Technical Report 1183 

Review of Aviator Selection 

Cheryl Paullin

Personnel Decisions Research Institutes, Inc. 

Lawrence Katz 

U.S. Army Research Institute 

 

Kenneth T. Bruskiewicz and Janis Houston 

Personnel Decisions Research Institutes, Inc.

Diane Damos

Damos Aviation Services

 July 2006 

 United States Army Research Institute 

for the Behavioral and Social Sciences 

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Personnel Decisions Research Institutes, Inc. 

Technical review by 

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Tonia Heffner, U.S. Army Research Institute 

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Review of Aviator Selection 

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6. AUTHOR(S) 

Cheryl Paullin, (Personnel Decisions Research Institutes, Inc.);

Lawrence C. Katz (U.S. Army Research Institute); Kenneth T. 

Bruskiewicz and Janis Houston (Personnel Decisions Research 

Institutes, Inc.); Diane Damos (Damos Aviation Services)

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14. ABSTRACT (Maximum 200 words): 

 This report presents a review of research in the aviator selection and general personnel selection domains. That 

information was used to identify knowledge, skills, attributes, and other factors that should be included in a job 

analysis focusing on the Army aviator job. It was further used to develop a recommended strategy for an Army aviator 

selection battery. 

15. SUBJECT TERMS 

Aviator selection; selection; military; personality; psychomotor; cognitive; job analysis; review

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ii 
Technical Report 1183 

Review of Aviator Selection 

Cheryl Paullin

Personnel Decisions Research Institutes, Inc. 

Lawrence Katz

U.S. Army Research Institute 

Kenneth T. Bruskiewicz and Janis Houston 

Personnel Decisions Research Institutes, Inc.

Diane Damos

Damos Aviation Services

Rotary-Wing Aviation Research Unit 

William R. Howse, Chief 

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

2511 Jefferson Davis Highway, Arlington, VA 22202-3926 

July 2006 

Army Project Number Personnel Performance and

633007A792 Training 

Approved for public release; distribution is unlimited 

iii 
iv 
REVIEW OF AVIATOR SELECTION 

EXECUTIVE SUMMARY 

Research Requirement: 

In June 2004, the U.S. Army Research Institute for the Behavioral and Social Sciences 

(ARI) was tasked with conducting the research and development towards a new Selection 

Instrument for Flight Training (SIFT). The Army’s stated objectives were: 1. Develop a 

computer-based and web-administered selection instrument for Army flight training with 

emphasis upon aptitudes for Future Force aviator performance within the Future Combat 

Systems environment; 2. Develop an aviator selection instrument that corrects or minimizes risks 

associated with several deficiencies identified in the current selection instrument – the Alternate 

Flight Aptitude Selection Test (AFAST); 3. Develop the selection instrument so that the Army

will be able to rapidly assess its current performance as a predictor, revise the instrument when 

necessary and adapt its application to selection for related occupational categories such as 

Unmanned Aerial Vehicle Operators and Special Operations Aviators; and, 4. Maximize 

utilization (by inclusion or adaptation) of existing tests as may be found in use or under 

development within the Department of Defense. The first task was to review the relevant 

selection literature. The overall goal of this initial task was to collect information that could be 

used to produce a rational recommendation for a specific selection and testing strategy for Army

aviation. 

Procedure: 

A focused review of aviator selection research, supplemented by relevant research from 

the general personnel selection domain, was conducted. The review identified more than 150 

potentially relevant articles. Rather than rely entirely on a narrative summary, a spreadsheet was 

developed to summarize information about various test batteries and to facilitate comparison of 

the test batteries when deriving a recommended selection strategy. From this analysis, a 

selection strategy for replacing the Army’s current aviator selection battery was recommended. 

The results of this review also informed the job analysis study conducted as part of the SIFT 

project. 

Findings: 

Research clearly suggests that cognitive ability, or general intelligence (g), will be an 

important predictor of aviator performance. However, there is reason to believe that measures of 

the following constructs may add incremental validity beyond that achieved by a battery that 

reliably and accurately measures general intelligence: psychomotor skills; selective and divided 

attention; working memory; aviation interest/knowledge; flying experience; and, personality. 

The recommended selection strategy is a two-stage testing process. The first stage of testing will 

measure cognitive and personality/motivational traits important for the aviator job. These tests 

v 
do not require any non-standard computer peripherals and can be administered via the Internet in 

virtually any location with access to a desktop computer, the Internet, and a test proctor. The 

second stage of the test battery will include performance-based measures of psychomotor and 

information processing skills. These tests do require non-standard computer peripherals and may 

better serve the needs of Army aviation as classification instruments, for tracking selected 

aviators into one of the four mission platforms. Both the U.S. Navy and the U.S. Air Force 

currently use an aviator selection test battery that measures cognitive abilities important for U.S. 

Army Aviators, and one of these two batteries should be adopted for Army aviator selection. The 

U.S. Army also possesses two non-cognitive inventories that can be adapted for use with the 

Army aviator applicant population. Finally, it is recommended that a small number of new 

ability tests and non-cognitive scales be developed to measure abilities or traits that are not 

currently measured by any of the readily-accessible test batteries or non-cognitive instruments. 

Utilization and Dissemination of Findings: 

This product is one of many emanating from the SIFT effort. The contents of this report 

flow mainly into decision processes conducted internally to the project, but also documents the 

overall conduct of the effort. Documentation of the development of this selection instrument is 

necessary to provide a basis to defend the scientific and theoretical underpinnings of the test and 

to provide a detailed base from which revisions can be made in time. This report provides 

information for use in transition of the selection instrument into operation.

vi 
REVIEW OF AVIATOR SELECTION 

CONTENTS

Page 

INTRODUCTION ...........................................................................................................................1 

Overview of Existing Army Aviation Accession Procedures...................................................1 

Brief History of Aviator Selection ............................................................................................2

FOCUSED LITERATURE REVIEW.............................................................................................4 

Literature Review Methodology ...............................................................................................4 

Findings from Aviator Selection Research Literature...............................................................5 

General Aviator Selection Reviews.....................................................................................5 

OBSTACLES AND ISSUES IN CONDUCTING AVIATOR SELECTION RESEARCH ..........7 

Training Performance as a Criterion Measure ..........................................................................7 

Statistical/Methodological Issues..............................................................................................8 

Low Base Rate...........................................................................................................................9 

Predictor Variables...............................................................................................................9 

Criterion Variables.............................................................................................................10 

FACTOR-ANALYTIC WORK IN THE AVIATOR SELECTION RESEARCH 

LITERATURE.........................................................................................................................10 

MODELS OF SKILL ACQUISITION..........................................................................................12 

EVIDENCE OF PREDICTIVE VALIDITY FOR FLIGHT TRAINING PERFORMANCE .....13 

Damos (1993) Meta-Analysis .................................................................................................13 

Hunter and Burke (1994) Meta-Analysis................................................................................13 

Martinussen (1996) Meta-Analysis.........................................................................................16 

Martinussen and Torjussen (1998) Meta-Analysis .................................................................19 

Summary of Meta-Analytic Validation Studies......................................................................19 

PERSONALITY RESEARCH IN THE AVIATOR SELECTION ARENA................................21 

FINDINGS FROM GENERAL SELECTION RESEARCH LITERATURE...............................21 

INCREMENTAL PREDICTIVE VALIDITY ..............................................................................23 

Aviator Selection Research Literature.....................................................................................23 

General Selection Research Literature ....................................................................................24 

Summary of Incremental Validity Evidence ...........................................................................24 

GROUP DIFFERENCES...............................................................................................................25 

Cognitive Ability Tests ...........................................................................................................25 

Psychomotor Tests ..................................................................................................................26 

vii 
CONTENTS (continued)

Speeded Information Processing Tests....................................................................................26 

Personality and Temperament Measures.................................................................................27 

WHAT SHOULD THE ARMY MEASURE?...............................................................................28 

REVIEW OF EXISTING AVIATOR SELECTION TEST BATTERIES....................................29 

SELECTION STRATEGY RECOMMENDATIONS ..................................................................29 

BEST BET PREDICTOR MEASURES........................................................................................31 

Stage 1: Cognitive Measures...................................................................................................31 

Aviation Selection Test Battery (ASTB)...........................................................................31 

Air Force Officer Qualification Test (AFOQT).................................................................32 

Cognitive Prioritization (Popcorn Test).............................................................................33 

Perceptual Speed and Accuracy.........................................................................................33 

Stage 1: Non-Cognitive Measures...........................................................................................33 

Test of Adaptable Personality (TAP).................................................................................33 

Assessment of Individual Motivation (AIM).....................................................................33 

Self-Description Inventory Plus (SDI+) ............................................................................34 

Armstrong Laboratory Aviation Personality Scale (ALAPS) ...........................................34 

New Non-cognitive Scales.................................................................................................34 

Stage 2: Psychomotor Skills and Multiple-Task Performance 

(Performance-Based Measures)...............................................................................................35 

Test of Basic Aviation Skills (TBAS) ...............................................................................35 

Wombat ©..........................................................................................................................35 

New Performance-Based Measure.....................................................................................35 

CONCLUSIONS............................................................................................................................36 

REFERENCES ..............................................................................................................................39 

APPENDICES 

A. Overview of Aviator Selection Test Batteries................................................................. A-1 

B. Overview of Non-Cognitive Inventories that may be Relevant for Aviator Selection.....B-1 

C. Recommended Selection Strategy for Army Aviators .....................................................C-1 

LIST OF TABLES 

TABLE 1. HUNTER & BURKE (1994) META-ANALYTIC RESULTS FOR 

VARIOUS PREDICTOR TYPES..............................................................................15 

TABLE 2. MARTINUSSEN (1996) META-ANALYTIC RESULTS FOR VARIOUS 

MEASUREMENT METHODS .................................................................................18 

viii 
REVIEW OF AVIATOR SELECTION 

Introduction 

In June 2004, the US Army Research Institute for the Behavioral and Social Sciences 

(ARI) awarded the Selection Instrument for Army Flight Training (SIFT) contract to Personnel 

Decisions Research Institutes (PDRI). The Army’s stated objectives were: 1) Develop a 

computer-based and web-administered selection instrument for Army flight training with 

emphasis upon aptitudes for Future Force aviator performance within the Future Combat 

Systems environment; 2) Develop an aviator selection instrument that corrects or minimizes risks 

associated with several deficiencies identified in the current selection instrument – the Alternate 

Flight Aptitude Selection Test (AFAST); 3) Develop the selection instrument so that the Army

will be able to rapidly assess its current performance as a predictor, revise the instrument when 

necessary and adapt its application to selection for related occupational categories such as 

Unmanned Aerial Vehicle Operators and Special Operations Aviators; and, 4) Maximize 

utilization (by inclusion or adaptation) of existing tests as may be found in use or under 

development within the Department of Defense. 

The project was divided into several tasks. This report summarizes efforts conducted in 

relation to Task 1: Review the existing Army aviation accession process and relevant literature. 

The overall goal of Task 1 was to collect information that could be used to produce a rational 

decision on a specific selection and testing strategy. 

Overview of Existing Army Aviation Accession Procedures 

A review of existing Army aviation accession procedures was conducted to provide the 

context for recommending a replacement for the AFAST. This included reviewing Army

regulations and other documents. US Army aviators are Commissioned or Warrant Officers. 

Commissioned Officers primarily come from a military academy, or from a Reserve Officer 

Training Corps (ROTC) or Officer Candidate School (OCS) program. Civilians and enlisted 

personnel from any branch of the US military may apply to become an Army Aviation Warrant 

Officer. Prior to volunteering for aviation duty, candidates must meet standards for becoming a 

Commissioned Officer or a Warrant Officer in the US Army. Among other things, this includes 

meeting physical and medical standards, and earning a qualifying score on the relevant 

admission exam (Scholastic Aptitude Test or the American College Test for Commissioned 

Officers; Armed Services Vocational Aptitude Battery (ASVAB) General-Technical (GT) 

Composite for Warrant Officers). 

Candidates who apply to become an Army aviator must meet additional standards beyond 

those described above. The selection process is rigorous and there are typically five to ten 

applicants for every available training seat. Selection standards are highly similar across all 

accession sources but the exact procedures vary to some degree, depending on whether the 

applicant is a Commissioned versus a Warrant Officer, the source from which he/she comes 

(e.g., US Army versus US Army National Guard or Reserve), and whether or not the applicant is 

already on active duty at the time of application. In general, all Army Aviator applicants must 

meet physical fitness and medical standards beyond those required to become a Commissioned 

or Warrant Officer, meet minimum and maximum age requirements, earn a qualifying score on 

1 
the AFAST, and be recommended by a selection board. Flight experience and post high-school 

coursework or degree are preferred but not required. 

The following Army regulations (AR) and other documents outline selection and testing 

requirements: 

● Selection and Training of Army Aviation Officers (AR 611-110, 14 Nov 2003) 

● Aviation Warrant Officer Training (AR 611-85, 15 June 1981) 

● Army Personnel Selection and Classification Testing (AR 611-5, 10 June 2002) 

● Appointment of Commissioned and Warrant Officers of the Army (AR 135-100, 1 

Sept 1994) 

● Warrant Officer Procurement Program (Department of the Army Circular 601-99-1, 23 

April 1999) 

● Warrant Officer Professional Development (Department of the Army Pamphlet 600-

11, 30 Dec 1996) 

● Order to Active Duties as Individuals Other than a Presidential Selected Reserve Callup, Partial or Full Mobilization (AR 135-210, 17 Sept 1999) 

● Policies and Procedures for Active-Duty List Officer Selection Boards (Department of 

the Army Memo 600-2, 24 Sept 1999) 

After candidates are selected as Army aviators, they report to Ft. Rucker, AL for training. 

All candidates complete an 18-week Initial Entry Rotary Wing (IERW) core training program

and a two-week Basic Navigation course, followed by 12 to 20 weeks of training in a specific 

operational aircraft. Student aviators are assigned, or “classified” into one of four tracks for 

aircraft-specific training: Scout, Attack, Cargo, or Utility. Classification decisions are currently 

based in part on academic grades in IERW and in part on the needs of the Army. Upon 

completion of aircraft-specific training, aviators are assigned to a Military Occupational 

Specialty (MOS) that corresponds to the type of aircraft they are qualified to fly, and they begin 

their first operational tour as an Army Aviator. 

Brief History of Aviator Selection 

The prediction of aviator performance played a prominent role in the military research 

and development arena for most of the last century. In a review of aviator selection research, 

Hunter (1989) explained that this continued emphasis is a result of the expense involved in 

aviator training, noting that, almost without exception, aviator training is the most expensive of 

the training programs conducted by the military services. The US Navy estimates that the sunk 

costs for student aviators who fail training range from $500,000 to $1,000,000, depending on the 

stage at which failure occurs (Helm & Reid, 2003). According to Carretta and Ree (2000), 

estimates of the cost of each person who failed to complete US Air Force (USAF) undergraduate 

aviator training range from $50,000 (Hunter, 1989) to $80,000 (Siem, Carretta, & Mercatante, 

1988). The amount approaches $500,000 per candidate by the end of flight school for US Army

aviators. 

2 
Since World War I, the military services have explored the relationships between 

measures of a wide variety of personal characteristics and aviator performance. As early as 

World War I, tests of mental alertness and emotional stability were found to be predictive of 

aviator success (North & Griffin, 1977). Between World War I and World War II, measures of 

psychomotor coordination received the primary emphasis in aviator selection research. A flurry 

of developmental activity produced “aircraft-like controls” for use in measuring complex 

coordination, two-hand coordination, rudder control skills, dual-task performance, and the like. 

A number of these psychomotor tests, especially those of a more complex nature, were found to 

be valid for aviator selection. However, in the early 1950s psychomotor tests were largely 

abandoned as a result of persistent problems with reliability and maintainability of these 

electromechanical devices (Hunter, 1989). 

With the advent of World War II, research on aviator selection and classification 

expanded to include measurement of additional abilities such as spatial orientation and the use of 

new testing tools (e.g., motion pictures, photographs). Much of what is known today about 

spatial and psychomotor abilities, as well as several other related attributes, stems from the 

classic Army Air Force (AAF) work (Guilford & Lacey, 1947; Melton, 1947) and the Navy’s 

Pensacola 1000 Aviator Study (Franzen & McFarland, 1945). After the war, Fleishman and his 

colleagues continued psychomotor abilities research (e.g., Fleishman, 1967, 1972; Fleishman & 

Hempel, 1954). Researchers also investigated personality characteristics related to attrition from 

aviator training and/or aviator performance (Griffin & Mosko, 1977). 

Within the last few decades, innovations in aviator selection and classification have 

centered on attributes such as multi-task performance (e.g., Griffin & McBride, 1986), division 

of attention (e.g., Carretta, 1987d), decision making speed (e.g., Carretta, 1988), and attitudinal 

and motivational traits (Foushee & Helmreich, 1986; Helmreich, Foushee, Benson & Russini, 

1986). Personality also received a good deal of attention in the past two decades. Much of the 

early work was exploratory in nature, attempting to determine which personality traits were 

related to various outcomes relevant for aviators, but not necessarily guided by any particular 

theory of personality or aviator performance. For example, several researchers administered 

personality inventories that had been well established as useful for purposes other than aviator 

selection, including the Minnesota Multiphasic Personality Inventory (Caldwell, O’Hara, 

Caldwell, Stephens, & Krueger, 1993), Eysenck Personality Inventory (Bartram & Dale, 1982; 

Jessup & Jessup, 1971), and the Edwards Personal Preference Schedule (Fry & Reinhardt, 1969). 

Other researchers developed their own inventory, for example, the programmatic research 

conducted by the USAF that eventually led to the NEO-PI and the Self-Description Inventory 

(Christal, 1975; Christal, Barucky, Driskill, & Collis, 1997; Tupes & Christal, 1961). 

Some of the research specifically focused on developing personality profiles for 

helicopter aviators (Caldwell, et al., 1993; Geist & Boyd, 1980; Harrs, Kastner, & Beerman, 

1991; Howse, 1995). Another arena of increasing importance is selection of individuals to fly 

unmanned aerial vehicles (UAVs). For example, US Navy researchers have examined the 

validity of a test battery designed to measure psychomotor, multi-tasking, and visuospatial 

abilities in a small sample of UAV operators, with promising results (Phillips, Arnold, & 

Fatolitis, 2003). 

3 
This report describes the specific procedures, findings, and implications of a focused 

review of the aviator selection literature. As an initial step in the development of SIFT, the goal 

of this review was to produce a rational recommendation for a specific selection and testing 

strategy for Army aviation. Therefore, consideration was given to methodological limitations 

and obstacles in conducting selection research, as well as to the incremental validities and 

practical issues associated with the tests being studied. 

Focused Literature Review 

As noted above, aviator selection and classification research has been conducted since the 

1920’s and a tremendous amount has been written on this subject. This focused literature review 

was designed to provide a research-based foundation for a recommended selection strategy. 

Therefore, no attempt was made to review every aviator selection study that has ever been 

conducted. Rather, the focus was on key studies related to currently or recently available 

selection batteries, particularly those studies conducted by the US military. 

The specific goals for conducting this literature review were to: 

1. Review studies that delineate the knowledge, skill, ability, and other characteristics 

(KSAOs) important for performing the aviator job, with particular emphasis on studies 

that involve helicopter aviators. This information would help inform the job analysis 

phase of the project. 

2. Review studies that focus on aviator selection batteries currently (or recently) in use 

by the US Air Force, US Navy, and other relevant organizations (e.g., foreign military,

commercial airlines). 

Literature Review Methodology 

The first step in this task was to identify currently or recently available test batteries that 

might be viable candidates for consideration as a replacement for the AFAST and, once those 

were identified, to locate and summarize key research about them. This step requires 

consideration of a wide range of possible tests or test batteries, with the expectation that, at a 

later date, a number of potential candidates would be ruled out with relative ease (e.g., test 

batteries that cannot be computerized or ones that involve prohibitively expensive licensing 

fees). There was a possibility that one or more existing test batteries would be recommended as 

an intact entity, with minimal changes, or of recommending specific subtests from a variety of 

existing batteries. 

Seven on-line databases were searched first, to obtain pertinent literature. These included 

PsychInfo, Defense Technical Information Center (DTIC), the Air Force Research Laboratory 

Research Archive Library, the Civil Aeromedical Institute database of technical reports, the 

Naval Medical Research Laboratory database of technical reports, and the archives of the Human 

Factors and Ergonomics Society (HFES). The HFES database covers all of the Society’s 

publications, including the Society’s bulletin and magazine. The sixth database searched was the 

United States Air Force Human Resources Laboratory (AFHRL, 1968-1998) Topics, hosted by 

the Innovation Center for Occupational Data, Applications, and Practices. All of these databases 

were searched using terms such as “aviator selection,” “ab initio” (from the beginning), 

“personality,” and “psychomotor.” Personnel Decisions Research Institutes (PDRI) also 

4 
searched its archives for articles and technical reports related to aviator selection, based on prior 

work with the US Air Force, particularly in the area of Crew Resource Management (CRM).

