 45K: TANK TURRET REPAIRER Gl

34F: DSTE REiAIRER EL 45L: ARTILLERY REPAIRER GM

34H:'ADMSE REPAIRER EL 45T: ITV/IFV/CFV TURRET MECH G

34Y: FA COMPUTER REP EL 518: CARPENTRY & MASONRY SP GM

358: ELCT INST REP EL 51C: STRUCTURES SPECIALIST cM

35E: SP ELEC DEVICES REP EL 51G: MATERIALS QUALITY SP GM

3SF: NUC WPN ELCT SP EL 51K: PLUMBER GM

35G: BIOME) EQ SP BASIC EL 51M: FIRE FIGHTER Gm

35H: CALIBRATION SPECIALIST EL 51R: INTERIOR ELECTRICIAN GM

35K: AVIONIC, MECmANIC EL 52C: UTIL EQUIP REP GM

35L: AVIONIC COMM iQ REP EL 520: PWR GEN EQUIP REP GM

35M: AVIONIC NAV/FLT CON EQ REP EL 538: INDUSTRIAL GAS PDN SP GM

35R: AIIONIC SPECIAL EQ REP EL 54C: SMOKE OP SP 3M

3oC: WIRE SYS INST/OP EL 558: AMMUNITION SPECIALIST GM

360: ANTENNA INSTALLER SP EL 55D: EDO SPECIALIST GM

3b: DIAL/MAN CEN OFR R29 EL 5G: NUC WPN MAINT SP GM

36K: TAC W*RE 3P SP EL 57E: LAUNDRY & 8ATH SP GM

3bL: ELCT SWITCHING SYS REP EL V/F: GRAVES REG SP GM

41E: AV EQUIP REP EL 57H: CARGO SPECIALIST &M

41i: SURVL PHOTO EQ REP EL 61F: MARINE HULL REPAIRER GM

46N: PERSH ELEC-MECH SUPV EL 62E: HV CONST EQUIP OP 3M

52G; TRANS & DISTR SP EL 62F: L[FTIN6/LOADING EQ OP GM

93F: FA MET CRMBR EL 62G: QUARRYING SPECIALIST GM

62H: CONC&ASPHALT EQ OP A

13b: CANNON CREWMAN FA 62J: GEM CONST EQUIP OP GM

13F: FIRE SUPPORT SP FA 68J: AIRCRAFT FC REPAIRER GM

15J: MLR.i/LANCE OP/FD SP FA 68M; AIRCRAFT WEAPON SYS REP GM

418: TOPO INST REP SP GM 12C: BRIDC CREWMAN 4

41C: FC INSTRUMENT REP GM 33S: EW/INTEP SYS REP MI

continuea on nex' page

A-2 4
List of MOS in the Army (cont'd)

Current CurrenL

MOS Name Composite MOS Name Composite

45E: M1 ABRAMS TRT MECH MM 16S: MANPADS CREWMAN OF

45N: M6OAI/A3 TRT MECH MM 64C: MOTOR TRANSPORT OP OF

615: WATERCRAFT OPERATOR MM 948: FOOD SERVICE SP OF

61C: wATERCRAFT ENGINEER MM 94F: HOSP FOOD SERVICE SP OF

628: CONSTRUCTION EQUIP REP MM

63d: LT W VEM & PWR GEN MECH MM 058: RAII) OPERATOR SC

630: SP FA SYSTEMS MECHANIC MM 05C: RADIO TT OPERATOR SC

63E: M1 ABRAMS TANK SYS MECH MM 05G: SIGSEC SPECIALIST SC

63H: TRACK VEM REPAIRER MM 13R: FIREFINDER RADAR OP SC

63J: QM & CHEM EQ REP MM 176: FA RADAR CRMBR SC

63N: M6OA1/A3 TANK SYS MECH MM 17C: FA TGT ACQ SQ SC

63S: HVY WNEEL VEM MECH MM 17L: AERIAL SENSOR SP SC

63T:. ITV/IFv/CFV SYS MECH MM 72E: CMBT TELECOM CTR OP SC

63W: WHEEL YEH REP MM 72G: AUTO DATA TELECOM CEN OP SC

63Y: TRACK VEH MECH MM 56H: AER SNS SP OV-ID SC

67G: AIRPLANE REPAIRER MM 67H: OBSN APIN REP MM 03C: PHY ACTIVITIES SP ST

67N: UTILITY MEL REPAIRER MM 050: EW/SIGINT IOENT/LrdC ST

67T: TAC TRANS MEL REP 1M 05m: Ew/SIGINT INTER-IMC ST

67U: ME)IUM MEL REPAIRER m U5K: EW/SIGINT N-M INTEP ST

67V: 08N/SCOUT MEL REP MM 13C: TACFIRE OPNS SP ST

67Y: ATTACK mEL REPAIRER MM 13E: CANNON FD SP * ST

688: ACFT POWERPLANT REP MM 54E: NBC SPECIALIST ST

6d0: ACFT POWERTRAIN REP wM 71P: FLIGHT OPNS COORDINATOR S7

68F: AIRCRAFT ELECTRICIAN MM 71Q; JOURNALIST ST

68G: AIRCRAFT STRUCTURAL REP MM 71R: BROADCAST JOURNALIST ST

68H: AICRAFT PNEUDRAULICS REP M 730: ACCOUNTING SPECIALIST ST

74D: COMPUTER/TAPE WRITER ST

13M: MLRS CREWMEMBER OF 74F: PROGRAMMER/ANALYST ST

150: LANCE CRMB/MLRS SGT OF 818: TECH DRAFT SP ST

15E: PERSHING MEL CRMBR OF 81C: CARTOGRAPHER ST

168: HERCULES MAL CRMBR OF 81E: ILLUSTRATOR ST

16C: HERCL-ES FC CR MBR OF 828: CONSTRUCTION SURVEYOR ST

160: HAWK MISSILE CREW OF 82C: FA SURVEYOR ST

16E: HAWK FC CRMBR OF 82D: TOPOGRAPHIC SURVEYOR ST

16F: LIGHT ADA CRWMN OF 83E: PHOTO & LAYOUT SP ST

16m: ADA OP-INTEL ASST OF 83F: PhOTOLITHOGRAPHER ST

16J: DEF ACQ RADAR OP OF 848: STILL PHOTO SP ST

16P: ADA SHORT RG, MSL CRMN OF 84C: MOPIC SP ST

16R: ADA SHORT RG GNRY CRMN OF 34F: AUDIO/TV SP ST

continued on next page

A-3
List of MOS in the Army (cont'd)

Current Current

MOS Name Composite MOS Name Composite

918: MEDICAL SPECIALIST ST 02S: SPECIAL BANDSPERSON AU

91C: PRACTICAL NURSE ST '2T: GUITAR PLAYER AU

91D: PHYSICAL THERAPY SP ST

91E: DENTAL SPECIALIST ST 008: DIVER

91F: PSYCHIATRIC SPECIALIST ST OH: BIOL SCIENCES ASST

9IG: BEHAVIORAL SCIENCE SP ST 19T: PATRIOT MSL C14BR

91H: ORTHOPEDIC SPECIALIST ST 24J: IH PULSE RDR REP

91J: PHYSICAL THERAPY SP ST 24T; PATRIOT SY$ MECH

91L: OCC TmERAP SP ST 26F: AERIAL PHOT SEN REP

91N: CARDIAC SPECIALIST ST 27M: MLRS REPAIRER

91P: X-RAY SPECIALIST ST 34C: DAS3 COMPUTER REP

91Q: PHARMACY SPECIALIST ST 34J: UNtVAC SYS REP

91R:-VETERINARY SPECIALIST ST 34K: IBM 360 REPAIRER

91S: ENVIRON HEALTH SP ST 35C: ATE REPAIRER

91T: ANIMAL CARE SP ST 35U: BIOMED EQ SP,ADV

91L: ENT SPECIALIST ST 36E: CABLE SPLICER

91V: RESPIRATORY SP ST 42E: OPTICAL LAS SP

91Y: EYE SPECIALIST ST 45R: M60A2 TANK TRT MECi

92B: MEDICAL LAB SP ST 5IN: WATER TRMT SPEC

92C: PETROLEUM .AB SP ST 52E: PRIME POWER PON SP

921: CtiEHICAL LAS SP ST 55R: AMMO STK CON & ACT

93E: FiETEROLOGICAL OBSERVER ST 55X: AMMUNITION INSPECTOR

93H: ATC TOWER OPERATOR ST 63G: FUEL & ELEC SYS REP

93J: ATC RADAR CONTROLLER ST 63R: M6OA2 TANK SYS MECH

968: MILITARY POLICE ST 658: LOCOMoTIVE REPAIRER

95C: CORRECTIONAL SP ST 65D: RAILAY CAR REPAIRER

96B: INTELLIGENCE ANALYST ST 65E: AIR64AKE REP:IRER

96C: INTERROGATOR ST 6SF: LOC'jOTIVE ELECTRICIAN

960: IMAGE INTERPRETER ST 65G: RAILWAY SEC REP

6fH: LOCOMOTIVE OPERATOR

U2B: CORNET TRUMPET PLAYER AU 62J: TRAIN CREWMEMBER

02C: BRTN EUPHMN PLAYER AU 65K: RAILWAY MOV COORD

U2D: FRENCH HORN PLAYER AU 57X: HEAVY LIFT HEL REP

02E: TROMBONE PLAYER AU 71E: COURT REPORTER

02F: TUBA PLAYER AU 72H: GEN OFC OFN OP

02G: FLUTE PICCOLO PLAYER AU 91W: NUCLEAR MED SP

OH: OBOE PLAYER AU 95D: SPECTAL AGENT

02J: CLARINET PL.AYER AU 97B: CI AGENT

02K: BASSOON PLAYER AU 97C: AREA INTELLIGENCE SP

02L: SAXOPHONE PLAYER AU 98C: EW/SIGINT ANALYST

OLM: PERCUSSION PLAYER AU 98G: EQ/SIGIT VOICE INTEP

02N: PIANO PLAYER AU 98J: EW/SIGINT NC &NTECP

A-4 )~ w
Table A-2

MOS with Multiple Tracks for SQT Testing

-19E -12F

TRACK I - M48AS/M60, t;60A1-Series TRACK I - APC Driver

TRACK 2 - M60A3 TRACK 2 - AVLB Operator

TRACK 3 - M551/M551A1 TRACK 3 - CEV Driver/Loader

-71L -lIH

TRACK 1 TRACK 1 - Tow

TRACK 2 - Postal ClerKs TRACK 2 - ITV (Improved Tow Venicle)

-056 -128

TRACK I TRACK 1 - APC Driver

TRACK 2 - (Special Forces)

-13R

-lC TRACK I - AN/TPQ-36 Operator

TRACK I - 81mm Mortar, Ground- TRACK 2 - AN/TPQ-37 Operator

Mounted TRACK 3 - AN/TPQ-35 Mecnanic

TRACK 2 - 4.2 inch Mortar, Ground- TRACK 4 - AN/TPQ-37 Mecnanic

Mounted

TRACK 3 - Special Forces -190

TRACK 4 - 81mm Mortar, Carrier- TAACK I - M113 Series

Mounted TRACK 2 - M151 Series

TRACK i - 4.2 incn Mortar, CarrierMounted -17K

TRACK 1 - AN/PPS-4A

-13B TRACK 2 - AN/PPS-5/5A

TRACK 1 - MI1Al TRACK 3 - AN/PPS-15

TRACK 2 - M102 TRACK 4 - AtN/TPS-33A (RC)

TRACK 3 - M114A)

TRACK 4/8 - M109/M1O9AI/155mm Atomic -17B

Projectile Assemoler TRACK 1 - AN/,PQ-4A Radar Crew Memoer

TRACK 5/7 - M1O7/M110/8-1nch Atomic TRACK 2 - AN/TPS-25 Radar Crew Memoer

ProjectilE Assembler TRACK 3 - AN/TPS-58 Radar Crew MemDer

TRACK 6 - M198

continued on next page
VIOS with Multiple Tracks for SQT Testing (cont'd)

-26C

TRACK 1 - AN/PPS-5 -31V

TRACK 2 - AN/PPS-4A TRACK 2 - (Special Forces)

TRACK 3 - AN/TPS-33A

TRACK 4 - AN/TRS-2 -36C

TRACK 5 - AN/PPS-15 TRACK 1 - (Operator)

TRACK 2 - (Installer)

-27G

TR{ACK 1 - Chaparral -450

TRACK 2 - Receye TRACK I - MlO9/MIO9Al owitzer

TRACK 2 - Redeye Mechanic

TRACK I - Chaparral TRACK 2 - MllO/MllOAl OR M107

TRACK 2 - Redeye Howitzer Mechanic

-31E -45L

TRACK I - Active Army TRACK I - Self-PropelleO Artillery

TRACK 2 - Reserve Components Repairer

TRACK 2 - Towed Artillery Repairer

-314

TRACK 1 - Low Capacity Equipment -54C

(6-12 Channels) TRACK 1 - Smoke Fuel Handler Tasxs

TRACK 2 - Medium Capacity Equipment TRACK 2 - Smoke Generator Operator

(12-24 Channels) TasKs

TRACK 3 - Frequency Division Multiplex (FOM) Equipment -54E

TRACK I - Decontamination Tasks

-31N TRACK 2 - Reconnaissance Tasks

TRACK 1 - S8-611/MRC TRACK 3 - NBC Operations Tasks

TRACK 2 - AN/TSC-76

TRACK 3 - S8-675/MSC

TRACK 4 - AN/TSQ-84

continued on nex: oge

A-6
MOS with MultiDle Tracks for SOT Testinq (cQnt'd)

-558-43M

TRACK I - Ammunition Specialist TRACK I - Clothing and Textile Repair

TRACK 2 - Ammunition Stock Control TRACK 2 - Canvas and Wen Repair

and Accounting Specialist

-24Q - CE LncTRACK 1 - Fire Control Mechanic

TRACK TUCK 2 2 -

- Laundry Bath Specialist Specialist TRACK 2 - Simulator (T1) Mecnanic

-630 -118

-R3C 1TRACK 1 - SKLVL 1 Rifleman/Others

TRACK 1 - M109/M109A1 Vehicle Mechanic TRACK I - SKLVL 2 Fire Team Leader

TRACK 2 - MttOAZ Vehicle Mechanic (Infantry), Assistant

Scout Squad Leader

-63S TRACK I - SKLVL 3 Infantry Squad TRACK I - M123 Vehicle Mechanic Leader TRACK 2 - M915 Veiicle Mechanic TRACK I SKLVL 4 Platoon SGT Infantry

-16C TRACK 2 - SKLVL 1 M60 Machinegunner

TRACK 1 - Director Station TRACK 2 - SALVL 2 Fire Team Leader TRACK 2 - Diracing Station (Mechanized)

TRACK 2 - Tracking Station TRACK 2 - SKLVL 3 Infantry Squad Leacer

TRACK 3 - AJ; H (Mechanized)

-82C TRACK 2 - SKLVL 4 Platoon SGT

TCK I(Mechanized)

TRACK 1 - Foftn Order Surveyor TRACK 3 - Special Forces

TRACK 2 - Fourth Order Surveyor TRACK 4 - SLVL I Squad Gunner

TRACK S - SKLVL 1 Scout (Infantry Only) -76x TRAC 5 - SKLVL 4 Scout (Infantry Only)

TRACK I - Accounting TRACK 6 - SKLVL 1 M203 Grenadier

TRACK 2 - Storage TRACK 7 - SKLVL 1 Dragon Gunner

A-7
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U000000000000000000 o0ooooo0000 00000 0000000

000000000000*00000 00000000000 00000 0000000009000'0'

.4ftP.~~~~ O 0 . . .OW .tNN .t0 .w . . . *

ooo00o000000000000 0000000oooo 00000 00000009000000

00000000000@0000 0000000000. 00000 00000000000000

0 -g ft. -

ar~nnor 1-~:~ 0nUtJ a-z MU UUbBP
o0000000o00oo000 00000000 @000000000 0o0@00000000000000

if 009000000 Ooo*ftO 0000000 0.0-.@000 00 -0900000006

60000000000000, 00000000 00@@000000 00 00000000000000

00006000000000660 00000000 0OGOO@00000 s0 00000000000000

000000o00o000000 00O0000O 600960000, 00 Dooono*OOO0ooO

** U 0 m ftftSCf. 0 1 v of go. go .A v etN

Ln 000000000000009 06000000, 040000000 00 0--e000000t00-e

IVooooooo aoooo @Oooeooo:a: 0 00000 1000

0000000000000000 00000000 000000000~0 00 00000000000000

- *oftow Moto 0~e~~u .4CCtdC* OW'ft~t 01. CCCCW4WEfu

L" @0 I0 00 00 00ft 0 0 000000 1 G!000N00!0C 0! cc ocu 0000000000000000 00000000 @00~000000 00 06000000000900

u 0006000009000000 00000000 @000000000 go 00000000000000

<cMN 0 .f a~C~ F. M 0~ NMCN@WNN a 0~q ftt i9. 4wC*WNf l v M

W ~ 0000000004000000 00000000 0000000000 00 00000000060000

to WfWOOC.000 .4eC4 i0CCt NoC$Wf0

0 I0O00000j000000 oooocooooc o4 c; c; ~ ' 0 oo 00 0oo0o0o0O000o

CCC 0E E b C; a; qE 4eN CCN i ft.CC C i C a; 4 C; ; C;6 NE, 0a d

-0000000000000000 00000000 0000000000 00 0O0oO0ooo000oo

0 000@00000000~0 ;4 0000 iC ;o-Cioca 00--04 ;c 00 ~0 6 a0 a ; C a0, a

0000000000000000 00000000 I 0000000000 00 00000000000000

*0"0000000000000 00000 OIoOOO 001 000000I 000000

V0WC N.NC 4C0*P.VI O.4 ~#tee~tE.4 *~ * CCCN 60W 8 ftb E C C C CN C V .N E C W

*000000000000000~-t 000000 00 0 00 0 001 00 0 0 00 0 0

CUOCCC P-CEC WO@ #9 WS AN 100P 1 C e ~ 0~ 0 W - P 0

* NOOOO00N000000- 0e000 0 qt0N0~
US00000000 @000000000 00000000@@ 00000 00000000000000

if OOOO~f@0.0000900 .@-.@t 0 Oo 6O00900000006

00000000000@0000 00000000000 orooc oooooeooooooo0

p.. ~ e"nv~a ~ ~ :s02N e0eo~-n e.nt6 t-eeef nt F%

0000000000 000000 00000 o00ooooooooeoo

ft 000400 0-01000IsP.WI0 faft10l 0-6'i. N AO4OO 0 0@ 00 0-.0 *o

000000000000000000 00000000000 00000 00000000000000

*0@aOO00o00000OO@O0 000000-'-e0O0 0000.0 0000.000.00-.0000

LA 666000000000090000 aocoooooooo 00000 0000000000000

o s;Gsan-aepceaafur-e 000000ee-000 00000 00tt-00000tO0000

4J U 0000000000000 - 00c-'o00eco0 to--0 000 000 00000

000000000000000000 00000000000 00000 00000000000000

S 00000-e000.Oo0000 000-00000000 00-00 0o00o0-0o0000

o o opooooo-oooo noo Ooooov oooooo

000000000000000000 00000000000 00000 @@0000000000000

-000900090000600006 00000000000 00000 00000000000000

. .e.rwn. .nnw.cea!e. r !sn ease - .. n .sc aee..17eeV seep! p o 00000'j000000000000 @0000000000 00000 60000060000000

- ~ ~ ~ ~ ~ ~ ~ e-nueoenn05z:ee-renN ~ ~ ~ p "Oe-ntt-eets- one

it 000-00-0000- U @0-O.@~o000 o00000w0

&:vwwa u~ 000000000000000000 0 0 0 0 0 0 0 00 0 0
Table A - s

SAMPLE CORRELATIONS FOR THE CURRENT COMPOSITES.

TRAINING CRITERION

i\

TCELL JA CL CO EL FA Gm i4 OF SC ST

71NLUS51 CL 0.36 0.36 0.38 0.42 0.33 0.30 0.32 0.37 0.35

73C5R121 CL 0.43 0.46 0.48 0.51 0.42 0.35 0.43 0.45 0.47

75B5E121 CL 0.17 0.23 0.26 0.24 0.24 0.19 0.21 0.22 0.23

75SDS05 CL 0.14 0.35 0.41 0.38 0.34 0.30 0.31 0.23 0.36

755E805 CL 0.40 0.46 0.49 0.49 0.46 0.41 0.46 0.48 0.47

76CEC101 CL 0.16 0.25 0.30 0.26 0.28 0.25 0.27 0.22 0.28

76PS!101 CL 0.26 0.34 0.40 0.44 0.31 0.23 0.27 0.26 0.38

76VEV101 CL 0.02 0.01 0.04 0.04 0.06 -0.01 0.04 0.02 0.12

76WDBOIl CL 0.20 0.20 0.08 0.13 0.10 0.22 0.18 0.25 0.03

76WPW1O1 CL 0.23 0.36 0.37 0.37 0.36 0.31 0.36 0.32 0.35

76XSX101 CL 0.07 0.10 0.11 0.14 0.07 -0.03 -0.00 0.06 0.07

76YEY101 CL 0.21 0.25 0.25 0.30 0.21 0.17 0.19 0.22 0.24

76Y5G101 CL 0.26 0.42 0.52 0.44 0.48 0.35 0.40 0.37 0.52

7GY6Y80s CL 0.12 0.14 0.14 0.19 0.13 0.11 0.11 0.13 0.13

11OIN809 CO 0.14 0.18 0.18 0.19 0.17 0.16 0.16 0.15 0.19

118N809 Co 0.18 0.26 0.23 0.26 0.22 0.21 0.24 0.21 0.26

11CIN809 CO 0.24 0.30 0.26 0.27 0.28 0.30 0.30 0.29 0.28

11HINg09 Co 0.17 0.24 0.24 0.23 0.23 0.23 0.24 0.21 0.23

12BAB807 CO 0.08 0.1S 0.13 0.13 0.14 0.16 0.13 0.12 0.10

12FAF807 CO 0.08 0.21 0.16 0.15 0.18 0.21 0.20 0.15 0.18

19D9D804 CO 0.22 0.25 0.25 0.24 0.26 0.26 0.26 0.26 0.24

19E9E804 Co 0.29 0.35 0.33 0.29 0.36 0.35 0.41 0.37 0.38

19F9F804 CO 0.24 0.40 0.37 0.32 0.39 0.39 0.40 0.33 0.37

17KGA301 EL 0.25 0.40 0.37 0.41 0.35 0.29 0.37 0.34 0.44

27E7E093 EL 0.31 0.40 0.33 0.35 0.34 0.37 0.39 0.39 0.32

3114D113 EL 0.19 0.25 0.25 0.27 0.20 0.19 0.23 0.22 0.25

31N4C113 EL 0.18 0.09 0.05 0.04 0.03 0.07 0.11 0.17 0.05

31V1V061 EL 0.25 0.37 0.36 0.38 0.34 0.35 0.34 0.31 0.31

36CA113 EL 0.12 0.13 0.10 0.14 0.07 0.10 0.10 0.14 0.03

36KAC113 EL 0.13 0.24 0.16 0.22 0.19 0.22 0.21 0.18 0.16

1383B810 FA 0.13 0.22 0.18 0.20 0.20 0.24 0.21 0.18 0.18

13F3F810 FA 0.40 0.53 O.SO 0.53 0.48 0.45 0.49 0.48 0.51

41CG7091 Gm 0.25 0.26 0.25 0.30 0.21 0.26 0.23 0.27 0.15

44BJ1091 Gm 0.19 0.30 0.26 0.26 0.25 0.30 0.29 0.24 0.23

45KK8091 GM 0.27 0.33 0.18 0.32 0.18 0.31 0.30 0.30 0.20

45KK9091 GM 0.26 0.36 0.29 0.34 0.32 0.37 0.37 0.30 0.34

SIKSK807 GM 0.17 0.22 0.25 0.23 0.26 0.24 0.22 0.22 0.17

S4CSS031 GM 0.04 O.OS 0.01 0.01 0.08 0.08 0.03 0.07 -0.02

5535B093 GM 0.28 0.32 0.38 0.32 0.35 0.30 0.32 0.30 0.33

57EPE101 cm 0.15 0.29 0.03 0.31 0.02 0.08 0.10 0.16 0.06

57HG1551 GM 0.15 0.14 0.13 0.14 0.14 0.08 0.17 0.16 0.24

62ECE807 GM 0.30 0.4S 0.47 0.40 0.48 0.48 0.46 0.38 0.4!

62FCF807 GM 0.16 0.29 0.27 0.25 0.30 0.38 0.34 0.24 0.25

68JW6551 GM 0.22 0.44 0.44 0.39 0.53 0.49 0.46 0.35 0.44

68MW8SS1 GM 0.31 0.47 0.30 0.43 0.33 0.42 0.40 0.40 0.28

(cont'd)

A-12
SAMPLE CORRELATIONS FOR THE CURRENT COMPOSITES

TRAINING CRITERION (Continued)

TCELL AA CL CO EL ?A GM 1 or SC ST

618G6551 MI 0.46 0.62 0.S9 0.60 0.57 0.55 0.66 0.58 0.65

61CH15S XK 0.25 0.47 0.44 0.50 0.36 0.35 0.35 0.30 0.38

62BCB807 4 0.22 0.43 0.36 0.39 0.39 0.38 0.37 0.33 0.35

6383D003 m1 0.16 0.32 0.28 0.26 0.32 0.34 0.34 0.26 0.29

63B3805 14M -0.01 0.18 0.17 0.11 0.21 0.19 0.15 0.09 0.14

63DSA171 M 0.10 0.13 0.06 0.07 0.11 0.10 0.13 0.16 0.08

63GM7091 1m4 0.01 0.21 0.09 0.10 0.11 0.18 0.16 0.10 0.07

63HH1091 mM 0.12 0.33 0.33 0.31 0.33 0.33 0.31 0.21 0.33

63NTS171 M14 0.06 0.19 0.12 0.15 0.15 0.21 0.17 0.12 0.10

63TF1171 10K -0.03 0.08 0.08 0.10 0.10 0.07 0.02 -0.01 0.07

63WW1091 MM 0.11 0.17 0.19 0.17 0.20 0.15 0.16 0.14 0.21

63YTV171 MM 0.07 0.11 0.10 0.11 0.13 0.14 0.13 0.10 0.11

67N65011 14M 0.S5 0.58 0.60 0.62 0.S4 0.50 0.61 0.60 0.61

67TL6551 MM 0.44 0.59 0.65 0.59 0.62 0.47 0.53 0.49 0.65

67UP1551 M 0.35 0.48 0.40 0.49 0.33 0.35 0.38 0.37 0.40

67V18011 HK 0.47 0.69 0.63 0.61 0.57 O.53 0.58 0.51 0.61

67YS1551 MM 0.25 0.23 0.24 0.17 0.30 0.39 0.33 0.33 0.20

680DT1551 MR 0.29 0.55 0.45 0.53 0.44 0.47 0.46 0.38 0.43

15V5D810 OF 0.09 0.18 0.12 0.16 0.13 0.16 0.19 0.14 0.13

isESEslO OF 0.09 0.19 0.16 0.17 0.18 0.19 0.22 0.15 0.19

16BA811 of 0.10 0.30 0.31 0.26 0.31 0.29 0.27 0.21 0.27

16BC811 OF 0.22 0.29 0.26 0.33 0.22 0.19 0.26 0.25 0.31

16CCA811 O 0.17 0.29 0.25 0.30 0.21 0.17 0.22 0.23 0.21

16DDB811 OF 0.32 0.48 0.38 0.40 0.46 0.47 0.46 0.45 0.37

16EEB811 OF -0.17 -0.10 0.03 -0.11 0.04 -0.04 -0.08 -0.14 -0.01

16HHB811 OF 0.23 0.44 0.44 0.42 0.43 0.36 0.41 0.31 0.46

16JJA81I OF 0.34 0.34 0.43 0.40 0.37 0.30 0.36 0.36 0.42

16PPA811 OF -0.02 0.07 0.01 -0.00 0.09 0.12 0.14 0.08 0.05

16RRA811 OF 0.12 0.20 0.17 0.18 0.19 0.17 0.20 0.18 0.18

16SSA811 OF 0.11 0.30 0.30 0.27 0.32 0.31 0.30 0.21 0.29

64CEC807 o -0.03 0.09 0.03 0.04 0.10 0.12 0.15 0.06 0.09

64C4C803 OF 0.0S 0.14 0.14 0.11 0.16 0.12 0.13 0.11 0.14

94BKA101 OF 0.16 0.21 0.20 0.20 0.20 0.19 0,20 0.2. 0.18

9434B803 o 0.24 0.43 0.44 0.44 0.39 0.34 0.36 0.31 0.43

9434B805 O 0.06 0.17 0.22 0.15 0.21 0.17 0.18 0.13 0.20

0582A113 SC 0.10 0.17 0.16 0.17 0.14 0.14 0.17 0.16 0.18

051CD113 SC 0.15 0.26 0.31 0.31 0.27 0.23 0.24 0.22 0.27

17C7C061 sC 0.06 0.22 0.22 0.19 0.21 0.18 0.22 0.20 0.23

72E3G113 SC 0.07 0.13 0.14 0.15 0.11 0.04 0.12 0.10 0.16

13E34810 ST 0.16 0.27 0.30 0.31 0.25 0.17 0.19 0.19 0.27

54ESA031 ST 0.32 0.36 0.36 0.39 0.33 0.33 0.34 0.34 0.36

82C2C810 ST 0.37 0.47 0.48 0.51 0.43 0.43 0.40 0.41 0.39

91S01929 ST 0.09 0.13 0.09 0.11 0.10 0.10 0.11 0.11 0.10

91C02929 ST 0.37 0.38 0.39 0.38 0.3S 0.34 0.36 0.41 0.35

91E05929 ST 0.26 0.33 0.38 0.41 0.30 0.22 0.26 0.28 0.37

92B25929 ST 0.18 0.48 0.49 0.49 0.45 0.42 0.40 0.30 0.47

95SB813 ST 0.24 0.37 0.41 0.32 0.41 0.37 0.42 0.34 0.43

A-13 ( c
Table A -

ADJUSTED CORRELATIONS FOR THE CURRENT COMPOSITES

TRAINING CRITERION

TCELL AA CL CO EL FA ON 101 or SC ST

71NLlSS1 CL 0.70 0.66 0.67 0.71 0.60 0.61 0.6S 0.70 0.66

73C5R121 CL 0.56 O.52 0.54 0.56 0.47 0.46 O.52 O.55 0.S3

75BSE121 CL 0.29 0.33 0.3S 0.34 0.33 0.31 0.32 0.32 0.33

75DSD805 CL 0.26 0.38 0.44 0.41 0.40 0.34 0.35 0.31 0.40

75ESE805 CL 0.63 0.61 0.63 0.65 0.59 0.S8 0.63 0.65 0.63

76CEC101 CL 0.44 0.49 0.52 O.51 O.SO 0.48 O.SO 0.48 0.52

76PSF101 CL O.SS 0.57 0.62 0.65 O.SS 0.50 O.SS O.55 0.62

76VEV1OI CL 0.13 0.13 0.1S 0.16 0.15 0.11 0.14 0.13 0.18

76WDS101 CL 0.58 0.58 0.51 O.S4 0.52 0.58 0.59 0.62 o.s

76WPW101 CL 0.56 0.67 0.68 0.67 0.66 0.64 0.67 0.63 0.67

76XSX101 CL 0.31 0.30 0.31 0.33 0.28 0.26 0.28 0.31 0.30

76YEY101 CL 0.40 0.40 0.41 0.44 0.37 0.35 0.38 0.41 0.41

76Y5101 CL 0.47 O.SS 0.64 0.56 0.60 O.SO 0.53 0.51 0.63

76Y6Y805 CL 0.31 0.30 0.30 0.34 0.27 0.28 0.29 0.31 0.30

1101N809 CO 0.20 0.22 0.23 0.24 0.22 0.20 0.21 0.20 0.24

IBNS1409 CO 0.32 0.38 0.35 0.38 0.33 0.33 0.36 0.35 0.37

11Ctg0 Co 0.35 0.41 0.37 0.39 0.39 0.41 0.41 0.40 0.39

11HINS09 CO 0.29 0.35 0.35 0.34 0.3S 0.3S 0.36 0.33 0.35

12BABB07 CO 0.10 0.18 0.16 0.16 0.16 0.20 0.16 0.14 0.13

12PA7807 CO 0.25 0.36 0.31 0.32 0.33 0.36 0.35 0.31 0.32

19D9D804 CO 0.31 0.35 0.35 0.34 0.36 0.36 0.36 0.35 0.34

19E9E804 CO 0.57 0.58 0.54 0.54 0.55 0.57 0.62 0.61 0.59

199F7804 CO 0.37 O.SO 0.49 0.45 0.51 O.SO 0.S1 0.45 0.49

17KGA301 EL O.53 0.60 O.S7 0.S9 0.SS 0.S4 O.59 O.57 0.62

27E7E093 EL 0.49 0.57 0.S3 0.S3 0.S4 0.ss 0.56 0.5s 0.52

31M4D113 EL 0.43 0.48 0.49 0.49 0.46 0.45 0.47 0.46 0.49

31N4Cl13 EL 0.36 0.30 0.29 0.28 0.28 0.31 0.34 0.36 0.30

31VIV061 EL 0.43 O.SS O.SS 0.55 O.SS 0.SS 0.54 0.50 0.53

36CAA113 EL 0.26 0.25 0.24 0.26 0.22 0.23 0.25 0.27 0.21

36KAC113 EL 0.23 0.32 0.28 0.30 0.30 0.31 0.30 0.28 0.28

13B39e10 IA 0.18 0.29 0.2S 0.25 0.27 0.30 0.27 0.23 0.24

13F3F810 FA 0.68 0.77 O.75 0.78 0.72 0.71 0.7S 0.74 0.76

41CG7091 G 0.34 0.38 0.36 0.39 0.33 0.37 0.35 0.36 0.31

443J1091 GM 0.33 0.41 0.39 0.38 0.38 0.41 0.41 0.38 0.37

4SKK8091 GM 0.40 0.42 0.30 0.41 0.29 0.39 0.38 0.41 0.30

45KK9091 GM 0.57 0.66 0.64 0.63 0.64 0.66 0.66 0.63 0.65

51KBK907 GM 0.33 0.44 0.45 0.43 0.45 0.44 0.42 0.40 0.41

S4CSSO31 GM 0.16 0.22 0.20 0.18 0.24 0.24 0.21 0.21 0.19

SSS093 GN O.SS 0.64 0.69 0.64 0.63 0.64 0.65 0.61 0.68

S7EPE101 GM 0.32 0.38 0.26 0.36 0.?3 0.28 0.30 0.33 0.26

S7HGlSS1 GM 0.38 0.41 0.42 0.42 0.42 0.39 0.43 0.41 0.46

62ECE807 GM 0.46 0.60 0.60 0.SS 0.62 0.63 0.61 0.55 0.59

62TCF907 G. 0.36 0.49 0.48 0.43 0.50 O.SS 0.S3 0.45 0.46

6!Jio6ssS GM 0.53 0.72 0.73 0.67 0.77 0.75 0.73 0.65 0.73

68MW8551 GM 0.54 0.65 0.57 0.62 0.59 0.63 0.62 0.61 0.57

(cont 'd)
ADJUSTED CORRELATIONS FOR THE CURRENT COMPOSITES

TRAINING CRITERION (Continued)

TCELL AA CL Co EL PA GM W04 or Sc ST

61BG6551 M04 0.61 0.66 0.66 0.66 0.65 0.64 0.71 0.6a 0.71

61CH1551 MM( 0.53 0.69 0.66 0.70 0.64 0.62 0.62 0.59 0.65

62BCB807 '('4 0.37 0.55 0.48 0.49 0.52 0.52 O.50 0.47 0.46

63B3803 MM 0.37 0.50 0.46 0.43 0.50 0.52 0.51 0.46 0.47

63B3BC05 M 0.08 0.22 0.21 0.16 0.24 0.23 0.20 0.15 0.18

63DSA171 M04 0.28 0.34 0.28 0.28 0.32 0.32 0.34 0.34 0.29

63GM7091 N0 0.23 0.38 0.30 0.30 0.34 0.38 0.36 0.31 0.29

63HH1091 14M 0.33 0.48 0.48 0.46 0.49 0.49 0.47 0.40 0.48

63NT5171 MM 0.18 0.33 0.28 0.28 0.32 0.35 0.32 0.26 0.27

63T?1171 MM 0.01 0.12 0.12 0.12 0.13 0.12 0.08 0.05 0.11

63WW1091 HK 0.23 0.27 0.28 0.27 0.29 0.25 0.26 0.26 0.30

63YTV17". MM 0.26 0.34 0.31 0.31 0.34 0.35 0.34 G.32 0.32

67N65011 104 0.79 0.83 0.81 0.82 0.80 0.60 0.8S 0.84 0.83

67TL6551 MM 0.73 0.02 0.85 0.82 0.83 0.79 0.82 0.79 0.85

67UP155 1M 0.58 0.68 0.62 0.68 1 40 0.67 0.64 0.63 0.63

67V18011 MM 0.68 0.78 0.80 0.78 0.77 0.76 0.78 0.75 0.78

67YS1551 MM 0.59 0.68 0.66 0.60 0.70 0.75 0.72 0 69 0.63

eeDT.55. "- 0.53 0.74 0.66 0.71 0.67 0.69 0.69 0.62 0.66

15DD810 OF 0.31 0.39 0.34 0.36 0.36 0.38 0.39 0.17 0.36

15E5E810 OF 0.34 0.42 0.39 0.39 0.42 0.42 0.44 0.4G 0.42

16BBAS11 OF 0.31 0.45 0.45 0.41 0.45 0.44 0.42 j.38 0.42

16BBC811 OF 0.39 0.42 0.40 0.44 0.39 0.36 0.40 0.41 0.43

16CCAS11 OF 0.22 0.29 0.29 0.32 0.25 0.19 0.23 r.24 0.27

16DDB8]l OF 0.46 0.62 0.56 0.54 0.60 0.61 0. i 0.57 0.5

16EEB811 OF -0.15 -0.06 0.01 -0.09 0.04 -0.02 -0.04 -0.10 -0.01

16HB811 OF 0.62 0.73 0.73 0.72 0.72 0.70 0.74 0.69 0.75

16JJA811 OF 0.56 0.57 0.61 0.60 0.68 0.54 0.59 0.59 0.62

16PPA811 OF 0.11 0.20 0.13 0.12 0.20 0.23 0.24 0.j.8 0.17

16RRASI1 OF 0.30 0.36 0.33 0.34 n.36 0.34 0.37 0.35 0.35

16SSA811 OF 0.36 0.49 0.50 0.47 r2 0.51 0.49 0.44 0.49

64CEC807 OF 0.19 0.26 0.21 0.22 ;.2 0.28 0.29 J.25 0.25

64C4C803 OF 0.17 0.23 0.23 0.2 .2 0.22 0.22 0.21 0.24

94BKA1O1 OF 0.35 0.39 0.38 0.3b .38 0.37 O.8 0.19 0.37

94B4B803 OF 0.49 0.60 0.61 0.61 3 0.54 0.56 0.5( 0.61

94BIt805 OF 0.26 0.32 0.35 0.31 ' 0.32 0.33 0.30 0.34

05B2Al13 SC 0.35 0.35 0.34 0.36 0.32 0.33 0.37 0.37 0,36

05C2D113 SC 0.35 0.38 0.41 0.41 0.38 0.37 1.37 0.37 0.39

17C7CO61 SC 0.35 0.38 0.35 0.36 0.34 0.34 0.38 0.3b 0.3-

72E3G113 SC 0.21 0.22 0.21 0.22 0.19 0.17 0.22 0.2 0,23

13E3E810 ST 0.32 0.40 0.41 0.41 0.39 0.35 0.37 0.35 0.40

54ESA031 ST 0.46 0.49 O.SO 0.52 0.47 0.47 0.49 0.49 0.51

82C2C810 ST 0.48 0.56 0.56 0.58 0.53 C.54 0.5 0.52 i.52

91301929 ST 0.16 0.19 0.17 0.18 0.17 C.17 0.18 0.1A 0.18

91C02929 ST 0.55 0.58 O.57 0.57 0.55 55 0.67 0.58 0.5b

91E05929 ST 0.57 0.63 0.65 0.66 0.61 .57 0.61 0.60 0.65

92B,15925 ST 0.36 0.52 0.53 0.32 0.51 0.48 0.47 0 42 0.51

95BSB813 ST 0.63 0.71 0.73 0.68 0.73 0.70 0.74 0.70 O.7c

A-15 /.I, -
Table A - 7

SAMPLE VALIDITIES FOR CUUPtNT COMPOTES

SQi CRITERION

PCELL AA CL CO ZL F GI m Or sC ST

71DO152 CL 0.21 0.37 0.36 0.37 0.33 0.27 0.29 0.27 0.37

71L1183 CL 0.34 0.39 0.44 0.44 0.38 0.32 0.38 0.37 0.43

7IL2103 CL 0.19 0.27 0.34 0.34 0.26 0.21 0 23 0.19 0.32

71/40182 CL 0.32 0.33 0.34 0.34 0.33 0.32 0.37 0.3S 0.38

73C0182 CL 0.44 0.S4 0.S4 0.97 0.S1 0.47 O.SO 0.49 0.52

73C0183 CL O.so O.S7 0.14 0.S4 O.S2 0.52 0.57 0.17 O.SS

7530182 CL 0.33 0.43 0.49 0.46 0.43 0.3a 0.41 0.37 0.45

7530183 CL 0.37 0.45 0.'.' 0.49 0.44 0.33 0.42 0.40 0.46

7SCO1i2 CL 0.39 0.45 0.44 0.50 0.48 0.42 0.45 0.4S 0.49

7SC0183 CL 0.22 0.4S O.S2 0.41 0.48 0.38 0.43 0.34 0.S1

750182 CL 0.1S 0.26 0.32 0.26 0.30 0.27 0.26 0.22 0.28

75D0183 CL 0.23 0.38 0.47 0.44 0.41 0.31 0.35 0.30 0.4S

750182 CL 0.26 0.32 0.39 0.31 0.37 0.31 0.3S 0.32 0.40

7SE0183 CL 0.25 0.42 0.44 0.4S 0.42 0.3i 0.41 0.34 0.4S

7POI83 CL 0.37 0.51 0.S5 0.S7 0.49 0.45 0.47 0.43 0.53

7400183 CL 0.21 0.30 0.37 0.32 0,.4 0.30 0.32 0.30 0.33

"4V0183 CL 0.20 0.29 0.34 0.28 0.31 0.22 0.26 0.25 0 35

7?W0182 CL 0.09 0.30 0.21 0.2S 0.26 0.22 0.21 0.21 0.21

76W0183 CL 0.29 0.41 0.46 0.39 0.45 0.37 0.40 0.36 0.45

11B 191 CO 0.22 0.29 0.26 0.28 0.25 0.28 0.27 0.25 0.24

1BO13 CO 0.23 0.42 0.46 0.40 0.46 0.,O 0.14 0.33 0.48

11B1182 CO 0.25 fo.33 0.34 0.33 0.33 0.31 0.32 0.29 0.34

1132182 CO 0.13 0.21 0.24 0.20 0.25 0.23 0.23 0.18 0.24

1156182 CO 0.18 0.21 0.26 0.20 0.27 0.25 0.28 0.21 0.32

1137182 CO 0.25 0.30 0.28 0.28 0.32 0.30 0.34 0.30 0.36

liC 181 CO 0.22 0.26 0.26 0.27 0.23 0.24 0.24 0.24 0.24

11CO183 Co 0.24 0.32 0.39 0.34 0.39 0.28 0.33 0.30 0.41

1101182 CO 0.23 0.36 0.3S 0.34 0.33 0.32 0.36 0.27 0.33

1102182 CO 0.27 0.33 0.38 0.32 0.37 0.36 0.34 0.32 0.33

11C4182 Co 0.23 0.26 0.26 0.26 0.26 0.2S 0.26 0.25 0.2c

11CS182 CO 0.33 0.45 0.45 0.44 0.44 0.42 0.42 0.39 0.43

115 1.81 CO 0.28 0.39 0.40 0.43 0.39 0.30 0.36 0.30 0.45

1H1182 CO 0.25 0.29 0.31 0.30 0.30 0.29 0.30 0.27 0.33

11B2182 CO 0.23 0.26 0.26 0.25 0.23 0.23 0.24 0.24 0.24

1230153 CO 0.19 0.33 0.32 0.Z2 0.32 0.31 0.31 0.25 0.33

1231182 co 0.1a 0.33 0.32 0.30 0.32 0.34 0.33 0.26 0.30

190 1II Co -0.01 0.10 4.14 0.22 0.16 0.07 0.03 0.0s 0.14

1%D0193 co 0.28 0.40 0.40 0.37 0.41 0.38 0.41 0.3S 0.42

M901182 co 0.27 A.3b 0.37 0.35 0.3% 0.32 0.35 0.31 0.37

1931182 co 0.24 0.34 0 32 0.31 0.33 0.33 0.36 0.32 0.35

113.103 CO 0.38 0.13 0.S2 0.49 0.13 0.S3 0.5S 0.47 0.56

193Z2183 co 0.30 0.46 0.,8 0.41 o.so 0.47 0.48 0.39 0.49

1933182 CO 0.22 0.31 0.29 0.24 0.31 0.31 0.32 0.28 0.30

C3SKLS3 EL 0.34 0.46 0.41 0.42 0.42 0.44 C.46 0.44 0.39

1792132 ZL 0.1S 0.27 0.17 0.25 0.2.S 0.21 0.26 P.21 0.18

2400193 IL 0.24 0.26 0.38 0.31 0.36 0.24 0.25 0.27 0.35 2730132 IL 0.18 0.23 0.09 0.19 0.15 0.19 0.24 C.23 0.19

2730163 UL 0.34 0.24 0.32 0.33 0.27 0.21 0.29 0.33 0.33 31J0183 IL 0.17 0.29 0.24 0.29 0.21 0.17 0.24 0.21 0.33

31140133 IL 0.19 0.43 0.47 0.38 0.s4 0.10 0.49 0.36 0.48

311132 EL 0.14 0.21 0.26 0.23 0.20 0.17 0.20 0.17 c zs 3I1282 EL 0.1.3 0.19 0.23 0.20 0.22 0.15 0.20 0.17 0.25

31V0153 EL 0.21 0.31 0.43 0.33 0.39 0.33 0.32 0.29 0.36

31V1182 EL 0.16 0.24 0.26 0.25 0.23 0.22 0.22 0.19 0.24 3iC2182 ZL 0.13 0.16 0.09 0..1 0.09 0.1S 0.1s 0.15 0.

