Percent Selected in SFAS 66%

Bottom 25% 261050% 51 to75%  Top25%

Score Category

Figure 6. SFAS FY98 Selection Based on the Equation Model

11



SFQC Overall Graduation and First Try
Graduation Rates by Branch Type

Branch Type
mus
OCMF 11
GCA-not 11
|DCSICSS

30% 30%

% Graduated ‘% Graduated-First Try

SFQC Overall Graduation and First Try
Graduation Rates by Airborne Qualification

Airborne
Qualification

1 Airborne Qualified!
Not Qualified

% Graduated

% Graduated-First Try



Airborne qualification and time in service also demonstrated a relationship with selection.
SPC/CPLs who were Airborne qualified were considerably more likely to complete SFAS (41%
success rate) than those without this training prior to SFAS attendance (29% success rate). In
addition SPC/CPLs who had three or fewer years time in service were more likely to be selected
from SFAS than those with four or more years time in service (38% vs. 27%, respectively) (see
Figure 2).

GT score demonstrated a modest relationship with success in SFAS for this sample, despite
the restriction in the range of scores. SPC/CPLs in the bottom third of the distribution for GT
score (GT below 112) demonstrated a 32% selection rate; not considerably different from the
39% selection rate seen among soldiers in the top third of the distribution of GT scores (GT
above 120). Again, it is likely that restriction of range effects have diminished the predictive
effect of this measure.

With respect to the Wonderlic, soldiers were significantly less likely to be selected from the
course if they eamed a raw score below 20, which historically equates to below the 20" percentile
for all SFAS candidates (see Figure 3). .

Developing Prediction Categories

Study 1 explored a categorical approach to combining these variables in order to identify
soldiers who would be most or least likely to complete SFAS. The intent was to develop a series
of rules to assist recruiters in identifying whether a subject was likely to be successful or

unsuccessful.

For the purposes of exploratory analyses, candidates were first categorized based on their
scores for five key variables: APFT score, time in service, airborne qualification, GT score, and
Wonderlic score. APFT, time in service, and airborne qualification were chosen because of their
demonstrated relationships with success in SFAS. Despite demonstrating only a modest
relationship with success, GT score and Wonderlic scores were used here because aptitude is
currently a pre-selection factor used by recruiters. In addition, their statistical relationship with
selection is likely to have been diminished by restriction of range effects.

Candidates were classified into top, middle, and bottom categories for APFT, time in service,
and GT score. For Wonderlic, candidates’ scores were dichotomized, being divided into raw
score of less than 20 correct on the test and greater than or equal to 20 items correct. The raw
score of 20 places a soldier’s score at the 20" percentile using a set of norms based on SFAS
candidates over the past decade. In other words, for a soldier to be rated in the low Wonderlic
category, his performance had to be in the bottom 20% of scores obtained among SFAS
candidates. Finally, Airborne qualification was also used as a dichotomous predictor.

We examined candidates’ success rates based on different combinations of their scores for
these five variables. Figure 4 demonstrates SFAS select rates for four different (not mutually
exclusive) combinations of variable scores. Group | includes soldiers who scored the lowest on
each of the variables; their GT score was less than 112, APFT less than 228, Wonderlic score was
below the 20" percentile, they had more than 3 years time in service. This group showed an 8%
success rate in SFAS for the FY90-FY96 sample.

Group 2 includes soldiers who had a GT score greater than 111, an APFT score greater than
227, and had 3 or fewer years time in service. This group achieved a 47% success rate. The third
group includes soldiers in the, top APFT group, that is, greater than 245, Wonderlic scores greater



success than APFT scores. Using these four variables (Branch type, APFT, GT score, and
airborne qualification), we were able to develop a prediction equation such that soldiers who
scored in the top 25% had a select rate of 66%, and soldiers who scored in the bottom 25% hada
select rate of 24%. A procedure was described that could be used to develop an order of merit list
for junior enlisted soldiers slated to attend SFAS.

Table 6. Backward Removal Stepwise Logistic Regression Analyses on First-Try
Graduation from SFQC.

DV: First-try Graduation from SFQC

Entered IVs b Wald’ ¢ df
Step 1: All variables
Branch Type .24 2.67 9.80* 3
Airborne Qualification .46 3.13
Age -46 2.17
Step 2: Age removed
Branch Type 24 2.68 7.66* 2
Airborne Qualification .46 3.16
Step 3: Branch Type removed -
Airborne Qualification .55 4.96* 4.99* 1
Note:
® The Wald test is the logistic equivalent of a t-test
*p<.05

The next question was to determine the relevance of these factors to predicting success in the
SFQC. Unfortunately, a SPC/CPL’s probability for success was not predicted well by the
available variables. Only branch type and airborne qualification were related to a soldier’s
success in the SFQC, although age contributed to predicting first-try graduation, and GT score
was predictive of academic failures. These results strengthens the importance of branch type,
airborne qualification, and GT score as pre-screening tools, but not yet offer specific information
concerning numerical values to assign to the order of merit list procedures.

Integrated Summary of Results

Results from both Study 1 and Study 2 emphasized the importance of APFT, ASVAB GT, and
airborne qualification in the SF pipeline. In addition, Study 2 demonstrated the importance of
branch type, both to success in SFAS as well as success in the SFQC. Both studies suggested that
a soldier’s scores on these key predictor measures could be used to determine which soldiers
would have the best chances for success in SFAS, and each proposed a method for combining this
information and developing an order of merit list to identify the highest potential soldiers.

Discussion

The intent of these analyses was to identify predictors that would be accessible to the recruiter
for establishing an order of merit system for junior level enlisted soldiers. These results could be
used to ensure that the highest potential soldiers are being contacted and recruited for SFAS, and

‘



Foreword

The U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) conducts
research on personnel performance and training in support of Army goals. A 1991 Memorandum
of Agreement between ARI and U.S. Army Special Operations Command (USASOC) established
a formal program of cooperative research to address the needs and concerns of Special Forces
(SF). In order to accomplish this we work in collaboration with the USASOC Psychological

Applications Directorate (@AD).