In addition, the Damos Aviation Services (DAS) database was searched, which consists 

primarily of articles related to aviator selection and performance. This database currently has 

over 3800 entries. The earliest entry pertaining to aviator selection in the DAS library dates 

from 1921. It contains references to both civilian and military aviator selection, and a substantial 

proportion of the entries are concerned with foreign aviator selection. The DAS database covers 

all of the International Journal of Aviation Psychology, all of the Proceedings of the 

International Symposium on Aviation Psychology, and the last 19 years of Aviation, Space, and 

Environmental Medicine. Any recent materials that had not yet been entered into the database

were searched by hand. Hand searches also were conducted on recently edited books that had 

not yet been entered into the database. Several individuals involved with aviator selection were 

also contacted to obtain updates on their current aviator selection research projects. 

Findings from Aviator Selection Research Literature 

Most of the research on aviator selection has been conducted by the military in the United 

States, the United Kingdom, and Norway. Some research was also published by military 

organizations in other countries (e.g., Israel, Turkey) and in the commercial sector. The Federal 

Aviation Administration (FAA) and National Aeronautics and Space Administration (NASA) 

have both conducted research in the arenas of cognitive and non-cognitive testing. Of most 

relevance for the present research is work conducted by NASA in the area of personality traits 

impacting aircrew performance (e.g., Helmreich, Foushee, Benson, & Russini, 1986; Musson, 

Sandal, & Helmreich, 2004) and work originated by the FAA’s Civil Aeromedical Institute 

(CAMI) on a test battery called CogScreen (King & Flynn, 1995). The following sections 

summarize key research found in the aviator selection research literature, as well as in the 

general selection research literature. 

General aviator selection reviews. A number of reviews of the aviator selection literature 

have been published (Carretta & Ree, 2000, 2003; Dolgin & Gibb, 1988; Griffin & Koonce, 

1996; Hunter, 1989; North & Griffin, 1977; Ree & Carretta, 1996, 1998; Rogers, Roach, & 

Short, 1986; Tirre, 1997; Turnbull, 1992), including one that focuses specifically on 

methodological difficulties and common shortfalls associated with such research (Damos, 1996). 

In their review of aviator selection methods, Carretta & Ree (2000) state, “Research results point 

to g [general intelligence] as the most important underlying construct in the prediction of aviator 

success. Clearly, three others have been shown to be important but to a smaller degree: flying 

job knowledge, personality, and general psychomotor ability” (p. 31). These authors note that, 

“Simulation-based tests may significantly increment the validity of cognitive tests when the two 

approaches are used together. These results are consistent with a large-scale meta-analysis of 19 

commonly used personnel selection methods across many occupations (Schmidt & Hunter, 

1998)” (p. 24). Regarding personality measures, Carretta and Ree comment that a great deal of 

research has been conducted in this area, with contradictory results. They go on to say that 

organizing the results according to the Big Five personality variables of Neuroticism, 

Extraversion, Openness, Agreeableness, and Conscientiousness (Norman, 1963; Tupes & 

5 
Christal, 1961) would likely be enlightening, but has not (yet) been done in the aviator selection 

arena. 

Griffin and Koonce (1996) also wrote a comprehensive review of aviator selection, with 

particular emphasis on measures of psychomotor skills. They review numerous research studies 

showing that several types of predictor measures are valid for predicting aviator performance, 

including: 

• aptitude (cognitive ability); 

• psychomotor skills; 

• work simulation; 

• divided attention (or multiple-task performance); 

• flying experience; and, 

• biographical information. 

According to these authors, uncorrected, zero-order correlations for psychomotor skills 

are in the .30 to .40 range and multiple regression correlations are in the .50 range in research 

studies involving continuous criterion measures such as instructor check/flight ride ratings. With 

regard to measures of psychomotor skills, the authors concluded, 

Automated versions of vintage psychomotor tests (developed in the 1930s and 1940s) 

seem to be as predictive of military aviator/aviator performance today as in the past. The 

use of computers may have enhanced the predictive power of the psychomotor tests by 

making their functioning dependent on digital electronic circuitry, rather than analog 

electromechanical devices, resulting in more reliable performance measurement. The 

psychomotor tests receiving the most attention today are the CCT [complex coordination 

test] and the THCT [two-hand coordination test], originally developed by Mashburn and 

colleagues before World War II (Mashburn, 1934). These tests were significant 

predictors of USAF and Navy pass-fail criteria in the past, and automated versions are 

predictive today. However, the tests are better predictors of normally distributed, 

continuous criteria such as flight grades and number of flight hours for the Navy and 

check rides and advanced training ratings for the USAF [than of traditional pass-fail] (p. 

143). 

Tirre (1997) made a useful distinction between two different approaches to aviator 

selection:

• Basic attributes – In this approach, the test battery measures specific attributes that 

are assumed to underlie aviator performance. Examples of this approach include the 

USAF’s Air Force Officer Qualifications Test (AFOQT) and Basic Aviator Test 

(BAT; Carretta, 1987a). 

• Learning sample (simulation) – In this approach, the test battery simulates tasks 

performed in flight, with varying degrees of realism. An example is the Canadian 

Automated Pilot Selection System (CAPSS). 

6 
Each approach has advantages and disadvantages, many of which are outlined by Tirre. 

The basic attributes approach has a long history outside aviator selection and is generally less 

costly and time-consuming to develop and administer than the learning sample approach. In fact, 

it has only been possible to use the learning sample approach widely and effectively with the 

advent of powerful desktop computers. The learning sample approach offers the advantage of 

dynamic (as opposed to static) measurement of cognitive processing skills and often involves 

measures that appear very realistic to test-takers. With either approach, the reliability and 

validity of the measurement tool depends critically on how carefully it was developed. 

Obstacles and Issues in Conducting Aviator Selection Research 

There are several obstacles to conducting research studies in the aviator selection domain, 

many of which have been recognized for a long time and many of which are exceedingly 

difficult to overcome. These issues have been described in several of the preceding reviews 

(e.g., Carretta & Ree, 2000; Damos, 1996), and the most important ones are summarized below. 

When reviewing the literature, it became clear that some researchers recognized these obstacles 

and acknowledged how their study results and conclusions were likely impacted; many others 

did not. 

Training Performance as a Criterion Measure 

The criterion measure in aviator selection research studies is almost always a measure of 

training performance. While training performance is clearly an important outcome measure, it 

certainly is not the only outcome variable of interest. Unfortunately, it is exceedingly difficult to 

obtain reliable and accurate measures of aviator performance after training. The reliance on 

training performance as a criterion measure is particularly problematic because researchers are 

typically unable to differentiate various types of “failure.” Different abilities or traits may 

underlie different types of failure, but the pattern of relationships will be difficult or impossible 

to detect if there is no way to identify and code the reason(s) for failure. 

The reliance on training outcome measures is also problematic when attempting to 

evaluate the validity of predictor measures that would not necessarily be expected to predict 

training performance (e.g., personality measures). Research conducted as part of the US Army’s 

Project A shows that measures of cognitive ability predict declarative knowledge and technical 

components of performance (McCloy, Campbell, & Cudeck, 1994) while measures of noncognitive characteristics predict motivational aspects of job performance (Campbell, Hanson, & 

Oppler, 2001; McCloy, Campbell, & Cudeck, 1994) and contextual performance (Borman, 

Penner, Allen, & Motowidlo, 2001; Campbell, Harris, & Knapp, 2001; Campbell & Knapp, 

2001). While motivational factors certainly play a role in training performance and most 

students are highly motivated to succeed, the type of training criterion measures typically used in 

aviator selection research do not separate technical performance and motivational aspects of 

performance. Thus, training criterion measures are likely more heavily weighted toward 

academic and technical aspects of performance (e.g., flight instructor ratings, grades, pass-fail 

status) and less heavily on motivational aspects of performance. 

7 
Statistical/Methodological Issues 

Aviator selection research is plagued by a number of statistical and methodological 

issues. Some of them are extremely difficult, if not impossible, to overcome. 

1. Using predictor or criterion measures of low or unknown reliability. In many cases, 

the reliability of predictor and criterion measures is not reported and may be quite 

low, particularly in the case of criterion measures. Thus, the impact of unreliable 

measurement on the outcomes of the study cannot be evaluated. 

2. The most common criterion measure is a dichotomous variable – pass-fail status at 

the end of training. When working with dichotomous criterion variables, the highest 

possible value of a correlation between any predictor measure and that criterion 

variable depends on the distribution of the dichotomous variable. The maximum 

possible value of the correlation is lower the more the distribution varies from a 50-50 

split. For aviator training pass-fail status, the pass-fail distribution is usually much 

more extreme than 50-50. It is possible to correct the correlation coefficient for 

dichotomization, and some researchers did this. It is important to note that, while 

pass-fail performance in training is impacted by the attitudes and skills of student 

aviators, it is also impacted by the policies of aviator accession and training 

organizations. When there is a strong need for aviators, for example during war, 

there is strong pressure to ensure that virtually all students will pass training. In 

addition, most aviator training programs make every effort to ensure that most 

students pass training because it is very costly to fail a candidate after several weeks 

of expensive training. 

3. Aviator selection research is based on a highly selected and homogeneous 

population. Before they begin an aviator training program, all applicants have been 

extensively screened, including meeting a required minimum score to enter the 

military, meeting a required minimum score on an aviator aptitude battery, meeting 

education requirements, and/or earning strong, positive evaluations and 

recommendations from a superior officer or a selection board. The samples used in 

most aviator selection research are also typically highly homogeneous in terms of 

race and gender. Screening occurs in multiple stages, with each stage serving to 

further restrict the sample relative to the general population. Correlations can be 

corrected for some types of range restriction, but there is disagreement about the 

extent to which such corrections should be made. Damos (1996) argues that it is not 

appropriate to make such corrections because aviators will never be selected from an 

unrestricted sample. In addition, some types of restriction cannot be corrected for, 

including the demographic composition of the sample. 

4. Failure to correct for capitalization on chance. A number of researchers in the 

aviator selection domain have used regression techniques to evaluate the validity of a 

test battery, without recognizing or correcting for the fact that such techniques 

capitalize on chance variations present in their sample. The reported multiple 

correlation may not generalize to a new sample, especially if the original sample was 

not very large. 

8 
5. Small sample sizes. In some aviator selection research, the sample is very small. 

This means there may have been very little power to detect significant relationships 

even if they did exist. 

6. Measurement method is confounded with measurement target. As Carretta and Ree 

(2000), Hough (2001), and others have noted, in some research studies, measurement 

method (e.g., biodata or personality inventory) is confounded with the measurement 

target (i.e., KSAOs). In some cases, there is a close correspondence between 

measurement method and measurement target. For example, “psychomotor tests” 

virtually always measure one or more psychomotor abilities, and typically very little 

else. In contrast, the “biodata” or “personality” measurement method can be used to 

target leadership tendencies, conscientiousness, stress tolerance, psychopathology, 

motivation, or other KSAOs. Summarizing findings across all biodata inventories or 

all personality inventories tells us little about which underlying traits are more and 

less predictive of aviator performance. The situation is worsened by the fact that not 

all biodata and personality inventories measure the same targets. Thus, across 

studies, there may be a great deal of variation in the extent to which relevant and 

irrelevant KSAOs are measured. 

Low Base Rate 

Predictor variables. Some tests are designed to identify applicants, aviator trainees, or 

experienced aviators who have a severe psychopathological problem or a neurological deficit. 

Tools such as CogScreen, dichotic listening tests, and the MMPI have been used for this purpose. 

King and Flynn (1995) describe CogScreen as “a self-administered screening tool, in which the 

subject uses a light pen on a cathode ray tube monitor. CogScreen may be superior to traditional 

neuropsychological testing in determining cognitive deficits after a central nervous system injury 

or dementing disease . . . . The CogScreen is very sensitive to the nuances of neuropsychological 

functioning and can be administered in a group setting” (p. 954). No validity studies for 

CogScreen could be located. The USAF explored the possibility of using CogScreen for aviator 

medical screening, but it was never used operationally for aviator selection. The US Navy is 

currently including CogScreen, or a variation of it, in their ongoing studies to enhance aviator 

selection. 

Severe psychopathology and neurological deficits are rare in the general population, and 

are even rarer in the highly-selected population of aviators (including applicants and trainees). 

While it may be exceedingly important to identify individuals in the aviator population who 

might or will experience these problems, doing so is literally like “looking for a needle in a hay 

stack.” The low base rate for these problems makes it extremely difficult to show a statistically 

significant relationship between test scores and outcome measures, even if the test is valid. In 

past research, the failure to find significant correlations for these types of tests was sometimes 

inappropriately generalized to all tools of a particular type, for example, all personality 

inventories. Callister, King, Retzlaff, and Marsh (1999) point out, “Testing for psychopathology 

has been shown to be of limited value in the assessment of the highly-functioning aviator 

population. On the other hand, measures of normal personality characteristics have been shown 

to be useful in a variety of settings and populations” (p. 885). 

9 
Criterion variables. As noted above, the most common criterion measure is pass-fail 

status at the end of aviator training, and the base rate for failure is typically low. The low base 

rate issue becomes even more extreme when researchers attempt to categorize failure according 

to type or reason, for example, failure due to lack of technical competence versus failure due to 

attitudinal problems, or when the training failure rate is mandated by policy to be extremely low. 

Factor-Analytic Work in the Aviator Selection Research Literature 

In spite of the aforementioned limitations, selection test developers have continued to 

search for measures that might predict aviation performance. Accordingly, researchers have 

factor analyzed scores on several aviator selection batteries to uncover which constructs yield 

incremental predictive validity. Most of the work was conducted by USAF researchers. Several 

of these studies are summarized below. 

Carretta and Ree (1997a) administered the Armed Services Vocational Aptitude Battery 

(ASVAB) and 17 psychomotor tests to enlisted USAF personnel (n = 429). They summarized 

their findings as follows: 

Confirmatory factor analysis yielded higher-order factors of general cognitive ability (g) 

and psychomotor/technical knowledge (PM/TK). PM/TK was interpreted as Vernon’s 

(1969) practical factor (k:m). In the joint analysis of these batteries, g and PM/TK each 

accounted for about 31% of the common variance. No residualized lower-order factor 

accounted for more than 7%. PM/TK influenced a broad range of lower-order 

psychomotor factors. The first practical implication of these findings is that psychomotor 

tests are expected to be at least generally interchangeable. A second implication is that 

the incremental validity of psychomotor tests beyond cognitive tests is expected to be 

small (p. 165). 

Ree & Carretta (1992) conducted a similar study, using the ASVAB and three 

psychomotor tests from the Basic Aviator Test (Carretta, 1987a). The sample was 354 USAF 

enlisted recruits. They found that the two types of tests correlated with each other, with average 

correlations in the .30’s (corrected for range restriction, but not for test unreliability). They also 

found that, as expected, there was a large first factor, which they labeled “psychometric g,” and 

that both the ASVAB and the psychomotor tests loaded on it. Confirmatory factor analyses 

revealed that both a seven-factor and a nine-factor model fit the data equally well. The more 

parsimonious seven-factor model includes psychometric g and a higher-order general 

psychomotor factor which accounted for 57% and 9% of the total variance respectively. Other 

factors included 1) Verbal-Technical (accounted for an additional 8% of the variance), 2) Nontechnical General Knowledge (10%), 3) Time-Sharing (4%), 4) Two Hand Coordination (7%), 

and 5) Complex Coordination (5%). 

Carretta and Ree (1998) compared the factor structure of the ASVAB with the factor 

structure of the AFOQT. The factor structure for each test battery was derived in a different 

sample of USAF personnel, because the two batteries differ in difficulty level and intended 

audience (with the ASVAB being taken by all Air Force applicants and the AFOQT being taken 

by Flight Officer applicants). The authors conclude “The AFOQT is comprised of five lowerorder factors: verbal, math, spatial, aircrew, and perceptual speed which accounted for 20% of 

the total variance, and g in hierarchical position accounted for 41% of the total variance. 

10 
Compared with the ASVAB, the AFOQT was less saturated [with g] but had more common 

factors and had a greater proportion of its variance associated with common factors” (p. 12). 

Carretta, Retzlaff, and King (1997) compared the AFOQT and the Multidimensional 

Aptitude Battery (MAB). The MAB is a broad-based test of intellectual ability patterned after 

the Wechsler Adult Intelligence Scale but designed for group administration. The sample in this 

study was approximately 2,200 USAF aviator candidates. A joint factor analysis of the AFOQT 

and the MAB revealed that each battery had a hierarchical structure. The correlation between the

higher-order factors from the two batteries was .981, indicating that both measured the same

thing, which these authors conclude is general intelligence (g). 

Ambler and Smith (1974) analyzed data for the seven tests of the Guilford-Zimmerman 

Aptitude Survey, the Hidden Figures Test, and four subtests from the US Navy-Marine Corps 

aviation selection battery [1) Aviation Qualification, which includes reading, math, and science 

questions related to a typical college experience; 2) Mechanical Comprehension; 3) Spatial 

Apperception; and 4) Biographical Inventory]. Scores were available for approximately 1,700 

aviation trainees (presumably all male, given that the study was published in 1974). The 

researchers factor analyzed the subtest scores in the total sample and in various subsamples and 

found that six factors appeared consistently across samples, which they labeled Mechanical, 

Spatial Manipulation, Perceptual Flexibility, Verbal Intelligence, Numerical Intelligence, and 

Flight Motivation. 

Martinussen and Torjussen (1998) factor analyzed scores on a multi-aptitude test battery 

used for aviator selection into the Norwegian Air Force. The battery is administered in a multistage process. Stage 1 includes 12 subtests intended to measure General Intelligence, Technical 

Comprehension, and Spatial Ability. Stage 2 includes seven subtests intended to measure 

Simultaneous Capacity and Orientation Ability. Finally, Stage 3 includes a personality inventory 

called the Defense Mechanism Test (DMT) which is described as a measure of psychodynamic 

defense mechanisms and was developed for use in selecting persons into high-risk professions. 

Very little information is provided about any of the subtests. 

The authors randomly selected 450 applicants from the applicant pool who had Stage 1 

and Stage 2 scores, and factor-analyzed the scores using Principal Component Analysis with 

Varimax rotation. The tests included in each stage were factor-analyzed separately. Three 

factors, labeled Mechanical Comprehension and Spatial Ability, Verbal Ability, and Numerical 

Reasoning accounted for 61% of the variance in the Stage 1 tests and three factors, labeled 

Spatial Ability, Time Estimation, and Perceptual Speed and Coordination, accounted for 62% of 

the variance in the Stage 2 tests. 

In summary, factor analyses of several aviator selection batteries suggest that it is 

possible to derive a hierarchical general intelligence factor, with sub-factors related to verbal 

ability, numerical ability, mechanical ability, spatial ability, and perceptual speed/flexibility. A 

general psychomotor factor, with some specific sub-factors also appears when the test battery 

explicitly contains psychomotor tests.

The factor-analytic work in the aviator selection domain is consistent with research 

conducted on the structure of human abilities that is not entirely based on military or aviator test

11 
data (e.g., see Fleishman & Mumford, 1988; Lubinski & Dawis, 1992; McHenry & Rose, 1988; 

Russell, Reynolds, & Campbell, 1994). It is worth noting that, with the exception of one subtest 

in the US Navy-Marine Corps selection battery (Ambler & Smith, 1974), the aviator selection 

batteries included in the factor-analytic studies described above did not include measures of noncognitive traits. It is not particularly surprising, then, that no underlying non-cognitive factors 

were found. 

Models of Skill Acquisition 

This section examines more closely the hierarchical general intelligence factor derived by 

the factor analyses described above. Specifically, the question, “How does intelligence relate to 

skill acquisition during flight training?” is addressed. 

Ackerman (1987; 1988; 1990) developed a model of skill acquisition that is applicable to 

the development of piloting skills. The theory is founded on the concept of attentional resource 

allocation, that is, the amount of attentional resources required by various tasks at various points 

in time, and the amount of attentional resources that individuals can bring to bear in any given 

situation. Ackerman’s model divides skill acquisition into three broad phases, with a 

corresponding type of ability that is the primary predictor of performance within each phase. In 

Phase I, the primary learning task is to comprehend the new task. Declarative knowledge and 

general intelligence are the primary predictors of performance in this phase. In Phase II, the 

primary learning task involves integrating the cognitive and motor processes required to perform 

the task. In this phase, knowledge compilation and perceptual speed are the primary predictors of 

performance. In Phase III, task performance becomes proceduralized (or automatic), and thus 

requires fewer attentional resources. Procedural knowledge and psychomotor abilities are the 

most important predictors in this phase. Tasks vary in the extent to which they can be 

proceduralized. In Ackerman’s terminology, tasks that can become proceduralized are called 

consistent tasks; those that cannot become proceduralized are called inconsistent tasks. 