J 0182 EL 0.11 0.24 0.18 0.20 0.20 0.22 0.23 0.1" 0.20

36K0113 EL 0.23 0.40 0.35 0.35 0.40 0.33 0.40 0.33 0.40

cont.*

A-16
SAMPLZ VALIOIrlES rot CURENT CONPOSITES

SQT CRITERION (Continutd)

PC!LL AA CL co Xi ?J 06 MM 0? sc ST

133 181 PA 0.06 O.2S 0.30 0.16 0.37 0.33 0.32 0.21 0.32 1380181 ?A 0.30 0.43 0.44 0.41 0.4S 0.44 0.44 0.39 0.45 1351182 PA 0.33 0.41 0.30 0.36 0.36 0.42 0.41 0.43 0.33

1382132 FA 0.25 0.3S 0.37 0.3S 0.38 0.37 0.37 0.Z3 0.38

133182 FA 0.20 0.43 0.40 0.40 0.39 0.41 0.40 0.31 0.38

1354182 PA 0.19 0.30 0.29 0.29 0.30 0.28 0.29 0.21 0.29

13BS182 FA 0.14 0.23 0.22 0.20 0.24 0.24 0.24 0.20 0.23

13FO182 FA 0.13 0.29 0.20 0.23 0.23 0.23 0.27 0.20 0.22

439C103 01 0.22 0.18 0.2S 0.21 0.25 0.21 0.23 0.24 0.22

S150182 GM1 0.21 0.27 0.25 0.28 0.27 0.26 0.30 0.26 0.27 S200182 Gm 0.24 0.33 0.27 0.32 0.27 0.34 0.37 0.28 0.33 5531182 GM 0.11 0.19 0.15 0.22 0.11 0.10 0.12 0.11 0.17 S7=183 ON 0.03 0.12 0.14 0.13 0.06 :.0 0.02 0.31 0.04 620182 GH1 0.30 0.42 0.34 0.37 0.36 0.43 0.43 0.38 0.36

6210132 IN 0.36 0.34 0.31 0.30 0.36 0.40 0.41 0.38 0.38 6IJ0182 GHq 0.01 0.23 0.06 O.L2 0.10 0.18 0.23 0.15 0.16

32cole2 W. 0.12 0.32 O.:S 0.27 0.26 0.31 0.27 0.19 0.26 12C0183 34 0.24 0.30 0.28 0.31 0.27 0.26 0.29 0.27 0.30

1250182 M6 0.29 0.45 0.41 0 41 0.44 0.42 0.46 0.40 0.46

6310182 M4 0.13 0.31 0.30 0.28 0.32 0.32 0.30 0.23 0.28

6380182 mm 0.17 0.32 0.33 0.20 0.34 0.32 0.33 0.26 0.33

63HO182 AM 0.02 0.06 0.14 0.10 0.12 0.06 0.12 O.05 0.15

63VW'82 mN 0.09 0.21 0.20 0.14 0.25 0.23 0.26 0.19 0.25

13Y0182 MM 0.12 0.30 0.20 O.ZU 0.26 0.30 0.33 0.21 0.27

67h 181 34 0.16 0.36 4.29 0.33 0.25 0.30 0 29 0.21 0.32

67N0162 34 0.06 0.24 O.25 0.23 O.2s 0.23 0.20 0.10 0.25

6711012 H 0.01 0.33 0.23 0.28 0.30 0.76 0.24 0.13 0.27

67VO182 M4 0.16 0.19 0.11 0.16 0.14 O.18 0.18 0.1# 0.14

67Y0182 3,4 0.11 0.26 0.23 0.19 0.33 0.29 0.31 0.19 0.32

68G01P2 34 0.06 0.24 0.37 0.27 0.34 0.22 0.27 0.14 0.36

1500182 o 0.14 0.35 0.30 0.32 0.29 0.30 0.3? 0.22 0.30

64C0182 OF 0.17 0.38 0.37 0.34 0.37 0.36 0.37 0.28 0.37

64C0183 OP 0.11 0.26 0.26 0.22 0.21 0.2S O.25 0.19 0.24

9440182 o 0.09 0.20 0.29 O.24 0.30 0.25 0.28 0.18 0.30

94501B3 OF 0.20 0.42 0.44 0.38 0.42 0.36 0.39 0.31 0.42

0S1182 SC 0.01 0.32 0.3s 0.29 0.36 0.32 t.35 0.26 f 36

0SC1182 SC 0.13 0.32 0.31 0.32 0.30 0.23 0.31 0.25 0.31

OSCO13 SC 0.05 0.47 0.49 0.41 0.Sl 0.47 0.47 0.30 0.48

OG0113 SC 0.21 0.43 0.27 0.34 0.30 0.36 0.41 0.39 0.33

7220i13 SC 0.12 0.46 0.49 0.42 0.51 0.%1 0.49 0.31 0 4'

C740323 ST 0.32 0.34 0.37 0.40 0.30 0.22 0.29 0.4 0.37

0551183 ST 0.49 0.43 0.43 0.51 0.37 0.33 0.42 0.51 0.45

13=182 ST 0.23 0.31 0.29 0.34 0.27 0.36 0.27 0.27 0.23

1330183 ST 0.20 0.30 0.40 0.36 0.36 0.2S ;.28 0.23 0.42

8IC1112 ST 0.23 0.37 0.39 O.W0 0.37 0.13 0.32 0.27 0.36

O2C2182 ST 0.29 0 39 0.13 O.41 0.26 0.31 0.32 0.31 0.29

9130182 Si 0.15 0.18 0.29 0.24 0.25 0.20 0.13 0.16 0.28

9110132 ST 0.25 0.24 0.24 0.27 0.21 0.26 0.14 0.25 0.22

9130112 ST 0.!3 0.31 0.26 0.31 0.24 0.23 0.27 0.40 0.24

9250182 ST 0.14 0.20 0.27 0.25 0.23 0.16 0.18 0.16 0.26

93110i@2 ST 0.09 C.27 0.30 0.30 0.24 0.24 0.24 0.14 0.31

9S30182 ST 0.13 0.25 0.26 C 2S 0.24 0.21 0.21 0.11 0.24

93013 ST 026 0.34 0.36 0.34 0.33 0.30 0.34 0.32 O.is

9630183 ST 0.32 0.35 0.4: 0.41 0.39 0.28 0.32 0.J2 0.44

98C0182 ST 0.30 0.39 0.36 0.52 0.29 0.16 0.25 0.32 0.39

A-17.
Table A - 8

ADJUSTED VALIDITIES ?02 CUIRET COMPOSITES

S0 CRITERION

PCZr.L AA CL CO EL IA ON MM OP SC ST

7100102 CL 0.26 0.37 0.39 0.38 0.36 0.29 0.32 0.29 0.40 71LI183 CL O.57 0,55 0.S4 0.60 0.53 0.50 0.$5 0.57 0.S9

71L2183 CL 0.43 0.42 0.47 0.49 0.41 0.39 0.42 0.42 0.47

71)0112 CL *0.O7 0.54 0.51 0.6s 0.49 0.52 0.S7 0.S 0.15

73CO112 CL 0.50 0.63 0.63 0.66 O.S9 0.S3 0.61 0.62 0.63 73CO183 CL 0.72 0.71 0.69 0.71 0.64 0.69 0.74 0.75 0.71

7S80182 CL 0.52 O.S6 0.60 060 0.5S 0.53 0.55 0.54 O.S

7550183 CL 0.59 0.62 0.64 0.5S 0.59 0.58 0.61 0.61 0.63

75C0182 CL 0.65 0.57 0.55 0.70 0.64 0.4S 0.49 0.70 0.68

75C0103 CL 0.41 O.SS 0.61 0.57 0.58 0.52 0.54 0.47 0.60

7500132 CL 0.33 0.38 0.42 0.39 0.40 0.38 0.37 0.36 0.40

7SD0183 CL 0.49 0.$5 0.61 0.60 O.SS 0.49 0.52 0.S1 0.60

7510102 CL O.S4 0.SO 3.5S O.52 O.S2 O.SO O.SS O.SS 0.S7

7510183 CL 0.43 0.51 0.54 O.54 0.49 0.43 0.50 0.44 0.53 7170113 CL 0.S7 0.61 0.64 0.66 0.57 0.$5 O.S 0.58 0.63

74C0193 CL 0.42 0.47 O.S2 0.49 0.43 0.46 0.48 0.45 0.49

76V0183 CL 0.47 0.49 0.52 O.so 0.49 0.44 0.48 0.49 0.53

76N0182 CL 0.11 0.34 0.31 0.28 0.34 0.30 0.27 0-21 0.2$

76"193 c.. 0.46 G.19 0.63 0.58 0.52 O.57 0.58 0.S3 0.62

113 181 CO 0.33 0.41 0.38 0.39 0.37 0.40 0.39 0.37 0.37 1130143 CO 0.40 O.51 0.SS O.SO 0.14 0.50 0.53 0.46 O.56 1181182 CO 0.37 0.44 0.43 0.44 0.43 0.42 0.43 0.41 0.44

1132182 CO 0.21 0.28 0.30 0.27 0.31 0.30 0.29 0.25 0.30

s16182 CO 0.29 0.32 0.35 0.35 0.33 0.32 0.34 0.31 0.37

1157162 CO 0.37 0.40 0.37 0.39 0.39 0.39 0.42 0.41 0.43 liC 11 CO 0.35 0.39 0.39 0.40 0.37 0.37 0.30 0.36 0.38

11C0183 CO 0.31 0.33 0.41 0.35 0.42 0.34 0.38 0.36 0.43 11C1132 CO 0.3S 0.46 0.46 0.46 0.44 0.45 0.47 0.40 0.48

11C2112 CO 0.35 0.41 0.45 0.41 0.45 0.44 0.43 0.40 0.43

11C4182 co 0.34 0.37 0.37 0.37 0.)S 0.3f 0.36 0.37 0.36

11C5182 CO 0.41 0.53 O.53 0.52 O.S2 0.51 0.51 0.47 0.51

11 131 CO 0.45 0.55 O.54 0.S7 O.52 0.43 O.52 0.49 0.S7

1191132 CO 0.37 0.41 0.42 0.42 0.41 0.40 0.41 0.40 0-43

1102182 CO 0.33 n.3s 0.36 0.37 0.33 0.34 0.34 0.34 0.34 1230133 CO 0.31 0.43 0.42 0.42 0.42 0.42 0.41 0.36 0.42

1281132 CO 0.33 0.46 0.4S 0.43 0.45 0.47 0.46 0.40 0.44

190 11 CO -0.09 0.05 0.07 0.06 0.10 0.01 0.00 -0.03 0.0s

1#00183 CO 0.4S 0.SS 0.54 0.52 0.S4 O.53 0.S6 0.51 0..6 19D1182 CO 0.41 0.47 0.49 0.47 0.46 3.4S 0.17 0.4S 0.48

1931112 CO 0.40 0.47 0.44 0.4S 0.44 0.45 0.40 0.4E 0.47

LSC1163 CO 0.SS 0.66 0.65 0.63 0.56 O.6S 0.68 0.61 0.68

192183 CO 0.43 O.S7 0 so 0.53 0.50 0.53 0.53 0.52 0.59

19331C2 CO 0.35 0.43 0.41 0.30 0.4k 0.43 0.44 0.41 0.42

C35KL13 RL O.so 0.66 0.54 0.65 0.64 0.66 0.67 0.64 0.63

1712122 XL 0.39 0.42 0.36 0.4: 0.34 0.42 0.43 0.41 0.37

26Q0113 EL 0.42 0.47 0.54 0.49 0.13 0.46 0.47 0.44 0.52 2730182 ZL 0.16 0.2u 0.13 0.10 0.16 0.19 0.23 0.21 0.19

2730113 9L 0.54 0.49 O.S2 0.54 0.49 0.47 0.S3 0.54 0.54

31J013 IL 0.46 0.So 0.5 O.5S 0.55 0.51 0.51 0.51 0.61

31m61383 EL 0 49 0 S6 0.70 0.43 0.73 0.70 0.49 0.61 0.70

31111132 EL 0.36 0.43 0.47 0.44 0.44 0.41 0.43 0.39 0.47 3182132 FL 0.36 0.41 0.44 0.42 0 4.x 0.40 0.41 0.39 0.45

31V0183 EL 0.45 0.55 0.64 0.57 0.62 0.57 0.56 0.S1 0.60 31VIII2 EL 0.35 0.43 0.45 0.43 0.44 0.42 0.42 0.39 0.44 3AC2102 EL 0.13 0.10 0.13 2.17 3.13 0.16 0.i& 0.19 0.14

36.0182 3:. 0.20 0.32 0.29 0.29 0.30 0.32 .31 0.26 0 3C 36K0183 KL. 0.40 O.St 0.57 O.53 0.58 0.5S 0.56 0.49 0.53

("ont'd)

A-18 1/ .
ADJUSTED VALIDITIES OR CURENT COMPOSITES

SQT CRITERION (Continued)

PCZLL AA CL CO 31. FA GN Nm Or SC ST

133 181 PA 0.14 0.26 0.31 0.21 0.37 0.33 0.32 0.23 0.32

1330113 ?A 0.41 0 52 0.S2 0.S0 0.S3 0.S1 0.S2 0.48 0.53

13$5A82 PA 0.44 0.49 0.42 0.45 0.4S 0.49 0.49 0.50 0.43

1332182 FA 0.37 0.43 0.44 0.43 0.44 0.44 0.45 0.41 0.4S

1333182 PA 0.31 O.SO 0.48 0.46 0.48 0.S. O.0 0.40 0.47

1354182 PA 0.29 0.38 0.37 0.37 0.37 0.30 0.37 0.34 0.37

135582 TA 0.19 0.27 0.27 0.24 0.29 0.29 0.28 0.24 0.27

1370182 FA 0.17 0.50 0.44 0.47 0.4G 0.49 0.43 0.43 0.45

4320183 GM 0.37 0.36 0.41 0.36 0.41 0.38 0.39 0.40 0.39

5130182 GM 0.27 0.37 0.35 0.34 0.30 0.37 0.39 0.34 0.38

S200132 GH 0.31 0.42 0.38 0.40 0.39 0.43 0.44 0.37 0.42

5551182 13 0.32 0.40 0.44 0.4S 0.42 0.36 0.33 0.3S 0.44

570183 GH 0.0s 0.13 0.13 0.17 0.139 0.10 0.06 0.0S 0.09

4230182 Q1 0.1. 0.60 0.S6 0.S7 0.57 0.81 0.61 0.S7 0.57

42r0162 Gl O.SS 0.S7 0.S7 0.53 0.16 0.61 0.42 0.59 0.59

48J0182 134 0.26 0.38 0.29 0.31 0.32 0.36 0.37 0.32 0.33

12C0102 NK 0.35 0.51 0.44 0.46 0.45 0.SO 0.47 0.42 0.44

12C0183 -¢ 0.41 0.44 0.42 0.4S 0.40 0.40 0.43 0.43 0.44

6230182 M 0.48 0.59 0.S4 0.56 0.S7 0.S 0.S9 0.SS 0.59

6330162 M 0.28 0.44 0.43 0.40 0.45 0.45 0.43 0.37 0.41

43 012 62 0.37 0.49 0.49 0.45 0.S0 0.48 0.49 0.44 0.49

63N0182 m4 0.11 0.16 0.19 0.16 0.13 0.16 0.18 0.14 0.20

63W0182 M4 0.26 0.3S 0.33 0.28 0.37 0.37 0.39 0.33 0.36 63Y0182 1M 0.47 0.64 0.S9 0.58 0.65 0.67 0.57 0.58 0.63

67N 131 6i4 0.39 0.59 0.52 0.55 0.53 0,S7 O.SS 0.47 0.S3

67N0182 MM 0.30 0.44 0.44 0.42 0.4S 0.44 0.42 0.36 0.44

b7=70182 34 0.28 0.S3 0.49 0.47 0.S2 O.SO 0.43 0.40 0.47

67VO182 34 0.37 0.42 0.37 0.30 0.38 0.41 0.41 0.41 0.38

67Y0132 NK 0.47 0.61 0.41 0.54 0.45 0.62 0.64 0.57 0.64

68G0152 m 0.29 0.38 0.48 0.39 0.46 0.40 0.43 0.35 0.46

1500182 OF 0.44 0.57 0.53 0.54 C.S3 0.54 o.SS 0.51 0.54

64C0132 Or 0.45 0.57 0.56 0.S4 0.54 0.S6 0.57 0.S2 0.57

64C0183 OF 0.30 0.40 0.39 0.36 0.40 0.39 0.39 0.36 0.39

9410182 OF 0.35 0.46 0.46 0.42 0.47 0.4S 0.46 0.41 0.47

9430163 or 0.47 0.59 0.60 0.57 0.60 0.56 0.58 0.54 0.S9

05B1182 SC 0.39 0.47 0.47 0.44 0.48 0.46 0.48 0.45 0.48

0$C0112 sC 0.41 0.48 0.46 0.47 0.45 0.45 0.47 O.45 0.47

0SC0103 SC 0.34 0.S3 0.S4 0.49 0.S6 0.S 0.52 0.44 O.54

OSt0183 Sc 0.64 0.72 0.61 0.48 0.62 0.67 0.71 0.70 0.67

72E0163 SC 0.40 O.54 0.S6 0.S2 0.59 0.56 0.S6 0.49 0.56

C74r0823 ST 0.65 0.69 0.71 0.70 0.67 0.62 0.69 0.69 0.72

0SH1183 ST 0.7S 0.72 0.70 0.75 0.66 0.66 0.73 0.77 0.73

1330132 ST 0.42 0.47 0.4S 0.48 0.44 0.44 0.46 0.45 0.45

1330183 ST 0.49 0.5S 0.61 0.58 0.S8 0.S2 0.S6 0.53 0.63

82C1182 ST 0.44 0.54 0.S7 0.57 0.54 0.4 0.S4 0.50 0.56

82C2182 ST 0.44 0.51 0.47 0.52 0.43 0.46 0.47 0.47 0.46

9120182 ST 0.43 0.45 0.51 0.48 0.49 0-4S 0.47 0.45 0.52

91PO182 ST 0.44 0.40 0.39 0.43 0.36 0.41 0.41 0.43 0.38

3110142 ST 0.61 0.59 0.S6 o.s9 0.55 0.54 0.S9 0.64 0.58

9250112 ST 0.31 0.34 0.40 3.38 0.36 0.30 0.33 0.32 0.39

9300182 ST 0.54 0.S 0.40 0.60 0.S7 0.54 0.S9 0.S4 0.62

955018z ST 0.32 0.41 0.42 0.41 0.41 0.39 0.39 0.36 0.41

9S30163 ST 0.57 0.63 0.64 O.f2 -.63 0.b1 0.65 0.42 0.64

940183 ST 0.60 0.62 0.70 0.64 0.4S 0.S7 0.43 0.63 0.70

94C0O12 ST 0.48 0.73 0.73 0.78 0.61 0.43 0.70 0.71 0.75

A- 19
~Table A - 9

Ridge Rcgresslon Coefficients for Training Criterion

TCELL AA N 12 R2 0S AR VE No CS AS MK MC El

d5U-2A-113 iC 519 .64 .02 .05 .16 .21 .9 .37 .16 .37 .11 -.05

15C-ZD-113 it 14 .11 .11 .06 .29 .00 .13 .03 .01 .31 o4 .19

118-ZN-809 V ; 76 .09 .98 .24 .20 .03 -.11 .22 .09 .10 .36 -.24

IIC-IN-809 -ad 57S .10 .99 .11 -.03 .65 .17 .15 .27 .14 .21 .03

IIH-IN-809 CC 444 .18 .05 -.10 .21 .22 .06 -. 3 .15 .14 .12 .10

121-AD-*847 CO 143 .04 -.04 -.11 .03 -.07 .O6 .04 .13 .09 .06 .10

t2F-AF-807 CC 224 .15 .01 .07 .6 .03 .14 .14 .17 -.01 .23 .03

1315-35-810 FA 1080 .07 .16 -. 8 .06-.11 .49 .08 .16-.10 .39 .25

13E°3E-St0 ST 483 .11 .19 .16 .40 .02 -.19 .15 .16 .31 .05 -.06

13F-3F-S1O FA $79 .33 .32 .19 .55 .35 .13 .32 .29 .38 .43 -.01

15-0-810 OF 295 .05 .01 .019 .10 -.03 .13 .07 .27 .09 .22 -. 14

13E*$[-810 OF 253 .06 .02 .04 .01 .11 .09 .02 .28 .18 .25 -.08

16]-BA-,11 OF 165 .12 .06 .16 .24 .03 -.05 .16 .21 .07 .14 .20

161-DC-811 OF 131 .14 .06 .13 .08 .1 6 .23 .17 .40 .21 -.21

16C-CA-911 OF 18 .t6 87 -.45 .49 .19 -.49 .16 Z9 .39 -.13 -.23

6D-D|-8 1 OF I2 .2 .21 .14 .16 -.02 .10 .23 .71 .15 .13 .12

16E-EB-Sti OF 137 .06 -.02 .14 .02 -.06 -.09 -.29 .04 -.01 -.09 .48

16H-HB-911 OF 155 .25 .16 .19 .32 .33 .01 .16 .24 .31 .31 .18

16J-JA-811 OF 119 .21 .13 .24 .19 .24 .11 .18 .02 .35 .06 .14

16P-PA-811 OF 11 .12 .02 .18 -.19 -.11 .21 -. 14 .51 -.03 .19 -.30

16R-RA-8t1 OF 407 .16 .13 .15 .08 .15 .02 .08 .31 .16 .08 -.11

16S-SA-811 OF 596 .12 .11 .09 .17 .15 -.01 .07 .26 .26 .23 .31

17C-7C-061 SC 188 .08 .12 .18 .21 .14 .03 .05 .14 .33 .04 -.08

17K-GA-301 EL 136 .25 .18 .40 .16 .36 -. 12 .34 .02 .12 .51 -.18

IMO-51-804 CO 215 .39 .44 .06 .04 .17 .13 .08 .20 .19 .02 .11

19E-9t-904 CC 171 .21 .15 .37 .11 .45 .25 .18 .22 -.17 .29 -.05

IIF-9F-804 CO 128 .19 .12 .24 .22 .13 .05 -.03 .27 -.13 .19 .10

27E-7E-093 EL 184 .18 .13 .15 .19 .05 .22 .23 .42 .13 .15 .02

SIM-4D-113 EL 664 .39 .07 .11 .27 .18 .19 .13 .12 .24 .14 .00

31M-4C-113 EL 193 .88 .03 .03 19 .42 .20 .13 -.06 -.39 -.05 .19

SIV-IV-061 EL 437 .19 .17 .11 .24 -. 13 .21 .36 .38 .51 .23 .17

36C-AA-113 EL 376 .15 .12 -.06 .32 -.04 .23 .67 .21 .09 -. 12 -.06

36K-AC-I13 EL 661 .07 .16 .14 .16 -. 18 .IS .14 .30 .18 .25 -.03

41C-07-091 G 105 .15 .15 -.15 .35 -.17 .35 .12 .17 .20 .03 .69

448-JI-091 GP 137 .11 .14 .12 .28 .16 .17 -.11 .17 .11 .16 .11

45K-K8-091 OR 101 .16 .96 -.04 .12 -. 19 .31 .40 .13 -.11 .41 .04

4SK-KI-091 CM 129 .18 .11 .31 .08 .19 .21 25 .'5 .09 .47 .19

SIK-IK-807 GM 167 .11 .05 -.06 .19 -.07 .19 .02 .36 .42 -.03 .16

J4C-5S-031 GM 183 .05 -.02 -.19 -.11 -.10 -.03 .17 .19 .04 -.07 .27

54E-SA-031 ST 272 .17 .1m .27 .21 -.81 .24 .16 .07 .24 .22 -.03

(cont'd) i. A-20 I
Ridge Regression Coefficients for Training Criterion (Continued)

TCELL AA M 32 RR2 05 A* VE NO CS AS MX lC El

553-55-193 GM 236 .f5 .11 .29 .31 .23 .15 .10 .14 .25 .15 .29

57E-PE-1S1 GM 125 .14 .26 -.11 .27 -. 2Z .02 .56 .22 .15 .38 -.36

57N-0I-551 GM 224 .16 .A2 .2 .O .29 .30 .11 .14 .12 .16 -.08

615-06-551t Mt 186 .47 .44 .5 .37 .71It. .130 .15 .37 -.33

6i¢-HI-531 mml 138 .27 .21 .09 .09 -. 17 -.87 .36 .22 .63 .61 .06

628-0-9607 MM 264 .22 .19 -,.tl .26 -. 155 -. 09 .33 .67 .30 .16 .1J6

62E-CE-807 GM 133 .26 .20 .34 .26 .0 .21 -.02 .28 .05 .27 .25

62F-CF-807 GM 149 .18 .12 -.28 .20 .17 .28 -.20 .31 .01 .33 .30

635-33-803 MM 35 .14 .12 .63 .19 .13 .14 .11 .60 .07 .22 .05

638-35-805 12M 381 .87 .04 .16 .11 -.22 -.06 -.02 .36 .02 .06 .12

63D-SA-171 MI 342 .05 01 .14 .11 .19 -.01 .20 .41 -.15 -.05 -. 03

43G-M7-091 11 161 .09 .02 .61 .31 0.18 .89 .04 .41 -.22 .24 -. 02

63H-HI-091 MM1 706 .14 .13 .13 .12 .A3 -.18 .98 .13 .21 .47 .31

63N-TS-171 1,Mr 309 .06 .14 -.16 .A7 -.15 .04 .AI .37 .10 .31 .21

63T-FI-171 MI 572 .04 .12 -.06 -.18 -.25 -.18 .11 .65 .33 .23 .21

63W-WI1-091 M 481 .07 .04 .10 -.08 .23 -.19 .20 ,43 .19 .A8 .16

43Y-TV-171 AM 173 .4 -.02 -. 11 -.17 It -. 0 .09 .15 .22 .11 .14

64C-EC-807 OF 202 .16 .61 -.07 -.20 .11 .89 -.11 .39 .13 .25 -. 19

64C-4C-803 OF 561 .04 .02 .A6 .05 .11 -.10 .06 .16 .10 .01 .07

67N-65-011 MM 163 .47 .43 .23 .31 .65 .39 .41 .33 .39 .30 .14

67T-L6-.51 MM 124 .50 .45 .53 .43 .34 -.07 .49 .15 .37 .24 .41

67U-PI-551 M 210 .27 .23 -. 13 .33 .25 -.13 .33 .08 .33 .64 .11

67V-18-911 WI 194 .45 .42 .16 .67 .38 .23 .12 .26 .37 .22 .28

67Y-51-551 WI 144 .21 .13 .33 .08 -.09 .60 -.12 .72 -.08 -.06 .58

68D-Ti-551 MMt 121 .36 .30 .25 .44 -.48 .18 .32 .64 .45 .75 -.20

68J-W6-531 GIl 120 .30 .24 .33 -.95 -.93 .13 .02 .55 .50 .44 .40

68M-1S-551 GM 134 .25 .18 .03 .23 -.17 .23 .45 .44 .15 .38 .09

71t-LI-551 CL 175 .22 .17 .08 .41 .32 .26 .44 T.07 .39 .05 .23

721-34-113 5C 143 .10 .12 .16 .29 .17 .61 -.05 .14 .10 -. 34 -.41

73C-5R-121 CL 202 .32 .29 -.00 .61 .39 .21 .14 .13 .37 -.08 -.20

755-51-121 CL 494 .08 .16 .19 .22 .08 .14 .05 .18 .25 -.07 .01

75D-50-803 CL 233 .21 .17 .12 .65 .1 -. 16 -.04 .14 .28 .02 .15

75E-5E-805 CL 276 .30 .27 .02 .32 .48 .23 .17 .24 .54 -.07 -.07

7C-IC-101 CL 1142 .1 .16 .11 .23 .28 .16 -. 12 .12 .40 .13 .16

76P-SF-I01 CL 560 .23 .22 -.07 .51 .41 -.86 .24 -.23 .87 .24 -.00

76VV-f-101 CL 41 .03 .19 .15 -.19 .13 -.18 .63 -.13 .25 .11 -.13

76W-0D-101 CL 138 .08 .66 .11 .13 .11 31 .25 .Z9 -.13 .11 .19

76-P,-101 CL 344 .20 .18 -. 09 .34 .39 -.02 .11 .39 .42 .19 .15

76X-SX-101 CL 158 .95 -.12 -. 13 .18 .17 -. 17 .13 -.30 .17 -.&,6 .12

76Y-EY-I0I CL 381 .15 .08 .06 .19 .15 -.00 .25 .04 .41 .05 -.07

(cont'd)

A-21
Ridge Regression Coefficients for Training Criterion (Continued)

TCELL AA N R2 RR2 6S AR VE NO CS AS MK MC El

76Y-SG-101 CL 298 .32 .30 .48 .38 .42 -.33 .20 -.17 .43 -.06 .18

76Y-GY-805 CL 470 .16 .04 .06 -.02 -.11 .18 .fS .07 .47 .12 -.09

82C-2C-810 ST 390 .30 .29 -.09 .57 -.19 .30' .13 .24 .42 .08 .27

9IB-41-929 ST 783 .12 .11 .19 .07 .01 .03 .10 .19 .00 .08 -. 03

91C-12-929 ST 233 .20 .16 .18 .35 .17 .22 .27 .20 .11 .00 .16

911-05-929 ST 162 .19 .13 .19 .34 .23 -.02 .31 .4 .*41 .17 .08

921-25-929 ST 133 .28 .22 .16 .41 -.02 -. 12 .15 .08 .22 .35 .20

948-KA-111 OF 627 .06 .15 .04 .08 .13 .03 .22 .17 .14 .02 .18

943-45-803 OF 237 .24 .20 .39 .39 .08 -.14 .37 .17 .23 .30 .11

941-48-805 OF 416 .15 .03 .12 .14 .10 .02 -.04 .12 .12 .02 .10

951-S-813 ST 725 .23 .22 .44 .28 .81 .17 .03 .31 .06 .16 .20

A-22
Table A - 10

Ridge Regression Coefficients for SQT Criterion

PCELL AA N 12 R12 03 AR VE NO CS AS M'K lC ET

65/1.1/82 SC 613 .1$ .14 .04 .21 .38 -.05 .08 .25 .12 .11 .10

1SC//l/82 SC 1197 .12 .11 -. 3 .29 .29 .00 .17 .12 .11 .24 .12

ISC/O/I/83 SC 1156 .28 .27 .15 .27 .12 -.26 -.02 .37 .27 .26 .19

SG/0/I/183 SC 119 .24 .17 .14 .12 .32 .13 .57 .38 -.12 .74 -.20

0IH/1/1/8, ST 110 .37 .30 -.26 .15 .S4 .23 .52 .32 .70 -.00 -.03

111/ 0111 CO 606 .16 .08 -.06 .25 .02 .13 .12 .11 .05 .25 .22

118/0/1/83 CO 423 .25 .23 .21 .24 .32 .00 -. 14 .17 .30 .25 -.00

113/1/1/32 CO 3896 .13 .12 .21 .15 .04 .05 .13 .t7 .25 .24 -.01

11B/2/1'82 CO 611 .07 .05 .07 .02 .019 .01 -.03 .11 .17 .13 .15

113/6/1/82 Co 419 .11 .09 .09 -.03 .23 .05 -.13 -.06 .30 .32 .03

118/7/1/82 CC 228 .18 .14 .17 -.47 .39 -.16 .30 .12 .29 .45 -.04

IIC./ /1/81 Co 171 .18 .32 .03 .16 .to .08 .09 .04 .13 .10 .12

1IC/0/1/83 CC 112 .20 .12 .27 .06 .26 -.13 .O4 .21 .38 -.01 -.06

IIC/1/1/8z CO 555 .18 .16 .10 .25 .18 .09 -.12 .08 .22 .58 -. 19

I1C/2/1/82 CC 186 .16 .11 .14 .16 .06 .11 -.42 .t2 .17 .03 .30

IIC/4/1.I82 CO 246 .18 .04 .09 .10 .10 07 .12 .15 .09 .10 .13

1IC/3/18f CO 217 .22 .18 .09 .27 .13 -.02 .15 .18 .18 .20 .20

IIH/ /1/81 Co 124 .26 .19 .47 .t4 .02 -.15 .24 .09 .46 .51 -. 33

11HN1/1/82 CO 442 .13 .11 .40 -.01 .03 .09 .21 -.04 .10 .30 .11

IIH/2/ /82 CO 321 .19 .06 .14 .18 -.03 .18 .13 .02 .16 .11 .08

123/0/1.'83 CO 1975 .13 .12 .04 t -.91 -.24 .07 .17 .25 .40 .07

123/1/1/62 Co 1103 .14 .13 .14 .37 -.97 .22 -. 12 .34 .08 .29 .12

12C0///82 11 175 .13 .67 .14 .23 -.09 -.01 .20 .19 -.05 .54 .17

12C/0/1/83 MM 271 .11 .07 .14 .13 .13 .07 .20 .08 .19 .20 -.01

133/ 11/91 FA 130 .18 .10 .29 -.12 .04 .37 -.23 .33 .13 .10 .11

138/0/1/83 PA 3902 .22 .22 .11 .16 .19 .07 .11 .25 .27 .26 .12

138/i/1/82 FA 109 .21 .12 -. 15 -.04 .09 .18 .27 .40 .13 .19 .13

131/2/1/82 FA 263 .15 .12, .16 .08 .11 .12 .02 .12 .17 .18 .11

133/3/t/32 FA 156 .25 .19 -.17 .40 -.13 .15 -.22 .38 .23 .42 .01

135/4/1/82 PA 1184 .11 .10 .23 .22 -.05 .15 .16 .28 .20 .16 -.10

138/5/t/82 FA 627 .16 .15 .11 .15 .03 .06 -.13 .18 .07 .12 .07

13E/g/1/82 ST 413 .13 .10 -.07 .15 .11 .05 .25 .21 .40 .38 .07

134E//1/83 ST 194 .20 .15 .28 .14 .34 -.16 .95 .10 .37 .18 .05

13F/1/1-482 FA 393 .11 .10 -.26 .14 .13 -.11 .23 .17 .05 .59 .34

4-5D/0/1.82 OF 259 .t4 .10 .11 .31 .16 .03 .14 .28 .13 .33 .nA

17K/2/I,82 EL 110 .11 .11 -.66 .22 -.04 .27 .15 .10 .!4 .37 -.04

19D/ /1/2' CO 184 .12 .12 -. 16 .32 .03 -.39 .10 .31 .37 -.13 -.07

190/0/1/83 CO 334 .20 .18 .16 .21 .57 -. 1 .i8 .18 .04 .27 .12

19D/l/I/82 CO 742 .15 .1 .11 .21 .27 .01 .12 .11 .18 .17 .09

19E/1/1/8-, CO 676 .14 .2 .12 .12 .28 .07 .11 .19 .05 .27 -.02

(cont'd)

A-23
Ridge Regression Coefficients for SQT Criterlon (Continued)