During fiscal year 1998, the PAD and ARI were working toward the development of
longitudinal databases that would enable us to examine predictors of success in the SF selection
and training pipeline. The current report utilizes those databases to identify prescreening tools
that could be used to ensure that the soldiers with the best chances for success are recruited for

the SF Assessment and Selection program (SFAS).

The objective of this report is to provide the Special Operations Proponency Office at the U.S.
Army John F. Kennedy Special Warfare Center and School (USAJFKSWCS) with information
and a method that could be used to maximize recruit performance in SFAS. The results of this
report were briefed to the Commanding General of USASOC (LTG Tangney) and Commanding

General of USAJEKSWCS (MG Bowra) in April of 1999.

‘A M. SIMUTIS
hnical Director



SFAS Select Rate by Airborne Status

Percent Selected in SFAS

10%.

60%.

50%.

40%.

30%.

20%.

10%.

0%. de ENE fiat a
Not Airborne Qualified Airborne Qualified
(n= 292)* (n= 165)

‘n= total sample size



Percent Selected in SFAS.

10%

60%

50%
0% 14
40%.
30%.
20%.
10%.

0%.

Bottom 25% 261050%  $1t075% Top 25%

Score Category

Figure 7. SFAS FY97 Selection Based on the Equation Model

Percent Selected in SFAS

10%.

60%, we
50%
40%.
30%.
20%.
10%.

0%.

Bottom 25% 2610 50% 51t0 75%  Top25%

Score Category

Figure 8. SFAS FY96 Selection Based on the Equation Model

12



References

Abelson, H. I. (1954). Factors Related to the Effectiveness of Special Forces Personnel.
(HumRRO Control No. A-3513). Washington, D. C.: Human Resources Research Office.

Brooks, J. E. (1997). Special Forces recruitment and manpower planning. In J.E.. Brooks
and M.M. Zazanis (Eds.), Enhancing U.S. Army Special Forces: Research and Applications (ARI
Special Report 33). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social
Sciences, (AD A339 086)

Diana, M., Teplitzky, M. L., & Zazanis, M. M. (1995). Special Forces Qualification
Course Graduation and Attrition Statistics for Soldiers Selected for Training in FY89-FY91. (ARI
Technical Report 1023). Alexandria, VA: U.S. Army Research Institute for the Behavioral and

Social Sciences. (AD A292 902)

George, D. L. & Cassady, P. D. (1981). Training Attrition Problem, Institute for Military
Assistance (TAPIMA) Training Subsystem Effectiveness Analysis (' TSEA). (TRASANA TEA-13-
81), White Sands Missle Range, NM: U.S. Army TRADOC Systems Analysis Activity.

Kilcullen, R. N. (1999). Creating an Order of Merit List. (Computer software].
Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.

Kilcullen, R. N., Chen, G., Brown, F., & Zazanis, M. M. (in preparation). Identifying
High Quality Special Forces Candidates. (ARI Technical Report). Alexandria, VA: U.S. Army
Research Institute for the Behavioral and Social Sciences.

SFAS (1998). Special Forces Assessment and Selection FY98. (SFAS Database). Ft.
Bragg, NC: Directorate of Analysis and Evaluation, U.S. Army John F. Kennedy Special Warfare
Center and School.

Zazanis, M. M. (1996, February). Pull-up scores in Special Forces Assessment and
Selection (Information Paper). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.

Zazanis, M. M. (1998). Special Forces Qualification Course Longitudinal Database:
FY95-FY97, (ARI Database). Alexandria, VA: U.S. Army Research Institute for the Behavioral

and Social Sciences.
Zazanis, M. M. & Lappin, M. S. (1998). Predicting performance ratings using

motivational antecedents. (ARI Technical Report 1077). Alexandria, VA: U. S. Army Research
Institute for the Behavioral and Social Sciences. (AD A341 774)

19



less likely to be academic failures in the SFQC. Graphs of these relationships can be seen in
Appendix C.

Table 4. Correlations of Predictors with SFQC Overall Graduation (“Grad”), First-try
Graduation (“First”), and Academic Failures (“Acad”)

Grad First Acad Grad First Acad
Years Service -.07 -.07 08 Years Education 03-03 -.07
Age -00 -.20** .02 Branch Type 16** 13% -.17*
Airborne 16* 11 -.14* Pull-ups -.03 02 -.09
ASVAB GT 09 .06 -.13* ASVAB FA 07 .03 -.12

APFT score .00 02 = -.04

*p<.05; **p<.0l

Logistic Regression

Predictors that were significantly correlated with overall graduation were entered into a
simultaneous regression analysis in order to determine the relative importance of each of the
factors. A backward entry stepwise logistic regression was used to predict overall graduation
from the course and first try graduation. Results indicated that both branch type and airborne
qualification contributed significantly to the prediction of SFQC graduation (see Table 5).
Adding these variables, however, did not improve prediction of graduates in a practical sense; the
results still indicated that the best statistical model would be to predict a 100% success rate.

Table 5. Backward Removal Stepwise Logistic Regression Analyses on Graduation from
SFQC

DV: Graduation from SFQC

Entered IVs b Wald* x df
Step 1: All variables
Branch Type 40 4.03* 10.85 2
Airborne Qualification .63 3.65
Note:
* The Wald test is the logistic equivalent of a t-test
*p<.05

The second analysis predicted first-try graduation, and was not any more successful in
prediction (see Table 6). Of the 292 subjects in the analysis, 101 were first-try graduates and 191
were not, and none of the predictors were able to improve our prediction of which soldiers would
be first-try graduates. A logistic regression was not completed for the academic failure variable
because too few soldiers were academic failures.

Study 2: Summary
Results from the SFAS analyses in Study 2 supported those from Study 1. As found in Study
1, a SPC/CPLs chances for success in SFAS were predicted based on his APFT score, GT score,

and airborne qualification. Study 2, however, found that a soldier’s prior branch type was also a
significant predictor of success in SFAS, and, in fact, demonstrated a higher correlation with

14



APPENDIX C
Study 2 Predictors of SFQC Outcomes



(select rate of 43%), and a score greater than 8 (select rates around 65%) would be considered to
be the most qualified or lowest risk. Lowest risk candidates would then be preferentially
selected for attendance at SFAS. Appendix A shows a sample rating sheet that the recruiter could
use to categorize the subject using these four variables.