According to Ackerman’s theory, general cognitive ability is expected to be most 

important during the early stages of skill acquisition for all tasks and to remain important for 

inconsistent tasks. Processing speed is expected to be most important during intermediate stages 

of learning for any task. Psychomotor skills will become increasingly important as a task 

becomes better-learned, but may only outstrip cognitive ability in importance for inconsistent

tasks. Keil and Cortina (2001) found confirmatory evidence for the relationship between 

cognitive ability and performance on consistent and inconsistent tasks but did not find support 

for the relationship between perceptual speed and psychomotor skills and consistent and 

inconsistent tasks. Additional research by Ackerman and colleagues shows that both ability and 

non-ability factors (e.g., personality, vocational interests, motivation, and self-concept) play a 

role in determining performance on complex (inconsistent) tasks (Ackerman, Kanfer, & Goff, 

1995; Ackerman & Woltz, 1994). 

Ree, Carretta, and Teachout (1995) developed a causal model to explore the role played 

by general intelligence (g) and prior knowledge of flying on performance during aviator training. 

The measures of g and prior flying knowledge were based on AFOQT composite and subtest 

scores collected at the time of application to flight training. Criterion measures included 

measures of job knowledge (academic classroom performance) and work samples (check ride 

performance) collected at various points during a 53-week training program. When the model 

12 
was tested in a large sample of USAF aviator trainees (n = 3,428 males), the authors found that g 

directly influenced the acquisition of flight knowledge both prior to and during training and 

indirectly influenced work sample performance through the acquisition of job knowledge. Prior 

knowledge of flying had almost no influence on acquisition of job knowledge during the 

academic portions of aviator training, but directly influenced performance on early work sample 

measures. Early work sample performance was very strongly related to later work sample 

performance. Carretta and Ree (1997b) tested the same model in a sample of male USAF 

aviators (n = 3,369) and in a small sample of female USAF aviators (n = 59). The basic model 

was supported and appeared to work similarly for males and females, although the female 

sample was too small to draw any strong conclusions. 

Evidence of Predictive Validity for Flight Training Performance 

An enormous number of validation studies have been conducted in the aviator selection 

domain – too many to cover in this report. Fortunately, several meta-analyses focusing on the 

validity of selection tests have been published. In all the studies, measurement method is 

confounded with measurement target (KSAOs) to at least some degree. This section describes 

meta-analyses addressing validity evidence. 

Damos (1993) Meta-Analysis 

The first meta-analysis of aviation performance predictors was published by Damos in 

1993. She meta-analyzed 12 studies that involved a single-task performance-based measure, for 

example, tracking or dichotic listening, and 14 studies that involved multiple-task performancebased measures, that is, two or more single-task measures administered simultaneously, such as 

tracking plus dichotic listening. The mean correlation (uncorrected) between single-task 

performance and flight grades was .18 (n = 5,378); the correlation between multiple-task 

performance and the same criterion was .23 (n = 6,920). Moderator analyses suggested that the 

level of validity for multiple-task performance-based measures depended on the type of sample 

(military versus civilian) and level of flight experience (students versus fully-trained aviators), 

with higher validity in studies with a civilian sample or with a fully-trained aviator sample. 

Hunter and Burke (1994) Meta-Analysis

The second meta-analysis was published by Hunter and Burke in 1994.1

 They reviewed 

200 studies published between 1940 and 1990 that involved aircrew selection. Sixty-nine studies 

contained one or more usable validity coefficients, and the authors located or derived 468 

validity coefficients from these studies. It is worth noting that studies reporting only a composite 

score based on a multi-aptitude test battery were excluded from the meta-analysis. The majority 

of the validity coefficients were based on studies conducted in the US (77%), involving a 

military sample (94%), and/or a sample that was training to fly fixed-wing aircraft (86%). Most

of the studies used dichotomous pass-fail criterion measures (84%) which, as noted above, places 

 

1

 An earlier version of this meta-analysis was also published in Hunter and Burke (1992). The general

findings are the same in the two versions, but the specific values cited for various predictor types is not 

exactly the same. 

13 
a ceiling on the maximum possible correlation, with a lower ceiling to the extent the criterion 

distribution departs from a 50-50 split (as is likely the case in virtually all of the studies). 

Hunter and Burke (1994) categorized each validity coefficient according to one of 16 

predictor types. They then applied bare-bones meta-analytic procedures (Hunter & Schmidt, 

1990). Table 1 is adapted from a table of results published in Hunter and Burke (1994). It 

shows, for each predictor type, the mean sample-weighted validity (uncorrected), the percentage 

of variance explained by sampling error, and the lower bound for the 95% confidence interval.2

 

The predictor type with the highest mean sample-weighted validity is “Job Sample.” The 

authors do not describe this predictor type, but one might speculate that it includes flight 

simulation tests. Mechanical ability, gross dexterity, reaction time, biodata inventory, and 

information (General or Aviation) predictors also showed relatively high validity and a 

confidence interval that did not include zero. Recall that “biodata” is a measurement method. 

There is no way of determining, from this meta-analytic review, what KSAOs were measured. 

After conducting the bare-bones meta-analysis, Hunter and Burke applied two validity 

generalization decision rules: 1) Does sampling error account for more than 75% of the variance 

in observed validities? and 2) Does the 90% credibility limit include zero? Answering “no” to 

the first decision rule allows one to conclude that validity is generalizable across samples and 

settings. Answering “no” to the second decision rule allows one to conclude that the true 

validity in the population is greater than zero. None of the predictor types included in this metaanalysis met the first decision rule, but several met the second. For these predictor types, it is 

reasonable to believe that the true validity is greater than zero in any setting or sample, but the 

level of validity may vary from one setting or sample to another: Quantitative Ability; Spatial 

Ability; Mechanical; Aviation Information; General Information; Gross Dexterity; Perceptual 

Speed; Reaction Time; Biodata Inventory; and, Job Sample. 

 

2

 Hunter and Burke (1994) claim that, in keeping with decision rules established by Hunter & Schmidt

(1990), they calculated and used the 90% credibility limit, rather than the 95% confidence interval. In their 

table of results, however, they report the 95% confidence interval. 

14 
Table 1 

Hunter and Burke (1994) Meta-analytic Results for Various Predictor Types 

Predictor Type 

# of

Correlations 

Total 

Sample

Size Mean r 

% 

Variance 

Explained 

by 

Sampling

Error 

95% CI 

Lower 

Bound 

General Ability 14 8,071 .13 21% -.05

Verbal Ability 17 22,841 .12 6% -.09

Quantitative Ability 34 46,884 .11 28% .01

Spatial Ability 37 52,153 .19 14% .05

Mechanical 36 42,418 .29 8% .11

General Information 13 29,951 .25 4% .06 

Aviation Information 23 25,295 .22 12% .06 

Gross Dexterity 60 48,988 .32 13% .15 

Fine Dexterity 12 2,792 .10 45% -.09 

Perceptual Speed 41 33,511 .20 19% .05 

Reaction Time 7 10,633 .28 16% .16 

Biodata Inventory 21 27,004 .27 6% .07 

Age 9 13,810 -.10 11% -.25

Education 9 6,163 .06 12% -.16

Job Sample 16 2,814 .34 37% .19 

Personality 46 22,486 .10 11% -.16

Notes. 

1. Mean r is weighted by sample size, but has not been corrected for any other artifacts. 

2. When analyzing the data, validity coefficients were reflected for predictor types that would be expected to

show a negative correlation with the criterion variable, that is, those involving measures of speed. Thus, in the 

table above, positive correlations indicate that better performance on the predictor is associated with better 

performance on the criterion measures.

15 
According to Hunter and Burke (1994), the following predictor types may show non-zero 

validity in some settings or samples: 

• General Ability 

• Verbal Ability 

• Fine Dexterity 

• Age 

• Education 

• Personality – Recall that “personality” is a measurement method. Across studies, 

some of the personality scales likely were expected to show a negative correlation with 

criterion performance (e.g., Anxiety), while other scales were likely expected to show a 

positive correlation (e.g., Self-Confidence). For still other personality scales, there likely 

was no clear a priori expectation about the direction of the correlation (e.g., RiskTaking). One might argue that averaging across all the different types of scales does not 

provide an accurate representation of the true level of validity that might be achieved by 

measures of specific personality traits. 

Hunter and Burke conducted moderator analyses for a subset of the predictor types for 

which there were sufficient data. They examined four possible moderators: 1) time period in 

which the study was conducted (1940-1960 versus 1961-1990), 2) nationality of the study 

sample (US versus other), 3) service branch (Air Force versus other), and aircraft type (fixedwing versus rotary-wing). The most consistent finding was that the time period in which the 

study was conducted moderated the validity of several predictor types, with lower mean validity 

in more recent studies. The authors speculate that the decline in validity over time could be due 

to reduced variability in the applicant pool, more extreme splits on dichotomous criterion 

measures (e.g., farther away from a 50-50 split in the proportion of trainees who pass versus fail 

UPT), or changes in the nature of aviator training. The other moderator variables, at least as 

coded in this meta-analysis, provided very little explanatory power. 

Martinussen (1996) Meta-Analysis 

The third meta-analysis was published by Martinussen in 1996. She conducted a 

standard computerized literature search and also made a special effort to collect unpublished 

validation studies focusing on military aircrew selection from researchers in NATO countries. 

Studies that did not report the magnitude of nonsignificant correlations or only reported 

corrected correlations were excluded. (Hunter and Burke do not say how they handled such 

studies). Martinussen reports that she reviewed 134 studies, and located 66 independent samples 

in 50 studies that met her criteria for inclusion. Fifty percent of the studies were conducted in 

the United States. Most samples involved military aviators, with the bulk of those belonging to 

the Air Force. Two-thirds of the studies involved fixed-wing aviators and 21% involved rotarywing aviators (12% did not specify the type of aircraft). Twenty (40%) of the studies were 

unpublished material. All of the studies used performance during aviator training as the criterion 

variable – dichotomous pass/fail status, instructor ratings, or course grades. While the 

distribution of study types is similar to that described by Hunter and Burke (1994), comparison 

16 
of the reference lists reveals very little overlap in the studies included in each review. In fact, 

fewer than 20 studies appeared in both meta-analyses. 

Martinussen categorized each predictor measure into one of nine measurement methods 

(predictor types). Each is described below: 

1. Cognitive includes all tests designed to measure a specific type of cognitive ability 

(e.g., mechanical, spatial, verbal, quantitative). 

2. Intelligence includes tests specifically designed to measure global intelligence. 

3. Psychomotor/Information Processing includes all tests involving apparatus or a 

computer. Obviously, this could encompass several different types of ability 

measures (e.g., psychomotor skills, reaction time, etc.) 

4. Aviation information includes tests with questions about aviation. Martinussen points 

out that most psychologists interpret such tests as measures of motivation to become

an aviator. 

5. Biographical inventories collect background information about applicants, and then 

summarize the information according to a total score. Although Martinussen does not 

comment on the nature of the inventories, it is likely that many of them were 

empirically-scored. 

6. Personality tests include a variety of personality inventories. The data are not 

organized according to personality trait but, unlike Hunter and Burke (1994), 

Martinussen did attempt to take the expected relationship between the underlying 

scale and the criterion variable into account by reflecting the sign of the correlation, if 

needed, based on information in the original study. In cases where no expectation 

about the direction of the relationship could be derived from the original study, 

Martinussen coded the absolute value of the correlation, in effect making it positive. 

This has the overall effect of inflating the mean validity coefficient. 

7. Combined index was used when a validity coefficient was reported only for a 

combination of predictor measures. Martinussen does not report how, or if, she took 

account of the fact that such measures may capitalize on chance, for example, if they 

were created using a regression procedure. (Hunter and Burke excluded these 

studies.) 

8. Academics includes school grades or tests that measured mathematical or language 

proficiency. 

9. Training experience includes measures of flying performance prior to selection into 

the training program that was the focus of the study. It is not clear if these included 

self-reported or verified, objective measures of prior flight hours/performance, or 

both. 

Table 2 shows the number of correlations, total sample size, mean sample-weighted 

correlation (observed and corrected for dichotomization), percent variance explained by 

sampling error, and 90% credibility limit for each measurement method. Using decision rules 

similar to those applied by Hunter and Burke (1994), Martinussen suggests that the mean validity 

of the Academics measurement method (r = .15) is likely to generalize across samples and 

17 
settings, given that 70% of the variance in observed validity is explained by sampling error. For 

the remaining measurement methods, it appears that there may be moderator variables that 

impact the level of validity across settings and samples, but the 90% credibility limit is greater 

than zero for all but two of them (biographical inventory and personality). 

Martinussen (1996) also found a negative correlation between year of study publication 

and validity coefficients for each type of predictor measure except Training Performance. This 

is consistent with the finding of a decline in validity across time reported by Hunter and Burke 

(1994). She also conducted several moderator analyses. Of most interest for the present effort 

was her finding of a significant difference in the mean validity of two measurement methods – 

(general) intelligence and training experience — depending on type of aircraft. General 

intelligence tests showed higher validity in samples of rotary-wing aviators (mean uncorrected 

r = .27) than in samples of fixed-wing aviators (mean uncorrected r = .11). 

Table 2 

Martinussen (1996) Meta-analytic Results for Various Measurement Methods 

Measurement Method 

# of

Correlations 

Total 

Sample

Size Mean r

% 

Variance 

Explained 

by 

Sampling

Error 

90%

Credibility 

Limit 

Cognitive 35 17,900 .22 

(.24) 

12% .07

Intelligence 26 15,403 .13 

(.16) 

18% .03

Psychomotor/Info Processing 29 8,522 .20 

(.24) 

28% .10

Aviation Information 16 3,736 .22 

(.24) 

46% .14

Personality 21 6,304 .13 

(.14) 

24% .00

Biographical Inventory 13 11,347 .21 

(.23) 

4% .00

Combined Index 14 5,362 .31 

(.37) 

13% .19

Academics 9 4,267 .15 70% .11

Training Experience 10 5,806 .25 7% .07 

Note. Mean r is weighted by sample size. The value enclosed in parentheses is the sample-weighted mean r 

corrected for criterion dichotomization. 

18 
In contrast, training experience showed higher validity in samples of fixed-wing aviators 

(mean uncorrected r = .35) than in samples of rotary-wing aviators (mean uncorrected r = .12). 

The latter finding may be due to the fact that individuals who pursue a private pilot’s license 

prior to entering a formal aviator training program are more likely to do so in a fixed-wing 

aircraft. As a consequence, the training experience may more directly transfer to, and thus 

positively affect, performance in fixed-wing aviator training than in rotary-wing aviator training. 

This finding is also consistent with anecdotal evidence that “too much” prior experience or 

training in fixed-wing aircraft can be detrimental when learning to fly a rotary-wing aircraft. 

Martinussen and Torjussen (1998) Meta-Analysis 

The fourth meta-analysis, conducted by Martinussen and Torjussen (1998), focused 

exclusively on a test battery used for aviator selection into the Norwegian Air Force (NAF). 

Four studies were included, with two to five independent samples for each of 19 subtests 

included in the test battery. Sample sizes ranged from 244 to 977 per subtest. In all four studies, 

the test battery was used in the aviator selection process so there was direct restriction of range 

on the subtest scores. Furthermore, spatial abilities were measured in each of two successive

stages of the battery, albeit with different tests. As a consequence, the final sample was highly 

restricted in terms of spatial ability. Criterion measures were based on training performance, 

primarily pass/fail status, but also instructor ratings and course grades. 

Out of 19 subtests, 10 showed a 90% credibility limit greater than zero. The mean 

uncorrected validity was lower than .20 for all but two of them – Aviation Information (mean 

uncorrected r = .21) and Instrument Comprehension (mean uncorrected r = .26), both of which 

were administered in the first stage of testing. Martinussen corrected the validities for 

dichotomization of the criterion measure (when appropriate), but did not correct them for range 

restriction. The corrected validities are consistently somewhat higher. One can only speculate 

how high they might be if corrected for range restriction as well. 

Interestingly, this is the only study in which the 90% credibility limit was greater than 

zero for a personality measure, although the mean validity was still low and consistent with the 

level reported in other meta-analytic reviews (mean r = .06 and .12 for two non-independent 

scoring methods used within the same inventory). According to Martinussen and Torjussen, the 

personality inventory – the DMT – measures psychodynamic defense mechanisms, and was 

specifically developed to select personnel for high-risk professions. The Norwegian Air Force 

used it as a post-selection screening device for individuals who had already been selected into 

aviator training. 

Summary of Meta-Analytic Validation Studies 

As noted above, there was very little overlap in the studies included in three of the metaanalytic reviews. (The obvious exception is that all four of the studies included in the 

Martinussen and Torjussen (1998) review also appeared in Martinussen’s (1996) broader 

review.) Only four of the studies reviewed by Damos (1996) appear in the Hunter and Burke 

(1994) citation list, and only one appears in the Martinussen (1996) citation list. Fewer than 20 

references appear in both the Hunter and Burke (1994) and Martinussen (1996) reviews. 

Different authors also categorized the predictor measures differently, making it difficult to 

19 
compare the results from different reviews. Nevertheless, the following summary statements can 

be made: 

• Global intelligence tests showed about the same, relatively low level of validity in the 

two meta-analyses in which they were included (mean uncorrected r = .13), with 

support for validity generalizability in one study but not in the other. 

• The validity of specific cognitive ability tests seems to vary depending on the type of

ability being measured but tends to be higher than that of more global measures of 

intelligence. This statement is supported by the mean uncorrected validity of .22 for 

the cognitive measurement method, as opposed to the mean uncorrected validity of .13 

for the global intelligence measurement method, as reported in Martinussen (1996). It 

is also supported, to some degree, by Hunter and Burke’s finding that the mean 

uncorrected validities for two specific cognitive ability predictors types, Spatial (r = 

.19) and Mechanical (r = .29), are higher than that for the global intelligence predictor 

type (r = .13). However, the mean uncorrected validity of verbal and quantitative 

ability predictor types reported by Hunter and Burke (r = .12 and .11, respectively) is 

about the same as that reported for the global intelligence predictor type. This finding

may be at least partially due to higher content overlap between global intelligence and 

verbal and quantitative ability tests than between global intelligence and spatial or 

mechanical ability tests.

• There is some evidence that Mechanical ability tests are among the more valid 

measures of performance during aviator training, as evidenced by the mean uncorrected 

validity of .29 in the Hunter and Burke (1994) meta-analysis and the mean uncorrected 

validity of .26 for the Instrument Comprehension subtest in the Martinussen and 

Torjussen (1998) meta-analysis. (Factor analyses of the Norwegian Air Force test 

battery suggested that the Instrument Comprehension measures both mechanical and 

spatial abilities.) 

• Aviation Information tests showed about the same level of validity in the three metaanalyses in which they were included – about .22 (uncorrected). 

• The biographical inventory measurement method showed a relatively high mean 

validity in the two meta-analyses in which it was included (mean uncorrected r = .27 

and .21, respectively). However, sampling error explained very little of the variability 

in validity estimates across studies, suggesting that there are other factors that impact 

the validity of such inventories. One of the most important factors may be the extent to 

which the inventory was designed to measure KSAOs relevant for the aviator job. 

• At least some types of psychomotor and information processing tests are likely to 

exhibit a reasonable level of validity in almost any sample or setting, as evidenced by 

the Damos (1993) finding of mean uncorrected validities of .18 and .23 for single-task 

and multiple-task performance-based measures, respectively. This is supported by the 

range of mean validities from .10 to .32 for measures of dexterity, reaction time, and 

perceptual speed in Hunter and Burke (1994) and by the Martinussen (1996) mean 

validity of .20 for psychomotor/information processing tests. 

• Measures of spatial ability showed a mean uncorrected validity of .19 in the Hunter 

and Burke meta-analysis. In the Norwegian Air Force battery, subtests with titles that 

20 
appear most like traditional measures of spatial ability (Paper Forming, Rotating 

Patterns, and Figure Pattern) showed very low validity. However, two other subtests 

that contain a spatial ability component, Raven’s Matrices and Instrument 

Comprehension, showed much higher validity (mean uncorrected r = .16 and .29, 

respectively). Stage two of the NAF battery also includes spatial ability tests, and they

showed very low validity, but this could be due to the extreme restriction of range 

given that applicants had already been directly screened on spatial abilities during the 

first stage of the testing process. 

• Personality measures, in general, showed low validity for predicting performance in 

training. However, as noted above, the meta-analytic reviews did not calculate the 

mean validity for different types of personality traits, and averaging across scales more

and less relevant for the aviator job likely obscured the true level of validity that such 

measures can achieve. 

Personality Research in the Aviator Selection Arena 

As noted earlier in this report, a great deal of research has been conducted in the area of 

personality measurement for use in aviator selection, with contradictory results. Lambirth, 

Dolgin, Rentmeister-Bryant, and Moore (2003) commented, “The US Navy, Air Force, and 

Army have investigated a variety of personality tests for use in pilot selection batteries. These 

efforts have had little impact on the selection of pilots or other aircrew because of response bias 

and the inappropriateness of the clinical measures selected for a homogeneous, non-clinical 

population. However, personality tests that emphasize positive attributes, rather than 

psychopathology, and performance-based personality measures, have proven to be more accurate 

descriptors of personality and predictors of performance” (p. 416). 