PCELL AA N R2 RR2 OS AR VE NO CS AS MK MlC El

19E/i1/1to83 CO 1911 .33 .32 .20 .15 .46 .15 .03 .29 .17 .48 .01

195,2//1/63 Co 855 .26 .26 .31 .19 .18 .34 -.05 .29 .13 .33 .17

195/3/1/82 CO 611 .11 .10 .24 .11 .66 .14 .16 .25 .01 .19 .04

26@/VSI/83 EL 142 .18 .11 .31 .12 .18 -. 88 .21 .66 .43 -. 10 .32

271i0t/182 EL 132 .10 .02 -.03 -.19 .21 .08 .12 .32 .16 .28 -.33

27E/0/1/83 EL 225 .21 .18 -.31 -.03 .86 .4 .12 .09 .71 -. 13 .12

31J/0/83 EL 130 .15 .07 .32 .25 .27 -.20 .30 .1 .17 .46 -. 06

311V0/I/83 EL 1449 .32 .31 .31 .19 .15 -. 16 .82 .54 .30 .35 .39

311/I1/82 EL 573 .08 .16 .22 .37 .18 .01 -.03 .12 .14 .14 .03

31M/2/1/82 EL 404 .07 .05 .13 .02 .23 -.03 .09 .42 .26 .15 .18

31V0/1/83 EL 151 .23 .17 -. 13 .35 .31 -.00 -.04 .06 .46 -.03 .59

31V/1/l/82 EL 336 .08 .05 .11 .18 .05 .06 .07 .07 .18 .18 .14

35K/ / / EL 112 .28 .20 -.22 .29 .Al .27 .17 .39 .31 .12 .32

36C/2/1/82 EL 390 .14 .01 -. 15 .15 .68 .11 .10 .08 -.08 .16 .05

36KS/sli82 EL 939 .07 .86 -. i1 .16 .12 -.68 .86 .20 .81 .37 .11

36K/0/1/83 EL 847 .20 .19 .16 .34 .20 -.05 -. 06 .35 .21 .35 .06

43" IVI/83 Gm 100 .71 .01 .20 .09 .tO .27 -.02 .18 .22 -. 19 .05

518/0/1/82 G-1 196 .14 .09 -. 19 .02 .25 .17 -. 19 .41 .46 .20 -. 12

520/0/1/82 Gm t76 .17 .11 -.04 .10 .13 .23 -.10 .22 .17 .55 -. 11

55B/1/l/82 Gm 230 .08 .04 -.07 .09 .12 -. 10 .11 .06 .69 .17 -. 02

57H/0/1/83 GM 194 .09 .04 -.21 .31 -. 31 -. 07 .14 -.19 .23 .25 .25

62S/0/3/82 PI 220 .24 .21 .14 .13 .31 -.81 .15 .36 .24 .32 -. 07

621/0/1/82 G," 172 .20 .15 -. 04 .11 .18 .20 .15 .29 .14 .39 .23

62F/0/1/82 GM 117 .25 .17 .34 -. 17 .33 .30 .20 .00 -. 18 .41 .44

633/0/1/82 l 1471 .13 .12 -. 15 .25 .11 .03 -.67 .40 .22 .23 .28

i3H/0/1/82 MM 335 .14 .12 .05 .24 .27 -.06 .14 .22 .08 .16 .25

63N/0/1/32 MM 286 .05 .31 -. 11 .17 .21 .00 -.23 .06 .13 .05 -.05

63U/0/1/82 Ilr 180 .10 .84 .23 -.03 .16 .16 -.08 .35 .01 .15 -. 07

63Y/0/1182 MI,1 108 .18 .89 -. 13 -. 12 .28 .14 -. 4 .73 .48 .55 -. 14

64C/0/!/82 OF 1573 .17 .16 .03 .30 .31 -.02 .12 .28 .12 .33 .19

64C/0/,1/83 OF 2063 .19 .68 -.07 .32 .26 -.06 .04 .24 -.91 .11 .20

6714/ /1/l PI' 123 .17 .19 .01 .21 -. 12 -.10 .23 .14 .12 .71 .16

67N/1/1/82 M1I 386 .19 .16 .10 .16 .13 -. 11 .06 .AV .16 .36 .23

671U./0/1/82 M, 207 .16 .12 -. 01 .46 -. 19 -. 31 .13 .54 .26 .34 .0

67V/0/M/82 M 232 .15 -.00 .04 .10 .18 .87 .16 .69 .10 .12 .11

67Y/0/1/82 Mm 194 .16 .11 .33 .10 .44 -.08 -.03 .34 -.04 .25 .21

68(;/0/1/82 MM 121 .21 .12 .23 .31 .20 .13 -. 42 .16 .41 -. 02 -. 01

6J4/0',1/8Z GM 119 .11 .i2 .02 .12 .20 -.09 .15 .23 -. 25 .45 -.04

710/0/1/82 CL 115 .23 .14 -. 25 .29 .66 -. 63 .32 -. 21 .25 .26 .29

71L/I/1/83 CL 2628 .24 .24 .01 .49 .65 .03 .26 -. 10 .38 -. 01 -. 00

(cont'd)

A-24 1
Ridge Regression Coefficients for SQT Criterion (Continued)

PCELL AA N M2 m2 G Alr VE NO CS AS MK MC El

71L/2/1/83 CL 167 .20 .14 -.23 .33 .46 -. 13 .13 -.29 .50 .11 .27

71?f//1/82 CL 182 .17 .12 .22 .38 .39 .22 .17 -.00 .04 .29 -.05

72E//1I/83 SC 564 .28 .27 .06 .15 .12 .00 -.01 .21 .18 .33 .44

7310/1/82 CL 268 .34 .31 .10 .40 .17 .13 .27 .08 .39 .19 .08

73C/0/11/83 CL 415 .38 .36 -. 14 .32 .69 .36 .32 .24 .14 .22 .05

74D/ / / ST 132 .24 .17 .19 .60 .67 -.03 .36 .07 .24 -.02 .04

751/0/1/82 CL 423 .27 .25 .08 .59 .25 .16 -.02 -.01 .35 .10 .09

753//1/83 CL 63! .28 .27 .07. .53 .29 .19 .15 .12 .40 .11 .09

75CIh0/182 CL 118 .33 .27 -.00 .07 .34 .37 .13 .35 .78 .13 -.07

75C/0l/.83 CL 289 .29 .27 .21 .45 .23 -.15 -.02 .05 .40 .25 .13

75D/0//82 CL 370 .12 .19 .07 .27 .14 -.02 .04 -.00 .12 .02 .36

75W0.V 183 CL 45 .27 .26 .*1 .47 .42 -.29 .24 -.15 .63 . .14

75E/0/1/82 CL 175 .19 .13 .29 .09 .*1 .10 .02 -.13 .17 .04 .14

75E/0Co1/83 CL 279 .24 .21 -.00 .42 .25 -.01 .03 -.09 .27 .31 .20

75F/0'1/83 CL 1*4 .3i .30 .03 .51 .28 .03 .16 -.07 .42 .22 .13

76C/l/1/83 CL 320 .17 .14 -.02 .50 .22 .21 -.16 .17 .37 -. 13 .17

76V/0/183 CL 216 .17 .13 .27 .27 .44 -.25 .28 -.14 .08 .06 .13

761W/0.-I/82 CL 295 .13 .09 .33 .39 -.42 -.27 .24 .47 .08 .17 .01

76W/0//83 CL 321 .23 .20 .35 .34 .18 -.06 .09 .21 .21 .17 .17

82C/I/I/82 ST 209 .18 .14 .08 .31 .66 .02 .13 .14 .32 .28 .23

82/2/1/82 ST 133 .19 .12 .03 .48 -.03 .22 .20 .13 .16 .23 -.05

919/0/1/82 ST 203 .11 .06 .34 -.02 .08 .09 .10 -.08 .39 .12 .24

9IP/l/1/82 ST 159 .11 .15 .15 .09 -.02 .34 .20 -.03 .13 .18 .22

9113//1/82 ST 145 .19 .12 .22 .12 .23 .21 .51 .30 •18 -.09 -.02

928/0/1z82 ST 310 .11 .07 .63 .34 .-2 -.17 .07 -.07 .32 -.09 .15

93M/0/1/82 ST 114 .15 .05 -. 12 .29 .38 -.12 .14 -.19 .18 .39 .35

91/01/82 OF 1543 .11 .11 .25 .28 .22 -. 19 .07 .22 -.02 .21 .07

943/t//83 OF 2386 .23 .23 .17 .57 .41 -.29 .18 .28 .07 .09 .24

51/0/1/82 ST 1853 .18 .17 .14 .29 -. 12 -. 91 .11 .08 .20 .21 .17

953/0/1/83 ST 2580 .16 .16 .11 .32 .57 .12 .15 .26 .24 .16 .14

9163//1/83 ST 172 .29 .24 .45 .58 .69 -. 14 .16 -. 15 .23 -. 19 .22

98C'/1/82 ST 186 .32 .28 -. 14 .53 .62 -.23 .66 .17 1.65 .25 -.25

A-25 I
Table A - 1i

Ridge Regression Coefficiena for Combined Criteria

nOS AA N *23R2 GS AR VE NO CS AS MK IC EI

153 SC 113: .19 .08 .92 .18 .38 .03 .06 .19 .10 .16 -.06

Ise SC 2966 .16 .16 .03 .29 .17 -. 04 .19 .18 .22 .20 18

$o SC 119 .24 .17 .14 .12 .32 .13 .57 .38 -. 12 .76 -. 20

Is" ST 110 .37 .30 -.26 .15 .84 .23 .52 .32 .71 -.00 -.08

its CO 6153 .11 .11 .19 .10 .10 .13 .11 .14 .25 .28 -. 04

11C CO 1894 .12 .12 .11 .14 .11 .10 .06 .17 .20 .27 .04

It" CO 1207 .08 .07 .14 .13 .08 .11 .10 .05 .12 .17 .10

123 CO 3081 .13 .12 .04 .27 -.05 .03 -.00 .24 .19 .37 .10

12C IM 646 .10 .08 .09 .17 .06 .03 .21 .13 .12 .36 .03

IZF CO 224 .05 .01 .17 .06 .13 .04 .04 .17 -. 01 .23 .08

133 FA 6694 .16 .16 .10 .16 .05 .08 .04 .25 .23 .29 .09

13E ST 1692 .12 .11 .18 .26 .13 -. 05 .19 .17 .43 .13 -. 00

13F FA 1272 .19 .19 -. 07 .37 .26 .03 .39 .25 .22 .54 .16

1,0 OF 354 .08 .06 .11 .22 .02 .09 .12 .33 .09 .31 -. 11

15E OF 283 .06 .02 .04 .01 .11 .09 .02 .28 .18 .25 -.04

163 OF 296 .10 .07 .13 .21 .06 -.01 .14 .19 .22 .17 .06

16C OF 118 .16 .07 -. 05 .49 .09 -. 09 .16 .29 .39 -. 13 -. 23

160 OF 112 .28 .2t .14 .16 -. 02 .10 .28 .71 .t .13 .12

16H OF 105 .25 .16 .19 .32 .33 .01 .16 .24 .31 .31 .18

16J OF 119 .21 .13 .24 .19 .24 .11 18 .02 .35 .06 .14

16R OF 407 .06 .03 .05 .68 .15 .12 .08 .31 .16 .28 -.11

16S OF 596 .12 .11 .09 .67 .05 -.91 .07 .26 .26 .28 .31

17C 3C 188 .08 .02 .18 .21 .14 .13 .05 .14 .03 .04 -.08

17K EL 246 .14 .11 .20 .21 .17 .07 .23 .07 .64 .49 -. 13

190 CO 1291 .1 .14 .11 .18 .28 .02 .11 .15 .17 .17 .09

19E CO 3323 .21 .21 .24 .13 .28 .12 .15 .29 .08 .37 -. 01

19F CO 128 .19 .12 .24 .22 .13 .1 -. 03 .27 -.03 .19 .10

264 EL 142 .18 .11 .31 .02 .18 -.08 .21 .06 .43 -. 10 .32

27E EL '09 .16 .14 -.05 .68 .45 .19 .19 .26 .43 -.01 .05

31J EL 130 .13 .07 .32 .25 .27 -.20 .30 .01 .17 .46 -.06

31M EL 3030 .16 .15 .24 .23 .19 -.02 .05 .27 .29 .25 .23

31N EL 193 .08 .63 .03 .19 .42 .20 .13 -.016 -.39 -.05 .19

31V EL 944 .13 .12 .03 .28 -.00 .13 .05 .20 .40 .19 .30

331 EL 112 .28 .20 -.22 .29 .34 .27 .17 .39 .31 .12 .32

36C EL 376 .05 .02 -.06 .32 -_04 .23 .07 .21 .69 -.12 -. 06

36K EL '446 .18 .10 .03 .20 .06 -.02 .64 .29 .16 .38 .05

4C OM 105 .15 .05 -. 05 .35 -. 17 .35 .12 .17 .20 .03 .09

43E GI 10 .11 .01 .20 .09 .10 .27 -.02 .18 .22 -. 19 .05

448 OM 137 .11 .04 .02 .28 .06 .17 -. 01 .17 .01 .16 .11

451 GM 230 .13 .11 .17 .99 -. 06 .25 .33 .16 .05 .41 .15

continued on next page I - A -26
Ridge Regression Coefficients for Corbied Criteria (Cortinued)

noS AA N A2 Rt2 OS AR VE NO CS AS MK MC El

515 ,M 196 .14 .A9 -. 19 .12 .25 .17 ".g9 .41 .46 .20 -.12 SIK ON 167 .11 .15 -.16 .19 -.17 .19 .82 .36 .42 -. 3 .16 52D Gm 176 .17 .11 -.04 .11 .3 .23 -.10 .22 .17 .53 -.it 54E ST 272 .17 .14 .27 .21 -. 1t .24 .16 .07 .24 .22 -.03

551 am 466 .10 .68 .12 .22 .16 .63 .11 .tZ .St .17 .19 57E Gi 126 .14 .06 -.01 .27 -.23 .12 .56 .22 .f5 .38 -.36 57 GM 224 .66 .02 .20 .I1 .29 .t0 .11 .0' .12 .16 -.08

613 MM 186 .47 .44 .15 .37 .76 .11 .03 .30 .15 .37 -. 33 61C "ii 138 .27 .21 .89 .49 -.07 -.07 .36 .22 .63 .41 .06 625 IM 484 .21 .20 .02 .18 .17 -.17 .26 .55 .30 .24 .02

62E 4" 305 .21 .i9 .14 .17 .09 .22 .07 .31 .10 .36 .26 63F GM 266 .17 .14 -.064 .62 .27 .36 -.60 ,18 -. 07 .36 .54

63 MM 2026 .13 .12 -. 12 .20 .64 .68 -.05 .49 .18 .23 .22 630 Mm 342 .65 .61 .04 01 .19 -.01 .20 .4f -.05 -.05 -.83 630 m" 141 #IF .02 .05 .31 -.18 .09 .04 .4t -.22 .24 -.02

63H mm 10641 .14 .13 .03 .18 .12 -.09 .06 .18 .16 .37 .31 63H MM .09 .06 .04 -. 16 .07 -.15 .08 .01 .37 .10 .31 .21

63W MM 661 .06 .04 .f3 -.05 .19 -. 12 .12 .13 .13 .10 .09 63Y MM 283 .08 .64 -. t8 .12 .20 .65 .68 .45 .36 .31 .07 6fC OF 3616 .12 .12 -. 064 .32 .30 -. 05 .07 .27 .04 .22 .20

67N MM 549 .15 .14 .13 .22 .f& .64 .t9 .12 .25 .41 .23 67T mm 124 .50 .45 .53 .*3 .54 -. 07 .49 .15 .37 .24 .41 67U MM 417 .18 .16 -. 11 .42 .07 -.20 .33 .35 .30 .52 .07 67V lM 426 .17 .15 .09 .37 .23 .13 .23 .18 .10 .23 .21 67y MM 338 .14 .11 .32 .17 .26 .29 -.02 .50 -.08 .13 .39 68D MM 121 .36 .36 .25 .44 -. 48 .18 .32 .64 .45 .78 -.20 68G MM 121 .20 .12 .23 .32 .2 ) .13 -. 42 .16 .41 -. 02 -. 01 fiJ 04G 239 .15 .It .19 .18 .12 .04 .P9 .42 .09 .*8 .14 68M1 amt 134 .25 .18 .13 .25 -. 17 .23 .43 .44 .15 .38 .09 710 CL 115 .23 .14 -.25 .29 .66 -.63 .32 -.21 .25 .26 .29

71L CL 2795 .23 .23 -.32 .48 .66 .62 .25 -. 12 .39 .00 .02 VIm CL 182 .17 .12 .22 .18 .39 .22 .17 -.14 .64 .29 -.05 71K CL 175 .22 .17 .18 .41 .32 .26 .44 -. 07 .39 .05 .23 72E SC 564 .28 .2' .6 .13 .12 .00 -. 01 .26 .1 .33 .44 73C CL 885 .34 .33 -. 1t .44 .52 .30 .27 .15 .27 .13 -.03 740 ST f32 .24 .17 .f9 .60 .67 -. 05 .36 .17 .24 -. 02 .04 755 CL 1548 .20 .19 .018 .47 .23 .1 .1o .08 .39 .02 .06 75C CL 407 .28 .27 .15 .34 .27 .01 .02 .13 .52 .22 .06 75D CL 1253 .19 .19 .16 .56 .29 -. 22 .15 -. 11 .42 .03 .25

7S CL 730 .23 .22 .06 .36 .44 .13 .68 .$2 .36 .11 .69

contirueo on next page

A-27 / /t

i
Ridge Regression CLfficients for Combined Criteria (Continued)

MOS AA N R2 2 R2 @5 Alt VT NO CS AS MK PIC El

75F CL 144 .35 .31 .13 .51 .28 .03 .16 -.07 .42 .22 .13

76C CL. 1462 .12 .11 -.01 .29 .28 .16 -.15 .13 .41 .06 .17

76P CL 561 .23 .22 -.17 .30 .40 -.06 .24 -.23 .87 .24 -. 00

76V CL 216 .17 .13 .2? .27 .44 -.25 .28 -.04 .08 .06 .13

76W CL 1098 .14 .13 .18 .43 .07 -.04 .18 .38 .22 16 .14

76X CL 158 .05 -.12 -. 13 .18 .17 -.17 .13 -. 00 .17 -.06 .12

76Y CL 11.9 .10 .09 .13 .15 .13 -.03 .25 .14 .49 .06 -.04

82c ST 732 .23 .22 -.@3 .53 -.11 .20 .16 .18 .35 .21 .22

91C ST 233 .21 .16 .18 .33 .17 .22 .27 .20 .11 .00 .16

9ie ST 365 .13 .10 .31 .13 .15 .13 .21 -.05 .42 .13 .18

liP ST 159 .11 .05 .83 .9 -.12 .34 .20 -.03 .13 .18 .22

91R ST 145 .19 .12 .22 .12 .23 .21 .51 .30 .18 -.09 -.02

923 ST 443 .14 .12 .67 .41 .21 -. t8 .11 -. 04 .20 .06 .17

93H ST 1t4 .1 .05 -.02 .29 .38 -. 02 .14 -. 19 .18 .39 .35

9 a OF 5129 .15 .14 .14 .40 .30 -.12 .15 .23 .1 .10 .16

955 ST 5158 13 .12 .17 .31 .37 .67 .13 .19 .20 .19 .16

963 ST 172 .29 .24 .45 .58 .69 -.14 .16 -.15 .23 -. 19 .22

98C ST 186 .32 .28 -.14 .53 .62 -.23 .66 .17 1.05 .25 -.25

AA - Current Composite

N - Sample Size

R2 - Ordinary Least Square Squared Multiple Correlation

RR2 - Ridge Regression Squared Multiple Correlation

A-28 / I /.

6 '
Table A - 12

matrix of Correlations of Expected Outcome Functions for MOS, Combined TrAining and SQT Criteria

1*6% COS 0% 44% 1%1 ell lot ON C" 11P1 ell ell ell 0" ell 14 16 16 IS 14 16 16 17 17 19 IS 19 IS 17 31 )1 11 31 IS 16 16 41 41 44 41 51 $1 w .04 $S 51 $1 61 it

C a 8 C a 0 1 N, 0 a j 9 C 9 9 a p r a or it

%*QC N No

eta IS 911 M

It* 1, to 1% 91 41C to to 0% So to It

:19

lie I to So III to 99 91

a JT 044 94 11 66 111 as 68 17

:sI :01 :16 111

16 1 :1 :! Il in, it, :01 :9 :67 :16 :67 Ir *6 2) 11 IS *a Is 96 1.3 97 1.. 99 as :07 .0

I a as .:4 4: 1: V 9 94 1 to 4: 77 0: 14

96 7 7 1 to 9; N 1111 11 :11 :11 116 :6 :6 a? 111 99 so 16 a? 64 ti -12 93 1: 91 of a. 9. 9, 91 IS 67 ad 111 90

.1 11*1 :61 ,a N :6 :1 :1 :% N :: :7 :: :i N 1: :1 9: :4 lie :0 9: 91 13 597 97 97 91 06 It 911 96 is is 111 97 :a oil :6 937 S 91 :1 :6 :1 :6 :S :1 :4 :6 :1 :7 7 1 9 a 9 a a 7 a 97 tv 0 2 4 11 1: N N ;% :71: ;i V :1 a :1 6 9 9; :1 1 1 1: to$7 U :7 a, JGv It ad 07 V. as 9% 91 of 93 as at 04 21 to 98 96 Is So 97 to 91 94 94 91 07 11 91 219 91 : 7% :1 S .1 :4 It :7 :S 01 :: :7 :6 91 :: :6 :: :2 :6 9: :1 :1 :7 :4 :1, I k j I 1 4 -I I It 7 4 91 7 7 91, 7 1 a I 1 4 6 7 1 92 Joe 4 7 3 7 7 7 7: :6 :6 )1* $8 '01 :9 1: St :20 97 7*7 at 97 :7 97 97 9 1 9 7 ;$ :7 9: :4 :4 ;7 I'll 11 ad 11 1 2 1 1: 7 94 97 :6 IS tj 21 07 7: 11 1: IS NO 667 Is 7, 1 1 $07 95 96 to It 11% N 9: 161 9: 9:

Mit $1 01 11 IS :6 :4 of is 91 1: 97 11 to $6 9 Ile to ad 11 ad at No 44 79 43 11 :1 a '1 $1 Is as :7 :: :6 97 :1 :46 11 :1 9: :6 1: 11 1: 11 11 1: so - 91 :S 97 90 25 :1 :7 :9 99 :: 97 :7 :4 :7 :1 9; 9: 1: 94 9: :4 :4 7S 96 as 8 17 8 91 7 9 1 to to 1: 11 90 43 :1 :1 :1 9: :7 4 Ic :S :d 86 91 92 91 95 17 8% IS 91 92 1 to 1 7 7 444 9: to So to I, is go to 97 96 98 at :7 so 96 97 6, 9, 97 IS 96 96 96 IS 17 97 97 It 04 91 97 It It if 81 97 92 91 4W 11 94 IS No 16 97 Is 94 to is 96 91 to 97 is 21 #1 9% is $4 91 94 92 57 IS IS So so $1 91 91 61 is 91 111 91 91 69 11

I as 9, It 9. 1 ON :1 :,So :: :2 :1 :. 1 :13 :,1 :1 :7 :2 4 7: :S :: :7 :61 :7 1 , :1 :4

IS : :4 9% So 91 94 9 4 9 S 9 1 is 03 is 91 11 :1 911, 1: 19, :1 :1 9? 9: IS :4 :S :S 91 it :4 7: :S 79 :1 :6 :1 96 9; S: :2 91$ 97 7 99 1 97 to : 97 91 1 a 94 9 4 4 7 ' : I' III 97 1. 9, '9 1 IS :. 11 91 IS 19 97 IS a$ to 91 91 $9 26 96 91 96 97 99 9: 04 91 :7 9: 9 5 1: $7 :1 9t 0: 05 ': :. :1 11

79 7 IS it 84 so 77 Is 76 71 It It 71 7% At 19 74 16 if 75 77 74 76 17 72 82 73 If 61 IS 76 15 61 ISS 73 IS 21 71 84 68 71 43 Go 74 74 91 71

Id :4 :4 :4 97 16 9 Is 91 S% Os 91 9 '1 91 :: :4 as 97 :1 is 91 :7 97 :: 97 gil ;; :5 91 :S :1 91 :2 7S St :: *2 9 & 93 :1 :: 9: ;Z ;S 7; go 's a No 9, 9 1 :0 1 NO 17 :1 '1 91 16 :: go 91 06 :6 96 61 9. 'So : '1 14 91 :9 95 :1S I as :3 97 9 :2a :41 it 14 IV I '; 97 IS 91, 97 97 :4 96 9: 98 94 of as :1 91 1: :S 95 94 Of 97 91 :1 92 041 94 9 11 792 9*1 96 If :0 16 SO 9S 94 IS IS 94 92 9: 21 9941 91

62% 96 1: V% sip 07 9: is I a is 05 9: 9: 9: 44 " is " 94 is 92 0% 12 95 is 4 :14 :97 :9 95 9114 $01 :84 95 so 91 *6 Is 9: :1 :96 to :so :9 :4 19: :9 1414 11 i 119 :: 61 DO is so 92 4i ad at S 9% It :9 11 93 "1 :: 11 IS 96 :7 0: :6 :4 9: :: :: 9; :4 :6 :7 :7 :7 :6 17 $1 71 :1 01 91 :S 91 :; :: 91 6: :i :1 :i 6 01 1 :7 0% yl &1 isa 87 45 ofal 66Is 94 16 as 91 to s71 64 so as9 5: :: 44 'a IS 2 :1 74 2 1 :7 42 62 :1 11 8: 7: 71 7: 8: :1 41 74 $1 71 71 :0 :S 4: :1 07 77 1; :8 71 7: to I 111 94 It 84 91 1 01 96 S, 96 17 to 9 97 9 1: 99 92 $1 to 967 :7 12 1: IS 91 72 9: 7: 91 92 92 181 :4 &1 IS 65 911 11 o ad *7 11 IS 01 17 IS '1 45 68 9 1 14 7: 05 92 74 of 92 IS 7 4 11 :1 14 :S :1 71 77 1 S 7 ? 17 96 1% 11 96 9 :S 92 :4 :S IS 91 94 22 94 1% 79 111 97 *1 IS 96 91 93 91 96 9 6 9 6 at 96 1 A 4 Is 91 :6 1: of :1 :7 IS 9 1 : 11 & ly 91 IS 92 07 1 16 9 96 94 92 : 71 96 9: 1$ :1 :6 94 :0 96 97 :: St 94 IS $1 1 91 1 6 94 95 IS 97 12 85 91 la 97 91 IS 89 it 7k

I 4C to 21 it 04 9: 17 96 97 97 : 04 Ott is 97 9 11 4 6 9 1 96 941 9 4 go is 94 ti ol N 9 li, 7: 96 96 77 to 6) so 97 93 13 92 94 91 96 17 :1 91 No

9 91 :9 97 It :1 :9 97 97 :: :6 9% 9: :7 96 :: 96 91 :9 91 27 94 :5 :: :9 79 :: 97 1: 97 :: It :: :1 :1 9: :5 :6 124

'ju 4 9: 94 :0 J7 %1 9: :4 97 94 :4 16 97 91 :: of a, :: :1 91 11 97 Ii 11 1: S 91 :: 01 04 1: :11 91 96 78 9: Is 91 is 1: 1: :1 1141 No $6 ,IV 9 

% :6 :1 as It 99 

1. 91 

91 9: 

91 :' 

as 9: 

96 91 

No 9: 

'I to: 

97 94 

IS it 

:4 1: 

IS 06 

:7 It 

11 9: 

91 : 

14 

'6 96 

96 :7 

$4 :9 

It 99 

96 94 

97 :4 IS 9; 54 94 84 14 :9S IS :S 91 :4 96 97 11 94 It 97 106 1: 11 9: 92 It to 91 62 93 9 1 :1 Idu :4 11 as :1 :% 4 11 1 :1 IS go as it $1 a$ 91 6. a, 1: "1 91 :: 1: :6 :1 74 06 6 160 47 90 44 06 :: to :4 1 11 :1 :: 94 :1 12 IS 60 :1 93 :1 91 :: 12 :1 1: 84 71 96:: 71 :: : 7 7 v 4 1. 16 97 Is 9: $7 is :0 :3, 96 :0 so 17 1: so 97 :9 a, :11

as S 7 it 1: 11 17 97 IS IS I$ It 94 92 *4 91 :1 9: 54 Sol Of IS 91 91 97 77 :9% 91 76 It :t It S" Ito It 94 9) IS $1 17 it go 07% 111 2 7 No It 81 75, IS 62 7S 72 94 It 17 81 76 if 04 It 76 84 74 so 6) 64 71 77 SO 06 S? 64 74 12 7% 69 71 72 01 So 01 81 51

:1 67 ST :02 :1 9 % 6 7 NO, $1 :1 :.1 961 97 $6 1 :7 1: 91 :1 :% 44 No 13 so g7 15 67 84 94 IS 7) 94 94 91

:2 :0 04 $1 91 02 :1 :7 :1 :S :7 91 No I 1 0. 1$: so, 11 :1 0 01 9) :4 9 9 IS I] 1 1 4 :2 1 71 19, 84 to 96 as of 1. 1 9 2 4 94 IS IS $I 9S $7 *4 91 96 78 IS 97 1: :4 $9 91 91 9: 9: 91 91 1: 91 11) 71 07 24 1 $1 to Is I 1 6 11 19

or :7 1: :S 9: '1 9: :5 :: 1 9 97 1) It :61 is 11 91 66 94 17 to is 11 is a$ t4 IS '2 .01 :81 'G 71 : as :4 9% 16 1: :: 1: Is ;t 11 it 9" :. 17 16 :5 121 90 : "1 91 1: 91 ;: 9: :9 19, : Is 1 9 97 jj :: 16 :1 Is :9 jj 91 91 91 87 69 17 IS 16 7) 97 97 94 9: :4 IV :4 :! 96 97 92 1: 17 :4 17 IS 91 92 14 94 :1 17 99 :4 91 $7 94 :1 *4 9: 14 18 II)v I I '1 7 :6 9: V is IS 02 to 94 to V: 76 t: 9% 111

7 'PC 

of a, 91 1) 1? go: tfi is 9% 97 91 93 16 IS 96 is 9 IS as 9: 94 94 96 :7 to ?4 16

11 4 :1 :9 got 94 90 Sl as No 7 1) is 17 %is V as 996 96 to at 971 91 :S so I :S :: 7; :: 97 as is a, 11 .4, 111 to 92 911 IV I', :. 91 91 91 91 19 ad to 91 14 93 97 9 a 9 2 96 9 S S 17 a k of is is 97 92 11 97 It 71 97 117 14

7%r 96 94 ON IS 17 11 97 9) 96 10 94 92 96 93 it 67 93 80 07 IS 91 91 91 as 97 91 96 IS 91 NO IS 75 11 *1 41 91 it 92 :4 02 It go qj to If 76 94 t1l to 7 7 a 6 .1 91 9, 7 92 1, to 91 go 11 49 It 16 16 :7 :8. : 9 :1 Is No :7 :: 9, :7 11, : .1 141 :: 95 :: :6 1: :81 :S :11 V :. :11 :2 ::

IS :01 IS 92 91 It @G 79 %1 1 2 :4 87 :6 IS :7 91 'SO :3 96 9: No a& No 6 IN is 9% 94 93 So No No 2 97 1: 44, 6. 90 1 a, 03 i 11 :i : Is 11 It 41 615 IV is 11 76" 9 7 99 91 to 0: IS to of to :7 Ot Of 94 90 97 to of No 90 91 it 98 57 at at so so as 14 91 Is if Of 97 87 $9 so It to $4 14 Is 94 IS IN It 94 is It 769 SO 74 :7 :4 $1 8: :41 IS :2 :1 :7 71 7: :7 :9 76 1 11 41 :! :1 :8 7: :9 8: 74 :1 47 71) :S :1 :t :S 47 :: :6 7: :: IS 77

X 1: .64 :: 112 116 14, :7 11 :1 9 907 974 :1 :1 0 7 6 11 :3 96 96 No 93 03 94 as 93 V 51 Is 0) *1 7: 91 9; :1 11 111 :3 17 9 '1 It It ly IN gir 17 9S 10 24 56 9 :1 of 92 91 51 06 7 IS : 23 :7 :: 1: 91 9: :2 67 16 97 to 11 as a to 9: 6 is IS 9: 97 98 92 96 90 N : 96 9, 9 IS 01 , I N 9: I Ii 97 NO :7 :1 :1 VIP at .9 at 13 of 52 " 87 91 " 90 91 9 1 1 1 go 9: 80 17 91 :0 :: :4 67 6: 11 91 9: It 94 CS 04 111 Be 6: 96 is It as 96 9: 07 of

! s 4 :1, .10 a) *1 :1 49 07 64 91 01? 91 93 86 11 91 91 01 06 13 97 to at 07 eel 94 89 94 11 14 :0 94 No 91 1. 11 .1 :1 11 : 41 Vo :1 "1 :1 :1 :1 : : '1 :1 6 :6 :1 : at :6 : It :6 IS It 92 17 11 41 .1 11 11 41 :0 47 9) IN 61 1) 9) 16 t 17 9 94 94 92 S :91 :1

1 910 91 Ole %1 it 977 VC 97 96 97 9: 17 96 IS 11 :7 9S 9: 91 97 ol to of 9: 9: go 16 94 017 99' :1 IN all is' :11 16 :11 :11 :11 I'll I'll : 17 '1, I'll

11 as 9 111 of go t02 946 so 19as 91 to INof ISa 0 is to 91 1 06 

:: 1: 9: 41 so64 as IS 9917 9190 #7 1 11 : 15, :1 :1 :1 :1 :1 :1 :1 i, S 11 .1 SC 91 to IV 97 94 111 94 as 91 to :1 1 04 94 :1 91 it 9) $1 92 si 9) 9i 94 to 4: 941 9 11 9: 9; 7; 0: 111 at Ii 4: as :9 $9

.0 (COIlt'd) A-29 I .
Matrix of Correlations of Expected Outcome Functions for NOS, Combined Training and SQT Criteria (Continued)

"Q* 62 $1 61 $1 a) 61 it 111 61 it 64 61 67 61 41 67 66 so 68 68 71 71 71 71 71 73 74 ?$ 7% 75 75 71o 76 76 74 76 76 76 It 91 91 $1 91 92 93 04 95 04

6 It to 4 0 4 0 0 N I C 0 T 0 V 1 0 4 J A 0 L 4 P I C U 9 V 0 f P v w a I C C C P a

7

Is

lieu;

IN 67

6 "1 It 9)

6 bw 1 9 1 91 7 8 06 91

, 11 is 96 14 S? of 97 91 91 11 GoC IV $4 96 $7 $1 67 99 do 96 95

'IN :1 11 1: :1 I'll : 11 17, 11 IS a,

1110 to 61 91 :1 4: 17 91 S: : 71 :6 97 07 94

67V 96 to 91 91 IS @I Is 84 94 $1 91 19 to 91

9 , IS , 97 IS 9

6* Is V : 41 Sas 1 16: : : 91 : 1 S7 : : or. IS 97 :1 $1 71 71 0 73 1 :S 91 :4

:6.1 90 of 97 97 11 11 :8 91 :6 91 90 111 :1 11 :1 :1 :11 11

9" 91 96 92 It Is It 91 so 11? 91 97 Is 91 IS 9: 1: $7 16 11

71D It 16 7% 74 69 It 85 61 87 76 IS 64 IS IS 41 77 69 77 71 69

7:'- :: :7 : ' 71 '1 :' " to Is 71: 1111 It 07 8 1 :0 744 is 779 11; 64 to S'4 99S 19 :6 P6 87 a : Ii 21 17 of 96

9, 16 1 , ts 11, :61 :83 :1 :Is : Is all, so a :2 : :1 91, :7 :7 : ss 13 077, 9 97

91 97 4 04 0 71 09 7 92 66 94 1 9 It 7 9: 61 :7 :S :: IS to :7 97

77 92 91 1: 111 1: 71 It I: so 47 91 Is 9: 21 9*7 as go 0 1 9 to % 98 so

7%C IS Is of 91 0) 77 Is of 93 17 91 to to IS 07 91 S11 94 Is 91 84 IS 04 04 94 45 97 go 7%0 to 6 68 91 71 Of $4 S) IS 96 92 94 IS It 90 17 IS 90 06 SO 91 90 97 97 it

71o 4 1 1 87 77 So 14 9 1 It 94 97 is I I is of 49 of to I I. 17 99 07 10 $1 99 is of S?