Summary: Study 1

Results from Study lindicated that a SPC/CPL’s chances for success in SFAS could be
reasonably predicted by his APFT, GT, time in service, and airborne qualification, particularly
when used in combination with each other. Using these variables we were able to categorize
soldiers based on their potential for success in SFAS. This categorization indicated that soldiers
in the lowest scoring categories (4 through 7) had select rates below 35%; whereas soldiers in the
highest potential categories (9 through 12) demonstrated select rates above 55%, and as high as
100% selects. This scoring scheme is one method that could be used for identifying soldiers who
are most likely to succeed in SFAS.

Study 2: SFAS Results

In the second study, data from FY98 were used to reexamine the success of these predictors
using correlational analyses, and develop a statistical equation that could be used to predict
success in the current SFAS program.

Correlations & Crosstabs

Of the 508 SPC/CPLs in this sample, 41% were selected. Although the overall selection rate
in FY98 was slightly higher than that of the historical sample, as already demonstrated in Study 1,
the pattern of results concerning the predictors of success in FY98 mirrored those from the FY90-
FY96 sample in Study 1. Of the 11 variables that were examined in Study 2, APFT, pullups, GT,
FA, branch type, and airborne qualification were significantly correlated with selection from the
program (see Table 2). Correlations between selection and years in service, years of education,
and age were not significant, although crosstabs suggest possible trends in their relationships.
The relationship between SFAS select rates and each variable can be seen in Appendix B.

Table 2. Correlations of Predictors with SFAS Outcome

T Tr
Years Service -.08 Years Education 09
Age -.06 Branch Type .24**
Airborne .15** Pull-ups .11*
ASVABGT _ .13** ASVAB FA .16**

APFT score .19**

*p<.05; *p<.01

As expected, soldiers with higher APFT and pull-ups scores were more likely to be selected in
SFAS. While higher ranking soldiers tend to perform better in SFAS as discussed in the
introduction, within the sample of SPC/CPLs, soldiers with fewer years in service, and who were
younger than 22 years old had higher select rates. These results were similar to those found in
the historical sample, although the relationship for time in service was more linear in this study.
Soldiers with 14 or more years of education, and soldiers who scored higher on the GT and FA

‘



Prescreening Methods for Special Forces Assessment and Selection

Introduction

The Special Operations Proponency Office (SOPO) at the U.S. Army John F. Kennedy Special
Warfare Center and School (USAJFKSWCS) has considered various proposals recently to
identify methods to expand recruitment for the Special Forces Assessment and Selection (SFAS)
program. In the early history of Special Forces there were junior enlisted soldiers both in training
and in Special Forces units. In a sample taken in 1953 from the 10® Special Forces Group at Fort
Bragg, NC, for example, 33% of the enlisted soldiers in the sample were Privates (PVT/PV2) or
Privates First Class (PFC), 20% were corporals or sergeants, and 47% were higher level non-
commissioned officers (NCOs) (Abelson, 1954)!. Information stom SF training in the early
1980s suggests that even at that time there were junior enlisted soldiers attending SF training. Of
the soldiers attending training for SF in FY79 through the fifth class of FY80, 43% were Privates,
26% PFC and CPL, and only 31% NCOs (George & Cassady. 1981). Statistics on the correlates
of success, however, both in SF field performance and SF training. indicated that, as a group, the
lower ranking soldiers tended to be somewhat less successful ( Abelson, 1954; George & Cassady,
1981). This is not surprising, of course, since experience typically improves performance.

Under the current regulations, the lowest ranking Active Duty soldiers eligible to attend SFAS
are Specialists and Corporals (SPCs/CPLs), and data have suggested that, as a group, these
soldiers tend to be somewhat less successful in training than their NCO counterparts (Diana,
Teplitzky, & Zazanis, 1995). In analyses of the most recent selection and training databases, for
example, 41% of the SPCs/CPLs were selected from SFAS in FY98" as opposed to 51% of the
NCOs (SFAS, 1998). For the Special Forces Qualification Course (SFQC), 34% of the
SPCs/CPLs who started the SFQC between FY95 and FY97 graduated from the SFQC without
any recycles, in contrast with 45% of the NCOs (Zazanis, 1998). Given that higher ranking
soldiers will typically have had more opportunities for job-relevant experience, it is not surprising
that higher ranking soldiers are somewhat more likely to succeed.

Despite the fact that higher ranking soldiers, as a group, have higher rates of success, junior
level enlisted candidates (SPCs/CPLs) constitute a substantial number of the soldiers who go on
to attend and graduate from the SFQC. Specifically, for the 1998 training year, SPCs/CPLs
represented over 37% of all candidates attending SFAS, and centributed 33% of all the graduates.
While the experience associated with higher rank is important. research has identified other
factors that are more strongly associated with success in SF selection and training. One
extremely strong predictor of success in SFAS, of course, is the soldier’s score on the Army
Physical Fitness Test (APFT) (Kilcullen, Chen, Brown, & Zazznis. in preparation). Other factors
that have been related to success in selection and training include a soldier’s previous branch type
and Ranger qualification (Brooks, 1997; Zazanis & Lappin, 19°38). and the number of pullups
they can achieve (Zazanis, 1996). Thus, low ranking soldiers with higher scores on these other
critical factors perform to the level of the higher ranking soldiers.

SOPO requested help from the U.S. Army Special Operations Command Psychological
Application Directorate (PAD) and the U.S. Army Research Insvitute (ARI) in identifying

" Data from most older reports do not indicate whether samples were Active Duty only or both Active Duty
and National Guard/Army Reserve combined; thus, percentages o! ‘or enlisted soldiers in Active Duty
components only may have been somewhat lower than those indic:
? References to FY98 SFAS data in this paper include all FY98 classes except the final September class;
data were not yet available for that class at the time of the analyses.