Job analyses and other studies suggest that non-cognitive characteristics are important for 

aviator performance. Musson, Sandal, and Helmreich (2004) say, “Superior performance [among 

pilots] has consistently been linked to a personality profile characterized by a combination of 

high levels of instrumentality and expressivity along with lower levels of interpersonal 

aggressiveness. This personality profile has sometimes been referred to as the ‘Right Stuff,’ 

suggesting this is the ideal description of an astronaut or pilot. Inferior performance has been 

linked to personality profiles typified by a hostile and competitive interpersonal orientation . . . 

(the ‘Wrong Stuff’) . . . or to low achievement motivation combined with passive-aggressive 

characteristics (‘No Stuff’)” (p. 342). The authors point out that these profiles seem to be 

especially important in terms of working as part of a crew. 

As noted above, several of the obstacles to conducting good research in the aviator 

selection domain are particularly problematic for personality measures. These include the 

reliance on training outcomes as the criterion measure and summarizing results by averaging 

validity estimates across several different personality scales.

Findings from General Selection Research Literature 

There is a great deal of information about the validity of measurement methods in the 

general selection research literature. Cognitive ability tests have been shown to predict job 

performance, particularly technical or “can-do” aspects of job performance, in a wide variety of 

21 
jobs (Hunter & Hunter, 1984; McHenry, Hough, Toquam, Hanson, & Ashworth, 1990; Schmidt 

& Hunter, 1998; Vernon, 1969). Schmidt and Hunter (1998) collected meta-analytic evidence 

from a large number of sources and summarized it according to different types of personnel 

measures, that is, measurement methods, for predicting performance in training programs and for 

predicting overall job performance. Personnel measures with the highest validity for predicting 

performance in job training programs include general mental ability (GMA) tests (mean r 

= .56),3 integrity tests (mean r = .38), peer ratings (mean r = .36), employment interviews 

(structured and unstructured) (mean r = .35), conscientiousness tests (mean r = .30), and 

biographical data (mean r = .30). Personnel measures with the highest levels of validity for 

predicting overall job performance include work sample tests (mean r = .54), GMA tests (mean 

r = .51), structured employment interviews (mean r = .51), peer ratings (mean r = .49), job 

knowledge tests (mean r = .48), training and education ratings (mean r = .45), job tryout 

procedures (mean r = .44), and integrity tests (mean r = .41). As noted previously, there is a 

high degree of correspondence between method and target of measurement for some of the 

personnel measures, for example GMA tests, but a low degree of correspondence for other 

personnel measures, for example employment interviews. 

In many jobs, technical aspects of performance are not the only aspects that matter to the 

organization. For example, there is a great deal of interest in predicting organizational 

citizenship (Organ, 1994; Organ & Ryan, 1995), contextual performance (Borman & Motowidlo, 

1993), and “will-do” aspects of job performance (Campbell, Hanson, & Oppler, 2001). To 

identify attributes that underlie these non-technical aspects of job performance, personnel 

selection researchers turned to the vast literature on non-cognitive attributes. Research in the 

personality, biodata, and vocational interest domains has clearly shown that measures of noncognitive attributes can predict job performance (Barrick & Mount, 1991; Gellatly, Paunonen, 

Meyer, Jackson, & Goffin, 1991; Hunter & Hunter, 1984; McHenry, et al. 1990; Ones, 

Viswesvaran, & Schmidt, 1993; Tett, Jackson, & Rothstein, 1991), particularly when a careful 

effort is made to identify and measure attributes that one would expect to underlie different 

criterion constructs, and when the presence or importance of those criterion constructs is 

considered for different types of jobs (e.g., Hough, 1992; Hough & Ones, 2002; Hurtz & 

Donovan, 2000; Mount, Barrick, & Stewart, 1998; Ones & Viswesvaran, 2001a, 2001b; Reilly & 

Chao, 1982; Robertson & Kinder, 1993). Several researchers have meta-analyzed validity for 

personality measures, using the “Big 5” model (Norman, 1963; Tupes & Christal, 1961) or some

other model (e.g., Hogan, 1991; Hough, Eaton, Dunnette, Kamp, & McCloy, 1990) as an 

organizing structure. One well-established finding is that measures of conscientiousness appear 

to be a valid predictor of job performance in virtually all jobs (Barrick and Mount, 1991; 

Schmidt & Hunter, 1998). The validity of other personality characteristics seems to depend, to a 

greater extent, on the type of job. For example, extraversion appears to be more valid for 

predicting performance in sales and managerial jobs than in other types of jobs (Barrick & 

Mount, 1991; Hough, 1992). 

Finally, there is evidence that vocational interests, for example interest in becoming an 

aviator, can be a valid predictor of relevant job outcomes. It is generally assumed that interest in 

 

3

 In the Schmidt and Hunter (1998) meta-analysis, correlations were corrected for criterion unreliability and range 

restriction (if present). 

22 
a particular occupation will lead a person to be motivated to pursue that occupation and 

motivated to gain knowledge about it. In aviator selection research, there typically has not been 

a clear distinction between measures of interests and measures of knowledge or background 

experience, so it is not possible to estimate the likely validity of a stand-alone self-report 

measure of interest in aviation. There is, however, evidence from the US Army’s Project A that 

scores on a self-report vocational interest inventory are valid for predicting technical job 

performance in a variety of Army enlisted military occupations (McHenry, et al., 1990; Oppler, 

McCloy, Peterson, Russell, & Campbell, 2001). 

Incremental Predictive Validity 

It is clear from the information described in the preceding section that an aviator selection 

battery focusing on cognitive ability is likely to be a valid predictor of performance in training 

and on the job. So, is there anything to be gained by including measures of other KSAOs in the 

aviator selection process? Research suggests that there is. In addition, given the enormous cost 

of aviator training, even a small increase in validity can offer significant utility to the US Army. 

Aviator Selection Research Literature

Most of the research on this topic in the aviator selection arena was conducted by the 

USAF, and is based on adding the Basic Aviator Test (BAT) to the AFOQT. The BAT consists 

of several computer-administered tests measuring psychomotor skills, short-term memory, timesharing ability, and attitudes toward risk-taking. Across several studies, the BAT demonstrated 

increases in the amount of variance accounted for (i.e., R2

) ranging from zero to .08 (e.g., 

Carretta, 1987b, 1988; Carretta & Ree, 1996a), with higher incremental validity for criterion 

measures other than training pass-fail (e.g., Advanced Training Recommendation Board [ATRB] 

ratings). 

Only one study systematically examined the incremental validity of the individual BAT 

subtests (Carretta & Ree, 1993). This study found that a BAT-Psychomotor composite score and 

a BAT-Risk composite score each (separately), when added to the AFOQT-Aviator composite 

score, increased the multiple correlation (R) by about .04 for predicting pass/fail and class rank. 

Adding the BAT measure of Flying Experience to the AFOQT-Aviator composite score 

increased R by about .07 while adding BAT Information Processing scores did not increase R

significantly. Adding all BAT scores to the AFOQT-Aviator composite score increased R by 

approximately .13 for both pass/fail and class rank (which translates into an increase in amount 

of variance accounted for, R2

, by about .02). 

In research that did not involve the BAT, Ree (2004c) found that two dependent variables 

derived from the Test of Basic Aviation Skills (TBAS) increased the amount of variance 

accounted for in basic flight training performance scores by .02 to .03. TBAS includes measures 

of psychomotor skills, selective attention, spatial ability, and noticing and responding quickly 

and appropriately to an “emergency.” The report does not specify exactly on what the dependent 

variables are based, but they appear to involve psychomotor and spatial aspects of test 

performance. Retzlaff, King, and Callister (1995) and Carretta, Retzlaff, and King (1997) report 

that tests of aviation interest/aptitude included in the AFOQT have been shown to be useful for 

predicting aviator performance beyond measures of g and of specific cognitive abilities such as 

verbal, math, spatial, and perceptual speed. 

23 
Blower and Dolgin (1990) used a hierarchical regression model to examine the 

incremental validity exhibited by three tests: (1) Absolute Difference-Horizontal Tracking, (2) 

Complex Visual Information Processing, and (3) Risk Taking in predicting success in primary 

flight training over and above that of intelligence and demographic variables. Each resulted in 

approximately a 3.5 % increase in variance explained. This study also found that a 

psychomotor/dichotic listening test, a Manikin test (a mental rotation task), and a Baddeley test 

(an assessment of working memory) did not add incremental validity. 

Several other studies used a regression approach to examine the validity of various types 

of predictor measures for predicting undergraduate training performance (Bartram & Dale, 1985; 

Carretta, 1989, 1990; Morrison, 1988; Olea & Ree, 1994), but did not report incremental 

validity. However, the studies did report that psychomotor, spatial orientation, biographical data, 

working memory, and to a lesser degree personality, all predicted at least some unique variance 

in undergraduate aviator training. 

General Selection Research Literature 

In the general selection research literature, Schmidt and Hunter (1998) meta-analytically 

derived an estimate of the incremental validity likely to occur when any of several personnel 

measures were added to a measure of general mental ability for predicting a) performance in a 

training program or b) overall job performance. For predicting performance in a training 

program, their results show that the greatest incremental validity can be achieved by 

supplementing a measure of general mental ability with an integrity test or a conscientiousness 

test (increase in multiple R of .11 and .09, respectively). For predicting overall job performance, 

the greatest incremental validity can be achieved by supplementing a measure of general mental 

ability with an integrity test (increase in validity of .14), a conscientiousness test (increase in 

validity of .12), a work sample test (increase in validity of .12), or an employment interview 

(increase in validity of .09). 

Other research has also shown that measures of non-cognitive attributes can provide 

incremental validity beyond measures of cognitive attributes. This is especially true for 

predicting non-technical aspects or “will-do” aspects of job performance (Day & Silverman, 

1989; Mount, Witt, & Barrick, 2000; Ones & Viswesvaran, 2001b; Oppler, et al., 2001; 

Robertson & Kinder, 1993; Russell, Mattson, Devlin, & Atwater, 1990; Salgado, 1998) 

However, incremental validities have also been found in the vocational interest domain, even for 

cognitive (or “can-do”) aspects of job performance (Gellatly, et al, 1991; Hough, Barge, & 

Kamp, 2001). 

Summary of Incremental Validity Evidence 

Based on the research evidence described above, there is reason to believe that measures 

of the following constructs may add incremental validity beyond that achieved by a battery that 

reliably and accurately measures general intelligence: 

• Psychomotor skills 

• Working memory 

• Aviation interest/knowledge 

24 
• Flying experience – although the type of flying experience may make a difference 

(fixed-wing versus rotary-wing) 

• Personality (including factors such as conscientiousness and risk-taking) 

Group Differences 

As mentioned previously, the Army aviation applicant sample has historically been 

homogeneous, that is, relatively young, male, and Caucasian, with at least a high school degree 

and usually with some post-high school education. Some applicants already have or are working 

on a private pilot’s license, but very few are already certified to fly rotary-wing aircraft. Some

are already in the military, while others come from the civilian population. In fact, the Army is 

the only branch of the US military that allows civilians and military enlisted personnel to apply 

for slots in the aviation training program. (All branches allow military Commissioned Officers 

to apply for aviator training.) In the Army, those applicants who are accepted, but who are not a 

Commissioned Officer at the time of application, must complete Warrant Officer training before 

they enter aviator training. 

In the future, it is likely that the applicant population will become more diverse in terms 

of race and gender but, barring major policy changes, will likely not become more diverse on the 

other characteristics listed above. One of ARI’s objectives is to minimize, to the extent possible, 

adverse impact exhibited by the new aviator selection battery. The level of adverse impact 

exhibited by a test battery depends on various factors, including the selection ratio, the general 

characteristics of the applicant population, and placement of the pass-fail cutoff on a test battery. 

While the adverse impact cannot be estimated at this point, the research that might help to 

anticipate how race and gender subgroups are likely to score on an aviator selection test battery 

can be examined. 

Cognitive Ability Tests 

Research conducted on cognitive ability tests using military and civilian samples suggests 

there will be mean score differences on most cognitive ability tests when racial groups are 

compared, but that the tests will not be unfair to any racial subgroup (Campbell, 1996; Carretta 

& Ree, 2000; Roth, Bevier, Bobko, Switzer, & Tyler, 2001; Russell, Reynolds, & Campbell, 

1994; Sackett, Schmitt, Ellingson, & Kabin, 2001; Toquam, Corpe, & Dunnette, 1989; Wise, 

Welsh, Grafton, Foley, Earles, Sawin, & Divgi, 1992). Research suggests that there will be a 

standardized mean score difference of 0.6-1.0 between African-Americans and Whites, with 

Whites scoring higher on average. Other evidence suggests that the Hispanic-White 

standardized mean score difference will be about half as large as the African American-White 

subgroup difference, again with the White mean being higher. Finally, Asian subgroups 

sometimes earn a higher mean score than the White subgroup, and sometimes earn a lower mean 

score. Many different interpretations of and explanations for these findings have been offered 

(e.g., educational differences, subtle or overt racism, cultural bias), but no one has yet found a 

way to entirely explain or eliminate the differences, and efforts to ameliorate the differences 

have met with limited success (Sackett, et al., 2001; Schmitt, Sackett, & Ellingson, 2002). 

Research suggests that there are no gender differences in general cognitive ability (g), but 

that gender differences will appear on specific types of cognitive ability tests. Specifically, 

25 
females tend to perform better than males on tests of verbal ability and more poorly than males 

on tests of spatial, mathematical, and mechanical abilities (Geary, Saults, Liu, & Hoard, 2000; 

Maccoby & Jacklin, 1974; Maitland, Intrieri, Schaie, & Willis, 2000; Weiss, Kemmler, 

Deisenhammer, Fleischhacker, & Delazer, 2003; Wise, at al., 1992). Burke (1995) metaanalyzed gender subgroup differences on aviator aptitude tests and reported findings consistent 

with those from the general literature. As with the race subgroup differences, a variety of 

explanations have been offered for these findings, for example, differences in socialization 

experiences, but no one has fully explained or eliminated them to date. 

The magnitude of gender differences on spatial ability tests appears to vary considerably 

with the type of test (Linn & Peterson, 1985), with the largest differences occurring on tests that 

involve three-dimensional spatial rotation and the smallest differences occurring on tests that 

involve spatial visualization (e.g., paper-folding tests). Boer (1991) reviewed construct validity 

evidence for a variety of spatial ability tests and concluded that “the most important aspects of 

spatial ability are the identification of the optimal solution strategy and, perhaps, a final process 

called evaluation and confirmation. It seems that the actual execution of the solution process, 

including mental rotation, is less important.”(p. 108). 

The US Army’s Project A included several different spatial ability tests. Factor analyses 

suggested that all the tests load on a single underlying factor, but that some of the tests produced 

much larger race and gender subgroup differences than others (Russell & Peterson, 2001). 

Specifically, a spatial abilities test called Assembling Objects showed smaller gender differences 

than other spatial ability tests but was a valid predictor of behavior. A similar pattern of 

findings, using most of the same spatial ability tests developed during Project A, occurred in a 

large-scale study focused on revising the ASVAB (Russell, Reynolds, & Campbell, 1994). 

These researchers recommended adding the Assembling Objects subtest to the ASVAB, a 

recommendation that has since been enacted. 

Psychomotor Tests 

Males typically score considerably higher on psychomotor tests than females (Burke, 

1995; Carretta, 1997b; McHenry & Rose, 1988; Russell & Peterson, 2001) and the standardized 

mean score difference is often larger than 1.0. There is much less reported evidence for race 

subgroup differences on psychomotor tests but Russell and Peterson (2001) found standardized 

mean score differences ranging from .38 to .87 between African American and White enlisted 

personnel on Project A psychomotor tests. This finding may be at least partially explained by 

the correlation between psychomotor and cognitive abilities (Carretta, 1997a; Ree & Carretta, 

1992). 

Speeded Information Processing Tests 

In Project A and in a joint-services project (Russell & Peterson, 2001; Russell, Reynolds, 

& Campbell, 1994), there were small to no race or gender subgroup differences on speeded 

measures of information processing, for example, reaction time. Interestingly, males tended to 

perform somewhat better than females on measures that focus only on perceptual speed, while 

the reverse was true for measures that focused on both speed and accuracy. Both Carretta 

(1997b) and Burke (1995) report similar findings when examining gender differences in 

performance in samples of USAF and UK Royal Air Force aviator applicants respectively. 

26 
Personality and Temperament Measures 

Research suggests that personality and temperament measures typically show small or no 

racial subgroup differences (Bobko, Roth, & Potosky, 1999; Hough, 1998; Ones & Viswesvaran, 

1998; Russell & Peterson, 2001; Schmitt, Rogers, Chan, Sheppard, & Jennings, 1997). In 

contrast, there often are gender differences on personality and temperament inventories and, 

prior to passage of the Civil Rights Act of 1991, many test batteries used separate within-group 

norms for scoring and reporting purposes, that is, separate norms for males and females, and for 

persons of different racial backgrounds. The Civil Rights Act of 1991 prohibits adjusting scores, 

using different cutoffs, or otherwise altering the results of employment related tests on the basis 

of race, color, religion, sex, or national origin. As a consequence, the use of within-group norms

has essentially disappeared for cognitive ability tests. In the personality measurement arena, it 

appears that within-group norms are generally accepted when the results will be used for 

descriptive or diagnostic purposes (e.g., in a counseling setting) but are much more controversial 

if the results will be used for employment related decisions (see Sackett & Wilk, 1994). 

While research suggests there are practically meaningful subgroup differences between 

males and females on personality or temperament inventories, the direction and size of the 

difference depends on which personality or temperament characteristic is being measured 

(Sackett & Wilk, 1994) and, even then, is not always consistent. Furthermore, very little 

research has been conducted to determine whether or not these differences lead to differential 

prediction of job performance. Saad and Sackett (2002) analyzed data from the US Army’s 

Project A and found some evidence that personality scores over-predicted female performance, 

but no evidence of bias. 

Sackett and Wilk (1994) reviewed male-female effect sizes on the scales of several wellknown personality inventories. The results are difficult to summarize across inventories because, 

when the inventories were developed, there was no common set of scale labels and no agreedupon set of underlying constructs. Generally, males scored higher than females on scales 

measuring dominance, independence, aggression, and risk-taking, while females scored higher 

than males on scales measuring nurturance, agreeableness, affiliation, and conscientiousness. 

Many of the differences were not large, however. 

The temperament inventory developed as part of Project A (the Assessment of 

Background and Life Experience – ABLE) was developed with the intention of using the same

set of norms for both males and females. In a large sample of enlisted US Army Soldiers, the 

Male-Female effect sizes ranged from .00 to .54 across the 11 ABLE content scales. With the 

exception of the Physical Condition scale, all of the Male-Female effect sizes were .25 or lower. 

Female Soldiers scored at least somewhat higher, on average, than males on cooperativeness, 

conscientiousness, non-delinquency, traditional values, work orientation, internal locus of 

control, and energy level. Male Soldiers scored at least somewhat higher, on average, than 

females on emotional stability, self-esteem, dominance, and physical condition (Russell & 

Peterson, 2001). Finally, Ones and Viswesvaran (1998) meta-analyzed subgroup differences on 

overt integrity tests and found that females scored .16 standard deviations higher than males. 

27 
What Should the Army Measure? 

Research focused specifically on aviator selection, as well as general research, clearly

suggests that cognitive ability, or general intelligence (g), will be an important predictor of 

aviator performance. Researchers debate the usefulness of identifying more specific abilities 

within the cognitive ability domain (e.g., Jensen, 1993; Ree & Earles, 1992, 1993; Schmidt & 

Hunter, 1993; Sternberg & Wagner, 1993), but many personnel selection batteries include 

measures of different types of cognitive ability, including some combination of: 

1. General Reasoning; 

2. Spatial Ability; 

3. Mechanical Reasoning; 

4. Quantitative Ability; 

5. Verbal Ability; 

6. Multiple-Task Performance (also known as Timesharing or Divided Attention); and,

7. Information Processing (e.g., perceptual speed and accuracy, working memory, 

cognitive task prioritization). 

Research also suggests that including measures of the following abilities and 

characteristics is also likely to enhance the validity of the overall selection process: 

8. Aviation or Helicopter Knowledge 

9. Interest in Aviation 

10. Flying Experience - although the type of flying experience may make a difference 

(fixed-wing versus rotary-wing) 

11. Normal-Range Personality Characteristics — Based on the aviator selection and 

general research literature, traits that seem relevant for the aviator job 

include: 

a. Conscientiousness/Integrity; 

b. Achievement Orientation;

c. Stress Tolerance/Emotional Stability; 

d. Adaptability/Cognitive Flexibility; 

e. Interpersonal/Crew Interaction skills; 

f. Risk Tolerance; 

g. Internal Locus of Control; and, 

h. Dominance/Potency (including Self-Confidence/Self-Esteem). 

These, then, are KSAOs that should be included, at a minimum, in a job analysis study. 