7-If :7 99 Of " IS 17 :9 11 49 $1 91 91 97 11 '97 AT 91 49 IS 91 to is If 97 So If TO 19 It Is 19 76C 1 " It , I f 91 1 1 #1 1 1 97 7 r 111 :1 it ?1 :9 14: of :% 1 :1 : 91 :2 : 1 1127 :11 :: :1 : 11 : 1 :1 :06 :1 "1 : 41 :61 :1 911 ; 71 11: 1: Is I:v it to IV IV Is 1@ 11 49 :4 of 114 is to 9: ;S IS a 1 04 9: 04 41 974 94 I'll :4 It 90 9 1 94 to 1 1 :4 1 a to "I" " , 9, So so SO " " I, " " I. " I. .' Is I. I. " IS IV IS So 911, 11 is TV 11 70 14 is 60 41 $1 lif. 99 It 19 is 49 91 of 91 so 61 161 to to :14 go It 91 N :6 ;4 17 !G 1 91 7C 94 91 :0 Is is @I It IS 84 91 91 94 go IS 91 :11 :61 11: I'll :; ;I S 1 4 91 1 6 1 ) $7 11 17 :4 IS :1C 94 94 is of :9 79 Is If if 411 94 if is it IS 91 it 44 11 96 IS is if is 91 IS 91 of IS 91 91 16 1& It $1 06 as is 97 ,it 97 94 " 06 03 Is if 14 91 as 91 91 19 of 9 , 9 11 of @8 1, It 1111 96 lod IT It SO 11 is so of if 94 07 96 is 11 of of is 17

91 of of 79 12 to ?6 It 41 as 92 It 0, Is gy 19 76 #1 91 6% 91 to 97 4-1 94 1* It It @I IV -#1 91 IS 94 is 76 92 IS 16 93

it 1 911 110 I'll :.' It 14 : , : 5, :: " 2, :31 1 , , , ; 6 :4 , , . .1 :.' ": :l : " It: I 1 4? 91 so 1: IS 19 91 go go I@ " 9 it 8: : 11 91 9: :9 91 1-1 IS 94 91 ; 10 91 1 91 Is 0, 1 6 11 91 Is 4 It I Of 91 97 if : 6 1 66 is 04 96 ;: 94 9: 94 91 9@ 91 Ij of 91 Is It 91 it 9: 94 91 tit to It "1 6: :7 19*9 11 .9 Is " 9. 1 11 1 , I I, It IS I SO I I SO I , Is I I ts IS 1. 90 9, 1, to to 66 4: 70 111 91 :.l 14 14, 11 :1 11 :11 so 1: all 11 1 Is 9 94 94 1 0 N 4 9% 1 94

A-30 9, 4
Table A - 13

Adjusted Validities for Current and Alternative Composites, by Current Cluster and MOS

i 7 n 3 . 37 1 . 43 6 5 . 5 6 9 .03 3 1 . 2 89 4 .6 1 . 3 9 3 1 . 3 14 9 .1 2 .1 5 5 1. N. . 7 3 7 9 6 4

I4 714 .5 8.163 6.11 6.25J. 6.31 8.5214 . '1 .41 3316193:139' 6..8 a.55 6..3 95 1 731 6.4695 615 N.43 6.342 ::,7, NO5! 6.56 6.596 ITS4 64351 4.66 6.566 4,64 93

6 3544 6.554 6.5511 64 0.11#2 8.5511 6.5:6 6.497 6.1121 1.526 6.544 6.51 t.549 III 8a I 66 6.6 3 a.9 6.56 6.4 69 1.S 6.8 463 6.1 $1 65 .66 .55 .91 a6

6276 .486.6 6.137 6:.127 13 6.53 1.9 6.1 1.9 6.2 M.5 6.&1 6.86 2347 1 IS ~ 3,45 ~ N" 6It4 N49 NU45 .1 .9 642 *4 . I.4t..:694 631 #.493 2111

4 769 ,.4 :.4 A?56 6.544 6.46 6.59 1111f I6 4" .546 6, 6 1 6. :0, 6.1' 5.599 6.584 1713 1 15 INX 4.16M :64,9 :144 6.149 6.13 6.149 6.013 6.125 61132 1.147 6.19? 643 6.196 328 Is 76T 0.426 $.411 6.34: L.593 6.36S8669 1.342 0.33 6.5,7 1.599 6.633 6.346 6.466 72:: a

363 565 UC'. M57 AM CL IT 56 MI or to IA 3 toWt wuag 75 1 33

6 3.6. 6.1 .9 .5 0I .52 3 6.59 697 F.42 .4 .47 .96 .33 4661 C 19 66" 6.347 6.23 123 6. 1 .551 0.314 6.335 1.;44 1.3463 8.233 1.331 1.33Z 8.194 6.356 4644 C OF :1 ;71 I.461 x.%$: t.Gt2 6.458 t.112 2.*2 t.32 2 6.trl ?.*as V.41 t.i'9 411 6,46 4266 i. f ii 1,P 6.323 6.244 1.34 6.397 1.?&$ 6.24 6.2?? 4.38 6.23 6.255 0.251 1.23 6.24: 143

U 36.2 .35611 4 64 .47 .95 611 6.4: J.1 6.4$ 6.: .46 6.557 976 C P 34 19p 6.392 6.43q46.434 6.431 64.64 6.U426.377 6.4*6 6.W4 6.4:t 1.864 1.441 6.4:6 U I

des mos AC.. AST ACV ACO CL S7 16C WIR Of Co 66 69 9L T37619% T3H15 7383

M' U 6.37' 3.134 8 .4 6.56a S.489 I.569 6.53 6.44:* 6.9:22 Net6 I.T" N.4N 6.53 19 25 314 1.479 1.447 I.466 1.49:1 3.66 .36g; 9!41 6.45 8.94:9 6.9 4 6 6. 1 .-:$' 51 A

Jim ::Is .21 6.233 6.346 .34 .7 .1 .74 .34 .26S .258 L .2 46.266 6,2*6 94

33 388 1.3:6 -.2191 -.193 0.141 6.33 6.:8 1 61.327 4.5191 6.3359 6.A1 4.2t8 1:4.6 6.9 3698 6 9 i 34 696 1.8 645.5 6.435 6.566 6.4:5 6.349 6.414 6.04 6444 6.397 6.4Is:9 3.466 4697 a

3s% mo1 ACL AST AWO ACD C. IT sc Pas or U$ PA on OIL ?0F1t4 THIS IC23

35 3 36 6.:3 : 6.45:7 6.:1 6.4 6.33 6:492 6.4;;::1.41 6.411 ::454 t:.48 6.4§5 4.14 1 14:22 C F

18 13 .4? 6.2 6.88 639 S.44 .4:9 3.46 6313 .4*7 644 63* 6.613 6.64 U253 8 4

066 11 AO AST AM ACO 41L IT SO P or 69 P& in ft 73'76119 75815 7323

37 'C6.4 6.1.332291326 IM 6.:94 6.536 6.36 N.it5 6.33 6.143 1.3566 1 5 .3 6 6.334 I5

4 41 6.46 1.464 647 677 6.447 6.672 #.$if 1649 6.49 6.563 6.490 6.44236.443 1U5 I 91 53 1315 634 3 375 1.541 4.274 1.16z 1.33* 6.354 t.347 1.339 3.3 3.I63 4.153 A7l A3 I

43 52 .4 1.".! 6.342 6.57' U.P6 N. 6.39 621 6.311 6.491 N.267 :-.474 6.273? II3' a

49 536 6.Set 3.968 1.90t 6.0 . 16 % Is4 6.S3 6.455 6.44 4679 9.644 6.447 #.*It 6.539 1131 1 45 71, .6 @.2 .apt 6.395 6.9.2 2 4411 6.397 :1 6.3 2 .6 - 1.5:9 1.331 1.321 1.244 2 94 70 6.56 6.12 1.211 $..-It 6.378 6.339 6.25 .27 .296 4.21i639 6.3 $.Zvi 519 1 i7 13 6.549 1.544 6.56 6.515 6.446 6.556 #.Sit $.M1 6.314 6.55: 6.526 6.)4 1.31 773 1 P 48 62F 6.97 1694 4.516 6.313 Co414 644 0.41 6.33S 6.526 6.1 6.443a 6.16 6.49' 16# 1 P

56 44" .4 6: .325. 6.23 6 3 a.4 6.53 .4 .4 6.564 6.593 6.517 6.864 366 J4 4

continued on next oace

A-31
Adjusted Validities for Current and Alternative, Composites,

by Current Cluster and MOS (Continued)

ess pas AM. AV AM AMOPL *w IT 3P MR jcp FA 33"Ia a3 CL 61.e' TIC2953

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13 836 N.,9 6.443 3.5 83.114 1163 1.416 11.2846 .613 1.433 1.461 1.49 356 .4 79 * 3

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936 an S Ar AST AOP AMO Ci S7 ac m of go 11A a3 L TOT11t?.1 i t33

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665 "S AU. AV7 OP CL IT SIC m lip go PA on E 6793 75235il TSCI

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too a AC. AST ACP AC c a se on or a PA W3 11, TOM343 73319 733

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A- 32
Tabie A - 14

S Mple Validities for the Proposed Composites

Combined Criterion

NOS AA N ACL SE AST SE ACO SE AOP SE NORM SE

71D CL 114 0.38 0.09 0.36 0.08 0.36 0.07 0.3S 0.08 0.09

71L C6 2782 0.47 0.02 0.44 0.02 0.41 0.02 0.40 0.02 0.02

71M CL 182 0.35 0.06 0.35 0.06 0.36 0.06 0.36 0.06 0.07

710 C. 173 0.39 0.07 0.37 0.07 0.33 0.07 0.33 0.07 0.08

73C C, 478 0.56 0.03 0.55 0.03 0.53 0.03 0.53 0.03 0.05

ISB CL 920 0.44 0.03 0.43 0.03 0.39 0.03 0.38 0.03 0.03

75C CL 317 o.S2 0.04 o.S2 0.04 o.SO o.os 0.49 0.05 0.06

7SD CL 801 0.42 0.03 0.41 0.03 0.37 0.03 0.37 0.03 0.04

75E CL 417 0.47 0.04 0.46 0.04 0.44 0.04 0.44 0.04 0.0s

7S CL 1.7 0.58 0.07 o.SS 0.07 o.52 0.07 0.52 0.07 0.09

76C CL 1296 0.32 0.03 0.33 0.02 0.30 0.02 0.30 0.02 0.03

76P CL 559 0.4S 0.04 0.40 0.04 0.36 0.04 0.34 0.04 0.04

76V CL 214 0.34 0.06 0.33 0.06 0.32 0.0s 0.34 0.05 0.07

76W CL 684 0.32 0.04 0.36 0.03 0.34 0.04 0.35 0.03 0.04

7GX CL 158 0.17 0.08 o.1s 0.09 0.10 0.08 0.11 0.07 0.08

76Y CL 1136 0.29 0.03 0.28 0.03 0.26 0.03 0.24 0.03 0.03

lE CO 5761 0.31 0.01 0.32 0.01 0.32 0.01 0.32 0.01 0.0

11C Co 1482 0.32 0.02 0.34 0.02 0.34 0.02 0.34 0.02 0.03

1lH co 948 0.26 0.03 0.26 0.03 0.26 0.03 0.26 0.03 0.03

12B CO 2411 0.31 0.02 0.33 0.02 0.13 0.02 0.34 0.02 0.02

12F CO 224 0.12 0.06 0.16 0.06 0.19 0.0? 0.20 0.06 0.07

19D CO 1035 0.35 0.03 0.37 0.03 0.37 0.03 0.37 0.03 0.03

19E Co 2322 0.40 0.02 0.43 0.02 0.45 0.02 0.45 0.02 0.02

19F CO 83 0.33 0.09 0.39 0.09 0.3% 0.09 0.42 0.09 0.11

17K EL 179 0.29 0.06 0.29 0.06 0.31 0.06 0.32 0.06 0.07

26Q EL 142 0.33 0.07 0.33 0.06 0.31 0.06 0.26 0.06 0.08

27E EL 305 0.36 0.05 0.37 0.04 0.34 0.0s 0.31 0.05 0.06

31J EL 130 0.25 0.07 0.27 0.07 0.30 0.07 0.29 0.07 0.09

31M EL 1858 0.30 0.02 0.37 0.02 0.38 0.02 0.38 0.02 0.02

31N EL 193 0.06 0.06 0.06 0.06 0.03 0.07 0.09 0.07 0.07

31V EL 650 0.31 0.03 0.33 0.03 0.32 0.04 0.30 0.04 0.04

35K EL 121 0.42 0.07 0.48 0.06 0.46 0.06 0.46 0.06 0.09

36C EL 374 0.13 0.05 0.14 0.05 0.08 o.os 0.10 0.05 0.05

36K EL 1581 0.23 0.02 0.28 0.02 0.30 0.02 0.31 0.02 0.03

133 FA 4778 0.3S 0.01 0.38 0.01 0.39 0.01 0.38 0.01 0.01

13? FA 824 0.34 0.03 0.38 0.03 0.40 0.03 0.40 0.02 0.03

41C CH 103 0.24 0.08 0.25 0.08 0.19 0.08 0.19 0.09 0.10

43E CM 99 0.25 0.09 0.26 0.09 0.22 0.03 0.20 0.09 0.10

448 GM 137 o.2S 0.06 0.28 0.08 0.26 0.09 0.30 0.09 0.09

45K GM 228 0.22 0.07 0.25 0.07 0.30 0.06 0.30 0.07 0.07

SI1 ON 19S 0.29 0.07 0.32 0.06 0.33 0.0s 0.30 0.05 0.07

S1K am 167 0.23 0.08 0.26 0.07 0.23 0.07 0.21 0.08 0.08

s2D GM 176 0.29 0.06 0.31 0.06 0.36 0.06 0.35 0.07 0.08

ss GM 366 0.28 0.05 0.27 o.oS j.26 0.05 0.24 0.0s 0.05

57E GM 126 0.06 0.09 0.09 0.09 0.11 0.10 0.09 0.09 0.09

S7H GM 224 0.11 o.os 0.17 0.06 0.20 0.06 0.17 0.06 0.07

62Z GM 230 0.37 0.05 0.41 0.0s 0.43 0.05 0.44 0.05 0.07

62? GM 200 0.24 0.07 0.29 0.07 0.34 0.07 0.34 0.06 0.07

68J GM 188 0.22 0.07 0.29 0.07 0.35 0.07 0.35 0.07 0.07

68M GM 132 0.29 o.os 0.35 o.os 0.37 0.05 0.37 0.0s 0.09

(cont'C)

A-33
Sample Validities for the Proposed Composites

Combined Criterion (Continued)

MOS AA N ACL SE AST SE ACO SE AOP SE NORM SE

12C 104 355 0.26 0.05 0.28 0.05 0.29 0.05 0.29 0.05 0.05

618 M 183 0.64 0.03 0.65 0.03 0.65 0.03 0.66 0.03 0.07

61C N0 136 0.44 O.OS 0.44 0.06 0.41 0.06 0.39 0.07 0.09

628 H5 355 0.39 0.05 0.43 0.04 0.44 0.04 0.43 0.04 0.05

633 1M 1318 0.28 0.02 0.32 0.02 0.33 0.02 0.33 0.02 0.02

63D 1M 342 0.07 0.06 0.11 0.06 0.12 0.06 0.12 0.06 0.05

630 M0 161 0.06 0.08 0.12 0.07 0.12 0.07 0.18 0.07 0.08

63H MM 783 0.30 0.03 0.33 0.03 0.35 0.03 0.35 0.03 0.04

63N 144 509 0.10 0.04 0.14 0.04 0.17 0.04 0.17 0.04 0.04

63W 1M 527 0.19 0.05 0.20 0.0S 0.22 0.04 0.21 0.04 0.04

63Y M0 238 0.16 0.07 0.19 0.07 0.23 0.06 0.19 0.07 0.06

67N 1M 471 0.34 0.04 0.35 0.04 0.36 0.04 0.35 0.04 0.05

67T MM 124 0.62 0.06 0.63 0.06 0.61 0.06 0.62 0.06 0.09

67U M0 278 0.36 0.04 0.37 0.04 0.37 0.O5 0.38 0.04 0.06

67V 14 310 0.37 0.06 0.36 0.06 0.37 0.06 0.38 0.06 0.06

67Y MM 241 0.22 0.06 0.26 0.06 0.28 0.06 0.29 0.07 0.06

68D 4I 121 0.43 0.06 0.47 0.06 0.49 0.06 0.48 0.06 0.09

680 14 121 0.37 0.08 0.38 0.08 0.35 0.09 0.33 0.10 0.09

1SD OF 406 0.20 0.OS 0.23 0.04 0.24 0.04 0.26 0.04 0.05

ISE OF 280 0.16 0.05 0.19 0.05 0.22 0.O5 0.20 0.05 0.06

16B OF 288 0.28 0.05 0.30 0.05 0.30 0.05 0.29 0.05 0.06

16C OF 118 0.32 0.07 0.32 0.07 0.25 0.08 0.26 0.07 0.09

16D OF 112 0.32 0.07 0.44 0.07 0.46 0.08 0.45 0.08 0.09

16H OF 104 0.43 0.09 0.47 0.09 0.47 0.08 0.46 0.08 0.10

16J Of 119 0.43 0.07 0.42 0.07 0.37 0.07 0.35 0.08 0.09

16R Of 404 0.18 0.04 0.21 0.04 0.21 0.04 0.20 0.04 0.05

16S OF 592 0.26 0.04 0.30 0.04 0.33 0.03 0.31 0.04 0.04

64C OF 2959 0.28 0.02 0.32 0.02 0.32 0.01 0.34 0.01 0.02

94B OF 3322 0.33 0.02 0.36 0.02 0.35 0.01 0.37 0.02 0.02

OSB SC 890 0.27 0.04 0.29 0.04 0.28 0.03 0.28 0.03 0.03

OSC SC 1971 0.36 0.02 0.39 0.02 0.38 0.02 0.38 0.02 0.02

OSG Sc 119 0.23 0.09 0.31 0.08 0.36 0.08 0.38 0.08 0.09

17C SC 187 0.23 0.07 0.24 0.07 0.22 0.07 0.23 0.07 0.07

72E SC 562 0.42 0.03 0.48 0.03 0.50 0.03 O.S0 0.03 0.04

OH ST 110 0.53 0.05 O.50 0.06 0.43 0.07 0.37 0.07 0.10

13E ST 678 0.33 0.03 0.33 0.03 0.30 0.03 0.28 0.04 0.04

S41 ST 270 0.34 0.05 0.35 0.05 0.34 O.OS 0.32 0.05 0.06

74D ST 98 0.44 0.09 0.42 0.09 0.33 0.09 0.33 0.09 0.10

82C ST S36 0.42 0.04 0.43 0.04 0.39 0.03 0.39 0.04 0.04

91C ST 233 0.37 O.OS 0.39 0.0S 0.34 O.OS 0.36 O.OS 0.07

911 ST 301 0.31 0.OS 0.29 O.OS 0.26 0.04 0.23 0.05 0.06

91P ST 1S9 0.21 0.08 0.21 0.07 0.20 0.08 0.20 0.08 0.08

91R ST 145 0.27 0.07 0.29 0.06 0.23 0.07 0.22 0.07 0.08

92B ST 364 0.3S 0.05 0.33 O.OS 0.31 0.05 0.31 0.05 0.05

93H ST 114 0.30 0.09 0.28 0.09 0.28 0.08 0.21 0.08 0.09

9SB ST 3695 0.31 0.01 0.34 0.01 0.32 0.01 0.33 0.01 0.02

96B ST 172 0.49 0*06 0.45 0.06 0.37 0.07 0.39 0.06 0.08

98C ST 186 0.50 0.06 0.43 0.06 0.36 0.06 0.31 0.06 0,07

A-34 //.
Table A - 15

Adjusted Validities for the Proposed Composites

Combined Criterion

MOS AA N ACL SE AST SE ACO SE AOP SE NORM SE

710 CL 114 0.41 0.18 0.40 0.17 0.40 0.17 0.40 0.16 0.09

71L CL 278Z 0.62 0.02 O.S9 0.02 O.S 0.02 O.SS 0.02 0.02

71M CL 182 0.54 0.08 O.S3 0.07 0.53 0.07 0.S3 0.07 0.07

71N CL 173 0.70 0.0S 0.67 O.OS 0.63 O.OS 0.62 0.05 0.08

73C CL 478 0.68 0.03 0.67 0.03 0.64 0.03 0.64 0.03 O.OS

7SB CL 920 O.S7 0.03 O.$6 0.03 0.53 0.03 0.53 0.03 0.03

75C CL 317 0.64 0.06 0.64 0.06 0.62 0.06 0.61 0.06 0.06

7SD CL 801 0.S3 0.05 0.52 0.05 0.49 0.0S 0.49 0.05 0.04

7SE CL 417 0.60 0.06 0.59 0.06 O.57 0.06 0.56 0.06 0.05

7SF CL 137 0.67 0.11 0.64 0.11 0.61 0.10 0.61 0.10 0.09

76C CL 1296 0.53 0.04 O.S3 0.03 0.51 0.03 O.S0 0.03 0.03

76P CL 559 0.66 0.04 0.62 0.04 0.S 0.04 0.57 0.04 0.04

76V CL 214 0.53 0.08 0.S2 0.08 0.50 0.08 0.50 0.08 0.07

76W CL 634 O.SS 0.06 0.18 0.06 0.S8 0.06 0.58 0.06 0.04

76X CL 158 0.34 0.23 0.32 0.22 0.29 0.22 0.29 0.22 0.08

76Y CL 1136 0.44 O.OS 0.43 0.05 0.41 0.04 0.40 0.04 0.03

113 CO 5761 0.41 0.01 0.42 0.01 0.42 0.01 0.42 0.01 0.01

I1C CO 1482 0.42 0.03 0.44 0.03 0.45 0.03 0.44 0.03 0.03

11H Co 948 0.37 0.04 0.38 0.04 0.37 0.04 0.37 0.04 0.03

128 CO 2411 0.41 0.02 0.44 0.02 0.44 0.02 0.45 0.02 0.02

12r CO 224 0.28 0.09 0.32 0.09 0.3S 0.09 0.36 0.09 0.07

19D CO 1C35 0.47 0.03 0.48 0.03 0.48 0.04 0.48 0.04 0.03

19E CO 2322 O.53 0.02 0.56 0.02 0.57 0.02 O.57 0.02 0.02

197 CO 83 0.46 0.12 O.SO 0.12 0.51 0.12 0.53 0.12 0.11

17K EL 179 0.49 0.08 O.SO 0.09 0.51 0.09 0.52 0.09 0.07

26Q EL 142 0.51 0.09 0.51 0.09 0.50 0.08 0.48 0.09 0.08

27E EL 30S O.SS 0.06 0.11 O.OS 0.53 0.05 O.52 0.06 0.06

31J EL 130 O.58 0.13 0.S8 0.13 0.58 0.12 O.59 0.12 0.09

31M EL 1858 0.57 0.02 0.60 0.02 0.60 0.02 0.60 0.02 0.02

31N EL 93 0.31 0.12 0.30 0.12 0.29 0.12 0.32 0.12 0.07

31V EL 650 0.52 0.04 O.53 0.04 0.53 0.04 0.52 0.04 0.04

35K EL 121 0.65 0.08 0.68 0.08 0.66 0.07 0.66 0.07 0.09

36C EL 374 0.26 0.10 0.26 0.10 0.22 0.09 0.23 0.09 0.0s

36K EL 1581 0.39 0.04 0.43 0.04 0.44 0.04 0.44 0.04 0.03

138 FA 4'78 0.43 0.02 0.46 0.01 0.47 0.01 0.46 0.01 0.01

13F FA 824 0.63 0.03 0.6S 0.02 0.65 0.02 0.65 0.02 0.03

41C GM 103 0.35 0.17 0.36 0.17 0.32 0.17 0.33 0.16 0.10

433 ON 99 0.39 0.15 0.40 0.16 0.37 0.15 0.36 0.16 0.10

443 GM 137 0.30 0.14 0.41 0.15 0.39 0.15 0.41 0.15 0.09

4%K GM 228 0.48 0.09 O.SO 0.09 0.52 0.09 O.S2 0.09 0.07

SIB GH 195 0.37 0.03 0.40 0.08 0.42 0.08 0.40 0.08 0.07

SIK GH 167 0.43 0.12 0.46 0.12 0.44 0.11 0.43 0.12 0.08

52D GH 176 0.39 0.09 0.41 0.09 0.44 0.09 0.44 O.uq 0.08

SSB GM 366 0.59 0.06 0.S9 0.06 0.58 0.06 O.S7 0.06 0.05

57 GM 126 0.28 0.29 0.29 0.30 0.28 0.30 0.29 0.29 0.09

S7H GM 224 0.43 0.14 0.44 O.15 0.44 0.15 0.43 0.14 0.07

621 GH 230 O.54 0.05 O.S9 0.0s 0.60 0.OS 0.61 0.05 0.07

62r G14 200 0.49 0.10 0.53 0.09 o.ss 0.09 0.56 0.08 0.07

68J G4 13e 0.13 0.09 0.19 0.09 0.62 0.09 0.62 0.09 0.07

68H GM 132 0.54 0.06 O.S9 O.Oe 0.60 0.03 0.10 O.Oa 0.09

(cont d)

A-357
Adjusted Validities for the Proposed Composites

Combined Criterion (Continued)

os AA N ACL SE AST SE ACO Se AOP SE NORM SE

12C MW 3SS 0.41 0.06 0.43 0.06 0.44 0.07 0.45 0.06 0.05

61B MW 183 0.70 0.04 0.72 0.04 0.71 0.04 0.71 0.04 0.07

GIC M 136 0.66 0.07 0.67 0.08 0.67 0.09 0.66 0.09 0.09

629 M 3SS 0.49 o.os 0.54 o.os 0.s5 o.os o.SS 0.04 0.05

63B M 1818 0.40 0.03 O.4S 0.02 0.47 0.02 0.47 0.02 0.02

63D HE 342 0.27 0.11 0.32 0.11 0.32 0.11 0.33 0.11 0.05

630 WEM 161 0.26 0.12 0.33 0.13 0.34 0.13 0.37 0.13 0.08

63H M 783 0.4S 0.04 0.48 0.04 O.SO 0.04 O.SO 0.04 0.04

63N MW 509 0.24 0.07 0.30 0.07 0.32 0.07 0.32 0.07 0.04

63N HM S27 0.28 0.07 0.30 0.08 0.31 0.08 0.30 0.08 0.04

63Y 10M 238 0.43 0.12 0.48 0.13 0.51 0.14 0.50 0.14 0.06

67N M 471 O.S8 0.05 0.60 0.05 0.61 0.05 0.61 0.05 O.0S

67T WI 124 0.83 0.02 0.84 0.03 0.83 0.04 0.83 0.04 0.09

67U M 278 O.55 0.06 0.59 0.07 0.60 0.08 0.60 0.08 0.06

67V M 310 0.59 0.06 0.61 0.07 0.61 0.07 0.62 0.07 0.06

67Y M 241 0.57 0.07 0.64 0.08 0.65 0.08 0.66 0.08 0.06

68D M 121 0.63 0.07 0.68 0.07 0.70 0.07 0.70 0.07 0.09

68G M 121 0.48 0.16 0.49 0.17 0.46 0.18 0.45 0.18 0.09

1SD OF 406 0.43 0.06 0.46 0.07 0.47 0.07 0.48 0.07 0.05

iSE OF 200 0.39 0.09 0.42 0.09 0.44 0.10 0.43 0.10 0.06

163 OF 288 0.45 0.08 0.46 0.08 0.46 0.08 0.46 0.08 0.06

16C OF 118 0.34 0.14 0.33 0.1S 0.28 0.15 0.27 0.15 0.09

16D OF 112 O.52 0.11 0.59 0.10 0.60 0.09 0.60 0.10 0.09

16H OF 104 0.73 0.10 0.75 0.09 0.75 0.09 0.75 0.10 0.10

163 OF 119 0.63 0.11 0.61 0.12 0.60 0.13 0.58 0.13 0.09

168 OF 404 0.34 0.OS 0.37 0.05 0.37 0.06 0.37 0.06 0.05

16S OF 592 0.46 0.06 O.SO 0.05 0.52 0.0s 0.51 0.05 0.04

64C OF 2959 0.45 0.02 0.48 0.02 0.49 0.02 0.49 0.02 0.02

94B or 3322 0.51 0.02 0.S3 0.02 0.S3 0.02 0.S3 0.02 0.02

05B SC 890 0.43 0.05 0.44 0.06 0.44 0.06 0.44 0.06 0.03

OSC SC 1971 0.47 0.03 0.49 0.03 0.49 0.03 0.49 0.03 0.02

0SG Sc 119 0.62 0.12 0.65 0.11 0.68 0.10 0.69 0.20 0.09

17C SC 187 0.37 0.11 0.38 0.11 0.36 0.10 0.30 0.11 0.07

72E SC 562 0.52 0.05 0.56 0.05 0.58 0.05 O.58 0.05 0.04

OSH ST 110 0.76 0.07 0.75 0.07 0.73 0.07 0.71 0.07 0.10

135 ST 678 0.48 0.04 0.48 0.04 0.47 0.04 0.46 0.05 0.04

S4E ST 270 O.50 0.08 0.so 0.08 0.49 0.08 0.48 0.08 0.06

74D ST 93 0.75 0.10 0.74 0.10 0.70 0.11 0.71 0.10 0.10

82C ST 536 O.Ss 0.06 0.56 0.06 0.S4 O.0S 0.S4 0.06 0.04

91C ST 233 0.57 0.0S 0.S8 0.0S O.SS 0.0S 0.56 0.05 0.07

OLE ST 301 0.S6 0.06 0.SS 0.06 0.S4 0.06 0.S3 0.06 0.06

91P ST 1S9 0.38 0.13 0.37 0.13 0.36 0.13 0.36 0.13 0.08

S1 ST 14S 0.58 0.11 0.59 0.11 0.57 0.11 0.57 0.11 0.08

92B ST 364 0.46 0.07 0.45 0.07 0.42 0.07 0.43 0.07 0.05

93H ST 114 0.62 G.12 0.61 0.12 0.60 0.12 0.60 0.12 0.09

9S ST 3695 0.58 0.02 0.60 0.02 0.S9 0.02 0.59 0.02 0.02

963 ST 172 0.72 0.06 0.70 0.06 0.66 0.06 0.67 0.06 0.09

98C ST 166 0.79 0.08 0.77 0.08 0.74 0.08 0.72 0.09 0.07

A--36
Table A - 16

Sample Validities for the HAGE Composites

Combined Criterion

MOS AA N N SE A SE a SE E SE NORM SE

71D CL 114 0.29 0.08 0.21 0.09 0 38 0.09 0.36 0.09 0.09

71L CL 2782 0.33 0.02 0.33 0.02 0.46 0.02 0.43 0.02 0.02

71M CL 162 0.34 O.OS 0.32 0.07 0.36 0.06 0.34 0.06 0.07

71M CL 173 0.28 0.07 0.36 0.06 0.3S 0.07 0.38 0.08 0.08

73C CL 478 0.46 0.04 0.46 0.03 0.55 0.03 0.S3 0.03 .0O5

758 CL 920 0.32 0.02 0.29 0.03 0.41 0.03 0.42 0.02 0.03

75C CL 317 0.44 O.05 0.27 0.05 0.48 0.04 0.51 0.04 0.06

75D CL 801 0.32 0.03 0.19 0.03 0.41 0.03 0.41 0.03 0.04

75E CL 417 0.38 0.04 0.31 O.OS 0.45 0.04 0.45 0.04 0.05

7SF CL 137 0.44 0.08 0.37 0.08 0.55 0.07 0.55 0.08 0.09

76C CL 1296 0.25 0.02 0.18 0.03 0.29 0.02 0.32 0.03 0.03

76P CL 559 0.24 0.04 0.26 0.04 0.39 0.04 0.40 0.04 0.04

76V CL 214 0.30 0.05 0.20 0.06 0.37 0.06 0.34 0.06 0.07

76W CL 684 0.32 0.04 0.21 0.04 0.31 0.04 0.34 0.03 0.04

76X CL 158 -0.02 0.07 0.07 0.06 0.15 0.08 0.11 0.08 0.08

76Y CL 1136 0.21 0.03 0.19 0.03 0.25 0.03 0.28 0.03 0.03

11B CO 5761 0.30 0.01 0.22 0.01 0.29 0.01 0.31 0.01 0.01

11C CO 1482 0.32 0.02 0.25 0.03 0.31 0.03 0.33 0.02 0.03

11H CO 948 0.24 0.03 0.22 0.03 0.25 0.03 0.27 0.03 0.03

12B CO 2411 0.31 0.02 0.19 0.02 0.29 0.02 0.32 0.02 0.02

12? CO 224 0.21 0.07 0.08 0.05 0.14 0.06 0.16 0.06 0,07

19D CO 1035 0.33 0.03 0.26 0.03 0.35 0.03 0.36 0.03 0.03

19E CO 2322 0.44 0.02 0.30 0.02 0.41 0.02 0.42 0.02 0.02

197 CO 83 0.42 0.09 0.24 0.09 0.37 0.09 0.37 0.09 0.11

17K EL 179 0.30 0.07 J.21 0.06 0.29 0.06 0.28 0.07 0.07

26Q EL 142 0.24 0.07 0.24 0.08 0.28 0.07 0.38 0.06 0.08

278 EL 305 0.25 0.06 0.33 0.06 0.32 0.05 0.32 0.O5 0.06

31J EL 130 0.25 0.08 0.17 0.07 0.25 0.07 0.26 0.08 0.09

31H EL 18S8 0.35 0.02 0.17 0.02 0.30 0.02 0.36 0.02 0.02

31N EL 193 0.04 0.06 O.18 0.08 0.16 0.07 0.05 0.06 0.07

31V EL 6SO 0.26 0.04 0.22 0.04 0.26 0.03 0.33 0.03 0.04

3SK EL 121 0.36 0.07 0.34 0.08 0.42 0.07 0.41 0.06 0.09

361 EL 374 0.0s 0.05 0.12 0.05 0.10 0.05 0.10 0.04 0.05

36K EL 1581 0.29 0.02 0.16 0.03 0.23 0.02 0.25 0,03 0.03

13B FA 4779 0.37 0.01 0.25 0.01 0.34 0.01 0.37 0.01 0 01

13? PA 824 0.3S 0.03 0.28 0.03 0.3S 0.03 0.36 0.03 0.03

410 GM 103 0.12 0.10 0.25 0.11 0.19 0.09 0.25 0.07 0.10

43E G1 99 0.17 0.08 0.22 0.10 0.23 .n9 0.25 0.09 0.10

448 GM 137 0.25 0.10 0.19 0.08 0.28 0.0 0.26 0.08 0.09

4SK GH 226 0.29 0.06 0.26 0.06 0.20 0.07 0.24 0.07 0.07

SIB Gm 195 0.25 0.05 0.21 0.07 0.25 0.07 0.2S 0.06 0.07

SIK GM 167 0.16 0.08 0.17 0.08 0.17 0.09 0.2S 0,07 0.08

S2D GM 176 0.32 0.06 0.24 0.07 0.26 0.07 0.27 0.06 0.08

SS GM 366 0.20 0.05 0.20 0.06 0.23 O.0S 0.23 0.05 0.05

57Z ON 126 0.10 0.08 0,S 0.09 -0.01 0.09 0.03 0.09 0.09

57H GM 224 0.16 0.06 0.1S 0.07 0.16 0.06 0.13 0.05 0.07

628 GH 230 0.42 0.OS 0.30 0.06 0.37 0.05 0.40 0.05 0.07

F2F GH 200 0.32 0.07 0.26 0.07 0.26 0.06 0.29 0.06 0.07

58J GM 18 0.37 0.06 0.15 0.07 0.22 0.07 0.26 0.07 0.07

68M GM 132 0.34 0.06 0.31 0.0 0.26 0.06 0.30 0.06 0.09

(cont'd)

A-37 // ,
Sample Validities for the MAGE Composites

Combined Criterion (Continued)

MOS KA N x SE A SE c SE R SE NORM S

12C NK 35S 0.27 O.OS 0.19 0.06 0.25 O.0S 0.27 0.05 0.05

613 MH 113 0.61 0.04 0.46 0.05 0.66 0.04 0.59 0.03 0.07

61C 894 136 0.28 0.08 0.25 0.07 0.36 0.07 0.44 0.0S 0.09

628 M 355 0.41 0.04 0.2S 0.04 0.38 0.0S 0.39 0.04 0.05

638 MN 1818 0.31 0.02 0.14 0.02 0.27 0.02 0.30 0.02 o.02

63D MN4 342 0.12 O.OS 0.10 0.07 0.09 0.06 0.06 0.06 0.05

63 1M 161 0.18 0.08 0.01 0.08 0.10 0.01 0.09 0.07 0.08

63H MM 783 0.33 0.03 0.14 0.04 0.30 0.03 0.33 0.03 0.04

63N M04 509 0.16 0.04 0.06 0.04 0.09 0.04 0.12 0.04 0.04

63W !'24 S27 0.21 0.04 0.10 0.04 0.18 0.0S 0.19 0.04 0.04

63Y M 238 0.17 0.06 0.09 0.06 0.12 0.07 0.14 0.07 0.06

67N MK 471 0.30 0.04 0.22 0.0S 0.33 0.04 0.36 0.04 0.0S

67T M94 124 0.54 0.06 0.44 0.06 0.62 0.06 0.6S 0.0S 0.09

67U MK 278 0.29 0.05 0.20 O.OS 0.34 0.04 0.35 0.05 0.06

67V MN 310 0.32 0.06 0.30 0.06 0.38 0.06 0.37 0.06 0.06

67Y 14 241 0.31 0.06 0.17 0.06 0.24 0.07 0.77 0.06 0.06

68D 1M 121 0.45 0.06 0.29 0.08 0.37 0.06 0.45 0.06 0.09

680 44 121 0.29 0.09 0.08 0.12 0.34 0.09 0.37 0.07 0.09

1SD OF 406 0.23 0.04 0.12 O.OS 0.19 0.04 0.20 0.05 0.0s

1SE o0' 280 0.20 0.05 0.09 0.05 0.14 0.06 0.16 0.05 0.06

168 Or 288 0.26 0.05 0.16 0.06 0.25 O.OS 0.28 0.0S 0.06

16C OF 118 0.14 0.08 0.17 0.07 0.30 0.07 0.25 0.09 0.09

16D OF 112 0.46 0.09 0.32 0.09 0.29 0.07 0.38 0.07 0.09

16H OF 104 0.37 0.08 0.23 0.09 0.43 0.08 0.44 0.08 0.10

16J OF 119 0.27 0.08 0.34 0.08 0.40 0.08 0.43 0.06 C.09

16R Of 404 0.19 0.04 0.12 0.04 0.17 0.04 0.17 0.04 0.Ls

16S o S92 0.31 0.03 0.11 0.04 0.24 0.04 0.30 0.03 0.04

64C OF 29S9 0.30 0.02 0.13 0.02 0.30 0.02 0.30 0.02 0.02

948 OF 3322 0.32 0.02 0.15 0.02 0.34 0.02 0.35 0.02 0.02

OSB SC 890 0.25 0.03 0.09 0.04 0.28 0.04 0.26 0.03 0.03

05C SC 1971 0.35 0.02 0.10 0.02 0.36 0.02 0.38 0.02 0.02

OSG SC 119 0.37 0.08 0.26 0.10 0.27 0.09 0.27 0.09 0.09

17C SC 187 0.22 0.07 0.06 0.07 0.24 0.08 0.22 0.07 0.07

72E SC 562 0.49 0.03 0.12 0.04 0.41 0.03 0.49 0.03 0.04

OSH ST 110 0.26 0.09 0.49 0.07 0,48 0.06 0.43 0.06 0.10

13E ST 678 0.21 0.04 0.19 0.03 0.29 0.04 0.31 0.03 0.04

S4E ST 270 0.30 O.OS 0.32 0.05 0.30 0.05 0.36 0.05 0.06

74D ST 98 0.22 0.02 C.32 0.10 0.47 0.00 0.37 0.09 0.10

82C ST 536 0.30 0.04 0.32 0.04 0.38 0.04 0.43 0.03 0.04

91C ST 233 0.29 O.OS 0.37 0.05 0.38 O.OS 0.39 0.05 0.07

912 ST 301 0.19 O.OS 0.20 O.0S 0.26 0.05 0.32 0.0S 0.06

91P ST IS9 0.16 0.08 0.25 0.03 0,19 0.08 0.24 0.07 0.08

91S ST 145 0.16 0.08 0.38 0.07 0.27 0.06 0.26 0.07 0.08

928 ST 364 0.25 O.OS 0.15 0.04 0.34 0.0S 0.34 0.05 0.05

93H ST 114 0.22 0.09 0.09 0.09 0.30 0.09 0.30 O.O 0.09

9S8 ST 3695 0.26 0.01 0.21 0.02 0.31 0.01 0.33 0.01 0.02

96B ST 172 0.30 0.07 0.32 0.06 0.50 0.0S 0.48 0.06 0.08

98C ST 186 0.17 0.07 0.30 0.06 0.42 0.06 0.36 0.06 0.07

A-38 /7
Table A - 17

Adjusted Validities for the MAGE Composites

Combined Criterion

MOS A N N SE A SE G SE E SE NORM SE

71D CL 114 0.33 0.14 0.26 0.25 0.43 0.18 0.39 0.16 0.09

71L CL 2782 0.47 0.02 0.56 0.02 0.61 0.02 0.6 0.02 0.02

71K CL 152 G.4S 0.06 o.s7 0.10 o.SS 0.06 0.S1 0.07 0.07

71N CL 173 0.54 o.os 0.70 0.06 0.69 0.05 0.67 0.0s 0.08

73C CL 475 0.57 0.03 0.66 0.04 0.63 0.03 0.65 0.02 0.05

7S1 CL 920 0.46 0.03 0.49 0.0s O.SS 0.03 O.SS 0.03 0.03

7SC CL 317 0.56 0.06 O.SO 0.09 0.61 0.07 0.63 0.06 0.06

7SD CL 501 0.43 O.OS 0.40 0.07 0.52 0.06 0.53 0.0S 0.04

7sE CL 417 o.so 0.06 0.53 0.09 0.59 0.06 0.S6 0.06 0.05

7s5 CL 137 0.52 0.09 0.57 0.16 0.65 0.11 0.64 0.10 0.09

76C CL 1296 0.45 0.03 0.44 0.05 0.51 0.04 o.S2 0.03 0.03

76P CL 559 0.48 0.04 0.SS 0.0S 0.62 0.04 0.62 0.04 0.04

76V CL 214 0.46 0.07 0.47 0.10 0.54 0.08 O.S2 0.08 0.07

76W CL 684 0.56 O.0S 0.45 0.09 0.55 0.07 0.58 0.06 0.04

76X CL 158 0.22 0.21 0.31 0.28 C.33 0.23 0.31 0.22 0.08

76Y CL 1136 0.36 0.04 0.39 0.06 0.42 O.OS 0.43 0.04 0.03

118 CO 5761 0.40 0.01 0.34 0.01 0.40 0.01 0.41 0.01 0.01

liC CO 1482 0.43 0.03 0.36 0.04 0.42 0.03 0.43 0.03 0.03

11H CO 948 0.35 0.04 0.34 0.04 0.37 0.04 0.38 0.04 0.03

129 CO 2411 0.43 0.02 0.31 0.03 0.40 0.02 0.43 0.02 0.02

12F CO 224 0.36 0.09 0.25 0.09 0.29 0.09 0.31 0.09 0.07

19D CO 103S 0.45 0.04 0.40 0.04 0.47 0.03 0.47 0.03 0.03

19E CO 2322 0.S6 0.02 0.45 0.02 0.53 0.02 0.54 0.02 0.02

19F CO 83 0.52 0.11 0.37 0.14 0.48 0.12 0.49 0.12 0.11

17K EL 179 0.49 0.10 0.47 0.10 0.S0 0.08 0.49 0.08 0.07

26Q EL 142 0.47 0.09 0.42 0.10 0.49 0.10 0.54 0.09 0.08

27E EL 305 0.46 0.06 0.52 0.07 0.S3 0.06 0.52 0.06 0.06

31J EL 130 O.56 0.11 0.46 0.12 0.58 0.13 0.S8 0.13 0.09

31H EL 18S8 0.58 0.02 0.45 0.03 0.56 0.03 0.60 0.02 0.02

31M EL 193 0.27 0.11 0.36 0.11 0.35 0.12 0.29 0.12 0.07

31V EL 650 0.49 0.04 0.42 0.0s O.SO 0.04 0.54 0.04 0.04

3SK EL. 121 0.S9 0.07 0.61 0.09 0.6S 0.09 0.64 0.08 0.09

36C EL. 374 0.19 0.09 0.26 0.09 0.25 0.10 0.24 0.10 0.0s

36K EL 1S01 0.43 0.04 0.30 0.04 0.39 0.04 0.41 0.05 0.03

13B FT 4778 0.45 0.01 0.3S 0.02 0.43 0.02 0.45 0.02 0.01

13r YA 824 0.61 0.02 0.56 0.03 0.62 0.03 0.63 0.02 0.03

41C C4 103 0.29 0.16 0.34 0.20 0.33 0.17 0.36 0.16 0.10

43E GP 99 0.36 O.1S 0.37 0.14 0.37 0.15 0.41 0.15 0.10

441 GH 137 0.38 0.16 0.33 0.13 0.39 0.1S 0.39 0.14 0.09

4SK Gi 228 0.51 0.09 0.49 0.08 0.47 0.09 0.51 0.09 0.07

SIB GM 19S 0.37 0.08 0.27 0.10 0.35 0.08 0.35 0.08 0.07

SIK GH 167 0.41 0.11 0.33 0.12 0.39 0.12 0.45 0.12 0.08

S2V ON4 176 0.42 0.09 0.31 0.09 0.30 0.09 0.38 0.09 0.08

S5B G4 366 0.53 0.07 0.46 0.07 0.56 0.06 0.60 0.06 O.0S

S7E GM 126 0.26 0.28 0.32 0.24 0.26 0.28 0.26 0.30 0.09

S7H GM 224 0.41 0.15 0.38 0.13 0.43 0.13 0.42 0.1s 0.07

62E GM 230 0.s9 0.06 0.49 0.07 O.SS 0.OS 0.58 0.05 0.07

62? G ! 200 0.S4 0.08 0.4S 0.09 O.SO 0.09 O.S2 0.09 0.07

fl cm 1C 5 0.63 C.08 0.45 0.10 0.54 C.09 0.S7 0.09 0.07

68M CM 132 0.58 0.08 0.54 0.09 0.S3 ..01 0.57 0.09 0.09

(A-39'd)
Adjusted Validities for the MAGE Compotites

Combired Criterion (Continued)