‘



SFAS Select Rate by FA Score

Percent Selected in SFAS

70%.

60%,

50%.

40%.

30%.

20%.

10%.

0%.

Less than 110
(a=139)¢ FA Score (a"289)

‘n= total sample size

SFAS Select Rate by GT Score

Percent Selected in SFAS

70%.

30%
60%

50%.

40%,

30%.

20%.

10%.

0%.
Less than 110 110+

(o=156)" GT Score —_("=305)

*n= total sample size

B-4



APPENDIX B
Study 2 Predictors of SFAS Selection

B-1



PRESCREENING METHODS FOR SPECIAL FORCES
ASSESSMENT AND SELECTION

Executive Summary

Requirement:

The Special Operations Proponency Office (SOPO) at the U.S. Army John F. Kennedy Special”
Warfare Center and School (USAJFKSWCS) requested help from the U.S. Army Special
Operations Command Psychological Application Directorate (PAD) and the U.S, Army Research
Institute (ARI) in identifying prescreening tools that could be used to identify which soldiers have
the greatest chance for success in the Special Forces (SF) selection and training pipeline. This
information would allow SOPO to minimize recruitment of soldiers with little chance of
completing Special Forces Assessment and Selection (SFAS), and allow slots to be filled by
soldiers who have a greater probability of success.

Procedure:

Aseries of variables was chosen from the SFAS database which included variables that had
either previously been shown to be related to success, or were currently collected as prescreening
information by recruiters. Analyses focused on junior level enlisted soldiers (Specialist/Corporal)
(SPC/CPL), who have lower success rates in SFAS than the non-commissioned officers. Two
studies were completed using samples from different fiscal years to examine SPC/CPL
performance in SF selection and training. The first study examined a point-based method of
combining variables to predict success, and the second study examined an equation-based method
of combining variables to predict success.

Findings:

Results indicated that the Army Physical Fitness Test (APFT), branch type, the Armed
Services Vocational Aptitude Battery (ASVAB) General/Technical (GT) score, and airborne
qualification provided optimal prediction of success in SFAS. Prediction models indicated that a
soldier’s scores on these key predictor measures could be used to identify soldiers who would
have the best chances for success in SFAS. While success in SFAS was also related to higher
scores on pull-ups, ASVAB Field Artillery scores, Wonderlic Personnel TestTM scores, and a
greater number of years of education, these relationships were not as strong or were overlapping
with those of the four key variables. One surprising finding was that SPC/CPLs with fewer years
in service, and who were younger, showed higher rates of success in SFAS.

Unfortunately, a soldier’s probability for success in the Special Forces Qualification Course
(SFQC) was not predicted well by the available variables. Only branch type and airborne
qualification were related to a soldier’s success in the SFQC, although age contributed to
predicting first-try graduation, and GT score was predictive of academic failures. These results
do, however, strengthen the importance of branch type, airborne qualification, and GT score as
pre-screening tools.

Using the key variables identified as critical, two methods were developed for creating an
order of merit list to identify junior enlisted recruits with the highest potential for success in



Acknowledgments

We would like to extend a special note of thanks to Ms. Doe Ann Crocker, Statistician, Analysis
and Evaluation Division, Directorate of Training and Doctrine, U.S. Army John F. Kennedy
Special Warfare Center and School, for her continual assistance with cleaning and preparation of
the SFAS and SFQC data, and her insightful comments and reviews of our research.



tests also had somewhat higher select rates. Because the relationships between GT and selection
and FA and selection were nearly identical, we only included GT in future analyses, since that is
the measure that is currently used as a prerequisite.

Finally, soldiers who came to SFAS with airborne qualification, and soldiers who came from
an Infantryman background (11B) had high select rates. Soldiers who came from CMF 11 (other
than 11B) showed a moderate select rate, and soldiers from other combat arms branches or from
non-combat arms branches had low select rates (see Appendix B). While it had been expected
that the strongest predictor of success in SFAS would be APFT, the correlation between Branch
type and success (.24) was significantly larger than between APFT and success (.19) (t(458)=-
2.889, p<.01).

Logistic Regression

In order to determine the relative importance of each of these factors, the variables with
significant zero order correlations with selection from SFAS were entered into a simultaneous
regression analysis. A backward entry stepwise logistic regression was used to predict select or
non-select from SFAS based on APFT, pull-ups, ASVAB GT, branch type, and airborne
qualification.*

Results indicated that pull-ups did not contribute significantly to predicting selection in SFAS,
but each of the other variables did (see Table 3). While the regression coefficient for airborne
qualification was not significant at the .05 level, it was not recommended for removal, so was
retained. By using APFT, branch type, GT score, and airborne qualification, we were able to
correctly predict 77% of the soldiers who were not selected, and 49% of the soldiers who were
selected, for an overall classification average of 64% correct.

Developing Weighted Scores

The results of the logistic regression were used to develop an equation that would allow us to
predict a soldier’s chances for success, based on his APFT score, branch type, GT score, and
airborne qualification. The coefficients from the logistic regression procedure generated the
following equation for the prediction of select/non-selects in SFAS: (.017*APFT) +
(.5248*Branch type) + (.0284*GT score) + (.3841*Airborne qualification).* The resulting score
distribution for FY98 data ranged from 6.77 to 10.31, with the quartile scores at the 25" =7.71,
50" = 8.12, and 75" = 8.57.

Soldiers who scored in the top 25% on this composite predictor variable had a selection rate of
66%, while soldiers who scored in the bottom 25% had a 24% select rate (see Figure 6). Similar
success rates were seen when the equation was cross-validated using SPC/CPL samples from
FY97 and FY96 (see Figures 7 and 8).

* Results presented here treated these variables as continuous variables in the regression analyses for two
reasons: (1) it is more parsimonious from the standpoint of the prediction equation, and (2) results that
treated branch type and airborne qualification as categorical variables generated nearly identical results.

5 Note that the coefficients in this equation are based on the raw scores for each variable, not standardized,
and therefore cannot be directly‘compared to'each other.