Some of them may be defined more narrowly than shown here, based on taxonomic work in the 

28 
field of individual differences. There are other areas that should be considered in the overall 

Army aviator selection process, for example, screening for serious medical conditions, 

neurological deficiencies, or psychological disorders. However, these measures would be 

designed to “select out” applicants that do not belong in Army aviation training, while the 

present project is intended to identify those batteries that would be useful in “selecting in” the 

most qualified applicants. 

Review of Existing Aviator Selection Test Batteries 

Even using a focused approach to the literature review, more than 150 potentially 

relevant articles were identified. Rather than rely entirely on a narrative summary, a spreadsheet 

was developed to summarize standard information about various test batteries and to facilitate 

comparison of the test batteries when deriving a recommended selection strategy. The following 

questions were identified as potentially having some bearing on testing recommendations: 

1. What subtests, if any, are part of the battery?

2. Who uses the battery?

3. How long does it take to administer the battery?

4. Is the battery already computerized? Web-enabled? 

5. Does the battery require non-standard equipment (e.g., joystick, timing card)?

6. What validity evidence is available for the battery? 

7. What are the key studies and references describing validation efforts?

An answer for each question was provided (when possible) for a number of current or

recently available test batteries, and documented in the aforementioned spreadsheet. The results 

are shown in Appendix A. 

When considering potential measures of non-cognitive characteristics such as 

conscientiousness, it is clear that no single, existing inventory measures every characteristic that 

might be important for the aviator job. Therefore, a second spreadsheet was created, shown in 

Appendix B, to summarize information available for several inventories that have been 

administered in an aviator selection setting, or that are already owned by the US military. This is 

not intended to be a comprehensive review of all possible personality inventories. There are 

dozens of commercially-available personality and biodata inventories that could be used to 

measure characteristics important for aviators, but none that are specifically designed for aviator 

selection and none that appear to be significantly more comprehensive or likely to exhibit 

significantly higher validity than those already available to the US military. 

Selection Strategy Recommendations 

The following approach was used to develop a recommended selection strategy: 

1. Identify KSAOs important for the aviator job through the literature review and a job 

analysis. In other words, take a construct-oriented approach to this effort.

29 
2. Identify, to the extent possible, existing measures of those KSAOs with known 

validity. 

3. Construct a set of recommendations outlining best-bet choices for predictors that

measure critical KSAOs, taking into account what is known about expected subgroup 

differences. 

As noted above, one of the primary considerations in recommending an existing test 

battery is whether or not there is validity evidence to support its use. Based on the literature 

review, and as summarized in Appendix A, there are several aviator selection batteries that have

demonstrated a reasonable level of validity for predicting Undergraduate Pilot Training, which is 

typically operationalized as pass/fail, but sometimes also includes measures of training grades or 

instructor aviator ratings. Almost no one has attempted to predict aviator performance outside of 

training and, when they have, have not had a great deal of success. This is likely due to issues 

with criterion quality, as there are significant obstacles to developing good measures of aviator 

performance on the job. 

The most viable candidates for replacing the AFAST appear to be test batteries developed 

by the US military, several of which are described below.4

 There are also some aviator selection 

batteries developed by foreign military services or commercial organizations with demonstrated 

evidence of validity, as shown in Appendix A. The latter test batteries are less viable than 

batteries developed by the US military because: 1) there is no evidence that they are any more 

valid than test batteries developed by the US military, and 2) it would likely be difficult and/or

expensive for the US Army to gain access to them. 

The recommendations resulting from this review are presented in overview form in 

Appendix C. It would seem to be an efficient use of Army testing resources to create a two-stage 

testing process. The first stage would include measures of cognitive abilities such as spatial 

ability, mechanical reasoning, verbal ability, numerical reasoning, and perceptual speed and 

accuracy, as well as a measure that would attempt to tap motivation to become an aviator, such 

as an Information subtest. The Army may be able to take advantage of the fact that the US Navy 

has a web-enabled aviator selection battery that currently consists of a reasonable set of cognitive 

tests and an Information subtest that assesses aviation and nautical knowledge. Including a noncognitive inventory in Stage 1 is also recommended. Such an inventory may provide 

incremental validity beyond the cognitive test battery, and it may help ameliorate race subgroup 

differences on the cognitive tests. The inventory could include scales from several different 

inventories that have been developed by the US military. The Stage 1 battery could be 

administered via the Internet on any standard desktop computer and would not require any nonstandard peripherals or hardware. Thus, it could be administered virtually anywhere that a 

computer is available, along with a reliable Internet connection and a test control officer. 

 

4

 Re-using any of the existing AFAST subtests was not considered because Army researchers believe that the 

content may have been compromised over the several years in which it has been used. 

30 
The second stage of the test battery would focus on psychomotor skills and multiple-task 

performance. These types of tests are often combined and labeled “performance-based” 

measures. Alternately, given practical considerations, this Stage 2 test battery might assist in the 

classification of selected Army aviators into mission/aircraft types. 

Best Bet Predictor Measures 

Stage 1: Cognitive Measures 

Aviator Selection Test Battery (ASTB). The ASTB is the US Navy’s primary aviator 

selection instrument. It grew out of the Pensacola 1000 Pilot Study, which examined over 60 

psychological, psychomotor, and physical tests (North & Griffin, 1977). The current version of 

the ASTB includes subtests measuring Reading Comprehension, Mathematical Ability, 

Mechanical Comprehension, Spatial Apperception, and Aviation and Nautical Interests. Navy 

researchers are currently building an adaptive version of the ASTB and anticipate transitioning to

adaptive testing within three to five years. 

The ASTB subtests are used to create several composite scores, including the Academic 

Qualification Rating (AQR) and the Pilot Flight Aptitude Rating (PFAR). Validity data for 

FY98-FY04 are summarized or reported in graphic form in a series of ASTB Workshop Briefing 

Slides (Operational Psychology Department, 20 July 2004). Navy researchers found that AQR 

scores predict performance in ground school (r = .46 for USN student aviators and r = .39 for 

USMC student aviators) while PFAR scores predict performance in primary flight school 

(Primary NSS) (r = .32 USN student aviators and r = .21 for USMC student aviators). This 

research suggest reported that validity for predicting attrition from aviator training was in the 

high teens for student aviators in both the USN and the USMC, using AQR or PFAR scores. 

Sample sizes were not provided in the briefing slides, but include thousands of cases (personal 

communication, Captain John Schmidt, Operational Psychology Department, USN, October 29, 

2004). 

The US Navy has developed a web-administration system for the ASTB, called 

Automated Pilot Examination (APEX). The APEX system is being widely used throughout the 

Navy and is expected to account for the bulk of ASTB administrations by the end of FY 2005 

(personal communication, Captain John Schmidt, USN, October 29, 2004). This is currently the 

only web-administered aviator selection test battery. 

The ASTB was designed to select Commissioned Officers who will enter training to 

become a Navy or USMC aviator. All Commissioned Officers, by definition, have completed a 

four-year college degree. Therefore, to ensure that the ASTB is not too difficult for the Army

aviator applicant population (which includes persons with less than a four-year college degree), 

the US Navy administered the ASTB to a sample of incoming Army student aviators. In 

February 2005, the Operational Psychology Department of the Naval Operational Medicine 

Institute administered a paper-and-pencil version of the ASTB to 73 student aviators at Ft. 

Rucker, AL. The Navy scored the data and provided summary information to the ARI monitor 

and PDRI project team. There was a reasonable degree of variability in the Army scores, and no 

evidence that it was too difficult for Army student aviators (i.e., no floor effect). The Navy also 

provided, for comparison purposes, ASTB summary data for Navy personnel with varying levels 

of education. (The ASTB is administered to Navy personnel for some purposes other than 

31 
aviator selection.) Overall, the Army sample scores were similar to those in a mixed-education 

Navy sample, and to those in a sample of Navy personnel who had at least a bachelor’s degree. 

The Army aviator sample included in this effort had already been selected via the AFAST, and 

thus does not represent the full range of intelligence in the Army aviator applicant population. 

Nevertheless, it appears that the ASTB will not prove to be too difficult, overall, for the US 

Army aviator applicant sample. 

Air Force Officer Qualification Test (AFOQT). The US Air Force developed the AFOQT 

in the early 1950s as a tool for selecting civilian applicants for officer precommissioning training 

programs and for classifying commissionees into aircrew job specialties (Rogers, Roach, & 

Short, 1986; Skinner & Ree, 1987). The Air Force has periodically revised the AFOQT to 

update items, ensure test security, and improve predictive validity. The first form of the AFOQT 

was implemented in 1953, Form R is currently in use, and Form S is scheduled for 

implementation in the near future. Form R has 16 subtests and Form S has 11 subtests that tap 

verbal, quantitative, spatial, and mechanical aptitudes. Form S also includes a measure of noncognitive characteristics, called the Self-Description Inventory. Scores on the AFOQT subtests 

are used to form five distinct but partially overlapping composites: Pilot, Navigator-Technical, 

Academic Aptitude, Verbal, and Quantitative (Sperl & Ree, 1990). The Pilot and NavigatorTechnical composites are used for classification into Undergraduate Pilot Training (UPT) and 

Undergraduate Navigator Training (UNT), respectively. The AFOQT has been validated for 

more than 36 officer jobs as a predictor of technical training grades (Arth, 1986; Carretta & Ree, 

1998). Carretta and Ree (1994) found that the AFOQT showed a multiple correlation of .20 for 

predicting rank in UPT, and Shore and Gould (2003) reported a multiple correlation 

(uncorrected) of .34 with UPT final grade for Form S of the AFOQT. According to Carretta 

(2002), “the predictiveness of the AFOQT for aviator training performance comes almost 

entirely from its measurement of g and aviation job knowledge.”(p. 1). 

As new forms of the AFOQT have been constructed in recent years, key features of the 

subtests have deliberately been held constant to ensure equivalent measurement. Thus, the more 

recent versions are equivalent in terms of subtest content, subtest length, item difficulty, testing 

time, and stylistic features. Further, about one-half of the items in each form are taken directly 

from the previous form, and analyses are conducted to equate the new form to the old (Glomb & 

Earles, 1997). None of the AFOQT forms, to date, have been computerized, but the USAF 

intends to develop this capability. 

Like the ASTB, the AFOQT is designed primarily for use with a Commissioned Officer 

population. Therefore, USAF provided access to, and permission to analyze, a normative 

database containing scores on the soon-to-be-implemented AFOQT Form S for a Basic Military 

Training (BMT) enlisted personnel likely to apply for the Airman Education and Commissioning 

Program (n = 509), Air Force Reserve Officer Training Cadets (n = 679), and Officer Training 

School cadets (n = 462). The analyses are described in Gould and Damos (2005). They 

conclude, “As expected, the AFOQT was more difficult for the Air Force enlisted personnel than 

for other commissioning source applicants. However, the subtest and composite score 

distributions are sufficient to discriminate well between enlisted personnel if the AFOQT or 

similar aptitude test is used for [aviator] selection” (p. 1). If the US Army chooses to implement 

the AFOQT, these authors recommend that Army-specific norms and passing score(s) be 

established. 

32 
Cognitive Prioritization (Popcorn Test). The cognitive prioritization test follows a 

format originally developed by NASA researchers, and is colloquially known as a “popcorn” 

test. It is a measure of cognitive processing, specifically the ability to prioritize several moving 

stimuli that appear on a computer screen. No operational pilot selection test battery has included 

a popcorn test, although other test batteries, for example, Wombat© (Aero Innovation, 1998), 

likely measure the same or a similar underlying ability. It is recommended that the US Army

include this type of test in its Army aviator selection battery because this ability may become

increasingly important in the future, as the cognitive load associated with flying rotary-wing 

aircraft increases. Scores on this test may also be related to measures of situational awareness. 

Perceptual Speed and Accuracy. One possible measure of perceptual speed and accuracy 

is the Table Reading test that is a subtest of the AFOQT. This test has been in use for aviator 

selection since 1942. It continues to account for unique variance in prediction of aviator 

performance, and is part of the AFOQT Pilot Composite score. A commercial version of the test 

is also available. 

Alternatively, it would be possible to develop a new measure of perceptual speed and 

accuracy, using stimulus materials that are face valid for Army aviators. PDRI has developed 

many different measures of perceptual speed and accuracy, and could do so efficiently in the 

current project. 

Stage 1: Non-Cognitive Measures 

Test of Adaptable Personality (TAP). The TAP was developed by the US Army for use 

in training and developing Special Forces Soldiers and officers. It consists of biodata items that 

were written to target constructs such as achievement orientation, fitness motivation, cognitive 

flexibility, peer leadership, and interpersonal skills. In Special Forces samples, the achievement 

orientation, fitness motivation, and cognitive flexibility scales have proven valid for predicting 

peer and supervisor ratings of performance (personal communication, R. Kilcullen, November, 

2005; Kilcullen, Goodwin, Chen, Wisecarver, & Sanders, undated; Kilcullen, Mael, Goodwin, & 

Zazanis, 1999). 

Assessment of Individual Motivation (AIM). The AIM is a forced-choice non-cognitive 

inventory that measures several constructs potentially important for aviator selection. It was 

developed by researchers at ARI, and was developed to measure most of the same constructs as 

the Assessment of Background and Life Experiences (ABLE) developed during Project A. In 

Project A, the ABLE was predictive of volitional aspects of performance in a variety of military 

enlisted jobs, and it exhibited incremental validity when added to a cognitive test battery (Russell 

& Peterson, 2001). However, the ABLE was never implemented for selection purposes due to 

concerns about its fakability (White, Young, & Rumsey, 2001). 

The AIM specifically addresses fakability concerns by using the forced-choice 

methodology. This methodology has long been suggested as a way to make an inventory 

resistant to faking, and there is evidence to support this claim, some of it specifically based on 

the AIM (Jackson, Wroblewski, & Ashton, 2000; White, et al., 2001). ARI is also currently 

funding efforts to explore an Item Response Theory (IRT)-based approach to administering and 

33 
scoring the AIM, in an attempt to make it even more resistant to faking (Stark, Chernyshenko, & 

Drasgow, 2003). 

To date, research on the AIM has focused primarily on predicting attrition, but there is 

some evidence that it predicts job performance and personal discipline among correctional 

officers in military prisons as well as success in explosive ordinance disposal training for 

military personnel (White, et al., 2001). Project A results suggest it is reasonable to believe that 

the AIM will predict volitional aspects of job performance for the Army aviator job, because it 

measures characteristics important for performing that job. 

The AIM is currently used for operational recruit screening as part of the US Army’s 

GED Plus program, and it has shown promise for use in pre-enlistment screening of Non High 

School Graduate (NHSG) recruits (White, Young, Heggestad, Stark, Drasgow, & Piskator, 2004, 

2005). It is also being evaluated for potential use in screening of US Army recruiters and drill 

sergeants. Researchers have also developed various scoring methods in an effort to enhance the 

validity of the AIM for predicting attrition, including empirical scoring procedures (White, 

Young, Heggestad, Stark, Drasgow, & Piskator, 2005), an IRT-based scoring approach 

(Chernyshenkso, Stark, & Drasgow, 2003), and a decision tree approach (Lee & Drasgow, 

2003). 

Self-Description Inventory Plus (SDI+). The SDI was developed by the USAF, and is

currently considered an experimental subtest within Form S of the AFOQT. It was originally 

developed to measure the Big Five personality factors. In recent years, USAF researchers wrote 

two additional scales to measure Team Orientation and Commitment to Military Service (Service 

Orientation). It contains 220 items (see Christal, et al., 1997). According to USAF researchers, 

the value of the SDI is in generating profiles for people and ultimately profiles for organizations 

and job families to facilitate person-job match and strategic force development (J. Weissmuller, 

personal communication, February 28, 2005). It is not specifically intended for personnel 

selection. Nevertheless, validity data are currently being collected in a broad USAF sample, 

including some aviators.

Armstrong Laboratory Aviation Personality Scale (ALAPS). The ALAPS was also 

developed by the USAF (Retzlaff, King, Callister, Orme, & Marsh, 2002). It includes five 

“personality” scales (confidence, socialness, aggressiveness, orderliness, and negativity), six 

“crew interaction” scales (dogmatism, deference, team orientation, organization, impulsivity, and 

risk-taking), and four “psychopathology” scales (affective lability, anxiety, depression, and 

alcohol abuse). A large-scale validation study is currently underway by the USAF. The US 

Navy is also planning to conduct validation research on this inventory. However, further 

investigation into this inventory revealed the unfortunate fact that the items and scoring key have 

been published in a USAF technical report that is available to members of the general public who 

are savvy enough to locate it. Therefore, it would be unwise for the US Army to use this 

inventory for selection. 

New Non-cognitive Scales. Based on our review of the existing inventories, there are 

several non-cognitive characteristics that may be predictive of aviator performance that are not 

measured by any of the inventories readily accessible to the US Army. Therefore, it may be 

34 
advisable to write new scales targeting these characteristics, emulating the style and format of 

the items in the TAP. 

Stage 2: Psychomotor Skills and Multiple-Task Performance (Performance-Based Measures)

Test of Basic Aviation Skills (TBAS). This test battery was developed by the USAF as a 

replacement for the BAT. It includes three subtests designed to measure spatial orientation 

(tracking tasks) and multiple task performance skills (tracking plus directed listening), as well as 

the ability to make decisions under stress. TBAS is scheduled for fielding in 2006 and the US 

Navy is also considering adding it to their aviator selection process. Ree (2003) analyzed TBAS 

data from USAF aviator trainees (n = 531) who had already been selected on other measures and 

found that the spatial orientation and decision-making subtests showed low but significant 

correlations with various training criterion measures. The multiple correlation for predicting a 

combined training performance measure (based on check ride scores, instructor ratings, and quiz 

scores, among other things) was .33; the multiple correlation for predicting UPT pass/fail was 

.31. It does not appear that these correlations were corrected for shrinkage but the author notes 

that they were downwardly biased due to a high degree of range restriction on the predictor 

measures. There are some concerns about the stability of scores on the decision-making subtest. 

Ree (2004b) examined 90-day and 180-day test-retest reliability in a small sample (n = 126) of 

USAF aviator trainees. Reliability was very low for the decision-making subtest (.15) and 

acceptable for the other subtest scores (.56-.75). Further investigation of TBAS with USAF 

personnel revealed the unfortunate fact that no documentation regarding the computer 

programming appears to exist, nor any documentation about how the dependent variables are 

calculated. For this reason, it is not recommended that the TBAS be used for Army aviator 

selection unless and until program documentation can be located. 

Wombat©. The Wombat© (Aero Innovation, 1998) is a commercially-available, 

computerized test battery that involves learning and operating a complex system. It does not 

involve discrete subtests, but rather involves continuous performance on a primary tracking task, 

with secondary performance on any of three “bonus” tasks. The bonus tasks are worth varying 

amounts of points at different times. All of the measures are combined to create a total 

efficiency score. During the testing period, examinees are given continuous feedback on their 

performance which can help them maximize their task performance strategies, to the extent that 

they have the attentional and cognitive capacity to do so. There has been little published on the 

validity of the Wombat©, but two studies suggest that scores are correlated with academic 

performance in flight school and flight hours (Cain, 2002; Frey, Thomas, Walton, & Wheeler, 

2001). The Wombat© has been used extensively for aviator selection in Canada, but has not 

been used operationally by the US military, as the advertised pricing is prohibitive. 

New Performance-Based Measure. If neither the TBAS nor the Wombat© are viable 

alternatives, it is recommended that the US Army develop its own performance-based measure of 

psychomotor skills and multiple-task performance. This recommendation is being made because 

there does not appear to be another performance-based measure that: 1) has proven validity; 2) is 

programmed in a modern programming language; and, 3) is readily available and free to the US 

Army. 

35 
The new test battery could include subtests similar to psychomotor tests with a long 

history and proven validity, for example, the Complex Coordination test and the Rotary Pursuit 

test, but programmed in a modern programming language. Multiple-task performance could be 

assessed by combining a directed listening test, or some other secondary task, with a 

psychomotor task. For example, it might be possible to use the TBAS as a model for 

development but with careful documentation of the programming and development of scoring 

variables. 

Conclusions 

This report presents a review of a great deal of research in the aviator selection and 

general personnel selection domains. That information was used to identify KSAOs that should 

be included in a job analysis study focusing on the Army aviator job. It was further used to 

develop a recommended strategy for an Army aviator selection battery. 

Research focused specifically on aviator selection, as well as general personnel selection 

research, clearly suggests that cognitive ability, or general intelligence (g), will be an important 

predictor of aviator performance. More specific cognitive abilities that may be of importance 

include: general reasoning; spatial ability; mechanical reasoning; quantitative ability; verbal 

ability; multiple-task performance (also known as timesharing or divided attention); and 

information processing (e.g., perceptual speed and accuracy, working memory, cognitive task 

prioritization). Research also suggests that measures of aviation or helicopter knowledge, 

interest in aviation, flying experience, and normal-range personality characteristics are likely to 

enhance the validity of the overall selection process. Non-cognitive traits that seem relevant for 

the aviator job include: conscientiousness/integrity; achievement orientation; stress 

tolerance/emotional stability; adaptability/cognitive flexibility; interpersonal/crew interaction 

skills; risk tolerance; internal locus of control; and, dominance/potency (including selfconfidence/self-esteem). 

