Improved military air traffic controller selection methods as measured by subsequent training performance.
BACKGROUND
Over the past decade, the U.S. military has conducted several studies to evaluate determinants of enlisted air traffic controller (ATC) performance. Research has focused on validation of the Armed Services Vocational Aptitude Battery (ASVAB) and has shown it to be a good predictor of training performance. Despite this, enlisted ATC training and post-training attrition is higher than desirable, prompting interest in alternate selection methods to augment current procedures. The current study examined the utility of the FAA Air Traffic Selection and Training (AT-SAT) battery for incrementing the predictiveness of the ASVAB versus several enlisted ATC training criteria.


METHODS
Subjects were 448 USAF enlisted ATC students who were administered the ASVAB and FAA AT-SAT subtests and subsequently graduated or were eliminated from apprentice-level training. Training criteria were a dichotomous graduation/elimination training score, average ATC fundamentals course score, and FAA certified tower operator test score.


RESULTS
Results confirmed the predictive validity of the ASVAB and showed that one of the AT-SAT subtests resembling a low-fidelity ATC work sample significantly improved prediction of training performance beyond the ASVAB alone.


CONCLUSIONS
Results suggested training attrition could be reduced by raising the current ASVAB minimum qualifying score. However, this approach may make it difficult to identify sufficient numbers of trainees and lead to adverse impact. Although the AT-SAT ATC work sample subtest showed incremental validity to the ASVAB, its length (95 min) may be problematic in operational testing. Recommendations are made for additional studies to address issues affecting operational implementation.
Carretta
Thomas R
TR
Air Force Research Laboratory, Wright-Patterson AFB, OH, USA.
King
Raymond E
RE
D000328:Adult; D001077:Aptitude Tests; D001359:Aviation / Q000193:education; D004521:Educational Measurement / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008889:Military Personnel / Q000193:education*; D010560:Personnel Selection / Q000379:methods* / Q000592:standards; D011237:Predictive Value of Tests; D012044:Regression Analysis; D014481:United States; D000078329:Workforce