10



PRESCREENING METHODS FOR SPECIAL FORCES
ASSESSMENT AND SELECTION

CONTENTS

DISCUSSION ........ sees
APPENDIX A. Sample Rating Sheet for Recruiters

B. Study 2 Predictors of SFAS Selection...
C. Study 2 Predictors of SFQC Outcomes...

LIST OF TABLES

Table 1. Points Assigned for Scores on Key Variables ..
Table 2. Correlations of Predictors with SFAS Outcome
Table 3. Backward Removal Stepwise Logistic Regression Analyses on Selection

Academic Failures

Table 5. Backward Removal Stepwise Logistic Regression Analyses on Graduation

from SFQC 14
Table 6. Backward Removal Stepwise Logistic Regression Analyses on First-Try

Graduation from SFQC 15

LIST OF FIGURES

Figure 1. SFAS FY90-FY96 Outcome by APFT Performance Category
Figure 2. SFAS FY90-FY96 Outcome by Time in Service

Figure 3. SFAS FY90-FY96 Outcome by Wonderlic Percentile Scor
Figure 4, SFAS FY98 Outcome by Different Prediction Groups.
Figure 5. SEAS FY98 Outcome by Potential Success Score ....
Figure 6. SFAS FY98 Selection Based on the Equation Model
Figure 7. SFAS FY97 Selection Based on the Equation Model
Figure 8. SFAS FY96 Selection Based on the Equation Model

ix



could potentially be used to assign the most qualified soldiers on a priority basis to SFAS. How
the results are used will be determined largely by the availability of candidates for SFAS.

These results clearly indicated that lower ranking soldiers can succeed in the SF pipeline, and
that they can actually perform quite well if they meet certain prescreening criteria. While lower
ranking soldiers successfully complete training at a lower rate in the SF pipeline than non-
commissioned officers, both APFT scores and previous branch type were stronger predictors of
success in SFAS than rank. This is not surprising, since branch type and APFT are direct
measures of skills and abilities that are relevant to successful performance in SFAS and SFQC.
Taken together, APFT, branch type, GT score, and airborne qualification provided a strong
distinction between soldiers who had high or low chances for success.

It was surprising to discover that soldiers with less time in service, and soldiers who were
younger, had higher selection rates. This may be due to differential motivation in SPC/CPLs in
their first versus second enlistment term who are seeking SF training; that is, soldiers who are
more interested and motivated to join SF tend to join as early as possible. Also, in most
occupational specialty groups (particularly combat arms), good soldiers are generally promoted to
Sergeant before their fourth year of service. The group of SPC/CPLs with 4 or more years of
time in service may contain a greater proportion of individuals who have not progressed to the
next rank due to marginal performance or some type of disciplinary action.

Developing an Order of Merit List

The current data suggested that these variables could be used to ensure that the most qualified
soldiers would be targeted for SF recruiting. In addition, the most qualified soldiers could
potentially be given the first opportunities to attend SFAS. Two strategies were offered for
accomplishing these goals. The first strategy used a point system to determine a soldier’s
Potential Success Score. Each of the nine Potential Success Scores was associated with an
expected success rate. The second strategy used an equation to generate scores. Again, these
scores were associated with expected success rates. Either of these strategies could be
implemented to identify the soldiers most likely to succeed in SFAS.

Ata minimum, this information could be used to target soldiers for recruiting who have the
best chance for success, and advise a recruit regarding steps he could take to improve his chance
for success. Specifically, marginally rated soldiers could be directed on improving their APFT
scores, attending jump school, or studying and retaking the GT portion of the ASVAB as part of
an academic remediation program. Beyond this, these methods could potentially be used to at
least select out those soldiers who have the least chance of success in SF. For example, if the
soldier who has applied only has a 25% chance of success in SFAS, he would not be allowed to
attend. Further, a moderate cutoff rate could be set for selection of junior enlisted soldiers to
attend SFAS, so soldiers would have at least a 40-50% chance of success, which would
approximate the current overall success rate in SFAS.

Future Directions

Beyond the variables that were tested in these analyses, we are pilot-testing new measures in
SFAS that could potentially be useful prescreening tools, such as measures of integrity, cognitive
flexibility, and background reviews of criminal activity or financial problems. In addition, the
Psychological Applications Directorate has recently begun collecting information on prior
military course experience (other than airborne and ranger qualification) that requires some
degree of intellectual or physical challenge. We expect that success in these courses (e.g., Expert

«



Technical Report 1094

Prescreening Methods for Special Forces
Assessment and Selection

Michelle M. Zazanis
U.S. Army Research Institute

Gary A. Hazlett
U.S. Army Special Operations Command

Robert N. Kilcullen and Michael G. Sanders
U.S. Army Research Institute

May 1999 19990701 045

United States Army Research Institute
for the Behavioral and Social Sciences

Approved for public release; distribution is unlimited.

TIC QUALITY INSPECTED 4



SPC/CPL population in order to examine success rates. In this study, categories were expanded
to four for most variables. For the correlational analyses, although branch type is a categorical
variable, numerical values can logically be assigned based on the similarity of skills to those in
Special Forces. Infantrymen (11B) skills are the most similar, so they were coded as “3”; Career
Military Field (CMF) 11, but not Infantrymen were coded as “2”; and all others were coded as
“1, .

While Ranger qualification has historically been a strong predictor of success, it was not
examined in these analyses because only 8 of the SPC/CPLs in the SFAS sample and 15 of the
SPC/CPLs in the SFQC sample were Ranger qualified. Similarly, not many other junior level
enlisted soldiers would have already earned the Ranger tab.

Beyond these 11 variables, there are some new motivational and personality tests currently
under investigation in SFAS and SFQC that may be able to help predict successful performance.
Since these tests are still under investigation, they would have to be incorporated into the
prescreening process at a later time. The first step is to make better use of the information we
already have by determining the relative importance of the variables for predicting success.

Criteria

Both SFAS samples used select/non-select from SFAS as criteria. While there is often
particular concern about soldiers who choose to voluntarily withdraw from the program, the
distinction among the various reasons for non-selection from SFAS can be nebulous. For the
purposes of this investigation, therefore, only the overall select/non-select criteria were examined.