The results of this review, then, suggest a selection strategy for Army aviation that 

includes measures of cognitive abilities such as spatial ability, mechanical reasoning, verbal 

ability, numerical reasoning, perceptual speed and accuracy, and cognitive prioritization, as well 

as a measure that would attempt to tap motivation to become an aviator. In addition, incremental 

validity may be achieved by including non-cognitive measures such as the TAP and AIM, as 

well as other normal-range personality inventories. 

From a practical perspective, the test battery could be administered via the Internet on 

any standard desktop computer and would not require any non-standard peripherals or hardware. 

Thus, it could be administered virtually anywhere that a computer is available, along with a 

reliable Internet connection and a test control officer. In fact, the Army may be able to take 

advantage of the fact that the US Navy has a web-enabled aviator selection battery that currently

consists of a reasonable set of cognitive tests. 

The addition of measures that focus on psychomotor skills and multiple-task 

performance, often labeled “performance-based” measures, is recommended. However, practical 

constraints on time and resources might suggest that these tests be considered as candidates for 

inclusion in an aviator tracking battery, to assist in the classification of selected Army aviators 

into mission/aircraft types. This recommended “Stage 2” in the selection/classification process 

36 
is, in fact, the next scheduled research and development effort for the ARI Rotary-Wing Aviation 

Research Unit at Fort Rucker. 

37 
38 
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Appendix A 

Overview of Aviator Selection Test Batteries 

Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Tests Usedby US Military MultiTrack Test Battery (MTTB)US Army 

> 1 day n = app. 3000. Attempted to use MTTB 

to predict 19 primary grades, setbacks, and attritions. Rs ranged from .29 to .39 for flight grades and .21 to .36 for academic grades. Discriminant analysis showed sign difference between setbacks and others. No differences in multiple discriminant analysis between pass and fails. Prediction of flight grades mainly from complex coordination task. Prediction of academic grades mainly from cognitive DVs. 

Currently used by US Armclassify pilot trainees into Attack/Scout or Cargo/Utiliaircraft training tracks. Already Computerized. Requires non-standard equipment. 

Complex Cognitive Assessment Battery (CCAB; devel by ARI) 

Probably can be Web-Ena

TowerFollowing directions

Word Anagrams

Logical relationsMark Numbers

A-1 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[MTTB con’t]

Information Purchase

Number & Words 

Route planning

Porta-Bat; selected tests only 

complex coordination

2 hand coordination 

mental rotationdecision making speed

timesharing

serial mental arithmetic 

embedded word

Manikin unknown whether it can be 

Web-Enabled. 

Word Knowledge

Cockpit Management Attitudes Battery 

Can be Web-Enabled. 

Work and Family Orientation 

Questionnaire 

Revised Jenkins Attitude 

Survey 

A-2 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[MTTB con’t]

Extended Personal Attributes 

Questionnaire 

Cockpit Attitudes Management 

Questionnaire 

Complex Coordination/MultiTasking Battery(CCMB; NAMRL)

combination of psychomot

and dichotic listening task probably cannot be WebEnabled. 

Psychomotor-stickDichotic listenting

Psychomotor and dichotic 

Psychomotor -stick and rudder 

Stick, rudder, dichotic listening Stick, rudder, throttle 

A-3 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Aviator Selection Test Battery (ASTB) US Navy 

2.5

hours 

Accdg to Operational Psychology Dept Briefing Slides, PFAR predicted flight grades in and attrition from primary flight training; from same presentation, validity data is summarized for FY98-FY04 -- AQR exhibited validity of .46 for Student Naval Aviators in predicting academic aspects of flight training, and PFAR exhibited validity of .32 for predicting peformance in flight training for USN SNAs, and .21 for USMC SNAs (Ns not provided, likely in the thousands; charts suggests that validity for predicting attrition is in the high teens for SNAs (USN & USMC), using the AQR or the PFAR. 

became operational in 199accdg to ASTB Workshop Briefing Slides, 1992 versiowas validated separately foSNAs and SNFOs and was developed to be bias-free gender/ethnicity with ETS Already Computerized. Can be Web-Enabled. Doerequire non-standard equipment. 

Compared the paper and pencil version 

of the ASTB with the computrized version administered via APEX. No significant differences in means,variance, model, or predictive validity. However, N = only 82. 

Reading Comprehension Reading & Math are often 

combined and called the Math/Verbal subtest. 

Math 15 min

Mechanical Comprehension 10 min Spatial Apperception Test 15 min 

A-4 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[ASTB con’t]

Aviation and Nautical 

Knowledge 

serves as a maximal meas

of interests/motivation to bpilot 

Aviation Interests 20 min. has been dropped 

biographical inventory has been dropped 

Air Force Officer Qualification Test (AFOQT) US Air Force US Navy 

Verbal (V), Quantitative (Q),Academic Aptitude(AA), Pilot(P), Navigator/Tech (N) 

Form Q, 16 subtests; Form S, 11 subtests. Form S dropped RC, DI, MC, EM, SR 

4.5 

hours 

(Q), 3.5 

hours 

(S) 

low but significant validity of the Pilot Aptitude Rating as a stand-alone for predicting UPT P/F (R=.168) & UPT Rank (R=.20) (Carretta & Ree, 1994); UPT final grade Form S (R=.34,uncorrected) (Shore & Gould, 2003) 

rank includes subjective evaluation. Using regressanalysis with test scores rathan the composite producsome increase in predictive validities. Criterion data obtained up to 5 years aftetesting. Not computerized, except SDI+ Can be Web-Enabled. Doerequire non-standard equipment, except TR test

Form S: P=AR, MK, IC, TR 

+ AI. N=VA, AR, MK, BC,TR + GS. V=VA + WK. Q=AR + MK. AA=V + Q. (subtest definitions follow) 

predictive validity ranges from .00 to .27 

for individual test scores predicting academic test grades; from .00 to .13 for individual test scores predicting check ride scores 

BAT + AFOQT combined to make 

PCMS score. N= 676. Correlation between pass/fail and PCMS was 0.34 uncorrected and 0.48 when the criterion variable was corrected fordichotimization 

BAT was composed of 5 teat this time 

A-5 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[AFOQT con’t]

Form 0. n=695 (1982-1984). 

uncorrected predictive validities from -.01 to .34 for pass/fail. R for pass/fail=.395. Pilot composite with 8 tests had R =.21 

First 8 tests below are used in the Pilot Composite

Verbal Analogies (VA) 9 min 

Mechanical Comprehension

(MC)

23 min dropped from Form S after 

factor analyses showed thaother subtests adequately measured the cognitive predictor space. 

Electrical Maze (EM) 13 min dropped from Form S after 

factor analyses showed thaother subtests adequately measured the cognitive predictor space. 

Scale Reading (SR) 18 min dropped from Form S after 

factor analyses showed thaother subtests adequately measured the cognitive predictor space. 

Instrument Comprehension

(IC) 

9 min 

Block Counting (BC) 5 min 

A-6 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[AFOQT con’t]

Table Reading (TR) 9 min This subtest has been in u

since 1942, and it still accofor unique variance in prediction of pilot performaRequires non-standard equipment (Large table). 

Aviation Information (AI) 9 min Arithmetic Reasoning (AR) 30 min Reading Comprehension (RC) 19 min dropped from Form S after 

factor analyses showed thaother subtests adequately measured the cognitive predictor space. 

Data Interpretation (DI) 25 min dropped from Form S after 

factor analyses showed thaother subtests adequately measured the cognitive predictor space. 

Word Knowledge (WK) 6 min 

Math Knowledge (MK) 23 min 

Rotated Blocks (RB) 15 min General Science (GS) 11 min Hidden Figures (HF) 10 min 

A-7 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[AFOQT con’t]

Self Description Inventory

(SDI) (Form S only) 

41 min Currently considered experimental This is the "original" Big 5: 

Conscientiousness, Agreeableness, NeuroticisOpenness, Extroversion + Service & Team OrientatioInventory 

Basic Aptitude Test (BAT) US Air Force 

Initial description of BAT in 

Carretta (1987a). Became operational in 1993. Already Computerized. Requires non-standard equipment, dep on test. Can be Web-Enabled, deptest. 

Only Flying Experience showed decent 

validity as a stand-alone measure, but adding all of them to the AFOQT enhanced predictive validity for UPT P/F (R=.295) and UPT Rank (R=.333) (Carretta & Ree, 1994) 

BAT less predictive than in previous samples. May be caused by prediction to Rawhich contains a subjective evaluation. 

Composite of AFOQT, BAT, and self 

reported flight hours correlated 0.23 with class rank and -.11 and -.18 with extra flying hours for T-37 and T-38 training. In this study, the BAT included a risk taking measure that was subsequently deleted. 

sample from 1986-1992. 

[BAT con’t] Psychomotor By 1993, psychomotor DV

were combined into a composite 

A-8 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Two-hand Coordination 10 min test-retest reliabilities range from .650 

to .823 for retests from 2 weeks to 6 months on each of the 2 DVs 

Probably cannot be WebEnabled. Requires non-standard equipment. 

Complex Coordination 10 min test-retest reliabilities range from .668 

to .827 for retests from 2 weeks to 6 months on each of the 3 DVs 

Probably cannot be WebEnabled. Requires non-standard equipment. 

Time sharing--tracking with a 

choice RT task 

30 min n=212. Timesharing DVs not related to 

pass/fail or check flight scores. Were related to track selection (classification). test-retest reliabilities range from .474 to .808 for retests from 2 weeks to 6 months on each of the 2 DVs 

unusual adaptive variable; combined with choice RT tProbably cannot be WebEnabled. Requires non-standard equipment. 

Item Recognition (Sternberg 

Memory Search) 

20 min test-retest reliabilities range from .518 

to .792 for retests from 2 weeks to 6 months on each of the 2 DVs 

Probably cannot be WebEnabled. Requires non-standard equipment. 

Mental Rotation 25 min Addition of mental rotation scores to 

AFOQT-Pilot composite only improved prediction of track selection (classification). 

angle of rotation not linearrelated to RT. Dropped. Requires non-standard equipment. Probably cannot be WebEnabled. 

Attitudes Toward Risk 10 min Dropped. Probably can be 

Web-Enabled. Does not require non-stanequipment. 

A-9 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[BAT con’t] Flying Experience (selfreported) ?? Probably can be Web-Ena

Does not require non-stanequipment. 

Dot estimation 6 min Dropped; measure of 

firmness/quickness of decimaking Probably cannot be WebEnabled. Requires non-standard equipment. 

digit memory 5 min Dropped. Probably cannot 

Web-Enabled. Requires nostandard equipment. 

encoding speed 15 min n=2219. Did improve prediction of 

AFOQT composite. However, test results did not show normal pattern of data. 

Test eventually dropped because of negative weighregression equation -- may have been a problem with programming. Probably cabe Web-Enabled. Requires non-standard equipment. 

immediate/ delayed memory 25 min n=2219. Did not improve prediction of 

AFOQT composite 

Data did not show normal pattern of results. Dropped may have been a problem the programming. Probably cannot be Web-Enabled. Requires non-standard equipment. 

A-10 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[BAT con’t] choice reaction time 20 min Dropped -- may have been 

problem with programmingProbably cannot be WebEnabled. Requires nonstandard equipment. 

embedded figures 15 min no significant relation to training 

performance. Did not increase predictive validity of AFOQT pilot composite 

Dropped. Probably can be Web-Enabled. Requires non-standard equipment. 

self-crediting word knowledge 10 min Eventually dropped becaus

negative weight in regressequation; intended as a measure of Self-ConfidencProbably can be Web-EnaDoes not require non-stanequipment. 

activities interest 10 min test-retest reliabilities range from .655 

to .871 for retests from 2 weeks to 6 months on each of the 2 DVs 

The Activities Interest scalforced-choice instrument thmeasures risk-taking attituin situations that involve thto physical survival. Can bWeb-Enabled. Does not renon-standard equipment. 

A-11 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

EnhancedFlight Screening Program US Air Force 

Armstrong Laboratory Aviation Personality Survey (ALAPS) 

20-30

min 

describes test development. Conversion tables for men and women. Cronbach alphas 0.73-.90. Administered with Neo-PI-R. Except for Openness, high correlation between some of ALPS scales and NEO summary scales. No NEO equivalent for 7 ALAPS scales 

Validity data not currently available. Test questions ascoring key published in an Armstrong Laboratory tech report. US Navy intends to evaluate at least some of tscales from this inventory fpotential use in their pilot selection process. Has diffscoring conversions (essenorms) for men vs womenbe Web-Enabled. May reqnon-standard equipment. 

5 "personality" scales: 

confidence, socialness, aggressiveness, orderliness, & negativity 

6 "crew interaction" scales: 

Dogmatism, Deference, Team Orientation, Organization, Impulsivity, & Risk-Taking 

4 "psychopathology" scales: 

Affective Lability, Anxiety, Depression, & Alcohol Abuse 

A-12 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[EFSP con’t]

NEO-PI-R Big 5 scales: Extraversion; 

Conscientiousness, Agreeableness, Emotional Stability, Openness 

~40 

minutes 

Archival analysis of accident data.Pilots with higher scores on Competence are 4 times more likely to have an accident; those with highscores on Dutifulness are 2 times more likely. (Competence & Dutifulness are two facets of the Conscientiousness domain score.) 

General literature shows ththe NEO-PI-R is valid for a veriety of jobs, but almost of the criterion-related validevidence (to date) is specithe pilot job; inventory was carefully developed and is psychometrically sound in terms of reliability and convalidity. Does not require nstandard equipment. 

Cluster analysis revealed no domain 

combinations related to UPT pass/fail. Attempted to analyze only Self-Initiated Terminations (SIEs) but needed larger sample to do odds ratios. 

First attempt to form profilefrom NEO and calculate likelihoods. Seems promisespecially for SIEs. Must kin mind, however, that the percentage of SIEs is typicvery small, so the low base will make it difficult to show a predictor does better thachance alone. 

Multi Attribute Battery (MAB) II

Does not require non-stan

equipment. 

InformationComprehensionArthimetic

SimilaritiesVocabularly

A-13 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[EFSP con’t]

Digit Symbol

Picture Completion

Spatial

Picture Arrangement

Object Assembly

CogScreen (battery) 

CogScreen originally deve

for the FAA's Civil AeromeInstitute (CAMI); USAF did some research with it relatpilots, but not for selectionwas eventually dropped by USAF due to high royalty f

MicroCog replaced Cogscreen

attention/mental control

memoryreasoning/calculationspatial processing

reaction time

information processing

accuracy 

information processing speed 

cognitive functioningcognitive proficiency

A-14 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Test of Basic Aviation Skills (TBAS) USAF & US Navy are studying 

N=531 USAF pilot trainees (already 

selected on other measures); horizontal tracking (rudder pedals), "airplane" tracking (joystick), and Emergency Scenarios showed low, but significant correlations with training pass/fail and training performance (composite of grades, check rides, etc.). (Multiple-task test did not.) Multiple R for predicting T37 Total score (check ride, instructorratings, quiz scores, etc) was .33; Multiple R for predicting UPT pass/fail was .31. 

Doesn't look like regressiowere corrected for shrinkabut author notes that they downwardly biased due to high degree of range restriN=126 USAF pilot trainees test-retest reliability was velow for Emergency Scenarscore (.15) and acceptable not impressive for the othescores (.56-.75) Documentation describing score variables were derivdoes not appear to exist, ndoes computer programmidocumentation. Probably cannot be WebEnabled. Requires non-standard equipment. 

Direction orientation Spatial orientation test. Multi-Tasking (Tracking & 

Directed Listening tasks performed simultaneously) 

A tracking task requiring usrudder pedals and a joysticperformed at the same tima Directed Listening task (trigger on joystick when anseveral pre-specified digits heard out of a stream of randomly-presented digits)

A-15 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[TBAS con’t]

Emergency Scenarios Test-taker must notice and 

respond quickly to warning signals indicating that an “emergency” is occurring. 

Armed Services Vocational Aptitude Battery (ASVAB) US Army, US Air Force, US Navy 

In a large sample of enlisted US Army 

Soldiers, a composite of ASVAB factor scores demonstrated an average multiple correlation, across several MOS (N ranged from 73 to 553 per MOS), of .62 for predicting Core Technical Job Proficiency, .66 for predicting General Soldiering Proficiency, .37 for predicting Effort and Leadership, .17 for predicting Maintaining Personal Discipline, and .16 for predicting Physical Fitness and Military Bearing. The multiple correlations were corrected for range restriction and adjusted for shrinkage. 

Currently used as a prescreening tool by US Afor Warrant Officer selectiointo rotary wing pilot traininand as a prescreening for enlisted personnel in all branches of the US militaryDoes not require non-stanequipment. 

General Science (GS) 11 min Arithmetic Reasoning (AR) 36 min Word Knowledge (WK) 11 min 

Paragraph Comprehension

(PC) 

13 min 

Auto & Shop Information (AS) 11 min Mathematics Knowledge (MK) 24 min 

Mechanical Comprehension

(MC)

19 min 

A-16 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[ASVAB con’t]

Electronics Information (EI) 9 min 

Assembling Objects (AO) 9 min added in 2002 to replace t

Coding Speed test; good variance, smaller gender differences 

Numerical Operations (NO) -- dropped as of 2002 due to 

unacceptably large gender differences 

Coding Speed (CS) -- dropped as of 2002 due to 

unacceptably large gender differences 

Tests/ Batteries Used by ForeignMilitary Services MICROPAT UK ARMY 

Unkno

wn 

Good validity as a stand alone predictor of success in Basic & Advanced rotarywing pilot training (shrunken R ~.50); added quite a bit of incremental validity when used in conjunction with a spatial/psychomotor composite & barebones biographical info. (Bartram, 1987), n=105. 

Bartram sample was rotarywing pilots. Tracking tasks predict basic training outconot advanced. Requires non-standard equipment. 

Psychomotor

Adaptive Pursuit Tracking Probably cannot be WebEnabled. 

A-17 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[MICROPAT con’t.]

Compensatory Tracking Probably cannot be WebEnabled. 

Information Management Ability 

Risk - figure out risks, which 

change over time, and adapt strategies accordingly 

Almost identical to NAMRL Automated Risk AssessmeTask. Both based on Slovi(1969) paper. May be WebEnabled, dep on degree of timing accuracy needed. 

Signal - signal detection Signal detection theory tas

Measure beta, decision spand accuracy (d'?). May be Web-Enabled, dep degree of timing accuracy needed. 

Dual Tasks (tracking & mental 

arithmetic) 

May be Web-Enabled, dep degree of timing accuracy needed. 

Landing (simulated aircraft 

landing) 

superfically resembles an aircraft landing simulator. Mbe Web-Enabled, dep on degree of timing accuracy needed. 

A-18 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[MICROPAT con’t.]

Scheduling (dynamic

speed/distance estimation task)

Resembles POPCORN tas

used by NASA. Correlates tests of mathematical reasand knowledge. May be Web-Enabled, dep degree of timing accuracy needed. 

digit span n=105. Pass/fail for basic r=.57 and .52 

for pass/fail at advanced (shrinkage to .50 and .44). With 16 PF, R=.75 for pass/fail in advanced 

digit span included with othtests in this article. Cattell PF also included and by itsr= .57 with pass fail for advanced training. MicroPwith 16 PF has same predvalidity as then current selection test plus bio. Ba(1987) does not include digspan in MicroPat

RAF (computerbased testing) RAF; Australian RAF; Netherlands RAF, British Army Air Corps 

Unkno

wn 

Meta analysis. Criterion is pass/fail on first attempt of initial flying training; data from Turkish AF, RAF, and AAC (British rotary wing). ns of about 2000 to 4000. Corrected validity for the average stanine score was .45 for the AAC, .42 for the RAF, and .35 for the Turkish AF 

May be Web-Enabled, dep test. Requires non-standard equipment. 

Sensory Motor Apparatus Traditional RAF test. Proba

cannot be Web-Enabled. Requires non-standard equipment. 

A-19 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[RAF con’t.]

Instrument Comprehension Traditional RAF test. Proba

can be Web-Enabled. Doerequire non-standard equipment. 

Control of Velocity Traditional RAF test. Proba

cannot be Web-Enabled. Requires non-standard equipment. 

CanadianAutomated Pilot Selection System (CAPSS) CanadianMilitary 

5 hours Validities in the .25-.35 range for 

predicting variables such as Training P/F, Course Grade, PerformanceRatings, and Academic Average. 

CAPSS scores are pretty hcorrelated with previous piexperience (~.35) Probably cannot be WebEnabled. Requires non-standard equipment (flight simulator

r=.47 between CAPSS score and

pass/fail 

non representative cadet sample; no females or Frespeakers ; 70% pass rate. Cadets prescreened 

Dichotic Listening Test Israeli Military 

25-35

min, 

dep on 

version 

Maybe can Web-Enabled. Requires non-standard equipment. 