)Os AA N m SN A SE a SE E SENORM SE

12C W4 355 0.43 0.06 0.38 0.08 0.41 0.06 0.42 0.06 0.05

61 HM 183 0.6S 0.04 0.61 0.06 0.72 0.04 0.66 0.04 0.07

61C W 136 0.61 0.10 0.S3 0.07 0.62 0.07 0.66 0.08 0.03

623 14 3SS O.53 0.04 0.41 0.05 0.49 0.0S 0.51 0.05 0.05

638 14M 1818 0.46 0.02 0.31 0.03 0.40 0.03 0.43 0.02 0.02

630 HM 342 0.33 0.10 0.28 0.12 0.29 0.11 0.28 0.11 0.05

630 19M 161 0.38 0.13 0.23 0.13 0.25 0.13 0.30 0.12 0.08

63H 19M 763 0.49 0.03 0.34 O.OS 0.45 0.04 0.48 0.04 0.04

63N !M 509 0.33 0.07 0.18 0.07 0.23 0.07 0.28 0.07 0.04

63W KM 527 0.30 0.07 0.23 0.07 0.28 0.07 0.29 0.08 0.04

63Y M 238 0.49 0.13 0.37 0.12 0.42 0.13 0.44 0.13 0.06

67N mm 471 0.58 0.06 O.SO O.0S 0.58 0.05 0160 0.05 0.0s

67T W19, 124 0.79 0.04 0.73 0.03 0.63 0.03 0.85 0.02 0.09

67U P94 278 0.57 0.08 0.43 0.08 0.54 0.07 0.56 0.07 0.06

67V MM 310 0.57 0.07 O.54 0.07 0.60 0.06 0.60 0.06 0.06

67Y HK 241 0.66 0.08 O.52 0.09 0.60 0.08 0.63 0.07 0.06

63D MM £21 0.70 0.08 O.53 0.09 0.60 0.07 0.66 0.07 0.09

68G MM 121 0.42 0.17 0.29 0.19 0.46 0.17 0.48 0.16 0.09

1SD OF 406 0.46 0.07 0.38 0.08 0.43 0.07 0.44 0.06 0.0s

1se OF 280 0.43 0.09 0.34 0.09 0.30 0.09 0.39 0.08 0.06

16B OF 288 0.44 0.08 0.36 0.10 0.43 0.08 0.46 0.07 0.06

16C OF 118 0.21 0.15 0.22 0.16 0.31 O.1S 0.29 0.14 0.09

16D OF 112 0.60 0.08 0.46 0.12 0.52 0.12 O.$6 0.10 0.09

16H OF 104 0.70 0.09 0.62 0.12 0.73 0.10 0.73 0.09 0.10

16J OF 119 0.53 0.13 0.56 0.13 0.61 0.12 0.61 0.10 0.09

16R O 404 0.35 0.06 0.30 0.07 0.34 0.06 0.33 0.05 0.05

16S OF 592 0.50 O.OS 0.36 0.07 0.45 0.06 O.SO 0.05 0.04

64C OF 2959 0.47 0.02 0.37 0.02 0.46 0.02 0.47 0.02 0.02

948 OF 3322 0.S0 0.02 0.41 0.02 0.52 0.02 0.52 0.02 0.02

0S8 SC 890 0.40 0.05 0.38 0.07 0.44 0.06 0.42 0.05 0.03

05C SC 1971 0.46 0.03 0.38 0.04 0.47 0.03 0.49 0.03 0.02

05G SC 119 0.66 0.09 0.64 0.13 0.64 0.12 0.61 0.11 0.09

17C SC 187 0.35 0.09 0.3S 0.14 0.38 0.11 0.35 0.10 0.07

72E SC 562 0.57 0.04 0.40 0.07 0.52 O.OS 0.56 0.04 0.04

OSH ST 110 0.61 0.07 0.75 0.07 0.76 0.08 0.70 0.07 0.10

13E ST 679 0.42 0.04 0.39 0.04 0.46 O.0S 0.47 0.04 0.04

54E ST 270 0.46 0.07 0.46 0.08 0.48 0.0 0.50 0.08 0.06

740 ST 98 0.63 0.10 O.65 0.10 0.76 0.10 0.71 0.10 0.10

82C ST S36 0.49 O.OS 0.47 0.06 0.53 0.06 0.56 0.0s C.04

91C ST 233 0.52 O.OS O.SS 0.06 0.58 O.O5 0.57 0.05 0.07

919 ST 3ul 0.50 0.0S 0.49 0.07 0.54 0.07 0.57 0.06 0.06

SIP 5T 159 0.33 0.1. 0.44 0.12 0.36 0.13 0.39 0.12 0.08

91R ST 145 0.52 0.11 0.61 0.09 0.58 0.11 0.56 0.11 0.08

923 ST 364 0.33 0.07 0.33 0.06 0.45 0.07 0.45 0.07 0.05

93H ST 114 0.53 0.12 O.54 0.13 0.63 0.12 0.60 0.11 0.09

953 ST 3695 0.56 0.02 O.50 0.02 0.53 0.02 0.59 0.02 0.02

96B ST 172 0.60 0.06 0.60 0.07 0.73 0.06 0.70 0.05 0.08

98C ST 186 0.62 0.09 0.66 0.08 0.76 0.09 0.73 0.08 0,07

A-40
Table A - 18

Sample Validities for High School Composites

Combined Criterion

MOS AA N HSAA SE HSMT SE HSOS SE HSSS SE HSEE St NORM SE

71D CL 114 0.33 0.09 0.33 0.07 0.39 0.07 0.38 0.03 0.36 0.01 o.OQ

71L CL 2782 0.46 0.02 0.36 0.02 0.45 0.02 0.43 0.02 0.43 0.00 0.02

71H CL 182 0.36 0.06 0.32 0.06 0.35 0.07 0.37 0.06 0.34 0.01 0.07

71N CL 173 0.35 0.07 0.32 0.07 0.42 0.06 0.35 0.07 0.38 0.01 0.08

73C CL 478 O.SS 0.03 O.SO 0.04 O.S4 0.03 0.54 0.03 0.53 0.00 O.OS

7S8 CL 920 0.41 0.03 0.37 0.02 0.39 0.03 0.40 0.03 0.42 0.00 0.03

7SC CL 317 0.48 0.04 0.47 O.OS 0.46 O.OS O.SO 0.04 O.S 0.00 0.06

7SD CL 801 0.41 0.03 0.36 0.03 0.37 0.03 0.40 0.03 0.41 0.00 0.04

7SE CL 417 O.45 0.04 0.41 0.04 0.43 0.04 0.45 0.04 0.45 0.00 O.OS

75F CL 137 O.SS 0.07 O.SO 0.08 O.54 0.08 O.56 0.07 O.5 0.01 0.09

76C CL 1296 0.29 0.02 0.29 0.02 0.26 0.03 0.30 0.02 0.32 0.00 0.03

76P CL 559 0.39 0.04 0.32 0.04 0.42 0.03 0.39 0.04 0.40 0.00 0.04

76V CL 214 0.37 0.06 0.30 0.06 0.33 0.06 0.36 0.0S 0.34 0.01 0.07

76W CL 684 0.31 0.04 0.3S 0.04 0.28 0.04 0.33 0.04 0.34 0.00 0.04

76X CL 158 O.IS 0.00 0.09 0.06 0.13 0.07 0.12 0.07 0.11 0.01 0.08

76Y CL 1136 0.25 0.03 0.23 0.03 0.30 0.03 0.25 0.03 0.28 0.00 0.03

1IB CO 5761 0.29 0.01 0.30 0.01 0.29 0.01 0.32 0.01 0.31 0.00 0.01

!IC CO 1482 0.31 0.01 0.33 0.02 0.30 0.02 0.33 0.02 0.33 0.00 0.03

11H CO 948 0.25 0.03 0.25 0.03 0.25 0.03 0.27 0.03 0.27 0.00 0.03

128 CO 2411 0.29 0.02 0.34 0.02 0.26 0.02 0.33 0.02 0.32 0.00 0.02

127 CO 224 0.14 0.06 0.21 0.06 0.10 0.06 0.18 0.06 0.16 0.01 0.07

19D CO 1035 0.35 0.03 0.35 0.03 0.34 0.03 0.36 0.03 0.36 0.00 0.03

19E CO 2322 0.41 0.02 0.43 0.02 0.37 0.02 0.44 0.02 0.42 0.00 0.02

19F CO 83 0.37 0.09 0.41 0.09 0.27 0.09 0.39 0.08 0.37 0.01 0.11

17K EL 179 0.29 0.06 0.29 0.07 0.28 0.06 0.34 0.06 0.28 0.01 0.07

26Q EL 142 0.28 0.07 0.2S 0.06 0.34 0.07 0.26 0.06 0.38 0.01 0.08

27e EL 305 0.32 0.0S 0.27 0.06 0.37 0.05 0.31 0.05 0.32 0.00 0.06

31J EL 130 0.25 0.07 0.22 0.08 0.25 0.07 0.32 0.07 0.26 0.01 0.09

31M EL 1853 0.30 0.02 0.37 0.02 0.26 0.02 0.36 0.02 0.36 0.00 0.02

31N EL 193 0.16 0.07 O.0S 0.06 0.10 0.08 0.11 0.07 0.05 0.01 0.07

31V EL 650 0.26 0.03 0.31 0.04 0.28 0.03 0.30 0.03 0.33 0.00 0.04

3SK EL 121 0.42 0.07 0.44 0.06 0.39 0.07 0.43 0.06 0.41 0.01 0.09

36C EL 374 0.10 O.OS 0.09 0.04 0.10 0.05 0.08 0.05 0.10 0.00 0.05

36K EL 1581 0.23 0.02 0.30 0.02 0,21 0.03 0.29 0.02 0.25 0.00 0.03

13B FA 4773 0.34 0.01 0.36 0.01 0.32 0.01 0.31 0.01 0.37 0.00 0.0.

13? FA 324 0.35 0.03 0.39 0.03 0.34 0.03 0.40 0.02 0.36 0.00 0.03

41C GM 103 0.19 0.09 0.21 0.09 0.22 0.10 0.18 0.08 0.25 0.01 0.10

439 GM 99 0.23 0.09 0.17 0.09 0.23 0.10 0.19 0.08 0.25 0.01 0.10

448 GM 137 0.20 0.00 0.30 0.09 0.16 0.07 0.29 0.09 0.26 0.01 0.09

4$K "s 228 0.20 0.07 0.30 0.06 0.26 0.06 0.29 0.07 0.24 0.01 0.07

S13 GM 196 0.25 0.07 0.27 O.0S 0.26 0.07 0.27 0.06 0.25 0.01 0.07

51K GM 167 0.17 0.0 0.24 0.07 0.20 0.07 0.17 0.08 0.25 0.01 0.08

S2D GM 176 0.26 0.07 0.33 0.06 0.25 0.07 0.34 0.06 0.27 0.01 0.08

55B G4 366 0.23 0.O5 0.23 0.05 C.25 0.06 n.25 0.05 0.28 0.00 0.05

SIE GN 126 -0.01 0.09 0.09 0.09 0.18 0.11 0.08 0.10 0.03 0.01 0.09

57H GM 224 0.12 0.06 0.09 0.06 0.19 0.07 0.19 0.06 0.13 0.01 0.07

628 GM 230 0.37 0.05 0.44 0.05 0.32 0.05 0.41 0.05 0.40 0.00 0.07

62r GM 200 0.26 0.06 0.36 0.06 0.24 0.07 0.32 0.0', 0.29 0.01 0.07

68J GM 133 0.22 0.07 0.35 0.06 0.19 0.07 0.31 0.06 0.26 0.Jl 0.07

68M GM 132 0.26 0.06 0.39 O.05 0.32 0.07 0.34 0.05 0.30 0.01 0.09

A-on1'd)

A-41 - -.. (72
p

Smple Validities for High School Composites

Combined Criterion (Continued)

KOS AA N 1ISAA SE 1ISMT Se HSOG SE HSSS SE HSEZ SE NORM SE

12C MM 3ss 0.2s 0.05 o0.2 o.o5 0.26 0.06 0.29 o.o5 0.27 0.00 0.05

61B NK 183 0.66 0.04 0.58 0.04 o.59 0.04 0.61 0.04 0.59 0.00 0.07

61C 19 136 0.36 0.07 0.39 0.07 0.40 0.06 0.40 0.07 0.44 0.01 0.09

628 194 3s5 0.38 o.os 0.42 0.04 0.37 o.os 0.41 o.os 0.39 0.00 o.os

638 MM 1013 0.27 0.02 0.34 0.02 0.22 0.02 0.30 0.02 0.30 0.00 0.02

630 MM 342 0.09 0.06 0.10 o.o5 0.11 0.07 0.08 0.06 0.06 0.01 o.os

630 MM 161 0.10 0.08 0.20 0.07 0.00 0.07 0.14 0.07 0.09 0.01 0.08

6311 1M 703 0.30 0.03 0.36 0.03 0.26 0.03 0.34 0.03 0.33 0.00 0.04

63N MM 509 0.09 0.0 0.21 0.04 0.0? 0.04 0.14 0.04 0.12 0.00 0.04

63W KM 527 0.18 0.05 0.19 0.04 0.19 0.04 0.19 0.04 0.19 0.00 0.04

63Y RM 238 0.12 0.07 0.16 0.07 0.17 0.07 0.16 0.07 0.14 0.01 0.06

67N MM 471 0.33 0.04 0.34 0.04 0.32 0.OS 0.36 0.04 0.36 0.00 0.05

67T MM 124 0.62 0.06 0.57 0.07 0.61 0.05 0.62 0.07 0.65 0.01 0.09

67U MK 275 0.34 0.04 0.38 o.os 0.34 0.04 0.38 0.04 0.35 0.00 0.06

67V 44 310 0.38 0.06 0.36 0.06 0.35 0.06 0.39 0.06 0.27 0.01 0.06

67Y MM 241 0.24 0.07 0.30 0.07 0.20 0.06 0.25 007 0.27 0.01 0.06

680 XM 121 0.37 0.06 0.48 0.06 0.40 0.07 0.45 0.06 0.45 0.01 0.09

68C 19 121 0.34 0.09 0.30 0.09 0.24 0.10 0.33 0.10 0.37 0.01 0.09

ISD OF 406 0.19 0.04 0.24 0,04 0.18 o.os 0.23 0.04 0.20 0.00 0.05

XSE OF 280 0.14 0.06 0.18 0.05 0.15 0.05 0.18 0.05 0.16 0.00 0.06

168 OF 288 0.25 0.05 0.28 o.os 0.25 0.06 0.28 0.05 0.28 0.01 0.06

16C OF 118 0.30 0.07 0.21 0.09 0.26 0.06 0.25 0.0 0.25 0.01 0.09

16D OF 112 0.29 0.07 0.46 0.09 0.35 0.08 0.36 0.08 0.38 0.01 0.09

16H OF 104 0.43 0.08 0.42 0.08 0.39 0.10 0.47 0.08 0.44 0.01 0.10

16J OF 119 0.40 0.08 0.31 0.07 0.43 0.07 0.38 0.08 0.43 0.01 0.09

16R OF 404 0.17 0.04 0.18 0.04 0.18 0.04 0.18 0.04 0.17 0.00 0.05

16S OF 592 0.24 0.04 0.33 0.04 0.23 0.04 0.28 0.04 0.30 0.00 0.04

64C OF 2959 0.10 0.02 0.33 0.02 0.23 0.02 0.32 0.02 0.30 0.00 0.02

94B Of 3322 0.34 0.02 0.34 0.01 0.27 0.02 0.3S 0.02 0.35 0.00 0.02

0SB SC 590 0.29 0.04 0.26 0.03 0.22 0.04 0.29 0.04 0.26 0.00 0.03

OSC SC 1971 0.36 0.07 0.38 0.02 0.29 0.02 0.38 0.02 0.38 0.00 0.02

o50 SC 119 0.27 0.09 0.34 0.08 0.2I 0.09 0.36 0.09 0.27 0.01 0.09

17C SC 187 0.24 0.08 0.2. 0.07 0.16 0.06 0.23 0.07 0.22 0.01 0.07

72E SC S62 0.41 0.03 o.52 0.02 0.31 0.04 0.4? 0.03 0.49 0.00 0.04

oSH ST 110 0.45 0.06 0.30 0.03 o.s7 0.05 0.41 0.06 0,41 0.01 0.10

13E ST 678 0.29 0.04 0.26 0.03 0.31 0.03 0.29 0.04 0.31 0.00 0.04

s4E ST 270 0.30 o.os 0.32 o.os 0.34 0.05 0.33 0.05 0.36 0.00 0.06

740 ST 98 0.47 0.08 0.26 0.08 0.41 0.10 0.39 0.09 0.37 0.01 0.10

82C ST 536 0.38 0.04 0.40 G.03 0.3e 0.04 0.40 0.04 0.43 0.00 0.04

91C ST 233 0.38 C.oS 0.34 o.o5 0.30 0.05 0.37 o.u5 0.39 0.00 0.07

91F ST 301 0.26 0.05 0.22 o.os 0.30 0.05 0.27 0.05 0.32 0.00 0.06

91P ST 159 0.19 0.08 0.21 0.08 0.23 0.09 0.22 0.07 0.24 0.01 0.0e

9IR ST 145 0.27 0.06 0.18 0.07 0.37 0.07 0.22 0.07 0.26 0.01 0.08

923 ST 364 0.34 o.os 0.30 o.o5 0.30 0.04 0.33 o.os 0.34 0.01 oO5

4311 ST 114 0.30 0.09 0.28 0.08 0.22 C.09 0.33 0.09 0.30 %.ol 0.09

9SB ST 3655 0.31 0.01 0.30 0.01 0.27 0.01 0.33 0.01 0.33 0.o0 0.02

968 ST 172 0.50 0.05 0.34 0.06 0.43 0.06 0.43 0.06 0.43 0.01 0.08

90C ST 186 0.42 0.06 0.22 0.06 o.5. o.os 0.39 0.06 0.36 0.01 0.07

A-42 -
Table A- 19

Adjusted Validities for High School Composites

Combined Criterion

OS AA N ISAA SE HSMT SE HSOS S HSSS SE HSEE SE NORM SE

71D CL 114 0.43 0.18 0.38 0.14 0.39 0.22 0.43 0.18 0.39 0.02 0.09

71L CL 278 0.61 0.02 0.50 0.02 0.61 0.02 0.59 0.02 0.58 0.00 0.02

71M CL 182 0.55 0.06 0.48 0.06 0.57 u.09 0.56 0.07 0.51 0.01 0.07

71N CL 173 0.69 O.OS 0.S8 O.OS 0.73 0.05 0.66 O.O5 0.67 0.01 0.08

73C CL 478 0.68 0.03 0.59 0.03 0.68 0.03 0.67 0.03 0.65 0.00 o.os

758 CL 920 0.55 0.03 0.49 0.03 0.54 0.04 0.54 0.03 0.55 0.00 0.03

75C CL 317 0.61 0.07 0.58 M.06 0.59 0.08 0.62 0.06 0.63 0.01 0.06

750 CL 801 0.52 0.06 0.46 0.05 0.49 0.06 0.51 0.05 0.53 0.01 0.04

'5E CL 417 0.59 0.06 0.52 0.06 0.58 0.08 0.58 0.06 0.58 0.01 0.05

75F CL 137 0.65 0.11 0.57 0 09 0.64 0.14 0.65 0.10 0.64 0.01 0.09

76C CL 1296 0.51 0.04 0.48 0.03 0.49 0.04 0.51 0.03 0.52 0.00 0.03

76P CL 559 0.62 0.04 0.52 0.04 0.64 0.04 0.62 0.04 0.62 0.00 0.04

76V CL 214 0.54 0.0 0.46 0.07 0.53 u.09 0.53 0.08 0.52 C.01 0.07

76W CL 684 0.55 0.0/ 0.58 o.o5 0.S1 0.08 0.57 0.06 0.58 0.01 0.04

76X CL 158 0.33 0.23 0.27 0.21 0.35 0.25 0.31 0.23 0.31 0.02 0.08

76Y CL 1136 0.42 0.05 0.37 0.04 0.45 0.06 0.41 0.05 0.43 0.00 0.03

11B CO 5761 0.40 0.02 0.40 0.02 0.39 0.02 0.42 0.02 0.41 0.00 0.01

1IC CO 1482 0.42 0.03 0.43 0.03 0.40 O.C3 0.44 0.03 0.43 0.00 0.03

1111 CO 948 0.37 0.04 0.36 0,04 0.36 0.04 0.38 0.04 0.38 0.00 0.03

128 CO 2411 0.40 0.02 0.45 0.02 0.36 0.02 0.44 0.02 0.43 0.00 0.02

12F CO 224 0.29 0.09 0.36 0.09 0.27 0.10 0.34 0.09 0.31 0.01 0.07

19D CO 1035 0.47 0.03 0.46 0.04 0.45 0.03 0.C8 0.04 0.47 0.00 0.03

19E CO 2322 0.53 0.02 0.55 0.02 0.50 0.02 0.56 0.02 0.54 0.00 0.02

19F CO 93 0.48 0.12 0.52 0.12 0.40 0.14 0.50 0.12 0.49 0.01 0.11

17K EL 179 O.SO 0.08 0.48 0.10 0.50 0.09 0.53 0.08 0.49 0.01 0.07

260 EI, 142 0.49 0.10 0.47 0.08 0.50 0.09 0.48 0.09 0.54 0.01 0.08

27E EL 305 0.53 0.06 0.48 0.06 0.56 0.06 0.53 0.06 0.52 0.01 0.06

31J EL 130 0.53 0.13 0.55 0.12 0.5S 0.12 0.61 0.12 0.58 0.01 0.09

31M EL 1858 0.56 0.03 0.59 0.02 0.52 0.02 0.59 0.02 0.60 0.00 0.02

31N EL 193 0.35 0.12 0.29 0.11 0.32 0.11 0.33 0.12 0.29 C.01 0.07

31V EL 650 0.50 0.04 0.53 0.04 0.48 0.04 0.52 0.04 0.54 0.00 0.04

35K EL 121 0.65 0.09 0.64 0.07 0.64 0.08 0.65 O.0S 0.64 0.01 0.09

36C E' 374 0.25 0.10 0.22 0.09 0.24 0.09 0.23 0.09 0.24 0.01 0.05

36K EL 1581 0.39 0.04 0.44 0.04 0.35 0.04 0.43 0.04 0.41 0.00 0.03

138 FA 4778 0.43 0.02 0.46 0.01 0.40 0.02 0.45 0.02 O.45 0.00 0.01

13F FA 824 0.62 0.03 0.64 0.02 0.62 0.03 0.66 0.03 0.63 0.00 0.03

41C GM 103 0.33 0.17 0.35 0.15 0.33 0.19 0.33 0.16 0.36 0.02 0.10

43E GM 99 0.37 0.15 0.35 0.15 0.38 0.16 0.35 0.15 0.41 0.01 0.10

448 GM 137 0.39 0.15 0.41 0.15 0.34 0.14 0.41 0.15 0.39 0.01 0.09

45K GM 228 0.47 0.09 0.52 0.08 0.50 0.08 0.52 0.09 0.51 0.01 0.07

SIB GM 195 0.35 0.08 0.38 0.08 0.33 0.09 0.37 0.01 0.35 0.01 0.07

51K GM 167 0.39 0.12 0.44 0.11 0.39 0.12 0.41 0.12 0.45 0.01 0.08

52D GM 176 0.38 0.09 0.42 0.09 0.34 0.09 0.43 0.09 0.38 0.01 0.08

55B GM 366 0.56 0.06 0.56 0.0' 0.55 0.06 0.57 0.06 0.60 0.01 0.05

57E GM 126 0.26 0.28 0.27 0.29 0.33 0.27 0.29 0.29 0.26 0.03 0.09

57H GM 224 0.43 0.)3 0.39 0.15 0.43 0.14 0.44 0.14 0.42 0.0' 0.07

62E CM 230 0.55 0.05 0.61 0.05 0.52 0.06 0.59 0.05 0.58 0.01 0.07

62F GM 200 U.50 0.09 0.56 0.08 0.46 0.10 0.54 0.09 0.52 O.O 0.07

68J GM 188 0.54 0.09 0.62 0.08 0.50 0.09 0.59 0.09 0.57 0.01 0.07

68M GM 132 0.53 0.08 0.50 0.08 0.56 0.08 0.58 0.08 0.57 0.01 0.09

(Cort'd)

A-43j /
Adjusted Validities for High School COmposites

Combined Criterion (Continued)

NOS AA IN HSAA SE HSMT SE HSOS SE HSSS 3Z HSEE SH NORM SE

12C 1 3SS 0.41 0.06 0.44 0.06 0.41 0.07 0.45 0.06 0.42 0.01 0.05

61B 9183 0.72 0.04 0.6c o.os 0.66 0.04 0.72 0.04 0.66 0.00 0.07

61C M 136 0.62 0.07 0.65 0.10 0.63 0.06 0.66 0.06 0.66 0.01 0.09

628 M 35S 0.49 o.os o.SS 0.04 0.48 o.os o.S2 o.os 0.51 0.00 0.0S

638 MM 1816 0.40 0.03 0.48 0.02 0.36 0.03 0.44 0.02 0.43 0.00 0.02

63D M 342 0.29 0.11 0.32 0.11 0.29 0.12 0.30 0.11 0.28 0.01 0.05

63; MM4 161 0.ZS 0.13 0.39 0.13 0.23 0.12 0.33 0.13 0.30 0.01 0.08

63H MM 783 0.45 0.04 o.S 0.03 0.41 0.04 0.49 0.04 0.48 0.00 0.04

63N MM 509 0.23 0.07 0.35 0.07 0.21 n.07 0.28 0.07 0.28 0.01 0.04

63W KM 527 0.25 0.07 0.29 0.08 0.28 0.07 0.29 0.07 0.29 0.01 0.04

63Y MM 238 0.42 0.13 0.50 0.14 0.42 0.13 0.46 0.13 0.44 0.01 0.06

67N MM 471 0.58 0.05 0.60 0.05 0.$6 0.05 0.61 0.05 0.60 0.00 0.05

67T M 124 0.83 0.03 0.30 0.04 0.81 0.02 0.84 0.03 0.8S 0.00 0.09

67U M 278 0.54 0.07 0.60 0.06 O.52 0.06 O.59 0.07 O.56 0.01 0.06

67V M4 310 0.60 0.06 0.60 0.07 O.58 0.06 0.62 0.07 0.60 0.01 0.06

67Y MM 241 0.60 0.08 0.66 0.08 0.54 0.08 0.63 0.08 0.63 0.01 0.06

60D MM 121 0.60 0.07 0.70 0.07 0.59 0.08 0.67 0.07 0.66 0.01 0.09

68G MM 121 0.46 0.17 0.4? 0.18 0.36 0.17 0.45 0.18 0.48 0.02 0.09

15D OF 406 0.43 0.07 0.46 0.07 0,41 0.07 0.46 0.07 0.44 0.01 0.05

ISE OF 280 0.38 0.09 0.41 0.09 0,37 0.08 0.42 0.09 0.39 0.01 0.06

16B OF 288 0.43 0.08 0.45 0.08 0.42 0.08 0.45 0.08 0.46 0.01 0.06

16C OF 118 0.31 0.15 0.24 0.1S 0.31 0.14 0.28 0.15 0.29 0.01 0.09

16D OF 112 0.52 0.12 0.61 0.09 O.50 0.11 0.55 0.10 0.56 0.01 0.09

16H OF 104 0.73 0.10 0.72 0.09 0.69 0.10 0.75 0.10 0.73 0.01 0.10

16J O 119 0.61 0.12 0.54 0.12 0.62 0.11 0.60 0.13 0.61 0.01 0.09

16R OF 404 0.34 0.06 0.34 0.05 0.33 0.06 0.35 0.06 0.33 0.01 0.05

16s OF 592 0.45 0.06 0.52 0.05 0.43 0.06 0.49 0.05 0.50 0.01 0.04

64C OF 2959 0.46 0.02 0.49 0.02 0.42 0.02 0.48 0.02 0.47 0.00 0.02

94B OF 3322 0.52 0.02 O.52 0.02 0.47 0.02 0.53 0.02 0.52 0.00 0.02

05B SC 090 0.44 0.06 0.41 0.05 0.41 0.06 0.44 0.06 0.42 0.01 0.03

05C SC 1971 0.47 0.03 0.48 0.03 0.44 0.03 0.49 0.03 0.49 0.00 0.02

05G SC 119 0.64 0.12 0.64 0.09 0.66 0.12 0.69 0.11 0.61 0.01 0.09

17C SC "87 0.38 0.11 0.34 0.10 0.36 0.12 0.38 0.11 0.35 0.01 0.07

72E SC 562 O.52 0.05 0.59 0.04 0.47 0.06 0.56 0.06 0.56 0.00 0.04

OSH ST 110 0.76 0.08 0.64 0.06 0.80 0.06 0.73 0.07 0.70 0.01 0.10

13E ST 678 0.46 0.05 0.44 0.04 0.46 0.04 0.47 0.05 0.47 0.00 0.04

54E ST 270 0,43 0.09 O.45 0.07 0.49 0.09 0.50 0.08 0.50 0.01 0.06

74D ST 98 0.76 0.10 0.65 0.09 0.72 0.10 0.74 0.10 0.71 0.01 0.10

82C ST 536 O.53 0.06 O.54 O.05 0.52 0.06 0.55 0.06 0.56 0.01 0.04

91C ST 233 0.58 0.05 O.54 0.05 0.57 0.05 0.57 0.06 0.57 0.00 0.07

91E ST 301 0.54 0.07 0.50 0.06 0.56 0.06 0.55 0.06 0.57 0.01 0.06

91P ST 159 0.36 0.13 0.36 0.11 0.41 0.14 0.38 0.13 0.39 0.01 0.08

91R ST 145 0.58 0.11 0.51 0.10 0.63 0.10 0.57 0.11 O.56 0.01 0.08

928 ST 364 0.45 0.07 0.41 0.07 0.42 0.07 0.44 0.07 0.45 0.01 0.05

9311 ST 114 0.63 0.12 0.57 0.10 0.60 0.12 0.64 0.12 0.60 0.01 0.09

95B ST 369% 0.58 0.02 0.57 0.02 O.55 0.02 0.59 0.02 0.59 0.00 0.02

963 ST 172 0.73 0.06 0.61 0.06 0.68 0.06 0.70 0.06 0.70 0.01 0.00

98C ST 186 0.76 0.09 0.65 0.08 0.79 0.07 0.75 0.09 0.73 0.01 0.07

A-44Q
Table A - ?O

Cumulative Distributions for the Current Composites

Based on a 2% Sample of all FY81/82 Applicitnts

SM PCTCL PCTCO PCTL PCTFA PCTG4 PCTMM PCTOF PCTSC PCTST

40 0.2 0.1 0.0 0.1 0.1 0.2 0.1 0.2 0.1

41 0.8 O.S 0.4 0.3 0.6 0.7 0.9 0.8 0.6

42 0.8 0.5 0.4 0.3 0.6 0.7 1.0 0.8 0.6

43 0.9 0.5 0.5 0.3 0.6 0.8 1.0 0.8 0.7

44 0.9 0.5 0.5 0.4 0.7 0.8 1.0 0.8 0.7

45 1.0 0.6 0.6 0.4 0.7 0.9 1.1 0.9 0.8

46 1.0 0.6 0.6 0.4 0.7 0.9 1.1 0.9 0.8

47 1.1 0.6 0.6 0.5 0.9 0.9 1.2 1.0 1.0

48 1.2 0.8 0.7 0.5 0.9 1.0 1.2 1.1 1.0 49 1.2 0.8 0.7 0.5 0.9 1.0 1.3 1.2 1.1 so 1.3 0.9 0.8 0.6 1.1 1.1 1.5 1.3 .3

51 1.5 0.9 1.0 0.7 1.3 1.2 1.5 1.4 1.5

52 1.7 1.0 1.1 0.7 1.3 1.4 1.6 1.6 1.8

53 1.9 1.1 1.4 0.9 1.5 1.6 1.8 1.7 2.1

54 2.0 1.3 1.7 1.0 2.0 2.0 2.3 2.0 2.1

55 2.2 1.4 2.0 1.1 2.3 2.3 2.5 2.3 2.4

56 2.6 1.7 2.3 1.5 2.7 2.7 3.0 2.7 3.1

57 3.1 2.2 3.1 2.0 3.4 3.2 3.5 3.1 4.0

58 3.6 2.4 3.6 2.3 3.8 3.8 4.0 3.5 4.6 59 4.2 2.9 4.8 3. 4.5 4.4 4.8 4.2 5.1

50 4.5 3.2 5.4 3.5 5.4 4.7 5.6 4.7 5.5 61 5.3 4.0 6.0 3.9 6.7 5.6 S.J 5.2 6.8

62 6.1 5.2 7.3 4.8 8.0 6.5 7.2 5.8 8.0 63 6.5 5.7 8.1 5.2 8.6 7.4 8.1 6.4 8.6

64 7.5 6.2 8.9 5.7 9.4 8.5 8.9 7.3 10.1

65 8.4 7.3 10.5 6.8 10.9 9.6 10.3 8.4 11.5

64 9.3 8.5 11.4 7.9 11.8 10.9 11.1 9.7 13.0

67 10.6 9.2 12.2 8.6 12.7 11.5 12.4 10.5 13.8 68 11.2 ;0.6 13.1 9.3 14.5 12.8 13.5 11.7 14.1

69 11.9 12.0 14.1 10.1 15.2 14.3 14.7 12.9 16.5

70 13.4 13.4 16.2 11.6 17.1 15.8 16.0 12.8 18.4

71 15.1 15.0 17.1 12.4 19.0 17.5 17.5 15.1 ZO.1

72 15.9 16.0 18.' 13.1 20.0 18.2 19.0 16.4 21.1 73 17.6 17.0 20.2 14.1 20.9 19.9 20.9 17.9 23.2 74 19.3 18.9 22.Z 16.0 23.0 21.9 22.5 19.3 25.1

75 20.9 21.1 24.6 18.3 25.2 23.8 24.4 21.6 27.2

76 23.0 23.3 27.0 20.7 27.4 25.8 27.3 24.2 29.2 77 24.0 25.6 28.1 U.1 27.4 28.2 29.3 25.0 31.4

78 26.2 26.7 30.4 24.4 29.5 29.2 31.5 23.0 32.5

79 27.4 29.0 31.4 27.2 31.7 31.! 31.4 29.7 4. 8U 29.9 31.2 33.9 28.3 33.9 33.5 34.4 31.9 36.8

81 31.2 32.5 36.3 29.6 36.0 35.1 36.7 32.7 38.9

82 32.3 34.8 37.5 31.0 39.1 37.2 39.0 34.9 39.9 83 33.7 36.4' 38.8 32.2 40.3 38.3 40.1 36.2 41.0

84 34.9 37.4 40.0 34.8 42.5 40.7 41.1 38.6 43.1

85 37.9 39.8 42.3 36.2 43.6 43.2 43.4 40.7 45.2

86 39.2 40.9 43.3 37.6 45.6 44.4 45.8 41.8 46.1 87 40.7 43.3 44.5 40.2 46.9 46.7 46.8 43.0 47.2 a 42. 44.6 46.9 41.5 49.1 47.8 47.9 45.3 49.3 89 43.9 47.0 47.9 42.9 50.1 49.0 49.9 46.5 50.2

90 45.3 4a.2 48.9 45.3 52.2 51.4 52.1 47.8 51.1

91 46.8 49.5 51.1 "4.5 54.2 $3.5 54.3 %9.1 53.1

92 49.6 51.8 53.3 4?.Z 56.1 55.9 56.5 51.8 55.2

93 $1.3 54.1 "5.2 S.5 58.0 58.0 58.7 54.5 57.3

94 52.9 56.2 57.3 53.1 59.8 60.1 59.7 57.1 59.2

95 56.1 58.3 59.3 55.5 60.3 52.3 61.6 58.4 62.9

% 57.7 50.5 60.2 57.5 62.9 54.3 53.3 60.7 52.9

97 -9.2 61.6 61.1 58.9 53.6 65.3 " .9 52.1 63.7

continued on n.ext page

A-45
Cumulative Distributions for the Current Composites

Based on a 2% Sample of all FY81/82 Applicants (Continued)

SCORE KTP . PCTCO PCTE. PCA PUGH PCT PCTOF PCIsc POST

98 61.1 43.7 62.8 61.3 65.7 67.3 67,1 63.4 65.4

99 62.9 4.7 64.6 63.6 67.6 69.4 69.3 65.8 67.1

100 66.z 66.5 66.5 65.6 69.5 71.3 71.3 6.3 U.9

1u1 67.5 68.5 68.2 66.6 71.1 72.3 72.3 69.5 70.8

102 59.0 70.A 70.0 63.4 72.8 73.3 73.3 70.7 71.6

103 70.4 72.5 ?1.S 70.1 74.6 75.4 75.3 73.0 73.1

104 71.9 74.5 73.2 72.0 76.3 77.1 77.2 75.5 74.8

lOS 73.1 76.2 74.8 73.9 78.0 79.1 78.1 76.6 76.3

106 74.8 77.2 76.3 74.7 78.9 80.0 79.1 77.7 77.1

107 76.0 78.0 77.7 76.4 80.3 81.7 60.9 80.0 78.4

103 77.5 79.0 78.3 77.8 51.1 52.7 81.8 80.9 79.3

10V 78.9 80.7 79.9 79.3 82.6 83.3 82.8 81.9 80.8

110 0.2 61.4 61.2 79.9 83.9 84.4 83.7 82.7 81.5

111 81.5 83.1 62.6 81.4 84.0 65.2 84.6 83.7 82.Z

112 2.7 53.9 53.9 82.6 84.7 86.9 85.5 84.7 83.6

113 85.1 85.5 85.2 64.0 86.2 87.6 87.3 86.5 84.9

114 86.2 87.1 86.2 85.4 67.5 88.9 8.1 87.4 86.2

115 a7.2 87.7 87.3 $6.5 58.1 89.6 U.9 8.1 87.3

116 8.2 89.1 8.3 87.7 89.2 91.0 90.5 U8.9 88.5

117 89.2 90.4 89.4 88.7 90.2 91.6 91.1 89.7 89.6

118 90.1 91.2 90.7 89.8 91.2 92.8 91.9 91.3 90.8

I19 91.0 91.8 91.6 91.0 92.2 93.5 92.5 92.0 91.8

129 91.9 92.5 92.5 91.9 93.1 94.1 93.0 92.7 92.7

121 13.6 93.6 93.8 93.3 94.1 95.0 94.3 93.9 93.6

122 94.2 94.6 94.6 94.2 95.2 95.5 95.0 15.0 94.4

12.3 95.4 95.7 95.6 95.5 96.2 96.4 96.0 96.0 95.6

124 96.0 96.5 96.7 96.2 96.8 97.2 96.8 97.0 96.4

125 96.5 96.9 97.2 96.9 97.3 97.5 97.6 97.4 97.1

126 97.0 97.5 97.7 97.4 97.8 98.1 97.9 8.0 97.6

127 97.9 98.2 98.3 97.9 98.3 98.5 98.5 98.5 98.2

128 8.3 98.5 98.5 98.4 98.6 98.8 98.7 9d.8 96.4

129 VO.6 98.5 98.7 98.6 98.8 99.0 98.9 99.0 98.7

13U 98.8 9.1 96.9 99.0 99.0 99.1 99.2 99.2 99.0

131 99.0 99.3 99.0 99.2 99.2 99.3 99.4 99.3 19.2

132 9.0 9.3 9".2 9".2 99.3 99.3 99.4 99.3 99.3

133 9.2 99.4 99.4 99.3 99.4 99.5 99.5 99.5 99.4

134 9.2 99.4 99.5 99.4 9.4 9.5 9T.5 99.6 99.4

135 99.4 99.5 996 99.5 99.5 99.6 99.7 99.6 99.5

136 9.4 99.6 99.6 9.6 ".5 9.6 99.7 99.7 99.6

137 H.5 99.7 9.7 "9.6 99.6 99.7 99.8 99.7 99.6

138 99.7 99.7 99.7 9.7 99.7 99.8 99.8 99.8 99.8

139 99.7 99.8 99.7 99.8 9.7 99.8 99.8 99.8 99.3

141 9.7 "9.8 99.8 99.9 99.3 99.8 99.8 99.8 99.3

142 99.7 99.8 "9.8 99.9 9.8 99.8 99.8 99.8 9'.8

143 9.7 99.9 99.8 99.9 99.8 99.9 99.8 99.8 99.8

144 99.8 9".9 9.8 "9.9 99.5 99.9 99.9 99.9 99.8

145 99A 99.9 99.8 9.9 99.9 99.9 99.! 99.9 9.?