For the SFQC analyses in the second study, three criteria were considered: (1) whether an
individual graduated from his first attempt at the SFQC, (2) whether an individual graduated after
multiple attempts at the SFQC, (3) whether an individual was relieved from the course for
academic or other performance reasons.

Results

Study 1: SFAS Results

Of the 990 SPC/CPLs in the sample spanning training years 1990-1996, 36% of the candidates
(352) were selected for attendance to the SFQC. Success in SFAS was related to APFT scores,
time in service, Wonderlic score, and airborne qualification. The General Technical (GT)
subscore from the Armed Services Vocational Aptitude Battery (ASVAB) was only marginally
predictive of success rates, probably because of the severe restriction in the range of soldiers’
scores, given that during this time period a GT score of 110 or higher was required. All of these
measures, however, were able to improve the prediction from the base rate of 35% selects
observed in the group as a whole. In combination, these variables provided a means to identify
soldiers who were substantially more likely to succeed in SFAS.

Crosstabs

As predicted, APFT score showed the strongest relationship with selection from SFAS.
SPC/CPLs who scored in the bottom third of the distribution for APFT (less than 228) had about
a 20% chance of successfully completing the selection program. In contrast, those who scored in
the top third of the distribution (greater than 245) had about a 50% selection rate (see Figure 1).

‘



prescreening tools that could be used to identify which soldiers would be expected to have the
greatest chance of success in the SF selection and training pipeline. This information could help
recruiters identify soldiers to contact who have the best chances for success, would allow SOPO
to minimize recruitment of soldiers with little chance of completing SFAS, and allow slots to be
filled by soldiers who have a greater probability of success. Improving select rates could
potentially decrease the shortfall between accessions and losses to the force.

A series of variables was chosen from the SFAS database which included variables that had
either previously been shown to be related to success, or are information collected by recruiters.
Based on previous research, it was predicted that the APFT would be the strongest predictor of
success in SFAS (Kilcullen et. al., in preparation). Specific hypotheses beyond this regarding the
relative importance of these predictors as prescreening tools were not made.

Method

The best way to identify predictors of success for junior level soldiers in the selection and
training pipeline was to examine predictors of success for the lowest current rank, SPC/CPL.
Two studies were completed to explore different procedures for creating prescreening tools.

Sample

Analyses for Study 1 used a sample of 990 active duty SPC/CPLs who attended SFAS and
completed SFQC between FY90 and FY96. Within this sample, 36% (352 soldiers) successfully
completed SFAS, and a total of 67 subsequently went on to successfully complete SFQC without
recycle. Prescreening methods were then applied to a sample of 655 active duty SPC/CPLs who
attended SFAS during FY98.

Study 2 used a sample of 508 active duty SPC/CPLs from SFAS FY98.° Because PT failures
can be predicted with 100% accuracy from the soldier’s APFT score, we removed 47 soldiers
from the sample who were non-selects due to PT failures. Analyses for the SFQC used a sample
of 309 active duty SPC/CPLs from SFQC FY95 through FY97. Information about rank and
component for both of the samples was based on data from SFAS.

Predictors

Study 1 selected a series of seven variables from the SFAS database to predict success in
SFAS: APFT score, age, years of education, time in service, Armed Services Vocational Aptitude
Battery (ASVAB) General Technical (GT) score, Airborne qualification, and Wonderlic
Personnel TestTM scores. Subjects were grouped into thirds along the dimensions of GT, time in
service, APFT scores, and Wonderlic percentiles. These key predictors were ones that, with the
exception of Wonderlic, are already part of the information that recruiters collect, and could
easily be incorporated as prescreening tools in the recruiting process without major changes to
recruiting operations. Wonderlic was included to explore its relationship with success in SFAS.

For Study 2, the list of variables was expanded to add four more variables from the SFAS
database: prior branch type, pull-ups, the ASVAB Field Artillery (FA) score, and the Assembling
Objects test. The Wonderlic was removed as a predictor for these analyses. As with the first
study, soldiers were grouped into categories based on the distribution of scores in the SFAS

3 Again, data from the 7-98 class was not available for inclusion in these analyses.
a



SFAS Select Rate by Years of Education

Percent Selected in SFAS

10%

60%

50%

40%

30%:

20%:

10%

0%.
Borfever MW ormre
‘n= ice O35)" (10)
tos sample size ”” vears of Education
SFAS Select Rate by Age
Percent Selected in SFAS
710%
60%
50%
40%.
30%
20%
10%.
0% os
21 or less 22+
(n=119)* Age (n=342)

‘*n= total sample size

B-3



SFAS Select Rate by Branch Type

Percent Selected in SFAS
68%

0%. :
CMFI1 — CA-not11_—— CS/CSS.
(0-45) (n=106) (0-223)
Branch Type

* n= total saumple size

SFAS Select Rate by Years in Service

Percent Selected in SFAS

70%.

60%. SI

41%

50%.

40%.

30%.

20%.

10%.

0% La
O-lyear years = Syears 4+ years
79)" (o=165) (0-91) (v=120)

+n=totalsample sie Years in Service



SFAS. The first method was based on a point-assignment strategy, and the second was based on
an equation model. Either method could be used to determine which candidates have the best

chance for success in SFAS.
Utilization of Findings:

These results can be used by SOPO to inform decisions that are being made about the
recruitment of junior enlisted soldiers for SFAS. They provide a method that could be used to
generate an order of merit list that would rank order recruits based on their probability for success
in the SFAS program. This information would enable recruiters to contact soldiers with the best
chance for success, allow SOPO to minimize recruitment of soldiers with little chance of
completing SFAS, and allow slots to be filled by soldiers who have a greater probability of
success. Potentially, soldiers could be selected for the Assessment and Selection program based
on their likelihood of success in the program. Improving select rates could potentially decrease
the shortfall between accessions and losses to the force.

viii



Table 3. Backward Removal Stepwise Logistic Regression Analyses on Selection from
SFAS