A-20 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[DLT con’t.]

n=100 cadets and n=95 pilots. Very low 

error rate (0.7 to 4.3%) in Part 1. Non significant correlation with intelligence test, mechanical and psychomotor test, and psychiatric evaluation. Correlates .26+ with 3-point pass/fail early/late criterion. Second study with n=95shows differences between transport pilots and jet pilots. 

Errors may be caused by speaking response as nexstimulus is presented. Tesshows rapid learning. 58%failure rate in flight trainingIsraelis use a cascade selesystem, not a multi-hurdle 

Pilot trainees who passed training made 

fewer errors than those who failed training; test scores added a bit of incremental validity when added to a battery of 15 undescribed subtests (probably cognitive), a psychomotor test, and an interview. 

Dichotic listenting test performed alone 

and with one, two, or three tracking tasks. N= about 675 student Naval aviators. Dichotic alone had max. possible right of 108; mean was 101.9. After transformation, dichotic scores did not predict pass/fail, but did predict flight grade and were independent of other measures. 

A-21 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Pilot Evaluation System (PES) Israeli AF 

22 scenarios withany combination of 

5 tasks; figure ofmerit DV 

90 min Internal validity study at USAF 

Academy. Then correlated PES scores with daily scores (not described further) and maneuver grades. Seemed promising. 

Simulates air-to-air engagements. US companmay have bought rights. Nother known English-languresearch. Probably cannot be WebEnabled. Requires non-standard equipment (static part-task simulator). 

Pilot Selection Tests not specifically developed by/for the Military WOMBAT-CS CanadianNational Flight College; commercial airlines, flight schools 

3.5

hours, 

but can 

be 

shorten

ed if 

desired 

Wombat has been used a

last-stage selection test by Canadian National Flying College in Quebec for 7 yeWombat scores are 70% ototal score for last-stage selection. Little published validity evidence because proprietary restrictions. Publisher is working on a wversion. Requires non-stanequipment. 

A-22 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[WOMBATCS con’t.]

Study 1: 32 ab initios. Criteria were 

academic performance, time to solo, and time to check ride recommendation. 

Bonus score significantly related to academic performance (r=.36). Study 2: 21 students from Phase 1. Simulated cross-country flight. SA estimates by questionnaire. Bonus score and total score correlated (r<.4) with SA measure. 

Study conducted at aviation dept. of US 

college. Subjects from all 4 years of program. Wombat scores related to flight hours (r=.35). Author believes WOMBAT can be used to measure SA and stress tolerance. 

total performance 

Two-Handed Tracking Task 

(Primary) 

Figure Rotation (Bonus) Quadrant Location (Bonus) Digit Canceling (Bonus) 

BFITS Developed under SBIR contract for AF 

several parameters fromflight simulator performance 

50

hours 

computer hardware and software may need to be updated. Cannot be Web-Enabled. Requires non-standard equipment. 

A-23 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[BFITS Con’t] 

Study at ComAir Academy (n=72). r=.33 

between time to gain private pilot's license and scores in first phase of BFITS (first 5 hours?). 

BFITS is a combination traand selection system conducted on PC-based flisimulator. 31 lessons leadto private pilot's license. Elesson has tutorial with procedural training. DV is to criterion performance. 

n=10 in experimental group; n=17 in 

control group. BFITS students soloed after 12.5 hours versus 17.6 for the control (sign at <01). 

initial validation study on cpilot candidates

181 total cadets at ComAir Academy. 

No data provide but authors claim same pattern as 1997 study. 

PILAPT varies;

long 

and 

short 

version 

availabl

e 

commercially available pro

May be as expensive as WOMBAT with additional pusage fees. Tests added adeleted over time. Probablcannot be Web-Enabled. Requires non-standard equipment. 

Sequences 8 min

Patterns 10 minDigits ??Concentration 8 min

Rules ?? not in current battery 

A-24 
Battery 

Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[PILAPT Con’t]

Views 20 min not in current battery Plans ?? spatial test; not in current 

battery 

Shapes ?? spatial test; not in current 

battery 

Gates ?? 2-d compensatory tracking

in current battery 

Ascent ?? job sample; not in current 

battery 

In-Flight ?? job sample; not in current 

battery 

Descent ?? job sample; not in current 

battery 

Deviation Indicator 7 min unselected sample of RAF University 

Air Squadron applicants. R based on 169 cadet = .55. Criterion was average training grade after 6 months. Another paper indicates that a composite PILAPT score increased 0.8 SD on retest in this study 

4 month test-retest =0.80 n=109 

Trax 5 min 3-d pursuit tracking; 4 mon

test-retest =0.84 n=109 

speed; accuracy; combination of two 

Hands 10 min also loads on a spatial fact

month test-retest =0.69 n=Spearman-Brown >.91 all 

A-25 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[PILAPT Con’t] 

Capacity 15 min. Not used in ab initio select

Primary task with two secondary tasks. Single, dand triple task performancassessed 

Tests Tried by Military But Not Currently Used Cognitive Laterality Battery (CLB) 

~ 90

minutes 

Visuospatial subtests differentiated pilot trainees who completed training from those who dropped out; verbosequential did not; don't know if test score added incremental validity beyond ASTB scores 

study conducted with US Npilot trainees, some of whowere rotary wing pilots. Macan be Web-Enabled. Requires non-standard equipment. 

visuospatial

Localization

Orientation (mental rotation) 

Touching Blocks

Form Completion

verbosequential

Serial Sounds

Serial Numbers

Word Production, Letters 

Word Production, Categories 

A-26 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

ComputerBased Performance Test (CBPT or CBT) US Navy is studying 

Unkno

wn 

n=372 Shull and Griffin (1990) used an early version of the CBT and examined different groups of aviators (2 jet groups, helos and student naval aviators). Helo pilots never did the best on any DV and were often the worst and performed more poorly than the students. Among the students, the ones who eventually went to helos did more poorly than the other groups. 

Street, Dolgin, & Helton (1describe 4 CBT tasks, as shown in this table. PortmaTiller, et al. (1998) describethe CBT has having 10 tasks:stick task; dichotic listening; stick and dichotic listening,stick and rudder tstick, rudder, and dichotic listening; stick, rudder, and throttle;horizontal tracking; absolute difference; manniand absolute difference anhorizontal tracking. Probabcannot be Web-Enabled. Requires non-standard equipment. 

n=106 Hierarchical regression found 

that all four CBT tasks significantly increased the R. Three scales from the Pilot Personality Questionnaire (assertiveness, competitiveness, and self-control) also significantly increased the R. Analysis focused on predicting advanced naval flight training (not primary or intermediate). 

n = 150. factor analysis of and 4 tests of ASTB (no biinventory) produces 4 factosolution. Dichotic listening (and combinations) define factor; stick and combinatiodefine another factor 

A-27 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[CBPT Con’t] 

Both Shull and Griffin (199

and Delaney (1990) descrithe transformation needed the dichotic listening scoreLog to the base 10(#errors They both mention extremskewness. Given the limiteinformation provided, it appthat many applicants make errors and a few make maneurological test? 

psychomotor/dichotic listening -0.36 uncorrected predictive validity 

with Advanced Flight Grade (AFG) 

absolute difference and 

horizontal tracking 

 -0.25 uncorrected predictive validity 

with Advanced Flight Grade (AFG) 

complex visual task -0.17 uncorrected predictive validity 

with Advanced Flight Grade (AFG) 

based on task analysis of sneeded to extract informatfrom visual displays (Robeand Castle, 1996) 

mannikin -0.29 uncorrected predictive validity 

with Advanced Flight Grade (AFG) 

A-28 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Complex Visual Information Processing Test US Navy is studying 

60-75

min 

n=451 student naval aviators undergoing primary training. Number correct r= -.27 with pass/fail. For students who pass training, correlation with each of the 3 DVs and flight grade was significan but low (r = .10). However, number correct exhibited significant correlation with all measures of primary flight training, including academic grades. Stepwise regressions to predict Pass/Fail, Composite Score, and Flight Grade were predicted by number correct, RTs, and biographical inventory, R= .26 to .32. FAR scores and AQT scores never entered the equation. 

DVs are number correct, tito read the question, and tto respond. Probably cannot be WebEnabled. 

Student naval aviators, n ranges 

from1077 to 337. Test battery. After controlling for intelligence, CVT accounted for about 3.5% of variance in pass/fail. Interacted with accession source and college major. Best linear model from battery included 7 demographic variables, 6 AQT/FAR variables, 3 CVT scores, and 12 interactions; still only accounted for 8.3% of variance in pass/fail. 

psychomotor/dichotic listentask did not add variance tmodel. 

A-29 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

Multi Attribute Battery (MAB) NASA uses for astronaut selection 

70 min

(7 min 

per 

subtest

) 

better than WAIS for discriminating at high end scale. Evidence of ceiling effects on some scales. In astronaut population, may 3 factor solution, not two. Dnot require non-standard equipment. Developer refuses to be WEnabled. 

highly regard by USAF 

Verbal IQ (crytallized intelligence) 

InformationComprehensionArithmetic

Similarities

Vocabulary

Performance IQ (fluid intelligence)

Digit symbol

Picture completion

Spatial

A-30 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[MAB Con’t] Picture arrangement

Object assembly

full scale IQ (comprised of all subtests) 

CogScreen (Aeromedical Edition) US Navy is studying; USAF considered 

most tasks havespeed, accuracy, and throughput score

45

min? 

no predictive validity evidence available that we can find. Being considered by the Navy as a pilot selection instrument. 

list of tasks and what they measure differs in King anFlynn (1995) versus CallisKing, and Retzlaff (1996), also give DVs. Callister enote that many of the meaaccuracy scores are 90+%Believe that software has scoring error in calculating scores on Pathfinder. Also some of the 65 variables aquestionable use. Probably cannot be Web-Enabled. Requires non-standard equipment. 

study of factor structure of 

CogScreen by USAF (CarrRee, and Callister, 1999). CogScreen does not appebe g loaded. Different studn=1015 pilot applicants. Ddo not fit the original 9 factKay model. No obvious sihiger-order factor. Best mohas 2 higher order factors 6 first levels 

A-31 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

[CogScreen con’t]

study of aging, flight 

perforamnce, and Cog Scrshows a 5 factor solution. However, only used 28 of 65 variables 

1 DV Backward Digit Span 3 DVs math problems 3 DVs visual sequence comparison 4 DVs symbol digit coding 

symbol digit coding-immediate 

recall 

listed in King and Flynn on

3 DVs matching to sample 3 DVs mannikin figures 7 DVs divided attention3 DVs auditory sequence comparison 12 DVs pathfinder 

symbol-digit coding--delayed

recall 

King and Flynn only 

16 DVs shifting attention Callister, King, and Retzlaf

only; sounds very similar to Wisconsin Card Sorting Tawhich measures persevera

10 DVs dual-task test Callister, King, and Retzlaf

only 

A-32 
Battery Name 

Composite Score/Test Subtest Length Summary of Validity Evidence Comments 

PASS 5 hours 223 officers and 232 warrant officer 

candidates. All qualified for flight training on basis of FAST scores. Predictive validity apparently about .50. 

Job sample test. Five 1-hosessions in UH-1 instrumeflight trainer. Data obtaine1978 and 1979. Analysis bonly on data from first sessstraight and level flight. Daanlaysis complex and costSimulator costs and analyscosts too high; PASS not ceffective. Cannot be WebEnabled. Requires non-standard equipment (sim). 

Complex CoordinationTest US Army USAF (may use) 

n = 1123. r = .50 for pass/fail flying 

only; about 0.29 for pass/fail for all reasons 

Included in preliminary FASvalidation. Probably cannot be WebEnabled. Requires non-standard equipment. 

Rotary Pursuit US Army 

n=1112. r =.30 for pass/fail flying only; 

about .25 for pass/fail for all reasons 

Included in preliminary FASvalidation. Probably cannot be WebEnabled. Requires non-standard equipment. 

A-33 
Appendix B 

Overview of Non-Cognitive Inventories that may be Relevant for Aviator Selection 

Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Inventories Considered orUsed for Pilot Selection Self Description Inventory, Plus (SDI+) US Air Force

Big 5: Neuroticism;Extraversion; Openness;Agreeableness;Conscientiousness; PLUS Service Orientation and Team Orientation 220 items; "behavior statements" with Likerttype responses 41 min 

D: Rational and factor-analytic S: Rational 

in progress No criterion-related validity

evidence for the SDI+; should beavailable in December 2006. Asmall pilot test (N= 71 enlisted airmen) based on the original 163-item version of the SDI (w/o Service or Team Orientation) showed raw correlations of .47 (Agreeableness), .20 (Conscientiousness), .01 (Extroversion), -.24 (Neuroticism), and .35 (Openness) with a composite supervisory rating variable called "Global 2:Interpersonal Proficiency." The validity coefficients for Agreeableness, Neuroticism, and Openness are statisticallysignificant at p<.05. The 5-factor model was originally developed using a sample of USAF enlisted personnel, but the factor structurewas confirmed in a sample of 523USAF commissioned officers, using a precursor of the SDI+. 

Skinner & Alley (2002);Christal, et al, 1997; Weismuller, et al., 2004 

16origand and cre5 pare the wriTeOriresWhall stalistAFconCo

B-1 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Self Description Inventory, Plus (SDI+) (Cont.) 

Ex

the of 5acrintemooutcorAgCoExand samranbe ret(stthointe

Th

AgresoffiSDstru

B-2 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Noncognitive Scales included in the BasicAptitude Test (BAT) US Air Force

at least 5 different noncognitive tests have been tried out, but most were eventually dropped; atpresent, it sounds like Flying Experience and,maybe, Activities Interest are still part of the battery. presumably biodata for Flying Experience; "activities" for Activities Interest 

Unknown Yes N= 678 pilot trainees. Carretta & 

Ree (1993) found that both Flying Experience and the ActivitiesInterest scale (labeled "RiskTaking" in this study) exhibited a significant correlation with UPT P/F(R=.167 and .101 for Fly Exp and Act Int, respectively) and UPT Rank (R=.190 and .108 for Fly Exp & Act Int, respectively). A surprise finding was that Flying Experience alone correlated almost as high asthe AFOQT score alone with these two criterion measures. When Flying Experience was added tothe AFOQT, R increased by .067for UPT P/F (from .168 to .235) and by .074 for UPT Grade (from.168 to .274). When Activities Interest was added to the AFOQT, R increased by .035 for UPT P/F (from .168 to .203) and by .036 for UPT Grade (from .168 to .236). 

Carretta & Ree (1993) [NOTE: Carretta & Ree (1994) may include the same data. 

ThformesituphyCo

NEO-PI US Air Force; NASA; (also used widely in private sector)

Personality: Neuroticism; 

Extraversion; Openness;Agreeableness;Conscientiousness240, 5-option items + 3 response validity items,takes ~40 minutes; NEOFFI has 60, 5-response items

D: Rational & Factor-Analytic S: Rational 

yes, but not 

operationally

General literature shows that the NEO-PI-R is valid for a variety ofjobs, but none of the criterionrelated validity evidence (to date) isspecific to the pilot job; inventorywas carefully developed and ispsychometrically sound in terms of reliability and construct validity.

www.rpp.on.ca/neopir. htm

Six the thaavaArmmacogArmvalper 

B-3 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

NEO-PI (Cont.) Compared 48 female AF transport 

and tanker pilots to 64 comparable male pilots and 103 femaleundergraduates on the NEO-FFI. Female pilots had significantlyhigher scores on the Extraversion, Agreeableness, and Conscientiousness scales than male pilots. Also showed large differences from controls (female undergraduates) on same three scales plus Neuroticism. 

King, Retzlaff, andMcGlohn (1997) 

scale norms are available for US 

Air Force student aviators(N=1031; 103 of them females)and for US Army student aviators(N=355) and active duty aviators(N=255); could potentially be used for profile matching purposes

Callister, King, Retzlaff, & Marsh (1999); Howse (1995) 

Canorcouset10~.6~.5~.3Agon Coare witgenand thaundbecindany

differences between male and 

female aviation studentsdisappeared by the third year. 8 of the 36 facets showed significant correlations with GPAs 

Pettitt and Dunlap(1995) 

B-4 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

NEO-PI (Cont.) no significant differences on any

scale between candidatesaccepted or rejected for astronaut positions

Musson, Sandal, Helmreich (2004) 

N=1031 individuals accepted into

US Navy's Enhanced Flight Screening program. Clusteranalysis revealed that no combination of domain (I.e., Big 5 dimension) scores were correlated with completion of the trainingprogram (88% completed). The authors also outlined amethodology for calculating the odds (based on logistic regression) that a pre-trainee with a particularNEO profile will behave like a benchmark profile (e.g., a stellar performer). Preliminary analysessuggests there might be some utility here, e.g., for predicting SelfInitiated Terminations (SIEs) butsample sizes would need to be much larger to go very far with this approach. 

Anesgart and Callister (1999) 

FirNEProinit

Archival analysis of accident data. 

Pilots with higher scores on Competence are 4 times morelikely to have an accident; those with high scores on Dutifulness are 2 times more likely. (NOTE: Competence & Dutifulness are facet scores within the Conscientiousness factor.) 

King, Retzlaff, andOrme (2001) 

Thcou

B-5 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Armstrong Laboratory Aviation Personality Scale (ALAPS) US Air Force

5 "personality" scales: Confidence (Narcissism), Socialness, Aggressiveness,Orderliness(Compulsivity), & Negativity (PassiveAggression) 240 True-False items; 20-30 minutes

D: Rational S: Rational 

Experimental no criterion-related validity

evidence until at least Dec 2006 

Retzlaff, King, McGlohn,& Callister (1996);Retzlaff, King, Callister, Orme, & Marsh (2002);Operational PsychologyDept (20 July 2004) 

Tepubtecevascapotproconfor Co

6 "crew interaction"scales: Dogmatism(Authoritarianism),Deference (Submissiveness), TeamOriented, Organization, Impulsivity, & Risk-Taking

In o

ALpiloand Feon lowDoalpcorNEfairexccorOpequsca

4 "psychopathology" scales: Affective Lability, Anxiety, Depression, & Alcohol Abuse 

US

pilotra

B-6 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Personal Characteristics Inventory (PCI) NASA; US Air Force; Commercial aviation

Personality: 

Instrumentality (pos & neg); Expressivity; VerbalAggressiveness; Mastery Orientation; WorkOrientation; Competitiveness;Achievement Striving; Impatience/Irritability; Negative Communion 225, 5-response items, ~ 40 minutes

D: Rational, inthat scales selected tomeasure specificconstructsS: Likely someof each

yes, but not 

operationally

n=259. No PCI or NEO-FFI scalesdistinguished between applicantsselected versus rejected for the astronaut program (analysesconducted separately for males & females). About 1/3rd of the astronaut applicants was classified into each of 3 PCI clusters; the percentage falling in each cluster did not differ between successful (N=63) and non-successful applicants (i.e., persons who ultimately were selected as an astronaut). Musson, et al. say thatsimilar clusters have been found tobe predictive of performance and cite some references, two of which sound like they involve pilots or pilot trainees. 

Musson, Sandal, and Helmreich (2004); King & Flynn (1995) 

Thof UScFlyconpreExQuFaand Thof tPC& cTexas the aviitem NErefNEove

B-7 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Personal Characteristics Inventory (PCI) (Cont.) 

N=23, three-man commercial 

airline crews. Examined performance of crews, separating them into comparison groupsaccording to the pilot's score onone of 3 clusters (Positive Instrumentality/Expressivity - "Right Stuff", Negative Instrumentality - "Wrong Stuff", or Negative Expressivity - "No Stuff". NOTE: Cluster configurations may not be exactly the same as in laterresearch. Found that the crewswith a "Right Stuff" pilot consistently performed effectively,although not necessarily alwaysbetter than crews with a "WrongStuff" pilot. 

Chidester & Foushee (1991) 

Mu(20capaspperexprefachexpinteconanastabeeresas al., ExStu("WMo

B-8 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Personal Characteristics Inventory (PCI) (Cont.) 

N=538 US military officers in

transport operations who heldpositions on the flight deck, and who attended a training program in cockpit resource management. Authors used cluster analysis to identify 3 clusters on the PCI which they labeled "Right Stuff," "WrongStuff," and "No Stuff." Authors don't say how many participants were in each cluster. Criterion measure was rate of promotion, which wasoperationalized as rank relative to self-reported total logged military flying hours. "Right stuff" participants had a significantlyhigher promotion rate than either ofthe other groups. No difference inpromotion rate between "Wrong Stuff" and "No Stuff." There werealso interpretable relationshipsbetween cluster membership and attitudes toward cockpitmanagement as measured by the CMAQ. 

Gregorich, Helmreich, Wilhelm, & Chidester (1989) (describe the 3clusters) 

Acof tin spera sappnot of twhhiggenscaastsigCoNewhscoExAcAufindsig(bama

B-9 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Personal Characteristics Inventory (PCI) (Cont.) 