147 99.9 99.9 99.9 99.9 99.9 99.9 9sk.9 99.3 100.0

148 99.9 99.9 99.9 9.9 99.9 100.0 99.9 99.9 100.0

149 99.9 99.9 9.9 9.9 99.9 100.0 99.9 99.9 100.0

150 9.9 9.9 100.0 9.9 9.9 100.0 100.0 99.9 100.0

151 99.9 99.9 OO.0 100.0 100.0 100.0 IOU.0 99.9 100.0

I2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

153 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

IS4 100.0 100.0 100.0 100.0 100.0 100.0 '00.0 100.0 100.0

151 ,,).0 100.0 100.0 100.0 100.3 100.0 ;00.0 100.0 100.0

A-46
Table A - 21

Conversions from Sum Scores to Composite Scores

ACL SUM AST SUM AOP SUM ACO SUM COMPOSITE CUM*

1- 91 1-123 1-117 1-118 40 0.1

92- 93 124-125 118-119 119-121 41 0.6

16042 0.6 96 120 122 43 0.7

123 45 0.8

95 127 121 0 46 0.8 . 128 122 124 48 C.9

96 129 123 125 50 1.1

1051 1.2

130 124 12i 52 1.4

98 131 125 127 53 1.6

126 128 54 1.8

99 132 127 129 55 2.1

t00 133-134 128 130-131 56 2.5

luI 135 129 132 57 3.1

102 136 130-131 133 58 3.5

137 132 134 59 4.3

103 138-139 133 135 60 4.7

104 140 134-135 136-137 61 5.5

10S 141 136 138-139 62 6,5

106 142 137 140 63 7.3

107 143-1" 138-139 141-142 64 8.1

108 145 140 143 65 9.4

109 146 141-142 144-145 66 10.5 l1U 147-148 143 146 67 11.4

111 149 144-145 147 58 12.5

112 150 146 148-149 69 13.6

113 151-152 147-148 150 70 15.1

114 153 149-150 151-152 71 16.6

115 154 151 153 72 17.7

116-117 156-156 152-153 154-155 73 19.2

118 157-158 154-155 156-157 74 21.0

119-120 159-16U 156-157 158-159 75 23.1

121 161-162 158-159 160-161 76 25.3

122 163 160-161 162 77 26.9

123 164-165 162-163 163-164 78 28.8

124-125 166 164-165 165-166 79 30.7

126 167-168 166-167 167-168 80 32.7

127 169-170 16a 169 81 34.4

128 171 169-170 170-171 82 36.2

129 172 171 172 83 37.5

130-131 173-174 172-173 173-174 84 39.3

132 175 174-175 175 85 41.5

133 176-177 176 176-177 86 42.9

134 178 177-178 178 87 44.5

135 179-180 179-180 179-180 88 46.0

136 181 181 181 89 47.5

137-138 182-183 182-110. 182-183 90 49.2

139 184-185 184-185 184-185 91 50.9

140-141 186-187 186-187 186-187 92 53.2

142 188-189 188-189 188-189 93 55.2

143-144 190-191 190-192 190-191 94 57.1

continued on next ;age

A-47/
Conversions from Sum Scores to CoMosite Scores (Continued)

ACL SUM AST SUM AOP SUM ACO SLIM COMPOSITE CUM%

145 192-193 193-194 192-193 95 59.1

146 194-195 195-196 194-195 96 61.0 147-148 196 197 196 97 62.2

149 197-19 198-199 197-198 98 64.1

150-151 200-201 200-202 199-201 99 66.0

152-153 202-203 203-204 202-203 100 68.0 154 204 205 204 101 69.3

155-156 205.206 206-207 205-206 102 70.7 157-158 207-209 208-210 207-208 103 72.5

159 210-211 211-212 209-210 104 74.3

160-161 212-213 213 211 105 76.0 162 214-215 214-215 212-213 106 77.1

163-164 216 216-217 214-215 107 - 78.6

165 217-218 218 216 108 79.5

166 219-220 219 217-218 109 80.9

167-168 221 220-221 219 110 81.8 169 222-223 222 220-221 111 82.9

17U-171 224-22S 223-224 222 112 84.1

172-173 226-228 22s-26 223-224 113 85.6

174-175 229-230 227 225-226 114 86.8

176 231-232 228 227 115 87.7

177 233 229-230 228-229 116 88.8

178-179 234-236 231-232 230 117 89.8

18-181 237 233 231-232 118 90.9

182 238-239 234-235. 233-234 119 91.8

183-184 240-241 236 235-236 120 92.6

185-185 242-243 237-238 237-238 121 93.8

187-188 244-246 239-240 239-240 122 94.6

189 247-248 241-242 241-243 1.3 95.7 19U-191. 249-250 243-244 244-245 124 96.4

192 251 245-246 246 125 97.0

193 252-253 247 247-248 126 97.6

194 254-255 248-249 249-250 127 98.1

195 256 250 251 128 98.4 196 257 251 252 129 98.6

258 252 253 130 98.9

197 259 254 131 99.1

25i 133 99.3

265 25i 134 99.3 198 261 25i 256 135 99.5

262 255 257 137 99.6

258 140 99.7 199 25i 259 142 99.7 263 257 143 99.8

260 144 99.8

264 145 99.9 255 258 26i 149 99.9

200 . . 11 99.9

* 266i, 152 99.9 . 262 153 100.0

25i 263 154 100.0

ZUI-3J 267'-00 260-300 264-300 1ss 100.0

A -4 07
Table A - 22

Now and Currtnt Composate Scores

Maintaining Constant AFQT ,teans for Eligibles

8ased on a 2% Sample of FY81/82 Applicants

--------------- NEWCO4PACL -----------..............-

MiAFQT CL CO EL FA GH MH OF SC ST NEWSCORE

39.4 40 40 40 40 40 40 40 40 40 40

39.5 42 43 43 46 42 41 42 43 41

39.6 42 44 45 50 44 43 41 43 43 43

39.6 43 5So 5o 52 47 43 42 43 46 46

39.7 43 52 51 54 SO 48 42 43 48 so

39.8 47 54 53 56 52 51 46 48 50 52

39.9 50 56 54 S7 54 53 50 51 S1 53

40.U 53 57 SS 58 55 54 53 54 53 55

40.1 55 58 57 58 56 55 54 55 55 56

40.3 57 60 58 59 57 57 56 57 56 57

4..5 58 61 58 50 S8 58 57 58 57 5.8

40.8 59 62 59 62 59 59 58 59 58 60

41.1 61 63 60 63 61 61 60 61 59 61

41.4 62 64 62 65 61 62 61 62 61 62

41.6 63 65 6Z 66 62 43 6! 64 62 6~3

Q.0) 65 66 63 67 6.3 64 63 65 62 54

42.3 65 67 64 68 64 65 64 66 63 65

42.6 67 68 65 69 65 66 65 66 64 66

43.0 68 69 66 70 66 67 66 68 65 67

43.5 69 70 68 71 68 68 67 69 66 68

43.8 70 71 69 73 69 69 68 70 67 69

4 .3 71 72 70 74 70 70 69 71 68 70

44.8 72 73 71 75 71 71 70 72 69 71

45.2 73 74 72 75 71 72 71 73 70 72

45.7 74 75 73 76 72 73 72 74 71 73

46.7 76 76 74 77 75 75 74 75 72 74

47.2 76 77 75 78 75 76 74 76 73 75

48.3 78 78 76 79 78 77 76 77 75 76

48.9 79 79 77 80 78 78 77 78 76 77

49.5 80 80 78 81 79 79 77 79 76 78

50.1 81 81 79 82 80 80 78 80 77 79

51.4 83 83 81 84 81 82 79 81 79 so

52.0 84 84 81 85 8Z 83 80 82 80 81

52.7 85 85 82 86 83 84 81 83 80 82

53.4 86 86 83 87 84 85 82 84 81 83

54.1 87 87 84 88 85 86 83 85 82 84

55.5 89 88 86 90 87 88 85 87 84 85

S6.2 90 89 87 91 88 89 86 88 85 86

56.9 91 90 88 91 89 90 87 89 86 87

57.6 92 91 89 92 90 91 88 90 87 88

68.4 93 92 90 93 91 92 89 91 88 89

59.1 94 93 91 94 92 93 90 92 89 90

60.6 96 94 93 95 93 94 91 93 92 91

61.5 96 95 93 96 94 96 92 94 92 92

continued on next page

A-49
New and Current Composite Scores

Maintaining Constant AFQT Means for E1libles

Based on a 2% Sample of FY81/82 Applicants (Continued)

-------------. ----------... NEWCOMP-ACL .------- -- aaaaaaaaaaaaaa

MKAFQT CL CO EL FA GN MM OF SC ST NEWSCORE

62.8 98 96 95 97 9 97 93 95 94 93

63.6 99 97 95 98 97 98 94 96 94 94

65.1 100 99 98 99 99 99 96 97 96 95

65.8 101 100 99 100 100 100 97 98 97 96

66.6 101 101 99 100 100 101 98 99 98 97

68.1 103 102 1U1 102 102 103 99 100 99 98

68.7 104 103 101 102 103 104 100 101 100 99

70.1 106 104 103 104 104 105 101 103 101 100

71.3 107 105 104 105 105 106 103 104 103 101

71.9 108 106 104 105 106 107 103 104 104 102

73.2 109 107 106 107 108 108 105 105 105 103

74.5 111 109 107 108 109 111 106 107 107 104

75.2 112 110 108 109 110 111 107 107 107 105

76.4 113 111 109 110 113 113 109 108 109 106

77.0 114 112 110 Ill 113 14 110 109 110 107

78.2 115 114 Ill 112 115 115 Ill Ill 112 108

78.7 115 114 iz2 113 115 116 112 112 113 109

79.3 116 115 112 113 116 117 113 113 113 110

80.5 118 116 114 115 118 118 114 114 115 111

81.0 118 117 114 115 118 119 115 115 115 112

82.2 120 118 116 116 119 121 116 117 116 113

83.0 121 119 117 117 120 122 117 118 117 114

83.9 122 121 118 119 121 123 119 119 119 115

84.4 123 122 119 119 122 123 120 120 119 116

84.9 123 122 119 120 123 124 121 121 120 117

85.9 124 123 121 121 123 125 122 127 121 118

86.9 126 124 122 122 125 125 123 123 122 119

87.5 127 125 123 123 126 126 124 124 123 120

88.2 128 126 124 123 128 127 124 124 124 121

89.2 130 127 125 124 130 128 125 125 125 122

90.0 132 128 127 126 132 130 125 127 127 123

90.6 133 128 128 126 137 133 127 128 128 124

91.5 136 130 131 128 138 140 128 129 131 125

91.8 137 131 132 128 140 142 129 129 132 126

92.5 137 133 155 129 142 144 131 130 136 127

93.0 143 135 155 130 155 144 133 133 145 128

93.7 148 138 15 134 155 145 135 134 155 129

94.3 148 152 155 144 155 145 137 135 155 131

94.5 149 152 155 145 155 145 137 145 155 135

94.8 149 153 155 147 155 148 137 144 155 142

95.0 149 163 155 148 155 149 140 144 155 151

94.1 149 153 155 148 155 149 140 144 155 155

continued on next page

A-50
New and Current Composite Scores

Maintaining Constant AFQT Means for Eligibles

Based on a 2% Sample of FY81/82 Applicants (Continued)

------------ * --------------- NEWCMPwAST ............------

MNAFIT CL CO EL FA GM M9 OF SC ST N EWSC..ORE

39.4 40 40 40 40 40 40 40 40 40 40

39.5 22 43 43 46 42 41 42 43 £1

39.6 43 47 47 50 44 43 42 43 43 42

39.6 43 49 50 52 47 43 42 43 45 46

39.7 43 51 51 53 50 47 42 43 48 48

39.7 45 53 52 54 50 50 42 46 49 50

39.8 48 54 53 56 52 51 47 49 0 51

39.9 So 55 54 57 54 52 49 50 51 53

40.0 52 S7 55 57 55 54 52 53 52 55

40.1 54 58 56 S8 55 55 54 55 55 56

40.3 57 60 58 59 57 57 56 57 56 57

40.5 58 61 58 60 68 58 57 58 57 58

40.6 59 61 59 61 59 58 SS 59 57 59

40.8 59 62 59 62 60 59 59 59 58 60

411? 62 4,3 61 4 41 62 60 62 W4 61

41.4 62 64 63 65 62 62 61 63 61 62

41.6 63 65 62 66 62 63 62 64 62 63

41.9 64 66 63 66 63 64 63 65 62 64

42.4 66 6.8 65 68 65 65 64 66 64 65

42.7 67 68 66 70 66 66 65 67 65 66

43.1 68 69 66 70 66 67 66 68 65 67

43.6 70 70 68 72 68 69 67 69 66 68

43.9 71 71 69 73 69 69 68 70 67 69

44.2 71 72 70 74 69 70 69 71 68 70

44.9 72 73 71 75 71 71 70 72 70 71

45.3 73 74 72 75 71 72 71 73 70 72

45.6 74 75 73 76 72 73 72 74 71 73

46.5 75 76 74 77 74 74 73 75 72 74

47.3 76 77 75 78 75 76 75 76 73 75

48.1 78 78 76 79 77 77 76 77 75 76

49.0 79 80 77 80 79 78 77 78 76 77

49.4 80 80 78 81 79 79 77 79 75 78

50.4 81 81 79 8 3 80 80 78 80 77 79

50.9 82 82 80 84 81 81 79 81 78 80

51.9 84 83 81 85 82 92 80 82 79 81

9 85 85 82 96 93 84 82 84 81 82

53.4 86 86 83 87 84 85 82 94 81 83

53.9 87 86 84 98 85 85 3 95 92 94

55.0 88 88 85 89 86 87 85 86 84 a5 55.6 89 89 86 90 87 88 85 87 84 8b

56.7 91. 90 88 91 89 90 86 88 86 87

57.2 92 91 89 92 83 91 87 89 86 a8

58.3 93 92 90 93 91 92 89 91 88 89

58.8 93 93 91 93 91 92 90 92 89 90

59.9 95 94 92 94 92 94 91 93 91 91

61.1 96 9S 93 96 94 95 92 94 92 q2 62.1 97 96 54 96 95 96 93 94 93 93

c=ninued on next ;age

A-51
New and Current Composite Scores

Maintaining Constant AFQT Means for Eligibles

Based on a 2% Sample of FY81/82 Applicants (Continued)

--------------------- NEWCOMP- AST .------------------------.

MNAFQT CL CO EL FA 61 M OF SC ST NEWSCORE

63.2 98 97 95 97 96 97 94 95 94 94

64.2 99 98 96 98 98 99 95 96 95 95

65.3 i10 99 98 99 99 1 9 98 6 96

66.5 101 101 99 100 100 101 98 99 97 97

67.0 102 101 100 100 101 101 98 100 98 98

68.5 104 103 101 102 102 104 99 101 100 99

69.6 105 104 102 103 104 105 100 102 101 100

70.6 106 105 103 104 105 106 102 103 102 1fli

71.1 107 105 104 105 105 106 103 104 103 102

72.1 108 106 105 105 106 107 104 104 104 103

73.5 110 108 106 107 108 109 105 105 105 104

74.5 111 109 107 10 109 111 106 107 107 105

75.5 112 110 108 109 111 112 108 108 108 106

76.5 113 112 U10 110 1M 113 l09 108 1C9 107

77.0 114 112 110 111 113 114 110 109 110 108

77.9 114 113 111 112 114 115 111 111 Ill 109

78.8 115 114 112 113 116 116 112 112 113 110

79.3 116 115 112 113 116 117 113 -113 113 Ill

80.2 117 116 113 114 117 118 114 114 114 -112

81.1 118 117 114 115 118 119 115 115 115 113

82.3 120 118 116 117 119 121 117 117 117 114

83.1 121 119 )17 118 120 122 117 116 118 15

83.7 122 121 118 118 121 123 118 119 I18 116

84.1 122 121 118 119 122 123 119 119 119 117

85.3 124 123 120 120 123 124 121 121 120 118

85.7 124 123 120 121 123 124 122 122 121 119

96.6 125 124 121 122 124 125 122 122 122 120

97.2 127 124 122 122 125 126 123 123 123 121

58.1 128 125 124 123 128 127 124 124 124 122

89.2 130 127 125 125 130 128 125 125 125 123

819.8 132 128 126 125 132 129 126 126 127 124

90.2 132 128 127 126 133 130 126 127 127 125

90.6 133 128 128 126 137 132 126 128 128 126

91.3 135 130 129 128 138 138 128 129 130 127

91.8 137 131 132 128 140 142 129 129 132 128

92.1 137 132 137 129 141 143 129 130 133 129

92.6 13a 133 155 129 143 144 131 131 144 130 92.9 138 135 155 130 155 144 133 '33 145 131

93.3 143 136 155 131 155 144 134 13 150 134

93.6 144 138 155 i33 155 145 13b 134 153 135

93.4 144 138 155 133 155 145 135 134 155 137

93.5 144 138 155 133 155 145 135 134 155 143

93.7 148 138 155 134 155 145 135 134 155 145

94.3 148 152 155 144 155 145 137 135 155 149

95.1 ISI 153 155 149 155 149 140 144 IS5 152 94.0 151 153 155 149 155 149 140 144 155 155

continued on nex: -aie
New and Current Composite Scores

Maintaining Constant AFQT Means for Eli ibles

Based on a Z% Sample of FY81/82 Applicants ?Continued)

................. -NEWCOlPs . AOP ----------.--.........

14WAQT CL CO EL FA GM M34 OF SC ST ftEWSCORE

39.4 40 40 40 40 40 40 40 40 40 40

39.5 42 43 43 46 42 41 42 43 41

39.6 43 48 48 5 46 43 42 43 43 43

39.6 43 49 50 52 47 43 42 43 45 46

39.7 43 52 5 54 50 47 42 43 48 48

39.7 46 53 52 55 50 50 44 47 49 so

39.8 48 54 53 56 53 51 47 49 so 52

39.9 50 56 54 57 54 53 50 51 51 53

40.0 52 57 55 57 55 54 53 53 52 54

40.1 54 58 56 58 55 55 54 5S 55 55

40.2 56 58 57 59 56 56 55 56 55 56

40.3 57 59 57 59 57 57 55 56 56 57

40.4 57 60 S8 60 58 57 57 57 57 58

40.7 59 62 59 62 59 59 .8 59 Se 59

40.9 60 62 60 63 60 60 59 60 59 60

41.1 61 63 60 63 61 61 60 61 59 61

41.4 62 65 62 65 62 62 61 63 61 62

41.6 63 65 62 66 62 63 62 64 62 63

41.8 64 66 63 66 63 64 63 64 62 64

42.3 66 67 65 68 64 65 64 66 64 65

42.6 67 68 65 69 65 66 65 66 64 66

43.1 68 69 66 71 67 67 66 68 65 67

43.3 69 70 67 71 67 68 67 68 66 6.8

43.9 71 71 69 73 69 69 68 70 67 69

44.2 71 71 70 74 69 70 69 71 68 70

44.7 72 73 71 75 71 71 70 72 69 71

45.3 73 74 72 76 72 72 71 73 70 72

45.7 74 75 73 76 72 73 72 74 71 73

46.4 75 76 74 77 74 74 73 75 72 74

47.1 76 77 75 78 75 75 74 76 73 75

47.7 77 77 76 79 76 76 75 76 74 76

48.5 78 79 76 80 78 77 76 77 75 77

49.2 80 80 77 81 79 79 77 7C 76 78

50.0 81 81 79 82 80 80 78 79 77 79

50.8 82 82 80 84 81 81 79 80 78 80

51.6 84 83 81 85 82 82 80 82 79 81

52.0 84 84 81 85 82 82 80 82 80 82

52.9 85 85 82 86 83 84 82 84 81 83

53.4 86 86 83 87 84 85 82 84 81 84

54.2 el A7 64 88 85 86 83 85 a2 85

55.1 89 88 86 89 86 87 85 86 84 86

S5.6 89 89 86 90 87 88 85 87 85 87

56.5 91 90 a8 91 88 90 86 88 85 8a8

57.4 92 91 89 12 90 91 87 89 87 89

57.9 92 92 89 92 90 91 88 90 as 90

56.a 93 93 91 93 9i 92 90 91 89 91

59.7 94 94 92 94 92 93 90 92 9 9z

continued on next page

A-53
New and Curren: Co.site Scores

Maintaining Constant AFQT Means for Eligibles

Based on a 2% Sample of FY81/82 Applicalfts (Continued)

... . . .......... - - EW-CC-1P AOP -----------. -----.---

.WAQT CL CO EL FA G0 MM OF SC ST NEWSCORE

*60.6 95 94 93 95 93 94 91 93 92 93

61.6 9% 95 94 96 94 95 92 94 93 94

62.8 98 6 95 97 96 97 93 95 94 95

63.8 99 98 96 98 97 98 95 96 95 96

64.8 100 99 97 99 99 99 96 97 96 97

65.2 100 99 98 99 99 100 9 97 96 98

66.1 101 101 100 100 100 97 97 99

67.5 102 102 100 101 101 102 99 l00 99 100

68.5 104 103 101 102 103 104 99 101 100 101

69.0 lU4 103 102 103 103 104 100 101 100 102

70.0 106 104 102 104 104 105 101 102 101 103

71.4 107 105 104 105 105 106 103 104 103 104

72.4 108 106 105 106 106 108 104 104 104 105

72.9 109 107 105 106 107 108 104 105 105 106

73.8 110 108 106 107 109 110 105 106 106 107

74.9 112 110 107 108 110 111 107 107 107 108

75.2 112 110 108 109 110 112 107 107 108 109

75.6 112 110 108 109 11 112 108 108 108 110

76.6 113 112 110 110 113 113 109 109 109 Ill

77.1 114 112 110 111 114 114 110 109 110 112

78.1 114 114 111 112 115 115 111 111 112 113

79.2 116 114 112 113 116' 117 113 113 113 114

79.8 117 115 113 114 117 117 113 113 114 115

80.3 117 116 113 114 117 118 114 114 114 116

81.6 119 117 115 116 119 119 116 116 116 117

82.5 120 118 116 117 120 121 117 117 117 118

82.9 121 119 117 117 120 122 117 118 1;7 119

84.1 122 121 118 119 122 123 119 119 11W 120

84.5 123 122 119 119 122 124 120 120 119 121

85.4 124 123 120 121 123 124 121 121 120 122

86.1 124 124 121 121 124 125 122 122 121 123

86.9 126 124 122 122 125 125 123 123 122 124

87.7 127 125 123 123 126 127 124 .24 124 125

88.9 130 127 125 124 130 128 125 125 125 125

89.3 130 127 125 125 130 128 12S 125 126 127

90.2 132 128 XZ7 126 133 130 126 127 127 128

90.5 133 128 128 126 137 131 126 128 128 129

90.4 133 128 128 126 137 131 126 128 128 130

90.6 133 128 128 126 137 132 127 128 128 133

91.0 134 129 129 127 137 137 127 128 129 135

91.8 136 130 132 1ZFI 140 142 129 129 132 137

92.5 137 133 155 129 142 144 131 130 136 142

92.7 138 124 155 130 143 144 132 12 144 143

94.0 148 139 155 1'S 155 145 126 134 IS 149

44.4 149 152 IS5 145 155 145 137 143 155 154

90.3 149 152 155 145 155 145 137 143 155 155

- continued on nex: ace
New and Current Composite Scores

Maintaining Constant AFQT Means for Elglibles

Based on a 2% Sample of FY81/82 Applicants (Continued)

.. ....... ....... ............. EWCOP *ACO ..- ...... ... ......

MRAFQr C. CO EL FA G0 MM OF SC ST NEWSCORE

39.4 40 40 40 40 40 40 40 40 40 40

39.5 42 43 43.4 43 42 41 42 42 41

39.6 43 47 47 50 43 42 43 43 43

39.6 43 49 So 52 47.43 42 43 45 45

39.7 43 so 51 53 49 45 42 43 47 48

39.7 45 53 52 54 50 50 42 46 49 so

39.8 47 54 53 56 52 51 47 48 50 52 39.8 50 55 54 56 53 52 49 50 51 53

39.9 51 56 54 57 54 53 51 52 52 S4

40.0 53 57 55 58 55 54 63 54 53 55

40.1 55 58 56 58 56 55 S4 55 55 56 40.4 S7 60 58 60 57 57 56 57 56 57

443.5 58 61 58 60 58 58 57 S8 57 5o 40.6 S8 61 59 61 59 58 58 59 57 59 40.1 9.9 62 59 6? !4 so- 69 sa. so 60

41.0 61 63 60 63 60 61 S9 60 S9 61

41.3 52 64 62 65 61 62 61 62 61 6Z 41.7 64 66 63 66 62 63 62 654 52 63 42.0 65 66 63 67 63 64 63 65 62 64 42.4 66 67 65 68 65 65 64 66 64 65

42.6 67 68 65 69 65 66 65 66 64 66

43.2 68 69g 67 71 67 67 66 68 65 67

43.4 69 70 67 71 67 68 67 69 66 68

43.7 70 70 68 72 689 69 68 69 66 69

44.3 71 72 70 74 69 70 69 71 68 70

44.6 72 72 70 75 70 71 70 71 69 71

45.2 73 74 72 7S 71 72 71 73 70 72

45.6 74 75 73 76 72 73 72 74 71 73

46.3 75 76 74 77 74 74 73 75 72 74 47.0 76 77 75 78 75 75 74 76 73 75 47.7 77 77 76 79 76 76 75 76 74 76

48.5 719 79 76 80 78 77 76 77 75 77

48.9 79 79 77 80 78 78 77 78 76 78 49.7 O0 80 78 82 79 80 77 79 77 . 79

50.5 81 81 79 83 90 81 78 60 77 so 51.4 83 8.3 81 84 82 82 79 81 79 81 51.8 84 83 81 85 82 82 80 82 79 82

52.8 85 85 82 86 83 84 81 83 80 83

53.2 86 86 93 87 94 95 92 S4 81 84 54.2 67 87 84 98 85 86 3 8 8 85 54.6 88 67 85 88 86 86 84 as 83 8 55.5 89 69 86 90 87 88 W.5 87 84 87 56.1 90 69 07 90 88 89 56 88 as Be S7.0 91 90 88 91 19 90 67 8 9868

57.5 92 91 89 92 90 91 87 89 87 90 58.5 93 92 91 93 91 92 89 91 88 91 59.6 94 93 92 94 92 93 90 92 90 92

5. 95 94 93 95 93 94 91 93 9 931

61.4 96 95 93 96 94 95 92 94 92 94

continued -.n next page
New and Current Composite Scores Maintaining Constant AFQT Means for Eligibles Based on a 2% Sample of FY81/82 Applicants (Continued)

.e e 0 .NEWCOMP-*ACO ----------------------

I4NAQT C. CD EL FA GM MM OF SC ST NEWSCORE

62.4 97 96 94 96 96 96 93 94 93 95 63.4 98 97 95 97 97 97 94 95 94 96 64.4 99 98 96 98 98 99 95 97 95 97 64.8 100 99 97 99 99 99 96 97 96 98

67.1 

65.8 100 100 98 99 100 100 97 98 96 99 102 101 100 101 101 101 98 100 98 100 68.0 103 102 101 102 102 103 99 100 99 101 68.6 104 103 101 102 103 104 100 101 100 102 69.6 105 104 102 103 104 105 100 102 101 103 70.8 107 105 103 104 105 106 102 103 102 104 72.0 108 106 104 105 106 107 103 104 104 105 72.5 109 106 105 106 107 108 104 104 104 106 73.6 110 108 106 107 108 109 105 105 105 107 74.6 111 109 107 108 109 111 106 107 107 108 75.0 112 110 107 108 110 111 107 107 107 109 75.8 113 111 109 109 111 112 108 108 108 110 76.3 113' 111 109 110 112 113 109 108 109 111 77.3 114 113 110 111 114 114 110 110 110 112 77.7 114 113 111 112 114 115 111 110 111 113 78.8 115 114 112 113 116 116 11? 112 113 114 79.8 117 115 113 114 117 117 11I i'3 114 115 s e 116 114 115 117 118 1 1 14 11' 116 . 119 Ill 115 I!S 119 119 ll Ii5 116 117

82.0 120 118 116 116 119 120 116 116 116 l18 82.8 121 119 117 117 120 121 117 118 117 119 83.8 122 121 118 119 121 123 119 119 118 120 84.7 123 122 119 120 122 124 120 120 119 121 85.3 124 123 120 121 123 124 121 121 120 122 86.2 125 124 121 121 124 125 122 122 121 123 87.3 127 124 122 122 125 126 123 123 123 124 88.0 128 125 124 123 127 127 124 124 124 125 88.5 129 126 124 124 129 127 124 124 124 126 89.1 130 127 125 124 130 128 125 125 125 127 89.7 131 127 126 125 131 129 125 125 125 128 90.2 132 128 127 126 133 130 126 127 127 129 90.6 133 128 128 126 137 132 127 128 128 130 91.1 134 129 129 127 137 138 127 128 129 131 91.7 136 130 131 128 138 142 128 129 131 134 91.7 136 130 131 128 138 142 128 129 131 135 92.2 137 132 137 129 141 143 130 130 133 137 92.7 138 134 155 130 143 4 13Z 1 ., 140 S i a 136 155 131 155 14 134 133 150 142 93.3 144 136 155 131 155 144 134 133 150 144 93.- 144 136 155 131 155 144 134 133 150 149 92.3 144 136 155 131 155 144 134 133 150 .153 92.3 144 136 155 131 155 144 134 133 150 154 94.0 148 139 155 135 155 145 136 134 155 155

A r e
Table A - 23

Aptitude Area Composite Cutoffs by MOS

AA MOS CUTOFF AA OS CUTOFF AA NOS CUTOFF M 140S CUTOFF AA NOS CUTOFF AA M4S CUTOFF

95 EL24K .CL.71C 95 EL 34Y 95 GM P1 9S MM 67u lo0 ST 74DIOU

CL 710 110 EL 24L 95 EL 358 90 M 51IK 85 AM 67V 100 51 74F 100

CL 71G 95 EL 24M 105 EL35E 95 G 511 45 MM 67Y 100 ST S1B 95

CL71L 95 EL 24N 105 EL 3SF 95 6M151K 85 MM ISUd 100 ST 81C a5

CL 71M1 95 EL 24P 105 EL 35G 110 G14 SIR 95 1V 680 lot; St T IE 95

C1. 1N 95 EL 240 105 EL 35H 120 M 52C 95 M 68F 100 ST 82C 95

C- C73C 95 EL 24U 105 EL 35K 95 6M 520 95 MM6864 MMq 68H 100 100 ST ST 82C 82D 9595

CL748 95 ELZJ 85 EL 3sL 100 GM4538 90 F 13M 100 ST 83E 85

CL753 95 EL ZSL 95 EL 35 100 GM 54C 95 OF 1so 95 ST 83F 85

CL7SC 95 EL 268 95 EL 35R 100 55 85 OF 15E 95 ST 848 9 L 750 95 EL6C 95 EL 36C 90 M 550 100 OF 16 85 ST 84C 85

CL.7SE 95 EL 260 85 EL360 90 G 55G 95 OF16C 95 ST 84F 95

CL 7SF 105 EL 26E 110 EL 36E 90 6 7E S0 OF 160 85 ST 918 95

CL 76C 95 EL 2M 95 EL36H 100 GM 7F 85 OF 16 9 95 ST 91C 95

cEl 95 2 K 0 36K 90 Gm 57m 85 OF 16F 85 ST 910 96

CL 76? 90 EL 26L 100 EL 36L 110 GM 61F 85 OF 16H1 95 ST 91E 95

CL.76V 90 EL 261 95 EL 41E 95 61 628 85 OF 16J 95 ST 91F 95

CL 76W 90 95 EL 416 85 GM 62F e5 OF 16P 85 ST 91G 105

CL 76X 85 EL Z6Q 95 EL 45G 95 GM 62G 90 OFIdR 85 ST 91m 95

CL 76Y 95 EL 26R 5 EL 46N 95 GM62. as OF 16 85 ST 91J 95

CO 11it 85 EL 27T 95 EL 526 9S GM 62J 85 OF 64C 85 ST 91L 95

CO 1 85 EL 26V i EL93 95 G 18 95 OF948 85 ST 91N 95

Co 19A 85 EL25Y 100 FA 12a 85 GM 6a1 90 OF 94F 95 ST 91P 10

co 1o as al; Sc 758 90 ST 91 95 CON 5 EL 278 95 FA 13F 100 mm 12C 85 SC 05C 95 ST 91R 100

CO 1X85 EL 27E 95 FA1SJ 100 M 33S 95 SC 056 95 ST 91S 95 CO 1 a EL 27F 9 G 95 M4M 9 ST 05 N 09S ST 91T 95

CO 12E95 EL 27G 95 GM 41C 90 M 45 95 SC 178100 ST91T 95

CO 12F8.5 EL 27H 95 GM 41J 85 w 618 85 SC 17C 95 ST 91V 95

C 19A a EL 27N 95 6 as -41X w 61C 100 SC 17L 95 ST 91Y 95

c 190 as EL 31E 110 GM42C 95 M 4623 86 SC 7E 90 ST 928 95

CO19E 85 EL 31J 110 ,1 420 95 MM 638 85 SC 72G 90 ST 92C 95

CD 19F 85 EL 31M4 95 GM 42E 95 MM 630 100 SC96%H 95 ST 92D 95

CO 19K 85 EL 31N 95 GM 43E 85 MM 63E 95 ST 03C 8S ST 93E 95

EL 17K 85 EL 31S 115 G4 43M 80 W 63G 100 ST 060 95 ST 93' 100

E L17M 85 EL-31T 105 ram4448 85 M 631 as STO05m 95 ST 93J 100

E LZIG 95 EL 31V 95 G4 44E 95 MM 63J 85 ST OSh 95 51T958 100

E L 21L S; EL 320 95 6 14 45a 85 994 63N 95 S T 13C 95 S T 95C 85

E L 22L 95 EL. 32c 11(1 6M 450 95 W9 63S 100 ST 13E 95 S T 968 95

EL 22.495 EL 32G 100 Q4 456 95 MM 63T 1OU ST lip 90 ST 960 95 EL 23N 95 EL 324 95 GM 454 95 w 63W 85 S71 5 5169

EL 23U 95 CL 3489~ H GM 45L 95 MM 53Y 100 ST 71Q 105 S T 9/5 105

E L 24C 105 EL. 34E 110 64 45R 95 M94 67G 100) ST 7IR 105 S T 98L 10S

E L 24E 105 EL 34F 11d GM 45T 90 944 67h 100 ST 730 105 S T 98J 105

E L24G 105 EL 34G 95 GM 518 85 M9 67N 100

EL 24m 95 EL 3-Jrt 110 GM SIC 85 1 67T 100

A-57
Table A - 24

ASVAB Subtest Means by Training Criterion Cell

Subtest

TCELL N GS AR VE NO CS AS MK MC El

0582A113 520 48.65 50.34 51.39 54.43 55.37 49.75 49.25 -,7.88 48.74

05C2Dl13 616 50.40 52.30 53.22 55.30 55.66 51.28 50.96 49.64 50.42

11IN809 978 50.15 51.44 50.59 50.86 51.49 52.14 49.53 50.92 50.51

11CIN809 579 50.75 52.49 51.08 51.68 52.39 52.29 50.53 51.97 50.97

11HIN809 446 51.28 52.29 52.04 51.73 52.08 53.02 50.56 51.32 51.92

1101N809 325 50.08 52.10 50.30 52.41 52.54 51.15 49.64 50.89 50.26

12BAB807 143 47.69 50.34 49.13 48.83 50.71 52.71 47.97 49.73 49.64

12FAF807 225 44.08 47.86 44.37 47.79 50.56 49.23 45.44 47.95 47.02

13B3B810 1080 43.69 48.70 45.13 49.83 50.70 43.18 48.52 45.77 44.88

13E3E810 487 54.58 56.21 54.94 52.91 53.01 53.26 55.87 54.29 53.63

13F3F810 679 51.39 56.03 52.13 53.22 53.97 52.84 53.90 54.14 52.36

1505D810 295 52.44 52.89 53.39 52.90 51.92 54.71 51.24 53.30 52.54

15E5E810 283 52.48 52.44 52.81 53.31 51.57 54.84 51.27 53.82 53.11

168BA811 166 46.56 46.95 48.74 50.04 48.23 48.30 46.12 46.63 47.56

1688C811 131 56.54 58.11 55.82 53.98 52.74 55.32 56.75 54.89 56.19

16CCA811 118 52.64 52.04 53.30 52.61 52.76 54.63 51.14 53.42 53.15

160DB811 112 56.24 57.25 55.33 53.39 53.02 54.31 55.76 54.63 56.61

16EEB811 139 56.22 58.67 55.55 53.04 53.19 55.37 58.33 55.53 56.99

16HH8811 105 53.86 56.35 55.30 54.67 53.73 54.46 55.03 54.50 55.46

16JJA811 119 52.35 52.68 53.57 51.70 49.69 53.76 50.71 53.98 52.98

16PPA811 115 46.51 47.23 48.80 52.16 48.81 47.78 46.26 47.00 48.41

16RRA811 420 47.24 46.96 48.29 50.11 47.21 49.31 46.11 47.89 48.05

16SSA811 597 46.45 47.09 47.49 50.53 48.72 49.58 46.42 48.48 47.74

17C7C061 188 45.35 48.36 49.31 56.20 59.83 45.71 48.73 44.52 45.60

17KGA301 136 52.96 52.94 52.96 53.55 53.94 54.15 50.89 52.13 52.49

19090804 215 49.77 51.40 50.20 50.77 51.83 51.95 49.40 50.67 49.87

19E9E804 171 49.58 51.23 49.97 50.61 51.03 51.71 48.12 50.80 50.65

19F9F804 129 49.17 50.70 49.79 50.95 50.93 51.68 47.96 50.39 50.29

27E7EO93 185 52.26 53.79 50.99 50.38 50.26 50.83 51.94 50.21 52.48

31M4D113 604 51.64 53.30 51.67 50.95 51.36 48.29 51.87 48.97 51.10

31N4Cl13 195 51.86 53.19 51.51 50.99 51.27 48.81 51.60 49.36 51.72

31VIV061 458 52.24 53.62 51.26 49.94 50.31 51.39 52.04 51.79 53.41

36CAA113 377 47.00 48.91 46.34 48.73 48.19 43.63 48.35 44.91 47.72

36KAC113 664 46.08 48.02 45.21 47.55 46.84 44.43 47.54 44.41 47.32

41CG7091 106 47.56 45.70 45.77 46.32 46.50 47.70 46.67 45.42 48.65

44BJI091 137 50.28 49.58 49.09 49.31 49.07 51.57 48.20 50.24 50.96

45KK8091 101 51.47 49.90 50.09 47.83 49.11 56.01 49.45 51.33 53.75

45KK9091 130 53.01 50.98 52.11 48.26 48.85 55.48 49.42 52.44 53.11

51KBK807 168 46.58 44.75 45.51 45.20 46.44 47.83 45.43 44.57 48.02

54CSS031 183 52.57 50.72 51.63 48.71 48.72 54.34 49.70 50.77 53.63

54ESA031 272 51.33 50.91 52.66 49.46 49.68 47.15 50.77 48.85 48.76

55B58093 236 47.28 43.85 47.34 44.56 46.45 45.97 45.75 42.70 47.39

57EPE1O1 126 43.37 41.98 44.34 45.45 45.93 40.29 44.68 40.00 44.02

57HG1551 224 46.58 44.05 45.92 45.40 46.84 45.20 45.83 42.56 47.17

A-5
ASVAB Subtest Means by Training Criterion Cell (Continued)