DV: Selection from SFAS

Entered IVs b Wald* ¢ df
Step 1: All variables
APFT Score .02 8.47** 46.85** 5
Pull-ups .04 1.10
Branch Type 53 16.65**
GT Score 03 5.18*
Airborne 37 3.00
Step 2: Pull-ups removed
APFT Score 02 11.30** 45.75** 4
Branch Type 52 16.31**
GT Score 03 5.13*
Airborne 38 3.34

Note:
* The Wald test is the logistic equivalent of a t-test
*p<.05; **p<.0l

Developing an Order of Merit List

This equation can be used to develop an order of merit list based on soldiers’ scores. A simple
computer program has been written that requests a candidate’s APFT score, GT score, branch
type, and airborne qualification status (Kilcullen, 1999). The program then uses the prediction
equation to generate an order of merit list score for each candidate. These order of merit list
scores can be entered in the recruiter’s database, and ordered in ascending order at any time to
identify the highest potential candidates in the database. If an individual improved his APFT
score or gained airborne qualification, the score could be recalculated and changed in the
database.

Study 2: SFQC Results

Of the 309 active duty SPC/CPLs in the SFQC sample, 34% graduated from their first attempt
at the SFQC, 32% graduated after two or more attempts, 9% voluntarily withdrew, 12% were
relieved from the course, and 13% were still in the pipeline. Overall, then, these analyses
attempted to discover systematic factors that would predict why 63 soldiers (21%) were not
successful, and 205 (66%) were successful.

Correlations & Crosstabs

Of the 11 predictor variables that were examined, the only variables that were significantly
correlated with overall graduation from the course were branch type and airborne qualification
(see Table 4). Thus, soldiers who were from 11-series MOS, especially 11B, and reported to
SFAS with airborne qualification were more likely to graduate. Soldiers from 11-series MOS and
soldiers who were younger were more likely to successfully graduate from their. first attempt at
the course. Finally, academic/performance failures were predicted by branch type, airborne
qualification, and ASVAB GT score; that is, soldiers who scored higher on the ASVAB GT were

13



SFQC Academic Fails by Airborne Qualification

30%,

25%,

20%
14%

18% @ % Academic Fails

10%. ve

5%.

0%.
Airborne Qualified Not Qualified
Ariborne Qualification

30%.

25%.

20%.

15%.

10%.

5%

0%.

SFQC Academic Fails by ASVAB GT Category

@ % Academic Fails

Less than 120

120+
ASVAB GT Score

c-4



APPENDIX A
Sample Rating Sheet for Recruiters



Technical Report 1094

Prescreening Methods for Special Forces
Assessment and Selection

Michelle M. Zazanis
U.S. Army Research Institute

Gary A. Hazlett
U.S. Army Special Operations Command

Robert N. Kilcullen and Michael G. Sanders
U.S. Army Research Institute

Organization and Personnel Resources Research Unit
Paul A. Gade, Chief

U.S. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

‘May 1999

fe
Army Project Number Manpower and Personnel
20363007A792

Approved for public release; distribution is unlimited.

iii



EISFAS Select

<3 Years 3 Years >3 Years
Years of Time in Service

Figure 2. SFAS Outcome by Time in Service

Select Rate

Figure 3. SFAS Outcome by Wonderlic Percentile Score



EIFY90-FY96
Selects

BIFY98 Selects

Figure 4. SFAS Outcome by Category

100%

WSelect Rate

80%:

60%:

%H

20%.

0%:

4 5 6 7 8 9 0 Hl 2
Potential Success Score

Figure 5. SFAS Outcome by Potential Success Index score



REPORT DOCUMENTATION PAGE

2. REPORT TYPE
Technical Report

1. REPORT DATE (dd-mm-yy)
May 1999

8. DATES COVERED (from. . . to)
September 1998 — April 1999

4, TITLE AND SUBTITLE
Prescreening Methods for Special Forces Assessment and Selection

5a. CONTRACT OR GRANT NUMBER

5b. PROGRAM ELEMENT NUMBER:
0603007A

6. AUTHOR(S) .
Michelle M. Zazanis (U.S. Army Research Institute),

Gary A. Hazlett (U.S. Army Special Operations Command),
Robert N. Kilcullen (U.S. Army Research Institute),

& Michael G. Sanders (U.S. Army Research Institute)

5c, PROJECT NUMBER
A792

5d, TASK NUMBER
12254

5e. WORK UNIT NUMBER:
Hot

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Research Institute for the Behavioral and Social Sciences
ATTN: TAPC-ARI-RP

5001 Eisenhower Avenue

Alexandria, VA_22333-5600

8. PERFORMING ORGANIZATION REPORT NUMBER

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
US. Army Research Institute for the Behavioral and Social Sciences
5001 Eisenhower Avenue

Alexandria, VA 22333-5600

10, MONITOR ACRONYM
ARI

41, MONITOR REPORT NUMBER
TR 1094

12, DISTRIBUTION/AVAILABILITY STATEMENT
Approved for public release; distribution is unlimited.

43. SUPPLEMENTARY NOTES,

144, ABSTRACT (Maximum 200 words):

The Special Operations Proponency Office (SOPO) at the U.S. Army John F. Kennedy Special Warfare Center and School
requested help from the U.S. Amy Special Operations Command Psychological Application Directorate and the U.S. Army
Research Institute in identifying prescreening tools to determine which soldiers would have the greatest chance of success in

the Special Forces (SF) selection and training pipeline.

Two studies were completed examining different methods for

predicting performance in SF selection and training. Analyses focused on junior level enlisted soldiers, who have lower

success rates than the non-commissioned officers.

Results indicated that Army Physical Fitness Test, previous branch type, Armed Services Vocational Aptitude Battery
General Technical score, and airborne qualification provided optimal prediction of success in SF Assessment and Selection
(SFAS). Soldiers in the highest prediction category achieved a select rate of 66%; whereas, soldiers in the lowest prediction

category showed a success rate of only 24%.

Two methods were proposed to generate order of merit lists that would identify recruits with the highest potential for
success in SFAS. This would allow SOPO to minimize recruitment of soldiers who have little chance of completing SFAS.