Howse (1995) collected data for

529 student aviators and 331active duty aviators (no indicationof gender breakdown) 

Howse (1995) PC

witmawe.35MuIns("RhigColiteCoof pmoBame

Cockpit Management Attitudes Battery; scales/clusters used inMTTB US Army

Personality: Cockpit Management AttitudesQuestionnaire; Work & Family Orientation Scale; Revised Jenkins Activity Survey; Extended Personal AttributesQuestionnaire 

D: Rational, inthat scales selected tomeasure specificconstructsS: Likely someof each

yes, but not 

operationally

In a series of regressions, Intano & Howse report zero-order validities(among other things) for clusters ofscores from the CMAB that wereincluded in regression analyses.For predicting Primary Overall Flight Grade (PFOG), the regression included one cluster("Cluster 1") that showed a zeroorder correlation of .07 with PFOG (N=2405 pilot trainees). For predicting Primary Overall Average Grade (POAG), the regression included 3 non-cognitive clusters -- "Cluster 1," "Comp15," and "Cockpit" with zero-order correlations of .07, .10, and.02respectively (N=2901 pilot trainees). Zero-order correlations were not reported for any noncognitive scales that didn't make it into the regression analysis.

Intano & Howse (1991);Intano & Howse (1992);Intano, Howse, & Lofaro (1991a, 1991b) 

clucomdiffdesweold

B-10 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Cockpit Management Attitudes Questionnaire 

3 underlying dimensionsare Communication & Coordination; Command Responsibility; and Recognition of Stressor Effects 

Gregorich, Helmreich,

Wilhelm, & Chidester (1989) 

Tha pusePC

Automated Aircrew Personality Profiler USAF

Five factor solutionextracted: hostility, selfconfidence, valuesflexibility, depression, andmania 202 items: Mixture of types

D: Rational for picking inventories onwhich AAPP is based, but some of those were developedempiricallyS: Factoranalytic

yes, but not 

operationally

N=325 USAF UPT students. AAPP administered with the BAT. Hostility, self-confidence and values flexibility factor scores weresignificantly correlated with UPT pass/fail (-.12, -.13, -.12, respectively). (Authors note that > 80% passed, so upper limit on point-biserial was about .70). Entered the AFOQT subscales,BAT subtests, and the 5 AAPP factors scores in a regressionequation to predict UPT graduate versus non-graduate. Sometimesincluded non-cognitive BAT subtests (Self-Crediting Word Knowledge & Activities Interest) and sometimes didn't. In all analyses, deleting the 5 AAPP factors scores reduced R, butnever to a statistically significant degree so author concluded that the AAPP didn't add any unique prediction.

Siem (1992) 202 

StaPeInteJenfacseldepitem somsca(19lateof dstuque

B-11 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Automated Aircrew Personality Profiler (Cont.) 

N=332, same overall sample of

USAF UPT students as reported in Siem (1992). Collected responselatency for each item. Calculated 5 different scale scores based on the94 MMPI items included in the AAPP (factor scales developed by Costa & colleagues in the MMPI item pool) -- Sociability(Psychoticism), Emotional Stability(Neuroticism), Extraversion, Competency (Inadequacy), and Cynicism. Three of the 5 factor scale scores showed low, but significant validity for predicting UPT P/F (Sociability .131, Competency .098, and Cynicism -.138). Factor analysis of the response latency and scale scoresdidn't produce a very interpretable factor solution. Conducted regression analyses to see if response latency added anypredictive validity for UPT P/F. Basically, it didn't, with the possibleexception of Extraversion, but even that finding seems pretty shaky.For example, the Extraversion scale score wasn't significantlycorrelated with UPT P/F to begin with. 

Siem (1991); Siem(1992) 

B-12 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Pilot Personality Questionnaire US Navy

12 Personality scales: assertiveness, interpersonal orientation, aggressiveness, hostility,verbal aggressiveness,submissiveness, highmastery motivation, highwork motivation, competitiveness, selfcontrol, fatalism, high social desirability 112 multiple-choice itemswith no specific time limit 

D: Rational, inthat scales selected tomeasure specificconstructsS: Unknown 

yes, but not 

operationally

n=106 SNAs. Threw 69 predictor DVs in a regression to predict Advanced Flight Grade (AFG). Thebest regression solution included23 of the DVs. Only 11 of the 23showed a significant contribution (p < .05) to the regression, so further examined those 11. Three of the DVs were PPQ scales. Of the 3, only the Assertiveness scaleshowed a significant stand-alone correlation with AFG (r=.20, p<.01).The Assertiveness scale, whenadded to the AQT/FAR (whichcome from the ASTB) increased Rsquared by .069 (R increased from.18 to .32). The Competitivenessscale, which didn't show a significant correlation with AFG asa stand-alone (r=12, ns), increasedR-squared by .080 when added to AQT/FAR (R increased from .18 to .34). Another scale, Self-Control,which didn't show a significant correlation with AFG as a standalone (r=.08), increased R-squared by .04 (R increased from .18 to.27). 

Street, Dolgin, andHelton (1993)

comWoPeQuDeIncDea rRatendescan con

N=45 SNAs; only the Crowne

Social Desirability scale correlated significantly with UPT pass/fail (-.293) and flight grade (-.451), suggesting that those who scored lower on social desirability weremore likely to pass and earn higher grades in UPT. 

Shull & Dolgin, 1989

B-13 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Pilot Personality Questionnaire (Cont.) 

n=211 SNAs; compared those who

passed all the way throughadvanced flight training with those who failed for academic or flightrelated reasons (N=45). The PPQ Competitiveness scale was theonly scale that differentiated those who passed from those who failed, in terms of mean score diff (t-test) and in a discriminant function analysis. SNAs with higher Competitiveness scale scores weremore likely to pass. 

Street, Helton, & Dolgin(1992) 

Tridimensional Personality Questionnaire US Navy (under review)

Personality: NoveltySeeking; Harm Avoidance; Reward Dependence 100 T-F items

D: Presumablyrational S: Presumablyrational 

yes, but not 

operationally

to date, have only compared student naval aviators and flightofficers with normative groups to check internal validity 

Lambirth, Dolgin et al (2003) 

tesper

Hand US Navy (under review)

projective test that isscored according to 15categories (listed below) & 6 summary scores, pluspsychopathology indexplus rigidity indexItem type: Stimulusobjects. 15 min. admin, 15 min to score

D: Unknown S: Clinical interpretation guided byscoringguidelines

yes, but not 

operationally

to date, have only compared student naval aviators and flightofficers with normative groups to check internal validity 

Lambirth, Dolgin et al (2003) 

shoresbe scoViron posatteNo

affection, dependence, communication, exhibition, direction, aggression, acquisition, active,passive, tension, crippled,fear, description, bizarre, failure 

B-14 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Jenkins Activity Survey US Navy examined

Composite Type ABehavior Pattern score, plus 3 factor-analyticallyderived subscales: Speed & Impatience (Factor S), Job Involvement (Factor J), and Hard-Driving & Competitive (Factor H); some articles say thatscores for achievementstriving and impatience/irritability can also be derived 20-30 minutes; 52multiple-choice items

D: Unknown S: Factoranalytic,perhaps alsoempirical 

yes, but not 

operationally

158 student naval aviators. Nosignificant correlation between anyJenkins Scale and pass/fail or with grades in primary flight training. 

Shull, Dolgin, and Gibb (1988) 

Intebehdiahas predishas numcogon GibscaTypto a comRePsynorwoof l

NAMRL Automated Risk Assessment TaskNAMRL (US Navy)

Unknown Yes 440 student naval aviators. No

correlation with any of the teststhat make up the AQT/FAR. No correlation between any measure of risk taking and pass/fail or withgrades in preflight or flight training. 

Shull, Dolgin, and Gibb (1988) 

Scand posposinteCo

Defense Mechanism Test Norwegian/Swedish/Danish Air Force

Unknown Yes Meta analysis shows correlation of 

0.3 to pass/fail for Scandinavians(n=1674) and 0.05 for othergroups. 

Martinussen andTorjuseen (1998) 

TemeNoAdinto com

B-15 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Minnesota Multiphasic Personality Inventory Used widely by clinicians andprivate-sector organizations; may be used for psychiatricscreening by the US military

10 clinical personalityscales and 4 response validity scales (which aresometimes interpreted like content scales); numerousother scoring profiles have been empirically derived from the MMPI item pool 566 T-F items

D: Empirical S: Empirical 

Yes Profiles for various pilot samples

have been developed and compared with general populationnorms; no evidence of validity for predicting training or job performance. 

Caldwell, O'Hara, Caldwell, Stephens, & Krueger (1993) 

Thpsyout"scsevthascodecscoPro

Eysenck Personality InventoryUK for wide variety of jobs; UK military

Personality: Extraversion, Neuroticism 57 true-false items

D & S: Factoranalytic No 495 selectees for British Army helicopter training. Score on

extraversion scale correlated with pass/fail. No correlations with aptitude measures. Addingextraversion scale to aptitudebattery score increases predictivevalidity about 4%.

Bartram and Dale (1982) 

EPenohelwoPro

B-16 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Eysenck Personality Inventory(Cont.) 

N=167 pilot trainees; criterion was

successful completion of Basic Flight training versus suspensionfrom same (N=58); Significant score difference between the twogroups (success versussuspended) on the Neuroticism factor with suspended traineesscoring lower; no difference on Extraversion factor. Refined theresults by categorizing each groupmember according to their quadrant on the 2x2 E-N factors,then calculating the percentage of failures falling in each quadrant.Found a significant chi-square primarily due to disproportionatelyhigh percentage of failures in the neurotic-introvert quadrant and disproportionately low percentage of failures in the stable-introvert quadrant. So, it's not as simple asavoiding neurotic candidates;should avoid neurotic introverts but consider selecting stable introverts. 

Jessup & Jessup (1971) 

Inventories Developed and/or Used by Military for Some Purposes but not (yet) tried for Pilot Selection 

B-17 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Test of Adaptable Personality (TAP) US Army

80 Biodata items, but only68 of them are scored 

D: Rational S: Rational 

No N=31-48 US Army Special Forces

Soldiers; four "rational biodata" scales (Intell Open, Tol of Ambiguity, Ach Orient, FitnessMotiv) were significantly correlated with peer ratings (.35-.45) and two (Ach Orient, Fitness Motiv) weresignificantly correlated with cadre ratings (external observer; .36 & .44) of performance in a 14-dayfield exercise; same study found lower (ns) correlations between Big5 personality measures and SelfEfficacy measures with ratings of field exercise performance. 

Summary documentsprovided by R. Killcullen, September, 2004; Kilcullen, Goodwin, Chen, Wisecarver, & Sanders (undated); Kilcullen, Mael, Goodwin, & Zazanis (1999) 

scatimfocKilcscaUsadjNo

B-18 
B-19 

Name Scales 

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s) Describing Test or Documenting Validity Co

Test of Adaptable Personality (TAP) (Cont.) 

N=210 US Army Special Forces 

solders; 20 "rational biodata" scales; calculated validity after excluding those who triggered more than 4 Lie scale items and keeping those who didn't trigger any of the Lie scale items (62% of 210). Criterion measures were supervisory ratings on BSS and self-reported awards & trg completed. Using the screened sample, Cognitive Flexibility, Work Motivation, & Ach. Orient showed significant correlations with supervisor ratings (.15, .22, & .25 respectively). Using the "NonFaking only" sample, Work Motivation (.30), Ach. Orient (.37), Dominance (.22), Cognitive Flexibility (.18), Anxiety (-.17), and Fitness Motivation (.20) were significantly correlated with SF job performance. A backward stepwise regression based only on the rational biodata scales revealed that the combination of Work Motivation & Achievement Orientation yielded a significant prediction equation (no R reported). NOTE: This study actually found that cognitive, demographic, and fitness predictors didn't contribute to predictions of performance beyond the motivation variables (which were measured using rational biodata). However, the authors note that the sample was extremely range restricted on these dimensions (although, presumably, there was also at least indirect range restriction on the motivational variables). 

Alp

(CoLeaIPSranof canbut whbecapp
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Test of Adaptable Personality (TAP) (Cont.) 

Work Motivation (aka Achievement Orientation) 

r=.39 (peer rtgs of field perf); r=.36 

(cadre ratings of field exerciseperf); r=.25 or r=.30 for predictingsupervisor ratings of fieldperformance, in screened & nonfaking samples, respectively. 

Killcullen, Goodwin, etal.; Kilcullen, Mael, et al. (undated) 

Cognitive FlexibilityFitness Motivation r=.45 (peer rtgs of field perf); r=.44 

(cadre ratings of field exercise perf) 

Killcullen, Goodwin, etal. 

list

Peer Leadership list

TA

Team Orientation (akaInterpersonal Skills - Team Player) 

list

TA

Diplomacy (akanterpersonal Skills - Diplomacy)

list

TA

Faking (Unlikely Virtues) list

TA

Intellectual Openness r=.37 (peer rtgs of field exercise

perf); r=.15 (ns) (cadre rtgs of field exercise perf) 

list

et a

Tolerance of Ambiguity r=.34 (peer rtgs of field exercise

perf); r=.07 (ns) (cadre rtgs of field exercise perf) 

Killcullen, Goodwin, etal. 

listet abe Fle

Affective Commitment to the Army

"ne

Kilc

Stress Tolerance "ne

Kilc

B-20 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Test of Adaptable Personality (TAP) (Cont.) 

Narcissism "ne

Kilc

Hostility to Authority Sc

Rigdevvar(Kicom

Respect for Authority exp

alre

Internal Locus of Control "ne

Kilc

Self-Efficacy "ne

Kilc

Cultural Tolerance "ne

Kilc

Attentional Focus "ne

Kilccom

B-21 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Assessment of Individual Motivation (AIM)US Army

Personality: Dominance (Leadership); WorkOrientation; Agreeableness;Dependability(Nondelinquency); Adjustment; Lie Scale 30 minutes

D: Rational S: Rational (forced choice) 

No White, Young, & Rumsey (2001) 

report that the AIM predicted attrition among Army enlisted personnel (N=5000+; r=-.15) and showed a similar level of validity forpredicting attrition in a sample of 8,500 USAF enlisted personnel.These say that the AIM alsoshowed validity for predictingperformance and personal discipline among correctional officers in military prisons and success in explosive ordinancedisposal training for militarypersonnel. 

White, Young, & Rumsey (2001); White & Young (1998); White, Young, Heggestad,Stark, Drasgow, &Piskator, 2004, 2005) 

Thinvkey ABconthe suscorof curCo

AIM 

enlScsomparevacomthe Lea

Cu

tetrInscom

B-22 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

Assessment of Background & Life Experiences (ABLE) US Army

Achievement Orientation;Dominance (Leadership); Dependability; Adjustment; Cooperativeness; Internal Control; PhysicalCondition; Social Desirability; Nonrandom Response 70-199 Personality & Biodata items dependingon which version isselected & which resp valscales included

D: Rational S: Rational 

No Project A research showed that the 

ABLE was moderately valid forpredicting "will-do" aspects of enlisted Soldier performance. Italso showed incremental validitybeyond cognitive measures for predicting will-do aspects of job performance. 

White, Young, & Rumsey (2001) 

Armsevincitemcona fotwo incitemthe itemconbioscrvalNo

Scale profile has been developed 

for aviator sample, could potentiallybe used for profile matching;however, visual inspection of scaleprofiles for a student versus an aviator sample suggest that the profiles are pretty similar;quantitative analysis may suggest some differences.

Howse (1995) 

Assessment of Right Conduct (ARC) US Army

Social Maturity; SelfEsteem; Traditional Values; Manipulativeness;Greed; Hostility toAuthority; Response Distortion. 80 Biodata items, ~ 20minutes

D: Rational S: Rational 

No construct validity evidence 

available, but no criterion-related validity evidence (yet) 

Kilcullen, White, Sanders, & Hazlett (2003) 

HaFoUsadjNo

B-23 
Name Scales

Development & 

Scoring 

Approach 

Been Tried 

for Pilot 

Selection? Summary of Validity Evidence 

Key Reference(s)Describing Test or Documenting Validity Co

California Psychological Inventory (CPI) used widely by clinicians andprivate-sector organizations

20 scales, plus 3 "vectors" (Internality, NormFavoring, & SelfRealization); several other scoring profiles have been developed over the years.434 Personality items, ~ 45 minutes

D: Some scales were empiricallyderived; othersrationallyS: Empirical 

No Gough & Bradley

(1996); Howse (1995) 

ThclinbeepursuiclinproScfor potmainsstusugsimsug

B-24 
Appendix C 

Recommended Selection Strategy for Army Aviators 

Broad Construct that Should

be Measured 

Recommended 

Stage Best Bet Predictor Measure Approx Admin Time Pr

Cognitive Ability  - Spatial  - Quantitative  - Verbal  - Mechanical (ASTB)  - Perc. Speed & Acc (AFOQT)

1 All cognitive subtests from the: 

1. ASTB (US Navy) or 2. AFOQT (USAF)

3 - 4 hours Both the ASTB and th

track record for aviatobe available to the Armhas the decided advanprogrammed for admiAFOQT includes morsubtests than the ASTdomain of cognitive adifficulty of the both tevidence that either onoverall, for the Army 

Perceptual Speed & Accuracy 1 Table Reading Test OR

Publicly-available measure OR Newly-developed measure 

part of AFOQT admin 

time if that battery is 

selected; ~10-15 

minutes if

administered as a 

stand-alone test

The Table Reading tePerceptual Speed & Afor predicting aviator a small amount of timunique variance to the performance. It is part since the 1940’s. If thadminister the AFOQvalidation study, but dimportant skill, it coulavailable version of thowned by DAS. It coumeasures of Perceptuaavailable in the public develop a new measur

C-1 
Broad Construct that Should

be Measured 

Recommended 

Stage Best Bet Predictor Measure Approx Admin Time Pr

Motivation / Attitude 1 Army-specific measure of 

Helicopter/Aviation InformationOR Aviation & Nautical Information(ANI) subtest from the ASTB OR Aviation Information (AI) subtest from the AFOQT 

20-30 minutes 

[Admin time for ANI 

or AI subtest is

included in the admin 

time for the ASTB or

AFOQT]

PDRI could, with assidevelop content for an Helicopter/Aviation InArmy decides to admiAFOQT in the prelimANI or the AI could athe ANI nor the AI haArmy aviation applicashown relatively high predicting performancprobably because they motivation to become assume that these subtof motivation for Armwe could gather empirassumption. Of the twANI has the decided aprogrammed for admi

Personality/Temperament 1 Selected scales from the US Army’s 

Test of Adaptable Personality (TAP) or Assessment of Individual Motivation (AIM), USAF’s SelfDescription Inventory, and/orUSAF’s Armstrong LaboratoryAviation Personality Inventory (ALAPS) And/orNew, highly-tailored scales developed by PDRI

~90 minutes for 

validation study; ~45-

60 for operational 

version 

No single inventory mcharacteristics likely tjob but, across the fouis good coverage of mthe job analysis resulta set of non-redundaninventories and/or wriscales that measure thtemperament characteconfigured inventory, like, others will be perexisting inventories bethere should be no chaWe can’t use the ALAbecause the items and USAF technical repor

C-2 
Broad Construct that Should

be Measured 

Recommended 

Stage Best Bet Predictor Measure Approx Admin Time Pr

Cognitive Task Prioritization 1 if Popcorn 

Test; 2 if WOMBAT

Popcorn Test or WOMBAT ~40 minutes 

[included in the 

WOMBAT admin time

if that battery is used] 

Cognitive task prioritimany years by NASA although this type of tselection purposes. Podevelop and DAS, witfrom AIR, could deverelatively little cost. Tthis ability, but it may it is, will likely not be selection tool, so if thmeasure of this imporscreen, it should deve

Psychomotor  1. Multi-Limb Coord. 2. Tracking

2 1. TBAS (USAF and US Navy)

2. WOMBAT 3. newly-developed psychomotortest battery 

60-90 minutes The TBAS is compute

administration), showbeyond the AFOQT in However, preliminary about the programminlocate any programmiinformation about how are derived. WOMBAtest used extensively iselection, although thevalidity evidence. It’s shortened by reducing drawback to using a sno longer measures petaxing task. The develadministered version. our team (DAS/AIR) battery. The battery coskills as well as TBAS but wouldn’t be as comWOMBAT. Time, buwould have to be nego

C-3 
Broad Construct that Should

be Measured 

Recommended 

Stage Best Bet Predictor Measure Approx Admin Time Pr

Multi-Task Performance 2 1. TBAS (USAF and US Navy)

2. WOMBAT 3. newly-developed test

included in the 60-90 

minute admin time

listed above

same as above; If DAthey would combine a psychomotor test battemulti-task performanc

Situational Awareness/ Stress Tolerance 

2 WOMBAT included in the 60-90 

minute admin time

listed above

The WOMBAT arguaawareness and stress timportant skills for avthese abilities would badministering the WOshortened version cleatolerance as well as th

C-4