Subtest

TCELL N GS .AR VE NO CS AS P1K MC El

61G6551 186 45.14 46.86 46.05 52.20 49.87 47.89 46.29 46.95 48.78

61CH1551 139 52.36 52.65 51.82 51.74 51.68 56.83 49.86 54.68 54.75

62BC8807 264 47.41 47.83 46.75 48.98 49.09 52.77 46.95 50.61 50.99

62ECE807 134 48.60 47.78 47.86 47.72 48.37 53.30 47.87 50.14 51.08

62FCF807 149 47.68 45.52 46.04 46.93 47.17 50.52 46.44 47.79 48.36

6383B803 555 46.27 47.52 46.42 50.25 49.34 51.76 46.62 48.83 49.63

63838805 382 46.04 47.09 46.02 51.23 49.74 50.63 46.49 48.30 49.81

63DSA171 349 50.05 51.01 49.84 50.52 50.37 57.13 48.45 54.23 53.45

63G17091 161 48.91 50.36 49.71 50.78 50.47 55.70 48.19 54.03 53.62

63HH1091 708 46.94 48.18 47.25 49.64 49.32 53.55 47.20 50.21 50.90

63NTS171 514 49.20 49.43 48.60 49.69 49.40 55,25 47.93 51.60 51.74

63TF1171 573 50.88 51.85 50.37 51.79 50.67 58.02 49.29 55.40 54.50

63WW1091 481 45.01 47.23 45.76 50.65 49.38 50.85 46.42 49.08 49.31

63YTV171 177 51.73 52.02 51.28 51.67 51.39 58.47 49.70 54.81 55.05

64CEC807 203 46.99 49.06 49.10 50.51 50.02 51.76 47.14 49.16 49.26

64C4C803 561 46.01 47.42 47.23 51.02 50.33 50.45 46.98 48.67 47.76

67N65011 164 55.06 55.06 54.16 52.96 52.52 58.04 53.80 57.20 56.62

67TL6551 125 55.50 54.40 54.62 52.32 52.54 58.90 54.11 58.26 56.72

67UP1551 211 55.49 55.38 55.00 52.99 53.24 58.27 54.65 57.06 56.14

67V18011 195 53.23 54.67 53.28 53.65 53.66 56.87 53.12 55.99 55.73

67YS1551 144 54.72 55.35 54.06 53.56 53.22 57.75 54.99 56.96 56.33

68DT1551 122 55.07 55.00 54.60 52.93 52.38 58.13 53.84 56.55 56.93

68JW6551 121 54.02 53.51 53.74 51.37 51.01 55.09 52.59 53.83 54.69

68MW8551 134 49.52 47.18 48.68 47.54 49.10 52.78 47.76 48.65 51.50

71NL1551 176 41.90 44.70 45.38 55.84 56.63 40.20 45.98 40.88 42.25

72E3G113 214 47.24 50.15 51.48 56.21 57.36 43.47 49.51 44.42 44.58

73C5R121 204 46.24 51.33 48.89 56.62 56.59 43.09 51.74 44.45 44.90

7585E121 497 45.28 48.66 48.34 56.37 56.60 44.48 48.96 44.57 46.00

75D5D805 233 43.06 47.35 46.37 56.01 56.27 42.19 47.05 42.90 44.18

75E5E805 277 45.47 48.54 48.70 56.42 56.76 43.34 49.16 45.34 45.76

76CECIOI 1146 41.96 45.06 44.89 54.12 54.25 41.28 45.42 41.89 43.03

76P5F101 560 40.90 43.96 43.65 52.91 52.65 39.30 44.81 40.31 41.38

76VEV1O1 402 39.00 42.14 42.25 50.98 50.97 38.24 43.24 39.07 39.80

76WD8101 138 38.71 41.89 40.97 51.26 50.68 37.69 43.08 39.10 39.12

76WPW101 344 39.77 42.95 42.53 51.73 51.28 38.32 43.86 39.31 40.47

76XSX101 158 38.34 41.41 41.53 49.98 48.92 37.62 42.45 38.11 39.15

76YEY1O1 381 43.65 47.18 46.51 56.41 56.20 42.80 47.17 44.29 43.74

76Y5G101 298 45.43 48.49 48.70 55.36 56.06 44.58 47.28 44.75 46.01

76Y6Y805 470 44.51 47.42 47.26 55.63 55.79 43.57 46.94 44.24 44.07

82C2C810 390 53.79 52.91 53.79 50.13 50.51 52.53 52.64 53.61 52.51

91801929 803 54.38 53.32 54.88 52.50 52.67 49.30 53.83 51.61 50.69

91C02929 234 53.93 54.29 54.99 52.97 53.77 49.10 54.27 51.11 50.50

91E05929 163 54.26 53.88 55.01 53.24 54.28 49.42 54.96 51.88 51.31

92825929 134 55.76 55.99 56.16 54.31 55.13 48.46 58.61 50.40 51.53

948KA101 627 47.12 48.61 49.16 51.16 49.78 48.09 47.16 48.23 47.07

94848803 237 47.50 48.20 49.90 50.81 49.58 48.22 46.78 47.86 47.79

94848805 417 46.81 47.71 48.52 51.03 49.22 47.93 47.02 47.43 46.69

958SB813 728 54.77 54.80 55.63 53.09 53.58 53.62 53.16 53.53 5Z.62

A-59
Table A - 25

ASVAB Means by SQT Criterion Cell

Subtest

PCELL N GS AR VE NO CS AS MK MC El

C35KL83 112 53.60 55.24 53.54 52.72 52.05 52.44 54.81 52.54 55.15

C740823 132 56.29 58.80 56.93 55.82 56.76 50.13 60.09 54.20 52.77

0581182 617 48.83 50.41 51.19 54.04 54.51 49.87 49.96 48.00 49.04

05C0182 1203 50.07 52.30 52.45 55.86 56.02 50.63 51.39 49.07 49.86

05C0183 1160 50.17 52.63 52.64 56.18 56.11 50.60 51.87 49.15 50.30

05G0183 119 54.32 57.83 56.33 57.22 58.26 53.39 59.36 54.64 53.18

05H1183 110 55.54 56.93 57.15 54.75 57.03 49.56 56.93 52.00 51.41

118 181 608 49.75 51.58 50.28 51.18 51.93 51.67 49.46 50.72 50.54

1180183 426 48.91 50.83 49.53 50.45 51.67 50.68 48.96 50.31 49.46

1181182 3912 49.30 51.36 49.84 50.99 52.08 51.50 49.46 50.53 50.01

1182182 614 49.40 51.05 50.04 50.15 51.44 51.23 49.22 50.57 49.83

1186182 422 49.15 50.97 49.64 51.45 52.29 50.49 49.51 50.32 49.45

1187182 229 51.00 51.85 50.59 51.69 52.06 52.09 49.93 51.31 50.51

IIC 181 171 49.02 50.91 49.94 50.51 50.90 51.31 48.65 50.27 49.66

11C0183 112 50.45 52.30 50.94 51.80 52.21 52.24 50,28 51.41 51.20

11C1I82 559 50.62 52.80 51.36 51.96 52.60 52.18 51.02 52.10 50.86

l1C2182 187 50.26 51.78 50.50 50.57 52.30 51.33 50.28 50.66 50.51

11C4182 247 50.89 53.04 51.23 51.77 52.46 51.41 50.98 51.19 50.67

11C5182 217 50.31 52.00 50.47 52.17 52.86 5,.25 50.62 50.65 50.31

IN 161 124 52.60 53.34 52.71 51.40 51.40 55.53 51.48 53.26 53.62

11HI182 443 52.36 53.18 52.56 51.98 52.25 53.88 51.69 52.27 52.63

IIH2182 321 51.52 52.01 51.80 51.28 52.05 53.24 50.98 51.57 51.97

1280183 1985 49.22 51.91 49.76 51.30 51.90 52.41 49.85 51.11 50.34

1281182 1112 49.32 51.36 49.63 50.76 51.45 52.03 49.03 50.56 50.22

12C0i82 176 45.98 49.43 46.38 50.00 49.98 51.75 47.11 49.97 49.22

12C3183 272 47.54 50.14 47.86 50.11 51.07 53.07 47.85 50.89 50.29

13B 181 130 45.15 48.55 46.83 49.75 50.91 47.13 48.43 46.95 46.72

1380183 3907 46.85 50.80 47.88 51.38 51.43 47.03 50.01 48.38 47.92

1381182 110 48.25 50.79 48.39 51.63 52.05 47.19 50.00 48.91 48.55

1382182 264 48.84 52.88 49.36 51.70 51.90 50.02 51.63 50.72 50.05

1383182 156 45.90 SU.42 47.23 50.33 51.96 46.67 48.85 48.54 47.46

1384182 1189 44.63 49.41 45.87 50.76 50.68 45.11 48.76 46.96 46.33

1385182 629 45.34 49.86 46.31 51.10 51.24 45.44 49.32 47.18 46.76

13E0182 417 54.88 56.54 55.34 53.22 53.41 53.32 56.47 53.98 53.79

13E0183 194 54.84 56.71 54.93 53.87 53.85 53.10 56.68 54.44 53.69

13F0182 596 51.59 56.39 52.17 53.76 54.31 53.06 54.46 54.13 52.57

150182 259 52.23 52.22 52.91 53.09 52.41 54.00 51.0" 53.17 52.23

17K2182 110 52.59 54.45 53.25 54.53 54.14 53.75 52.09 52.72 52.54

190 181 104 47.72 50.88 49.27 50.59 51.18 51.11 48.93 50.65 50.11

190183 336 51.96 53.69 52.23 52.18 52.26 53.86 51.69 52.89 52.48

1901182 746 50.55 52.24 51.07 51.19 52.18 52.02 50.70 51.83 50.95

19El182 680 49.62 51.60 49.74 50.92 51.96 51.76 49.41 50.87 50.40

A-60 .-
ASVAB Means by SQT Criterion Cell (Continued)

Subtest

PCELL N GS AR VE NO CS AS 14K MC EI

19E1183 l011 49.93 52.10 50.42 51.20 52.11 52.17 49.59 51.01 50.82

19E2183 858 49.85 52.20 50.18 51.86 52.23 51.87 50.26 51.02 50.47

19E3182 612 48.52 50.69 49.11 51.03 51.80 51.13 48.54 49.88 49,58

26Q0183 142 53.60 55.24 53.29 52.97 53.02 50.50 54.27 51.35 53.54

27E0182 133 51.56 54.42 50.35 50.93 50.98 51.24 52.38 50.71 52.17

27E0183 226 52.35 54.92 51.85 51.69 51.53 51.39 52.82 51.17 52.71

31J0183 132 57.71 59.67 56.85 53.70 53.88 54.63 59.02 55.07 56.90

31140183 1453 51.94 53.58 51.80 51.37 51.50 49.14 52.70 49.61 51.78

31M1182 576 51.74 53.24 51.47 50.76 50.94 48.85 52.42 49.37 51.60

31M2182 407 51.56 52.98 51.37 50.92 51.55 47.73 52.30 49.12 51.34

31V0183 152 52.59 55.20 51.65 51.24 51.40 51.61 53.89 52.41 54.09

31VI182 360 52.55 54.41 51.78 50.95 51.14 51.39 52.79 51.43 53.08

36C2182 390 47.17 49.52 46.49 48.57 48.46 44.42 48.66 45.17 48.46

36KO182 942 46.34 48.42 45.12 48.01 47.56 44.18 48.33 44.93 47.59

36K0183 850 47.15 49.23 46.12 48.93 48.02 45.14 49.00 45.62 48.35

43E0183 100 49.30 50.01 48.37 48.87 50.15 50.54 48.47 49.63 49.48

5180182 197 49.81 50.18 49.41 49.01 49.04 51.23 49.68 49.96 50.06

520182 176 50.43 48.79 49.94 46.94 47.73 53.30 49.06 49.29 51.88

5581182 231 45.95 42.61 44.94 44.05 45.12 45.49 45.53 41.42 47.47

57m0183 194 47.02 44.85 46.25 45.93 46.83 46.20 46.37 43.49 47.39

62B0182 222 48.27 48.60 47.32 49.29 49.58 53.36 47.39 51.30 51.33

62E0182 173 49.21 49.27 48.61 48.40 48.47 53.62 48.33 49.82 51.20

62F0182 118 48.58 47.58 47.82 48.56 47.65 51.84 46.93 48.86 50.14

6380182 1478 46.38 48.23 46.55 50.96 49.82 51.63 47.22 49.17 49.83

63H0182 335 46.70 47.88 46.75 49.80 49.59 53.15 47.33 49.99 50.99

63N0182 286 49.58 49.81 48.83 49.85 50.38 55.76 48.23 52.57 52.70

63W0182 180 45.01 46.86 45.27 50.35 48.92 50.84 46.21 48.05 48.96

63Y0182 108 51.96 53.39 51.39 51.85 50.71 58.22 50.25 55.06 55.63

64C0182 1583 47.08 48.24 48.31 50.68 50.07 51.63 47.06 49.26 48.57

64C0183 2053 47.14 48.58 48.48 50.87 50.33 51.46 47.56 49.40 48.51

67N 181 124 54.76 55.35 54.23 52.53 52.40 58.40 53.23 57.36 56.68

67N0182 389 55.48 55.76 54.77 53.57 53.14 57.95 55.01 56.73 56.47

67U0182 207 55.70 55.40 55.26 53.16 52.66 58.20 55.0 57.02 56.09

67V0182 236 54.17 55.78 53.64 53.75 54.08 57.08 54.22 56.45 55.69

67Y0182 194 54.71 55.76 54.26 53.77 53.39 57.93 54.92 57.29 56.06

68G0182 121 53.79 54.37 53.00 53.31 53.12 56.50 53.47 57.13 55.42

6830182 119 53.71 54.29 52.71 51.29 51.41 55.57 53.29 53.74 54.42

710182 116 53.45 56.66 56.46 59.35 61.53 48.07 56.99 49.68 49.78

71L1183 2629 46.96 50.71 50.85 56.97 58.04 42.51 50.99 44.47 44.82

71L2183 167 45.48 48.54 49.10 55.63 56.42 41.98 49.10 43.29 44.18

7110182 183 47.97 51.70 51.61 55.93 56.95 44.56 51.51 46.64 47.09

72E0183 564 48.33 50.43 51.66 55.79 56.34 46.15 50.41 45.58 46.94

73C0182 268 45.91 50.99 48.80 56.96 57.18 43.04 51.57 44.43 44.40

73C0183 415 45.65 50.68 48.84 56.81 57.07 42.32 51.63 44.05 44.30

7580182 424 45.27 49.00 48.33 56.53 56.63 43.73 49.55 44.40 45.60

A-0A
ASVAB Means by SQT Criterion Cell (Continued)

Subtest

PCELL N GaS AR VE NO CS AS MK MC El

75B0183 631 45.27 48.88 48.43 56.72 56.83 42.92 49.47 44.01 45.23

75C0182 118 45.06 48.03 48.46 55.89 56.50 41.77 49.11 43.54 44.07

75C0183 290 44.95 48.12 49.13 56.02 56.45 40.93 48.24 42.65 44.06

7500182 371 44.34 47.88 47.19 56.32 56.63 42.46 47.96 43.88 44.65

750183 650 43.67 47.65 47.36 56.34 56.54 41.48 47.98 43.12 43.86

75E0182 175 46.35 49.57 49.66 56.16 56.53 44.58 50.10 45.82 46.09

75E0183 280 46.18 49.42 49.64 56.31 57.10 43.21 49.80 44.85 45.07

75F0183 144 48.38 52.17 52.07 58.62 59.39 45.19 53.04 46.69 47.09

76C0183 322 41.95 45.87 45.07 54.69 53.95 41.48 46.66 42.70 42.96

76V0183 220 40.62 44.80 43.62 53.07 52.55 40.69 45.43 41.44 42.25

76W0182 295 39.50 43.61 42.47 52.17 51.83 38.42 44.38 39.72 40.38

76WO183 324 41.15 44.36 43.85 53.60 52.59 39.85 45.14 40.16 41.98

82CI182 209 54.07 55.11 53.84 51.58 51.70 53.11 54.64 53.98 53.49

82C2182 133 54.59 54.06 53.78 52.05 52.25 52.79 53.80 54.06 52.55

91EO182 204 53.86 53.92 54.19 52.99 54.70 47.50 54.50 50.69 50.17

91P0182 159 56.08 57.55 56.79 5.70 55.65 51.89 58.98 53.66 52.55

91R0182 145 54.99 55.10 56.26 53.25 54.54 49.28 54.48 51.14 51.11

9280182 313 56.55 57.49 56.58 56.04 56.63 48.89 59.92 51.23 51.83

93H0182 114 55.89 58.68 56.84 57.87 57.91 53.01 59.41 54.76 53.96

94B0182 1552 46.66 47.57 48.76 51.43 49.65 47.37 47.09 47.32 46.66

94B0183 2324 46.57 47.67 48.88 52.18 50.43 47.19 47.46 47.03 46.35

9580182 1861 54.75 55.24 55.40 53.44 53.87 53.33 54.02 53.66 52.96

9580183 2590 55.40 55.79 55.74 53.92 53.99 53.60 55.01 54.11 53.42

9690183 172 56.65 56.88 57.70 54.63 55.06 51.55 57.97 53.67 53.18

98C0182 186 59.64 62.24 59.59 57.37 58.57 54.62 63.26 57.46 56.93

A-62J
APPENDIX B
26 Aay 1983 (Revised: 11 July 1983)

TO: Gloria Guth (cc: DBrandt, DcLaughlin, iYoung, LLWise, PRossmelsil)

F&O: Ming-Mei tlang

SUBJECT: Editing of Training Cotcome Scores

In an effort to make sense out of the performance scores in the

training data file and to transform these scores into data that can

facilitate the upcoming validation analyses, I have worked out the

following rules for editing the three variables presently named TISCORE,

TIRAK, and rlRANK2. (A variable TiNRANK also exists in GGI's TRN81V3D,

and I will make use of it in the editing). The development of these

editing rules is based on existing information extracted from .Mike

Aumasey's summary, Ali's project document, and a series of frequency and

croastab runs made by GGI at my request. They should be applied to

TLI8lV3D in order to complete the editing of ALI's training data.

Defining Score Types

First, two aew variables (TISTYPEi and lS1YPF.2; 1 character code)

are created to indicate what kinds of performance scores are available for

each MOS at a given school/ATC. A third variable (TISTYPE3; 1 character

c.ode) is also created to preserve the additional information on MOS 05B,

05C, and 110. In .AOS O4 and 05C, a small number of trainees received

International Morse Code training and will be identified by T1STYPE3 - 'M'.

For training AOS 110, the specific training assignment (e.g., liB,

I1C, 11H) for the students was originally recorded in column 46 and

entered into the existing SAS file as the second byte of TlR.AK2. This

information will also be kept as TISTYPE3 in the edited training file (see

a later section for further explanation). In an earlier editing run,

TLMOSAWD - 110 was changed, where appropriate, to specific codes 11B, lc,

11H, or 1IX on the basis of this Information. At the same time, TlRA.'SK2

for these trainees in .4OS 110 was recoded to G (guaranteed), S (selected),

GS (guaranteed and selected), S2 (selected for 1IBC2), and A (attrited).

This run created an intermediate edited file TRN8lV3D which will be the

base file for the current editing.

The definition of the three score-type variables (TlSTYPEl, TlSTYPE2,

and TlSTYPE3) are given in the attachment to this memo. The values of

TlSrYPEl and TlSTYPE2 are explicitly defined for each KOS/school in that

attachment. The valtze of T1STYPE3 for MOS 053, 05C, and 110 can be

obtained from T.OsA) and TIRANK2 (as found in TRN8lV3D) using the

following table:

B-1
Variables in r8MV3D New Variable

TI40SAWD T1RANK2 TlSTYPE3

053 02G# K

05C '2G' 

11B 'G ' A

lic 'C ' B

11H 'G' C

11B 'GS' 0

lii iS' £

lIC 'S ' F

I1H 'S ' G

11B 'S2' R

liX 'A I I

110 " 4 blank

all others not relevant blank

I would suggest that a score-type file containing TtIOSAWD, T1SCHOOL,

TISTYPE1, and ?lSTYPE2 be setup using the definitions given In the

attachment. This score-type file can then be merged with TPh31V3D by

TLOSAOJD and TISCHOOL to create the first two score-type variables for

each record.

Special Notes for Training MOS 110

Training MOS 110 (at school 809) represents a mixture of .OS codes

within the 11 series. These students can be divided Into subgroups

according to their specific training assignments:

" Students who entered the school with KOS 11B, lC, or llH guaranteed

and received training in the respective 40S.

" Students who arrived with OS 113 guaranteed but were subsequently

selected for training as 113C2 (Dragon gunner).

" Students who arrived vith NOS code lX and were subsequently assigned

to training as 11B, 11C, lH, or 13BC2.

" Students who were attrited prior to assignment for training in a

specific NOS.

B-2
e Students for whom there was no inforaation on their specific tr-ining

assignments in the school. (There are 569 such cases in the current

training file, including multiple record counts.)

For the first three groups of students, TL4OSAWD has been changed to

correspond to the specific NOS code in which the student received

training. because only three characters are used for TIOSAVD. OS code

11SC2 ib also recorded as 1l1. The variable TlSTYPE3 may be used to

differentiate between trainees for 113C2 and those for 11B. (Those with

rlSTYPE3 - A or I received training as 11B whereas those with

TlSTYPE3 - D or H received training as 11SC2.) T1MOSAWD was recoded as

1IX for the fourth group, while no recoding was made for the fifth group

(i.e., rlMOSAMD - 110 for this group).

Additionally, it should also be noted that TIIOSAWD - lX indicates that

the students arrived at the school to be trained in C.F 11, but were

attrited prior to the assignment for training in a specific NOS code. This

meaning of lX for TL4OSAWD should not be confused with the lX for

AlPLKS, A1TRSOS, and A1T.4GMOS in the accessions file. I assie that

NOS l1X in the accessions data indicates that the soldiers were generally

assigned to trsining and subsequently to the first-tour service in

oCY 11. The exact meaning of lX (which represents about 4.71,

approximately 6300, of FYS accessions) is not clear at this time. dhen

we have learned what it really represents in the accessions fil.e, we .may have to change liX for TIMOSAWD to some other code such as lZ (which as

far as I know does not exist in the accessions data).

The training scores for students in this ADS also require special

editing. In an earlier editing run to split rLflOSA4D 110 into 11-, llC,

11H, I1X and 110, the third byte of the second performance scores for

these trainees was lost when the variable TlRAerA2 was redefined. A

subsequent rerun vas made to restore this information and create a

temporary variable TINRANK (which is a numeric equivalent of a character

variable formed by concatenating TRANIK and the first byte of T1RANK2).

Note that TlAA.K and TIRANK2 are arbitrary variables designated to store

the information in columns 43-44 and 45-46, respectively, when the first

SAS training file was created. It has been verified that all the lost

Information originally contained in TMAK2 has been recaptured with

TINRA.K. Thus in the editing specifications below, T1NAA .K will be used

to define a second performance score for this OS where appropriate.

Editing the Virst Performance Score (TISCO E I)

1. For T1STYPE1 - 1, 2, 3, 4, 5, 6, 7, 8, D, P, or Q:

9 If TiSCORE LE 0, set T1SCORE to missing.

o If T1SCOME GT 100, list the dots. Then if it appears that values

exc--d 100 because of failure to round the scores to integers, set

TlSCOE - ROUND(TlSCORE/l0); otherwise, set TlSCORE to missing.

* Rename TlSCORE to TlSCOREl.

B-3
2. For TiSTYPEl - A, B, or C:

* Concatenate TISCORE and TIRANK in character form, then convert the

resulting character variable to numeric form (with format 5.2).

" Name the new variable TiSCOREl.

* If TlSCORE1 LE 0, set TISCORE. to missing.

" Obtain frequency distribution of TISCOREl by T1MOSAWD and TISCHOOL to

determine needs for further editing of unusual and/or out-of-range

scores. (Except for a few obviously unreasonable values, I recommend

that most of the scores be retained to allow the analysts choices of

various methods to treat outliera, such as trimming, at the time of

analysis.)

3. For TISTYPE1 - E:

" If TISCORE LE 0, set TiSCORE to missing.

" Set TiSCORE - TlSCORE/IO.

" Rename TISCORE to TlSCOREl.

(Note that TlSCOREl is either 23 or 36 for 76V, and all equal to 16

for Course EY of MOS 76Y in school 101.)

4. For TlSTYPEl - X:

Obtain a frequency distribution of TISCORE by T1MOSAWD, and rename

TISCORE to TlSCOREl. Our current information indicates that there

are very few records (only 2 as far as we know) for MOS 12B in school

061. The scores for these cases are left unedited as it is unlikely

that they will be useful in our analysis. (Note that we have now

found that school 061 does not provide training for 12B and both

records have thus been edited on the basis of other information, such

as TICOURSE.) For T1MOSAWD = 09J, we have found that all such cases

are data entry errors and accordingly have been eliminated from the

edited file.

Editing the Second Performance Score (TlSCORE2)

A new variable T1SCORE2 will be created to contain this second

performance score. Upon completion of this editing step, drop T1RANK,

TIRANdK2, and TiNRANK from the data file.

1. For TISTYPE2 - 1 (progression index):

* Convert T1RANK to numeric and name the resulting variable TISCORE2.

" If T1SCORE2 LE 0, set TlSCORE2 to missing.

" Obtain frequency distribution of T1SCORE2 by TIHOSAWD and T1SCHOCL

to determine if further editing of unusual scores is required. (Note

that inspection of the score distributions by T1MOSAWD suggests that

the scores may not be meaningful progression indices with a standard

of 1 as the schools reported.)

B-4
2. For TlSTYPE2 - 2:

a If TrDISP - A, S, or C, convert TUI.NK to numeric and rname the

resulting variable T1SCORZ2; otherwise, set TISCORE2 to 0.

* Obtain frequency distribution of TISCOR2 by TLOSAUD to determaine if

further editing of unusual values is required. (In general, the

scores do not appear to require additional editing.)

3. For T1STYPE2 - C, set T1SCORE2 to missing.

4. For T1STYPE2 - D:

& Convert T?.ANK to numeric and name the resulting variable T1SCOREZ.

0 If TlSCOR.E2 LE 0, set TlSCORE2 to missing.

* Obtain frequency distribution of TlSCOR.E2 to make sure that all

scor-.s fall within the specified range (1-12 for MOS 91B; 1-13 for

%oS 94F. Note that all scores have been verified to be vithin the

range.)

5. For TISTYPE2 - E:

a Convert T1UANK to numeric and name the resulting variable TISCORE2.

a I TISCORE2 LE 0. set TISCORE2 to missing.

a Obtain frequency distribution to determine if further editing of

unexpected values is required. (Note that TlSCORE2 ranges from 23 to

41 for 76V, and from 16 to 23 for 76 .,)

6. For T1STY.PE2 - P:

a Concatenate TlR.AK and the first byte of TlRA K2 and convert the

resulting character variable to a numeric variable named TISCORE2.

a If TISCORE2 LE 0, set TlSCODRL2 to missing.

* If TlSCOR.2 r T 100, list the data. Then if it appears that values

exceed 100 because of failure to round the scores to integers, set

T1SCORE2 - ROUND(TSCORZ2/10); otherwise, set TlSCORE2 to missing.

7. For TISTYPZ2 - Q:

* Cnnvert TUlRAK to a numeric variable TlSCOR.E2.

" If TlSCOPL2 LE 0, set TlSCORPE2 to missing.

8. For TISTYPE2 - & Ad the first two bytes of T1-0SAJD FQ '11':

* set TlSCORE2 -TI NASK.

" If TlSCO.E2 IE 0, set T1SCORE2 to missing.

" If TISCO1.E2 GT 100, list data. Then if it appears that values exceed

100 beause of failures to round the scores to integers, set T1SCOPE2

to IOUND(TSCOR.E2/10); otherwise, set TlSCORE2 to missing.

B-S / 6 ,.
For all other cases with TISTYPE2 - X, set TlSCORE2 to missing unless

other information becomes available later to allow sensible editing

of the data.

9. For TlSTYPE2 - Y or Z, set TlSCORE2 to missing.

Additional Editing for MOS 110 (schcql 809)

If T1MOSAWD EQ '110' or '11X' (as found in TRN J1V3D), obtain

crosstabs of TISCOREl (recoded to missing versus nonmissing after

editing of UlSCORE1 as specified earlier in this memo) and T1DISP by

TLOSAWD in order to determine needs for further editing. (Note that

TiSCORE was found to be all missing for lIX, and for about 11%, 62

cases, for 110; while T1SCORE2 was known to be missing for all cases

in 1iX and 110.)

9-6 I ,,(J
M. Wang -- Editing Memo

July 11, 1983 (revision)

ATTACHMENT

Definitions of Score T ypes for the ARI Training Outcome Data

T1STYPEl (For 176 MOS/School)

This variable indic. es the score type for the first training performance

measure (TISCORE1, i, the edited file.

1 - Course GPA derived from averaging percents of "first-time GO's"

attained by students on each test; (Col. 40-42 in the raw data file)

School 061 : 13C, 13R, 17C

School 101 : 76X

School 113 : 05B, 05C, 31J, 31N, 31S, 31T, 32G, 35L, 36C, 36K, 72E

School 121 : 71D, 75B, 75C

School 161 : 71H

School 171 : 45N, 45T, 63D, 63N, 63T, 63Y

School 301 : 17K, 96B, 96D,

School 441 : 24C

School 804 : 19D, 19E, 19F

School 807 : 12F, 51B, 51C, 51K, 512, 51N, 5I, 62B, 62E. 62F, 62G.

62H, 62J, 63B, 64C

School 810 : 13E, 15E

Total : 49 MOS/School

2 = percent of "first-time GO's" achieved on End of Course Comprehensive

Test (EOCCT); (Col. 40-42 in the raw data file)

School 061 : 15J, 17B, 26B, 93F*

School 551 : 67U

School 803 : 63B

School 805 : 633. 76Y, 943

School 807 : 123, 12C

School 809 : 110 (also 11B, 1IC, 11H. lX as in the edited file)

School 810 : 13B, 15D, 82C

3chool 811 : 16P, 16S

(5 We have two conflicting informations on the scores for 93F: Mike

Luasey's dociment suiary indicates that the scores are percent of

'first-time GO's" on EOCCT; but the original ARl document indicates that

they are initial percentage score on EOCCT. Inspection of the core

distribution does not reveal evidence for choosing one type over the otheL.

Assuming that Ruasey's information is more up-to-date, we decided to

classify these scores under TlSTYPE1 = 2. If further information becomes

available later or evidence from subsequent data analysis support

classification of these scores under other types, we will revise this

document accordingly.)

Total : 17 OS/School (not counting -s1, lC, 1H, & llX)

B-7 r I
Definitions of Score Types for the

ARl Training Outcome Data (Cont.)

TISTYPEl

3 - Course CPA derived from averaging initial percentage scores attained

by students on each test; (Col 40-42 in the raw data file)

School 011 : 67N, 67V. 71P, 93H, 93J

School 093 : 21L, 24K, 27E, 27F, 27G, 554, 55G

School 101 : 43E, 43M, 57E, 76W, 92C, 94B

School 113 : 26L, 26Q, 26V, 31E, 31M, 32D, 32H, 35K, 35M, 36H

School 121 : 71C, 73C, 73D

School 441 : 24E, 24G, 2414, 24Q, 24U

School 551 : 61B, 61C, 67G. 67T, 68D, 68F, 68J, 68M, 71M

School 805 : 63S, 75D, 75E

School 810 : 13F

School 906 : 05D, 05G, 05H, 33S, 98C, 98J

Total : 55 MO0/school

4 - Initial percentage score achieved on End of Course Comprehensive Test

(EOCCT); (Col. 40-42 in the raw data file)

School 061 : 31V, 45D

School 101 : 76C, 76P, 76Y (for course '5G' only)

School 121 : 74D, 74F

School 803 : 94B

School 811 : 16B, 16C, 16D, 16E, 16H, 16J, 16R

Total : 14 405/school

5 - Second percentage score achieved on End of Course Comprehensive Test

(EOCCT); (Col. 40-42 in the raw data file)

School 803 : 64C

6 - Average of percent of "first-time GO's" achieved on End of Course

Coaprehensive Test (EOCCT) and time progressiou index (standard of 1),

each with 50 weight; (Col. 40-42 in the raw data file)

School 551 : 67Y

B-8
4

Definitions of Score Types for the

ARI Training Outcome Data (Cont.)

TISTYPEl

7 - Average of Course GPA and time progression index, each with 50%

weight. Course GPA is derived from averaging initial percentage scores

attained by student on each test. Time progression index has a

standard of 1 (100%); (Col 40-42 in the raw data file)

School 551 : 683, 68G

8 - percent of total points achieved on the first time tested (not

phased); (Col. 40-42 in the raw data file)

School 929 : 35G, 42D, 71G, 76J, 91E, 91G, 91Q, 91., 91S, 92B

Total : 10 MOS'(school

A - Course GPA derived from averaging the number of tries required to pass

each exam. The score is recorded with implicit decimal point after the

third digit (009.99) with the standard being 1.00 (one try per exam).

As such, this performance measure is expressed in a reversed direction,

i.e., lower values represent better performances. Our preliminary

analysis reveals negative correlations with ASVAB subtest scores. We

suggest that the reciprocal of this score be used as the criterion

measure in the validation.; (Col 40-44 in the raw data file)

School 091 : 34G, 41C, 44B, 44E, 45B, 45K, 45L, 63G, 63H, 63J, 63W

Total : 11 MOS/school

B - Initial or second percentage score achieved on End of Course

Comprehensive Test (EOCCT), recorded with two implicit decimal places

(999.99); (Col. 40-44 in the raw data file)

School 813 : 95B, 95C

B-9j1
Definitions of Score Types for the

ARI Training Outcome Data (Cont.)

TilSTYPE1

C - Course GPA derived from averaging initial percentage scores achieved on

each test, recorded with two implicit decimal places; (Col. 40-44 in

the raw data file)

School 031 : 54C, 54E*

School 551 : 57H

(* inferred on the basis of the data.)

D - first time pass rate (0 to 100Z) on performance tests (for lock-step

modules); (Col. 40-42 in the raw data file)

School 929 : 91B, 94F

9 Number of tests given in the course. This score by itself is not a

meaningful performance indicator. It should be used in conjunction

with a second measure (TlSCORE2, see later description for

TISCORE2 - E) to define an appropriate perforaance measure, such as

TISCOiE2/TlSCORZl representing the average number of tries required to

pass each test; (Col 40-41 in the raw data file)

School 101 : 76V, 76Y (for course 'EY' only)

(The number )f tests is either 23 or 36 for MOS 76V; and 16 for course

EY of .MOS 76Y.)

P - Percent of total points achieved on the first time tested in Phase I of

the course; (Col. 40-42 in the raw data file)

School 929 : 91C, 91D, 91F, 91P

(The training courses for these MOSs are divided into tvo phases:

Phase I consists of 4, 6, 6, and 13 weeks for 91C, 91D, 91F, and 91P

respectively.)

Q - Course GPA derived from averaging initial percentage scores attained by

student on each test for Part A of the course; (Col. 40-42 in the raw

data file)

School 906 : 05K

(The training course for chis NOS consists of two parts, each scored

the same way; see also TISTYPE2 - Q.)

B 10
Definitions of Score Types for the

ARI Training Outcome Data (Cont.)

TISTYPEl

I Uncertain (not documented)

School 061 : 12B

School 803 : 09J

(Note that all records with T12OSAWD - 09J have been found to be data

entry errors and therefore have been eliminated from the current file.

It has also been determined that school 061 does not provide training

for 12B and thus the two cases vith TIMOSAWD - 12B in this school are

data entry errors and have been edited on the basis of other

information, i.e. TiCOURSE.) I

I

B-li
Definitions of Score Types for the

ARI Training Outcome Data (Cont.)

TISTYPE2

1 - Progression Index. This index is defined as the ratio of the time spent

to complete the (self-paced) course to the expected time for

completioo, or the reciprocal of this ratio. (Std. 1, with an implicit

decimal place); (Col. 43-44 in the raw data file)

School 113 : 05B, 05C

School 811 : 16P, 165

(Note that inspection of the score distribution suggests that these

scores may not conform to the given definition. Be cautious when

using these scores in the analysis.)

2 - Number of weeks repeated or recycled in the course (mostly 00,

indicating graduation without repeat or recycle); (Col. 43-44 in the

raw data file)

School 113 : 26Q, 26V*, 31M*, 31S, 31T*, 32D, 32G, 32H, 35K*, 35*,

36C*, 36K, 72E*

all 00 in the training data file)

C No second performance score: Col. 43-44 in the raw data file have been

used in combination with col. 40-42 to record a single performance

score with format F5.2 (see TlSTYPE1 - A, B, or C)

School 031 : 54C, 54h (TlSTYPE1 - C)

School 091 : 34G, 41C, 44B, 44E, 45B, 45K, 45L, 63G, 63H, 63J, 63W

(TlSTYPE1 - A)

School 551 : 57H (T1STYPE1 - C)

School 813 : 95B, 95C (TISTYPEI - 3)

B-12
Definitions of Score Types for the

ARI Training Cutcome Par& (Cont.)

TISTYPE2

D Number of tests successfully passed (or number of self-paced tasks

completed) on the first trial; (Col. 43-44 in tho raw data file)

School 929 : 91B, 94F

(The number ranges from 1 to 12 for 91B, and 1 to 13 for 94F; see also

TISTYPOl - D.)

E - Total number of tries required to pass all exams given in the course;

(Col. 43-44 in the raw data file)

School 101 : 76V, 76Y (for course 'EY' only)

(The number ranges from 23 to 41 for 76V, and 16 to 23 for course EY

of KOS 76Y; SAe TlSTYPEI - E for suggestion of appropriate use of this

index as a performance indicator.)

P - Percentage of total points achieved in Phase II of the course; (Col.

43-45 in the raw data file)

School 929 : 91C, 91D, 91F, 91P

(Note that the training courses for these NOSs are divided into two

phases. Phase II consists of 12 weeks for 91C, and 6 weeks for 91D,

91F, and 91P. See also TISTYPEl - P.)

Q - Course GPA derived from averaging initial percentage scores attained by

student on each test for Part B of the course (same as Part A, see

TlSTYPE1 w Q); (Col. 43-44 in the raw data file)

School 906 : 05K

B-13
Definitions of Score Types for the

AlI Training Outcome Data (Cont.)

TlSTYP92

X Uncertain (data do not agree with documents and discrepancies Lannot

be resolved at this point, need further checking)

School 061 : ZIB (no longer exists in the edited file; see

T1STYPZ1 - X for explanation)

13C (should be '00', but there are nonzero entries)

School 803 : 09J (invalid NOS, no longer exists in the edited file)

School 805 76Y (should be ' ', but there are a large no. of

nonblank entries)

School 809 : 110 (Col. 43-45 say be '000' or initial percentage score

on the 40S unique test; Because of some unplanned

earlier editing which overrides data in Col. 45, special

editing of tLe second performance score for this HOS has

been made to recover the information. See page 5 of the

editing memo.)

School 906 : 98C (should be ' ' but there are miscellaneous nonzero

entries in the data file, we should ignore the scores

here.)

(,bte that except for T1HOSAWD - 110 in school 809, we have decided to

leave TISCORE2 unedited for TlSTYPE2 - 1. Current information

suggests that these data have little analytical values.)

Y uThree zeros; (Col. 43-45)

School 929 : 35G, 71G, 76J, 91E, 91G, 91Q, 91R, 91S, 92B

B-1&
Definitions of Score Types for the

ARIt Training Outcome Data (Coat.)

TISTYPK2

Z " Two zeros; (Col. 43-44)

School 093 : 27E, 27F, 27G, 55B, 55G

School 101 : 43E, 43M, 57E, 76C, 76P, 76W, 76X, 92C, 94B

School 113 : 26L, 31E, 31J, 31N, 35L, 36Hi

School 121 : 71C, 71D, 73C, 73D, 74D, 74F, 75B, 75C

School 171 : 45N, 45T, 63D, 63N, 63T, 53Y

School 441 : 24C*, 24G, 244, 24Q, 24U

School 551 : 67U, 67Y, 68B, 68G

School 803 : 94B**

School 805 94B

School 811 16B, 16C, 16D, 16E, 16H, 16J, 16R

School 929 : 42D

* Progression index indicated in the document, but data are all

zeros;

** = document indicates that this field contains course completion

time, but our data show all zeros)

Total : 53 MOS/school

blanks; (Col. 43-46)

School 011 : 6711, 67V*, 71P*, 93H*, 93J*

School 061 : 13R, 15J, 173, 17C, 26B, 31V, 45D, 93F

School 093 : 21L, 24K

School 161 : 71.1

School 301 : 17K, 96B, 96D

School 441 : 24E

School 551 : 61B, 61C, 67G, 67T, 68D, 68F, 68J, 684, 71N

School 803 : 633*, 64C*

School 804 19D, 19E, 19F

School 805 : 63B, 63S, 75D, 75E

School 807 12B, 12C, 12F, 51B, 51C, 51K, 5124, 51N 51R, 62B, 62E,

62F, 62G, 62R, 62J, 635, 64C

School 810 135, 133, 13F, 15D, 15E, 82C

School 906 05D, 05G, 05H, 33S, 98J

0 - Progression index indicated In document, but all zeros in data

file)

Total 66 MOS/School

B-iS !7"
Definitions of Score Types for the

ARI Training Outcome Data (Cont.)

T1STYPE3

M - Took International Morse Code training; (Col. 45-46 - '2G')

School 113 : 058, 054C

Special Codes for TfIOSAWD - '110' (school 809)

A - Arrived with KOS -ode iA (Light Weapons Infantry) guaranteed

5 - Arrived with NOS code 11C (Indirect Fire Crewman) guaranteed

C = Arrived with OS code 1l (Heavy Antiarmor Crewman) guaranteed

D - Arrived with 140S code lLB guaranteed and was selected for training

as MOS Code 113C2 'Dragon Gunner)

E - Arrived with KOS code lX (CKIF 11) and was selected in the fifth

week of trainitig as lOS code 11B

F - Arrived with KOS code l1X and was selected in the fifth week of

traiaing as ZOS code lC

G - Arrived with 140S code lX and was selected in the fifth week of

training as 1O code 110

H - Arrived as MOS code 11Z and was selocted in the fifth week of

training as K0S code 113C2

I - Arrived as K0S code 1iX and was attrited prior to selection for

training in a specific KOS code

(Note that blank means no information on specific assignment at the

training school is available for the trainee.)

These codes are originally recorded either in column 45 or 46 and will

be kept as TlSTYPE3 in the edited training data file.

For all other OS/school, TlSTYPB3 will be blank.

B-l6
APPENDIX C

* 

4-)
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED CO COMPOSITE

(CO - CS + AR + MC + AS)

SESS AIT SS SSSS ARM SS SSSS ARMY SB SSSS ARMY SS

s0 t0 131 57 182 89 233 121

sk 40 132 57 183 89 234 121

82 40 133 58 184 90 235 122

E3 40 134 59 185 91 236 123

F4 60 135 59 186 91 237 123

E5 40 136 60 187 92 238 124

i' 40 137 60 188 93 239 125

f 40 138 61 189 93 240 125

S;.0 139 62 190 94 241 126

E 40 140 62 191 94 242 126

40 141 63 192 95 243 127

5i 40 142 64 193 96 244 128

160 143 64 194 96 245 128

S3 40 144 65 195 97 246 129

40 145 65 196 98 247 130

40 146 66 197 98 248 130

'0 147 67 198 99 249 131

S- 40 148 67 199 99 250 132

S 40 149 68 200 100 251 132

940 150 69 201 101 252 133

11O 4D 151 69 202 101 253 133

1 40 152 70 203 102 254 134

152 40 153 70 204 103 255 135

I3 49 154 71 205 103 256 135

11 Z0 155 72 206 104 257 136

1.,5 40 156 72 207 104 258 137

1'. 157 73 208 105 259 137

j-- I. 15B 1'. 209 106 260 138

1.5 42 159 74 210 106 261 138

V-9 43 160 75 211 107 262 139

1.3 43 161 76 212 108 263 140

Ii 41 162 76 213 108 264 140

12 15 163 77 214 109 265 141

13 45 164 77 215 109 266 142

14 45 165 78 216 110 267 142

5 7 166 79 217 ill 268 143

1:6 47 167 79 218 ill 269 1&3

1:7 46 168 90 219 112 270 144

1:; 4 169 81 220 113 271 145

119 49 170 81 221 113 272 145

13 50 171 82 222 114 273 146

121 50 172 82 223 115 274 147

1:2 51 173 83 224 115 275 147

1:3 52 174 84 225 116 276 148

174 52 175 84 226 116 277 148

1:5 53 116 65 227 117 278 149

1:6 53 177 86 228 118 279 150

W 51 178 86 229 118 280 150

I' 55 179 87 230 119 281 151

1:9 i5 180 87 231 120 2F, 152

1jO 56 191 58 232 120 263 152

2;1 151 793 159 302 160 311 160

a, 1:,4 244 1S9 303 160 312 160

71.6 215 l1. 94 34 10 313 160

2;7 155 296 1l0 305 lA 314 160

2"1 155 2Q7 lbO 306 160 315 160

29 156 296 160 307 160 316 160

.10 157 299 160 308 16U 317 160

241 157 300 JbO 309 160 318 160

2Q2 158 301 160 310 160 319 ]t0 320 160
CONvERSION OF SUM OF SUBTEST STANDUARD SCORES

TO AW4Y STANDARD SCORES ALJUSTED EL COM'POSITE

(EL -AR + El MK+ GS)

SOS5 ALMW SS 5555 AAKYIS 55 555 AMYI SS 5JI5 AL"'y SS

so 40 111 41 262 90 231 all 111 40 132 62 1131 9 234 lit

32 40 133 52 214 91 235

6(v sO 3' 63 265 92 216 1-.0

60 sos 63 254 92 231 1.1

LIJ L 235 lei 93 228 2:.