15. SUBJECT TERMS

Prescreening methods, Special Forces, performance prediction

19. LIMITATION OF

ABSTRACT
16. REPORT
Unclassified

18, THIS PAGE
Unclassified

17. ABSTRACT

Unclassified Unlimited

20. NUMBER | 21. RESPONSIBLE PERSON

OF PAGES (Name and Telephone Number)
Dr. Michelle Zazanis

39 __ | 703/617-0318



SFQC Overall Graduation and First Try
Graduation Rates by Age

18% 16%

Age Category

21 or younger|
2 or older

SFQC Academic Fails by Branch Type

30%.

25%.

20%.

15%

15%.
Ei % Academic Fai

10%.

5%.

0%.

Branch Type



Infantry Badge, Expert Field Medical Badge, Air Assault, Pathfinder) will be correlated with a
higher rate of success in SFAS.

Finally, these analyses examined the importance of previous branch type using the 11B,
CMF11, and non-CMF11 categories. Some specific MOS, however, provide particularly relevant
technical training for the parallel SF specialties; for example, 91B Medical Specialist skills
correspond to those of 18D SF Medics. Future analyses could examine the relationship between
previous MOS and success in SF selection and training at a more specific level, generating
prediction models specific to each MOS. Another strategy would be a two-stage approach in
which initial identification of soldiers is made based on the variables identified here, and
classification is made based on more specific information such as specific prior MOS.

‘
17
—



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

A Directorate of the U.S. Total Army Personnel Command

EDGAR M. JOHNSON
Director

Technical Review by

Joseph Psotka, ARI
Doe Ann Crocker, USAJFKSWCS

NOTICES

DISTRIBUTION: Primary distribution of this Technical Report has been made by ARI.
Please address correspondence concerning distribution of reports to: U.S. Army
Research Institute for the Behavioral and Social Sciences, Attn: TAPC-ARI-PO, 5001
Eisenhower Ave., Alexandria, VA 22333-5600.

FINAL DISPOSITION: This Technical Report may be destroyed when it is no longer
needed. Please do not return it to the U.S. Army Research for the Behavioral and Social
Sciences.

NOTE: The findings in this Technical Report are not to be construed as an official
Department of the Army position, unless so designated by other authorized documents.



SFAS Select Rate by APFT Score

Percent Selected in SFAS

10%.

60%.
50%,
40%.
30%.
20%.

10%.

0%

Less than 220 220-239 240-253 254 or higher

ores ABP score "109 ont

n= total sample size

SFAS Select Rate by Pullup Score

Percent Selected in SFAS

70%.
60%

50%.
40%.
30%,
20%.

10%.
0% (al oe
Less than3 3t07

("58 NumbeFotButtups 8)

Wal
8 or more

‘n= total sample size

B-5



Assignment of Points

1 point 2 points 3 points Total
1) GT <112 GT = 113 to 120 GT > 120
2) APFT < 228 APFT = 228 to 245 APFT > 245
3) Time in service > 3yrs Time in service =3yrs Time in service < 3yrs
4) Airborne = No Airborne = Yes
Decision Rules Sum Total

1" Priority (Top recruit): Sum total = 9 to 12 points

2" Priority (Moderate recruit): Sum total = 8 points

34 Priority (Provide counseling/delay entry): Sum total = 4 to 7 points

A-2



@SFAS Select

<3 Years 3 Years >3 Years
Years of Time in Service

Figure 1. SFAS Outcome by APFT Performance Category



than or equal to the 20" percentile, and had less than three years time in service. This group
demonstrated a success rate of 48% for the original FY90-FY96 sample.

In the final example, Group 4 represents the same standings as Group 3, but with the added
value of attaching a requirement for airbome training. Thus, these soldiers have an APFT score
greater than 245, Wonderlic greater than or equal to the 20" percentile, less than three years time
in service, and are airborne qualified. This group demonstrated a success rate of 55% for the
FY90-FY96 sample. Although Group 1 and Group 4 capture only a small percentage of soldiers
(1.3% and 4.9%, respectively), these categories demonstrate how these variables could be used to
determine the soldiers who are least or best suited for recruitment.

This category scheme was then applied to the FY98 SFAS data, shown in the second series in
Figure 4. While the overall select rate for FY98 was somewhat higher, the same relative pattern
of success can be seen across the four categories. Group 1showed an identical select rate of 8%,
Groups 2 and 3 both showed a higher select rate of 64%, and Group 4 showed the highest select

rate of 79%.

These category schemes represent only five variations of a much larger number of possible
combinations of these five variables (GT, APFT, Wonderlic score, time in service, airborne
qualification). The next step was to select a strategy that is user friendly and reasonably allows
identification of those who have or do not have a reasonable chance of success in SFAS.

Developing an Order of Merit List

It would be possible, by assigning values to differing levels of performance across these
variables, to develop a cumulative “Potential Success” score that could be used by recruiters to
develop an order of merit list. Because recruiters do not have access to Wonderlic scores, only
the other four variables examined in the previous category scheme were used to develop an order
of merit list procedure. A proposed scheme is represented in Table 1. The scoring of the
Potential Success index would be based on the total number of points generated from adding the
category ratings for GT, APFT, time in service, and airborne qualification variables.

Table 1. Points Assigned for scores on key variables.

Variable Low Points Mid Level Points High Points
GT score <112 1 113-120 2 >120 3
APFT <228 1 228-245 2 >245 3
Time in Service >3yrs 1 3yrs 2 <3yrs 3
Airborne No 1 n/a Yes 3

Assigning points to candidates using these rules results in a range of scores from a minimum
of 4 to a maximum of 12. As an example, a soldier who has a GT score of 115, an APFT score of
250, 3 years time in service, and is not jump qualified, would earn a score of 8. Select rates for
each of the scores on this index (4-12) were determined based on the FY98 sample and are shown
in Figure 5. As shown in the graph, as the Potential Success score increases, there is a gradual
increase in probability that a soldier will be selected. The data do, however, suggest a set of
reasonable break points. For example, a score of 7 or lower is indicative of a relatively poor risk
candidate (select rates below 35%). A score of 8 could be viewed as indicating a reasonable risk

‘