Air Traffic Controller Trainee Selection.
Abstract
The purpose of this effort was to examine new and existing selection procedures for entry into Air Traffic Control Operator School, in an effort to reduce the high level of attrition during and after training. The existing selection measures were the General Aptitude Index (AI) and Administrative AI from the Armed Services Vocational Aptitude Battery (ASVAB). New tests that were examined included the Multiplex Controller Aptitude Test (MCAT), Object Completion Test (OCT), Rotated Blocks Test (RBT), Perceptual Abilities Test (PAT), and Electrical Maze Test (EMT). First, the relationships between ASVAB AIs and training performance were assessed. It was found that the Administrative AI had a smaller relationship with training performance compared to the General, Mechanical, and Electronics AIs which correlated well with the criteria. Second, the five new tests were administered and the test scores were compared to a dichotomous pass/fail criterion. Multiple regression analyses showed that the combination of MCAT and RBT yielded the best combined prediction and that their use would improve upon prediction made by using the ASVAB alone. In conclusion, the results of this investigation indicated that the General AI is a useful predictor of air traffic controller training performance and that the Administrative AI should be deleted as a skeleton requirement for entry into Air Traffic Control Operator School. Further, other tests, not included in the ASVAB, could make a significant contribution to the prediction of air traffic controller training outcomes.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1987
- Accession Number
- ADA187497
Entities
People
- Cheryl L. Batchelor
- David R. Hunter
- Linda T. Curran
- Peter Stoker
Organizations
- Air Force Research Laboratory