IO3?Is$ 93 239 1:.,

'0 1)$ 55 M8 91. 210 2773

6A 40 139 55 190 94, 241 1:3

4910 1.0 66 191 is 2'?1 1:4

4010 141 57 192 95 24.3 1.4

91 40 11.2 57 193 96 2".f 1:5 9;40 14.3 t' 194, 97 245 1:

&. 0 14. Go 195 97 V1612

4.0 11.5 59 195 98 2i. 1.,

95 41 14b 70 197 98 2&.8 12, 06 .1 147 70 298 99 21.9 1:8

'.2 I'. 71 199 100 253

99 11.9 71 200 l00 251 179 99 -3 ISO 72 201 ;01 75.1 1:;

ICO 44. 151 7 .120: 10i 253 130 101 152 73 Zf03 102 25. 131

I5 15.. 7. 205 103 250 077 I' it 15.) 75 1106 103 257 127

VS .6156 7S 207 1042 1:

106 is;15 76 208 105 259 1-.3

1? 1.- 7 155 74 2(;9 105 260 . VI 46 159 77 210 136 261 135

1!3 9 160 7; 211 106 252 135 1.c 9 161 7$ 212 107 763 13

$1 0 152 79 213 10? 26. 13t

2750 163 79 211. 108 ;55 13*

1.3 51 16-1 so 1:5 109 266 137

; 4 5; 165 V, 216 1(q 26' 1 - r 115 57 155 $1 217 110 ?'g 3

1;6 53 161I 218 110 .1b0

1.7 53 168 12 219 111 270 - 1*5 54 16 9 83 270 111 .171

1:9 54 170 81 221 112 :7 1

170 55 171 bi. 22: 113 2731.

1:1 55 172 3'. 223 113 :7'.1.

122 56 173 as 22'. 1146 275 1'.) I1 !57% 171. 9 225 !1L 2-16 1-3

1.-4 2775 85226 215 277 1'.

I:8s 176 87227 i15 278 ,6

22'S so 277 8' 228 15 279 1 L5

1:7 !9 's 1A 2: 1:6 290 - 1:8 59 179 so 230 217 2dI 14-t

179 60 M8 6L9 23! Ill .191 10,

1*0 t0 lot 69 23? 21t 281 147

26.%, 1&& 293 153 302 156 311 150

21$ 145 291. 153 303 1M 312 150

2'6i 11.9 295 15& 3,04 159 311 160

2;-7 149 296 154 305 159 311. 24f

2-8 150 4191 155 306 160 315 160

250 298 155 307 160 316 21.0

-0151 299 156 302 160 317,I.'

72 2300 W5 309 16.0 329 it.

223601 157 It01o 319

70 Ito0

C-2 ~
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORE: ADJUSTED GM COMPOSITE

(GM = MK + El + GS 4 AS)

SSSS A"lY SS SSSS ARIlY SS SSS5 ARIMY SS SSSS ARMY SS

so 40 131 60 182 90 233 11Q 11 40 132 60 183 90 23& 170

62 40 133 61 184 91 235 121 83 '0 134 61 185 91 236 123 84 ko 135 62 186 92 237 .12 P5 to 136 63 187 92 238 1:2

P6 40 137 63 188 93 239 123

A7 40 138 64 189 9. 240 1:3 9F i0 139 54 190 9; 241 124 89 40 140 65 191 95 242 125 cc 40 141 66 192 95 243 = 9i 1.0 11. 66 193 96 21.41

92 40 163 57 194 97 24, 1:6 93 40 141. 67 195 97 246 127 94 140 145 68 196 98 247 128

95 40 146 68 197 98 248 l28 96 40 147 69 19- 99 249 1'

97 40 1.6 70 199 100 250 129

96 40 149 70 200 1NO 251 130 99 41 150 71 201 101 252 131

#00 '2 153 7! 2"7 101 253 131 Q i 47 152 72 203 102 254 132 1.2 43 153 73 204 102 255 132

103 43 154 73 205 103 256 133 IC. 4. 155 74 206 104 257 133

Ic, 4. 156 74 207 104 258 12-

IK 4'5 157 75 208 ICs 25 1

I6 15F 76 209 105 260 135

10O 46 159 76 210 106 261 1-b IC9 47 160 77 211 107 22 136 110 47 161 77 2.2 107 263 137

111 48 162 78 213 108 26. 136 112 49 163 78 21L 108 265 1s 113 49 164 79 215 109 266 139 114 50 10; 80 216 109 267 1P9 115 50 166 80 217 110 268 1(0 116 5 167 81 2!5 111 2(9 l.

117 52 16b 81 219 II 270 1,1

11q 52 169 82 220 11: :71 1.2

h19 53 170 83 221 112 272 142 120 53 171 83 222 113 273 143 121 54 172 84 223 114 274 143 1:2 54 173 84 2:4 114 275 44 123 55 171 85 225 115 276 145 124 55 175 85 226 115 277 145

1:5 56 176 86 227 lb 278 16

126 57 177 87 228 116 779 11.,

177 57 178 87 229 117 260 147 128 58 179 88 210 118 281 147

1"9 59 180 88 231 116 -A 1-" 110 59 181 89 23? ]15 3 I -j

2A4 i(9 293 155 32 160 311 1bO

2F5 M50 294 155 3G3 160 311 !60 :,6 150 295 156 304 160 313 Ito 27 151 296 156 305 160 314 160 2ra 152 297 157 306 160 315 160 2S 152 298 157 307 160 316 160

153 299 158 308 160 317 160

153 300 159 3t,9 16G 318 It

154 301 159 310 160 319 If,

32 3110

C-3
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARC scorS ADJUSTED ST COMPOSITE

(ST VE + MK + MC + GS)

SSSS AR.Y SS SSS2 ARMY $5 SSSS ARM SS SSSS AL'.3 S

60 40 131 60 382 90 233 J.,

61 40 132 61 183 90 234 1.-0

82 40 133 62 184 91 235 370

33 40 134 62 185 91 236 1:1

84 40 35 63 186 92 237

85 d0 136 63 187 93 238 I:_

86 '0 137 64 188 93 239 1::

a7 .0 138 64 189 94 240 1:3

F8 40 139 65 190 94 241 I;_

99 40 140 66 391 95 242 1.

9040 141 b6 192 95 243 I

91 40 142 67 193 96 244 1:5

92 40 143 67 194 97 245 1.b

93 40 144 68 195 97 246 1 f

94 40 145 68 196 58 247 1:-

95 40 146 60 197 98 248 I7

06 40 147 70 199 99 249 £75

9' 41 148 70 199 99 250 1:9

98 42 149 71 20c 100 251 17

99 42 150 71 201 101 252 1I

100 L3 151 72 202 101 273 1 1

101 42 152 73 203 102

102 44 153 73 204 102 3"

103 44 15'. 7f 2n5 03 1Z

1U4 -5 155 74 206 104 .5 13

105 6 156 75 207 10-4 2i: 13

106 -6 157 75 208 165 259 1 j

107 47 158 76 209 105 269 1

W8 47 159 21o 105 241 17%

109 48 160 77 211 lob 242 136

I11 48 161 78 212 107 263 135

Ill 49 162 76 213 1vI 2l4 170

112 50 163 79 214 10 265 137

113 51 164 9 215 109 266 1.6

114 51 365 60 216 3U9 267 I?€

1.5 51 l|f. el 217 110 2t8 139

110 52 167 81 218 110 269 1.0

117 52 168 32 213 !11 770 140

W %) 169 82 .20 112 271 -i

119 5. 170 63 221 112 272 1&]

170 5,L 171 S3 222 113 273 12

'll 55 172 84 223 113 274 143

1:2 55 173 F5 224 ii'. 275 13

173 56 174 65 225 114 276 11

1:4 56 135 56 276 135 277 1&/

1V5 57 176 i6 227 116 278 145

126 58 177 87 2:8 116 279 1-5

3i7 5 175 67 229 117 280 1.6

17 5o 179 68 230 117 2A1 I-,

1;9 59 1FO 89 231 118 2bZ 14-

1M0 6n 161 89 232 119 2A3 146

2"i. 14$ 293 153 302 159 311 160

205 149 294 15. 303 157 312 (o

286 149 295 155 304 160 313 160

287 150 296 155 305 160 314 Ito

765 151 297 156 306 160 315 Ito

3p9 151 298 156 30? 160 316 160

200 12 299 151 309 3o0 317 160

791 152 300 157 )L.9 lfJ 318 1I0

252 15) 301 15P 32u 6lo 319 1 ..a

320 Ito

C-4 /,
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED FA COMPOSITE

(FA = AR + CS + MC + MK)

$SSS Ap, ss SSSS ARM SS 5855 AR.'l 55 SSSS Au rY SS

40 131 58 182 9F 233 120

81 40 132 59 183 9C 234 121

82 40 133 60 184 90 235 121

83 40 134 60 185 51 236 122

84 40 135 61 186 9: 237 12:

85 40 136 61 187 S. 238 1:3

16 40 137 62 188 91 239 124

s7 40 136 63 189 93 240 124

68 40 139 63 190 9' 241 125

B9 40 140 64 191 95 242 115

90 40 141 64 192 95 213 126

91 40 142 65 193 9t 2L4 127

9 40 143 66 19' 96 215 127

93 '0 144 66 195 97 216 128

9. .0 145 67 196 9E 2'7 128

95 '0 146 67 197 96 248 1:9

96 40 147 68 198 94 249 130

97 40 148 69 199 9- 240 130

98 40 149 69 200 1o 251 1

40 150 70 201 252 .5 131

l0 40 151 70 202 IG: 2!3 132

'40 152 71 203 102 214 1 3

102 '1 153 72 204 IQ: 255 133

103 4-4 154 72 205 1C3 256 134

1c 4; 155 73 206 10- 25- 13.

I5 43 156 73 207 1C; :55 125

106 43 157 74 208 Ie! 259 136

1C; 44 158 75 209 105 26D 136

lop 45 159 75 210 IFy,- ;*1 137

109 45 160 76 211 107 262 137

1;0 46 161 77 212 107 263 138

li; 46 182 77 213 IGF 264 129

Il 47 163 78 214 IGO 265 129

1'3 48 164 78 215 j01 266 I-0

114 48 165 79 216 110 267 140

1:5 49 166 so 217 11 nP26 1-1

116 '9 167 SO 218 111 2 fQ 132

117 53 168 81 219 111 270 142

1 51 169 81 220 112 271 1-3

1!9 51 170 12 221 113 2;2 1;.

1:0 52 171 83 222 113 2,3

1:1 52 172 83 223 114 274 145

122 53 173 e; 2;4 l15 275 145

13 54 174 84 225 115 276 146

124 54 175 85 226 116 277 146

125 55 176 86 227 116 278 1~7

3:6 5S 177 86 228 117 279 145

127 56 179 67 229 lit 280 1.8

178 57 179 b7 230 H18 1l 1- a

1 57 160 88 231 119 if2 149

37: SP 383 89 232 119 73

151 293 156 302 360 311 235 151 294 Is, 3u3 160 312 1hQ

7E6 152 295 157 304 160 313 16r

287 152 296 158 305 160 314 160

7 153 297 159 306 36O 315 160

7wo 154 298 159 307 160 316 160

]-'4 299 160 30 Ib 317 Ito

155 M 300 !(0 309 lbb 3'8 110

156 301 160 310 160 319 1(0

3,0 ItO

C-5
CONVERSION OF SUM OB SUBTEST STANDARD SCORES'

TO ARMY STANDARD SCORES ADJUSTED OF COMPOSITE

(OF = NO + AS + MC + VE)

$SSS AR" SS SSS5 ARI Sr SSSs AJy SS SSSS A RM SS

80 40 131 5; 162 89 233 170

:1 40 132 8 183 NQ 234 121

82 60 133 so 184 90 235 122

83 60 136 sf 185 91 236 1.2

t10 135 60 186 91 237 123 e5 40 136 63 187 92 238 12'

86 60 137 61 188 93 239 124

67 40 138 62 189 93 240 125

f 40 1314 62 190 94 241 125 6i 40 140 63 19! 9' 242 126

40 141 63 192 95 2&3 127

93 40 142 4' 193 96 244 127

92 40 143 65 194 96 75 128 03 40 146 65 195 9? 216 129

'. 40 145 66 196 98 247 129 95 40 1&6 67 197 98 248 120 96 60 147 6 198 99 249 130 97 60 1&8 61 199 99 :50 131

98 40 149 V8 200 100 251 132 99 40 10 6 201 .01 25? 132 1roO 40 151 70 202 101 253 1-3 1d 40 15i X 2 3 102 254 134 in: 40 153 71 .0- 102 255 1 4 103 .4 154 71 2.5 103 256 104 40 15 216 104 237 135 I05 '1 1!6 73 '07 104 258 136 106 2 15, 73 278 105 259 137 107 62 l5 74 279 10 260 137 108 43 1!9 7' 210 106 261 138

109 44 160 75 211 107 262 138 110 44 161 76 :22 107 263 139 III 15 162 76 2i3 108 264 10 112 45 163 77 :14 109 265 140 113 46 164 78 215 109 266 141 114 47 165 78 :.6 110 267 142 15 47 166 79 17 111 26S 142 1!6 48 167 80 2:6 ii1 269 1-3 117 49 188 80 219 112 170 3 118 49 169 81 220 11 271 1.4

119 50 170 8l 2:1 113 272 145 120 50 171 82 222 114 273 1L5 IN1 51 172 83 2:3 114 274 146 12 52 173 83 -,4 115 275 147 133 52 174 84 225 116 276 147 124 53 175 85 226 116 277 143 1:5 53 176 85 227 17 278 145 126 51 177 86 2:8 137 279 14.9 1:7 55 178 86 2.9 118 280 150 129 5s 179 87 230 119 :81 150 1 9 56 180 88 2,1 119 212 351 130 57 181 88 212 120 283 151 284 12 293 158 302 160 311 160 Mc5 153 294 158 303 160 312 360 2P6 153 295 159 301 6 313 1M0 20; 154 296 160 305 1(0 314 160 2O 155 297 160 306 160 315 160 2H9 355 298 160 307 160 316 160

0 356 299 160 308 160 317 160 156 300 160 309 160 318 160 357 301 lbO 310 160 319 160

320 1b6

C-6
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED SC COMPOSITE

(SC = VE + AR + AS + MC)

ssss ALIG Ss ssss ARMY SS SSSS AL9n S5 SSSS ARM1Y SS

o 40 131 60 182 89 233 119 IL 6o 132 60 183 90 234 120

82 40 133 61 164 91 23' 1:1

83 60 13' 61 185 91 236 121

160 135 62 186 92 237 1:2

40 136 62 I8 92 28 12.

40 137 63 18g 93 239 ]:3

40 138 64 199 94 240 324

pi 60 139 64 190 94 241 324

l &0 160 65 191 95 242 115

C3 60 141 65 112 95 243 125

63 1 162 66 193 96 244 126

460 1'A3 67 194 97 245 1;6

3 3 144 67 195 97 266 117

0. 40 15 68 196 98 247 1:9

C5 LO 166 6b 197 98 28 1:3

50 G3 147 69 198 99 249 129

9' .0 148 70 199 99 250 129

F. 40 149 70 200 100 251 130

41 150 71 201 101 252 131 1! 151 1i 202 101 253 13;

101 .2 152 7; 203 102 254 13Z

S 62 153 12 20 102 255 132

4 3'3 154 73 205 103 256 133

1'.,- 44 155 74 206 104 257 13.

1, 5 &1 156 74 207 104 258 1- l 157 75 208 105 259 135

-7 158 75 209 105 260 135

I-I '6 159 76 210 106 261 12f

Jr'9 ' 160 77 211 107 262 136

1:o 67 161 77 212 107 263 13;

IA 46 162 78 213 J08 26. 12

1_2 46 163 78 214 ICS 265 135 113 '9 164 79 215 1Q9 266 139

1:. !3 165 79 ^16 109 267 139

1,1 3 166 80 717 110 266 1-0

1,6 51 167 81 218 11 269 141

1.7 51 168 81 Z19 111 '70 1.4

t:8 1!9 $2 2:0 112 721 1-.

1,9 5? l8 2:1 12 2; 142

1.0 '3 171 03 222 113 273 1-3

1. 75 14 2'43 111 214 1 l

12 54 173 4 ;24 114 25 i;.

1:3 !5 17. a5 225 115 276 1.5

1:4 55 175 B5 2.6 115 277 1.5

1:5 56 176 86 227 116 278 146

1:6 57 177 67 228 117 279 146

1.7 57 17 87 229 117 280 1;

I:8 58 179 8 230 li 281 1..

124 51 IRO 8 231 118 282 14F

I0 l81 89 737 119 2%3 1.9

;'- R'9 203 155 302 260 311 2'0

l'0 294 155 303 160 312 10

26 151 295 156 )04 160 313 160

7a7 I1 296 156 305 160 314 160

7?8 15 297 157 306 160 315 160

2f15i 298 158 307 160 316 160

153 299 156 308 160 317 ltu

'-I 154 300 159 309 1 O 318 Ito

292 15& 301 159 310 160 310 1)

370 ;v

C-7
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

-0 ARMY STANDARD SCORES ADJUSTED W4 COMPOSITE

(MM NO + AS + MC + El)

SSS5 Ap£2 Sit SSSS AMY sS SSSS A~iUY SS Is55 AMY2" SS

so &o 131 Be 182 89 233 120 at 40 132 59 1'3 90 234 1.1 62 60 133 59 14 90 235 172 63 .0 134 60 it5 91 236 64 '0 135 60 16- 91 237 172 65 60 136 61 17 92 238 1.3 86 60 137 62 18 93 239 124 6Q o 138 62 119 93 240 121 48 0' 139 63 190 94 241 125 89 60 260 63 191 95 242 126 'o /0 141 64 192 95 243 126 91 'O 142 65 193 96 244 1Z7 92 .0 143 65 194 96 z45 j17 93 0 4 6 195 97 246 1:6 94 40 145 66 196 98 247 129 95 60 146 67 197 98 248 129 96 60 141 68 198 99 2&9 1 97 to 148 68 199 99 250 131 98 60 149 69 200 100 251 121 9; 40 150 70 201 101 252 12 ill, & ii 7 C' ZG Icl 753 11Z lei 40 152 71 203 102 25. 113

1f3 100L 153 712C-. 102 55 1 34 61 154 72 205 103 256 13 1V4 41 155 ?3 206 104 257 13 14' 42 156 73 207 12 21 1 35 (6 43 157 706 459 !05 135 0 13 158 7/ 209 106 260 137 Ics 44 159 75 :10 106 261 137 1:9 '4 160 76 211 107 26, 13 110 45 161 76 212 107 263 138 i46 162 77 213 l08 264 139 Li2 L6 163 77 214 109 265 ;.0 113 47 164 78 215 109 265 I- 1;. 8 165 79 216 110 267 115 48 166 79 217 110 268 1'1 116 49 167 80 218 111 269 12 111 49 168 t 219 112 270 143 1:9 50 169 81 220 112 271 143 I19 51 I0 62 2:1 113 2:7 1-4 120 51 171 82 222 113 273 1.5 121 

:2 52 172 83 223 11 .74 145 52 173 94 224 115 275 1.6 123 57 174 $. 225 115 276 1-6 2.4 54 175 85 .76 116 277 116 125 5i L76 85 227 116 278 146 12:6 55 177 66 228 117 279 148 127 55 178 87 2.9 Il1 780 1.9 i.1 56 179 87 230 114 2F1 1u 139 57 180 8 231 119 : 550 130 57 181 8 232 120 203 1) A4 151 293 157 302 160 311 10 285 152 294 157 303 160 312 160 2F6 152 295 I56 304 160 313 2"0 2r7 153 296 159 305 160 314 16 21 4 297 159 306 18o 31S 260 7-9 154 :so 160 307 160 316 160 2-11 1'5 299 160 308 180 317 150 l16 300 260 309 160 318 If,

15" 301 160 310 160 319 1 t

320

C-8
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED CL COMPOSITE

(CL = VE + AR + MK)

SSSS AMY IS SSSS ARSPY SS SSS$ ALM SI Ssss Ay

60 40 109 70 158 106 199 A

71 159 107 200

62 40 111 71 160 107 201 22

63 112 72 161 1 00 02

64 0 11? 73 162 109 2C3

ts -0 114 74 IE3 110 204

66 .0 115 ?4 164 i1o 205

64 40 116 75 Its 111 206 " 68 40 117 76 166 111 2CT 2-7

Q 9 Al118 77 16, 113 :0! I-2 70 41 19 77 168 113 209 71 2 120 78 169 i1t 210 A

72 43 Il2 79 170 115 21 -1 73 4 122 80 171 115 211 :4t 74 1.4 123 go 171. 116 213 75 45 124 31 11?3 117 2] 1-

,6 46125 $2 174 i18 215 I-E 7 7 12v 62 175 I18 216 1.8

79 4 128 8177 1:0 21F 15N eo 19 129 05 178 2.1 229

61 49 130 85 1-9 121 20 92 50 131 86 22 21

E -51 132 07La :;!

E4 5 "3 me 182 124 2:3 i1

51 135 09 183 124 :N. 5., 13f 90 184 175 22.5

*8 55 137 91 15 126 226 1!6 F9 S% 138 91 166 126 2:7 156 40 56 139 92 187 127 22 f.7

(' 57 243 93 18 178 229 11!

59 141 93 189 129 13z !!

58 142 9. 190 129 23' :!9

54 143 95 191 130 237 ;.b

60 96 192 131 233 Ito 6 uo 165 96 193 13. 234 ;o

97 61 146 97 194 132 235 It:.

c 62 147 98 195 133 23 Ito

@0 63 148 99 196 131 237 it

ICU 63 149 99 197 131 23F 160

64 150 300 198 135 239 1El

1(j 65 351 103 240 160

66 152 102 66 153 102

If's 67 15 103

V 6b 31 55 IP

307 69 156 104

1C- 69 157 105

C-9 -
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED GT COMPOSITE

(GT = VE + AR)

SSS$ AKMY SS SSSS ARM SS Sass A'Y SS

;0 40 91 90 142 145

41 40 92 91 143 146

42 .0 93 93 144 148

43 40 94 94 145 149

.. 1.0 95 95 146 150

45 41 96 96 147 15,

&.6 42 97 97 148 15:

4 43 98 98 149 153

49 44 99 99 150 154

9 45 100 100 151 U!5

o2 46 101 101 152 156

,47 102 102 153 157

52 48 103 103 154 158

53 49 104 104 155 159

54 50 105 105 i56 160

* 52 106 107 1!7 160

t6 !3 107 108 158 160

57 5. 108 109 159 160

!2 55 109 110 160 160

!4 56 110 ill

to !7 Ill 112

61 58 112 113

59 113 114

63 60 114 115

f.; 61 115 116

t5 62 116 117

6 63 117 118

67 64 118 120

c, 66 119 121

f 67 IN 122

'2 68 121 123

" 69 122 124

70 1:3 125

-3 71 124 126

-4 72 125 127

"4 73 126 128

76 74 127 129

* 75 128 130

7S 76 129 131

19 77 130 132

60 78 131 134

El 80 132 135

f2 81 133 136

63 82 134 137

f4 33 135 136

65 84 136 139

F6 85 137 140

F' F6 138 141

FS F7 139 142

F9 88 140 143

90 F9 141 144

C-b 0
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED OF COMPOSITE

(OF - NO + AS + MC + VE)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS

80 40 131 57 182 89 233 120

81 40 132 58 183 90 234 121

82 40 133 58 184 90 235 122

83 40 134 59 185 91 236 122

84 40 135 60 186 91 237 123

85 40 136 60 187 92 238 124

86 40 137 61 188 93 239 124

87 40 138 62 189 93 240 125

88 40 139 62 190 94 241 125

89 40 140 63 191 94 242 126

90 40 141 63 192 95 243 127

91 40 142 64 193 96 244 127

92 40 143 65 194 96 245 128

93 40 144 65 195 97 246 129

94 40 145 66 196 98 247 129

95 40 146 67 197 98 248 130

96 40 147 67 198 99 249 130

97 40 148 68 199 99 250 131

98 40 149 68 200 100 251 132

99 40 150 79 201 101 252 132

100 40 151 70 .202 101 253 133

101 40 152 70 203 102 254 134

102 40 153 71 204 102 255 134

103 40 154 71 205 103 256 135

104 40 155 72 206 104 257 135

105 41 156 73 207 104 258 136

106 42 157 73 208 105 259 137

107 42 158 74 209 106 260 137

108 43 159 75 210 106 261 138

109 44 160 75 211 107 262 138

110 44 161 76 212 107 263 139

111 45 162 76 213 108 264 140

112 45 163 77 214 109 265 140

113 46 164 78 215 109 266 141

114 47 165 78 216 110 267 142

115 47 166 79 217 111 268 142

116 48 167 80 218 111 269 143

117 49 168 80 219 112 270 143

118 49 169 81 220 112 271 144

119 50 170 81 221 113 272 145

120 50 171 82 222 114 273 145

121 51 172 83 223 114 274 146

122 52 173 83 224 115 275 147

123 52 174 84 225 116 276 147

124 53 175 85 226 116 277 148

125 53 176 85 227 117 278 148

126 54 177 86 228 117 279 149

127 55 178 86 229 118 280 150

128 55 179 87 230 119 281 150

129 56 180 88 231 119 282 151

130 57 181 88 232 120 283 151

C-11-.......... KQ. r3/'
C(VERSION OF SUM11 OF SUBTEST STANDARD SCORES

TO ARM STANDARD SCORES ADJUSTED OF CONtPOSITE

(OF - W0 + AS + MC + VE)

SSSS ARM SS SSSS ARMY SS SSSS ARMY SS SSSS ,ARMY SS

284 152 293 158 302 160 311 160

285 153 294 158 303 160 312 160

286 153 ?95 159 304 160 313 160

237 154 296 160 305 160 314 160

288 155 297 160 306 160 315 160

289 155 298 160 307 160 316 160

290 156 299 160 308 160 317 160

291 156 300 160 309 160 318 160

292 157 301 160 310 160 319 160

320 160

C-12
CONVERSION OF SUN OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED SC COMPOSITE

(SC a VE + AR + AS + MC)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS SSSS AIVY SS

80 40 131 60 182 89 233 119

81 40 132 60 183 90 234 120

82 40 133 61 184 91 235 121

83 40 134 61 185 91 236 121

84 40 135 62 186 92 237 122

85 40 136 62 187 92 238 122

86 40 137 63 188 93 239 123

87 40 138 64 189 94 240 124

88 40 139 64 190 94 241 124

89 40 140 65 191 95 242 125

90 40 141 65 192 95 243 125

91 40 142 66 193 96 244 126

92 40 143 67 194 97 245 i26

93 40 144 67 195 97 246 127

94 40 145 68 196 98 247 128

95 40 146 68 197 98 248 128

96 40 147 69 198 99 249 129

97 40 148 70 199 99 250 129

98 40 149 70 200 100 251 130

99 41 150 71 201 101 252 131

100 41 151 71 202 101 253 131

101 42 152 72 203 102 254 132

102 42 153 72 204 102 255 132

103 4a 154 73 20.5 103 256 133

104 44 155 74 206 104 257 134

105 44 156 74 207 104 258 134

106 45 157 75 208 105 259 135

107 45 158 75 209 105 260 135

108 46 159 76 210 106 261 136

109 47 160 77 211 107 262 136

110 47 161 77 212 107 263 137

ill 48 162 75 213 108 264 138

112 48 163 78 214 108 265 138

113 49 164 79 215 109 266 139

114 50 165 79 216 109 267 139

115 50 166 80 217 110 268 140

116 51 167 81 218 ill 269 141

117 51 168 81 219 Il1 270 141

118 52 169 82 220 112 271 142

119 52 170 82 221 112 272 142

120 53 171 83 222 113 273 143

121 54 172 84 223 114 274 144

122 54 173 84 224 114 275 144

123 55 174 85 225 115 276 145

124 55 175 85 226 115 277 145

125 56 176 86 227 116 278 146

126 57 177 87 228 117 279 146

127 57 178 87 229 117 280 147

128 58 179 88 230 118 281 148

129 58 180 s8 231 118 282 148

130 59 181 89 232 119 283 149

C-13
CONVERS1ON OF SUM OF SUBTEST STANDARD SCORES

TO AMNI STANDARD SCORES ADJUSTED SC COMPOSITE

(S C - V + A AS * N)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS

284 149 293 155 302 160 311 160

285 150 294 155 303 160 312 160

286 151 295 156 304 160 313 160

287 151 296 156 305 160 313 160

288 152 297 157 306 160 315 160

289 152 298 15 307 160 316 160

290 153 299 158 308 160 317 160

291 154 300 159 309 160 318 160

292 154 301 159 310 160 319 160

320 160
CONVERSION OF SUH OF SUBTESI STANDARD SCORES

TO ARMY S U RD SCOR.S ADJUSTED P9 COMI TE

(MM a NO + AS M 1C + El)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS SSSS AWnf SS

80 40 131 58 182 89 233 120

81 40 132 59 183 90 234 121

82 40 133 59 184 90 235 121

83 40 134 60 185 91 236 122

84 40 135 60 186 91 237 123

65 40 136 61 187 92 238 123

86 40 137 62 188 93 239 124

87 40 138 62 189 93 240 124

88 40 139 63 190 94 241 125

89 40 140 63 191 95 242 126

90 40 141 64 192 95 243 126

91 40 142 65 193 96 244 127

92 40 143 65 194 96 245 127

93 40 144 66 195 97 246 128

94 40 145 66 196 98 247 129

95 40 146 67 197 98 248 129

96 40 147 68 198 99 249 130

97 40 148 68 199 99 250 131

98 40 149 69 200 100 251 131

99 40 15I ?o 201 101 252 132

100 40 151 70 202 101 253 132

101 40 152 71 203 102 254 133

102 40 153 71 204 102 255 134.

103 41 254 72 205 103 256 134

104 41 155 73 206 104 257 135

105 42 156 73 207 104 258 135

106 43 157 74 208 105 259 136

107 43 158 74 209 106 260 137

10 44 159 75 210 106 261 137

109 44 160 76 211 107 262 138

110 45 161 76 212 107 263 138

i11 46 162 77 213 108 264 139

112 46 163 77 214 109 265 140

113 47 164 78 215 109 266 140

114 48 165 79 216 110 267 141

1!5 48 166 79 217 110 268 141

116 49 167 so 218 I11 269 142

117 49 168 so 219 112 270 143

118 50 169 81 220 112 271 143

119 51 170 82 221 113 272 144

120 51 171 82 222 113 273 145

121 52 172 83 223 114 274 145

122 52 173 84 224 115 275 146

123 53 174 84 225 115 276 146

124 54 175 85 226 116 277 147

125 54 176 85 227 116 278 148

126 55 177 86 228 117 279 148

127 55 178 87 229 118 280 149

128 56 179 87 230 118 281 149

129 57 110 88 231 119 282 150

130 57 181 88 232 120 283 151

C-15
CONVERSION OF StI4 OF SVUTEST STANDARD SCORES

TO ARKY STANDAW) SCORES ADJUSTED MM COMPOSITE

(MM1 - NO * AS * * El)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS SSSS ARM" SS

284 151 293 157 302 160 311 160

285 152 294 157 303 160 312 160

286 152 295 158 304 160 313 160

287 153 296 159 305 160 314 160

28e 154 297 159 306 160 315 160

289 154 298 160 307 160 316 160

290 155 299 160 308 160 317 160

291 156 300 160 309 160 318 160

292 156 301 160 310 160 319 160

320 160

C-16
CONVERSION OF SUM OF SUBTESr STANDARD SCORES

TO ARM': STANDARD SCORES ADJUSTED CL COMPOSITE

(CL - VE * AR + MX)

SSSS ARMY SS SSSS ARMY SS

60 40 109 7t,

61 40 110 71

62 40 111 71

63 t0 112 72

64 40 113 73

65 40 114 74

66 40 115 74

67 40 116 75

68 40 117 76

69 41 118 77

70 41 119 77

71 42 120 78

72 43 121 79

73 44 122 80

74 44 123 80

75 45 124 81

76 46 125 82

77 47 126 82

78 47 127 83

79 48 128 84

80 49 129 85

81 49 130 85

82 50 131 86

83 51 132 87

84 52 133 88

85 52 134 88

86 53 135 89

87 54 136 90

88 55 137 91

89 55 138 91

90 56 139 92

91 57 140 93

92 58 141 93

93 58 142 94

94 59 143 95

95 60 144 96

96 60 145 96

97 61 146 97

98 62 147 98

99 63 148 99

100 63 149 99

101 64 150 100

102 65 151 101

103 66 152 102

104 66 153 102

105 67 154 103

106 68 155 104

107 69 156 104

108 69 157 105

C-17 N

23
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO IJ.RY STANDARD SCORES ADJUSTED CL COMPOSITE

(CL VE + AR + K)

ii

SSSS ARi 55 CSSS ARtIIY SS

158 106 199 136

159 107 200 137

160 107 201 137

:,1 108 20? 138

162 109 203 139

163 110 204 140

164 110 2c5 140

165 111 206 141

166 112 207 142

167 113 203 143

!68 113 209 143

169 114 210 144

170 115 211 145

171 115 212 145

172 116 213 146

173 117 214 147

, 174 118 215 148

175 118 216 148

176 119 217 149

177 120 218 150

178 121 219 151

179 121 220 151

180 122 221 152

181 123 222 153

182 124 223 154

103 124 224 154

184 125 225 155

185 126 226 156

186 126 227 156

187 127 228 157

188 128 229 158

189 129 230 159

190 129 231 159

191 130 232 160

192 131 233 160

193 132 234 160

* 194 132 235 160

195 133 236 160

196 134 237 160

197 134 238 160

198 135 239 160

240 160

c-l
CONVERSION OF SUM OF SUBTEST STANDARD SCORES

TO ARMY STANDARD SCORES ADJUSTED GT COMPOSITE

(GT 

- VE + AR)

SSSS ARMY SS SSSS ARMY SS SSSS ARMY SS

40 40 91 90 142 145

41 40 92 91 143 146

42 40 93 93 144 148

43 40 94 94 145 149

44 40 95 95 146 150

45 41 96 96 147 151

46 42 97 97 148 152

47 43 98 98 149 153

48 44 99 99 150 154

49 45 100 100 151 155

50 46 101 101 152 156

51 47 102 102 153 157

52 48 103 103 154 158

53 49 104 104 155 159

54 50 105 105 156 160

55 52 106 107 157 160

56 53 107 108 158 160

57 54 108 109 159 160

58 55 109 110 160 160

59 56 110 111

60 57 111 112

61 58 112 113

62 59 113 114

63 60 114 115

64 61 115 116

65 62 116 117

66 63 117 118

67 64 118 120

68 66 119 121

69 67 120 122

70 68 121 123

71 69 122 124

72 70 123 125

73 71 124 126

74 72 125 127

75 73 126 128

76 74 127 129

77 75 128 130

78 76 129 131

79 77 130 132

80 78 131 134

81 80 132 135

82 81 133 136

83 82 134 137

84 83 135 138

85 84 136 139

86 85 137 140

87 86 138 141

88 87 139 142

89 88 140 143

90 89 141 144

C-19
ADDENDUM

AlternativeValidity Estimation Procedures

The procedure used to estimate the validities of the current composites are

described on pages 21 and 22, and this procedure was repeated in estimating

the validities of alternative composites. Briefly, this procedure involved

(1) computing composites by table look-up for sums of ASVAB subtest standard

scores, (2) computing correlations between the scaled composites and the

criteria in each MUS, (3) adjusting these correlations for restriction of

range, and (4) combining across MOS to estimate an average for all MOS using a

given composite, weighting the adjusted validities for each MOS by the number

of FY81/82 accessions for the M0. The validities presented in Tables 8, 10,

11, 12, 13, 19, 21, and 22 reflect this approach. The validities of MAGE and

HS composites, presented in Tables 23 and Z4, differ only in that simple sums

of suDtest standard scores were used, rather than table look-ups, in step 1.

In searching for optimal alternative composites and in comparing the differential validities of alternative composite sets, a slightly streamlined procedure was used, to allow both efficient and reliable estimation of validities

for a large number of alternatives. The computation of composite-criterion

correlations was chaniged in two ways, and these changes were applied uniformly

to botn current and alternative composites whenever comparisons were made.

First, both current and alternative composites were computed as simple sums

of suotest sthndardscores, because of the computational cost in creating a

rescaling of the "sum-of-subtest-standard" scores to make them exactly

co nparaole to the scalings of the current composites. The current procedures

for computing composite scores involve reference to conversion tables that

give slightly nonlinear translations of the "sum-of-subtest-standard" scores

in obtaining the operational composite scores. It should be noted that all

analyses in tnis report were carried out using traditional conversion tables

oeveloped using test scores for military personnel who served in Wo ld War II.

New conversion tables have now been adopted for operational use, based on a

new 1980 reference population (see Appendix C).

Second, ridge regressions were used in the process of estimating validities.

The composite-criterion validity for an MOS can be estimated as the product

of (1) the correlation of the composite with the "best linear predictor" of

the criterion using the AIPVAS subtest scores and (2) the correlation of that

"best linear predictor" with the criterion. Traditionally, the "best linear

predictor" is taken to be the ordinary least squares regression function.

In carrying out tnese calculations, however, ridge regression functions, as

descrioed on page 31 of the report, were used to estimate the *best linear

predictor" rather than the ordinary least squares regression functions. The

ridge regression functions have been found to hold up better in crossvalidation analyses in comparison to ordinary least squares functions, particularly for smaller samples. The result is that the validities presented

1-1 (QL4!
in Taoes 16, 1?, and 18, for botn current and alternative composites, tend to

be sligntly smaller in comparison to validities computed directly. The differential valaitiy estimates presented in Tables 27 and 28 were also computed in

tnis way.

Alternative Composite Scales and Cutoffs

After alternative composites were identified, conversion tables were developed

that yielded applicant distrioutions for the new composites that were as

similar as possiole to the applicant distributions averaged across the

existing composites. Further, tne new composites were equated to their

existing counterparts on tne basls of average AFQT levels so that alternative

cutoff points could be identified on these new scales (molding constant the

oFrT level of selected applicants). The procedures used in developing these

conversion ana equating taoles are described in detail in pages 103-104 of the

report.

AS tnese analyses were being completed, ARI was independently preparing a new

set of conversion tables based on the NORC 1980 reference population rather

tnan on tne cu-renL wwll norm group. These tmew conversion tables involve a

simple stanoaroization rather tnan a nonlinear conversion, except for truncation at tnree standard deviations. The conversion tables resulting from

tnis effort are presented in Appendix C. These will become the official conversion tables at tne beginning of FY85. The conversion tables derived as

part of tnis report and presented in Appendix A do not reflect the switch to a

new norm population. They were included here for comparison to the current,

soon-to-De-oosolete conversion tables. The development of the new norms

presented in Appenoix C is described in Mitchell and Hanser (1984).

Metnoas of Combining Criteria

Separate valioations were performed using training scores as criteria and SQT

scores as criteria. In addition, as described on pages 17 and 20 of the

report, a tnird set of validations was based on a combined file, using both

SQT and training scores as criteria. Two different methods were used for

conbining criteria. To understand these methods, one must first realize that

an initial step in the validation analyses was to standardize the criterion

scores in each "cell" to a mean of 100 and standard deviation of 20. A "cell"

was defined, on pages '3 and 16, as a group of soldiers in the same MOS who

eliner were in tne same training course, for training criteria, or took tne

same SQT forn. (year and track), for SQT criteria. For reasons stated in the

report, an indivioual soldier was included in only one training cell, even

tnougn he or she may have had scores from more than one course, wnile he or

she Could have oeen included in more than one SQT cell.

Tne combined criterion analyses used the scores from the cells included in tne

separate training anj SQT analyses, combining all cells in the same MOS. For

analyses that relied only on the predictor-criterion covariance matrices (all

analyses except the estimation of differential validity and the bootstrap

estimation of standard errors), tne method of combination was to compute the

1-2
weighted average of the covariance matrices; that is, to compute the powle

,it.Lin-cel] covariance matrix for eacn 140S. The weights used were the sample

sizes for the inoividual cells.

For tnose analyses for wnich the cavariance matrix was not sufficient, a file

of indvidual cases was constructed, one record per individual. For soldiers

represented more than once in tne oata base, this entailed selecting among

multiple records, each with a different criterion. The selection was arbitrarily set tc be the record with the highest criterion score, based on the

assumption that the soldier woulo spend a greater portion of his or her career

in the MOS, track, and duty position wnere his or her performance was best.

1-3

"(I