Race‐Based Differential Prediction in Air Force Technical Training Programs

Abstract

Bayesian Johnson‐Neyman methodology was used to investigate differential prediction by race in United States Air Force technical training programs. Meaningful Johnson‐Neyman regions of differences were found in eight of nine comparisons. In all nine training courses the regressions for blacks were flatter than for whites and the race‐differentiated regression lines intersected within the range of predictor test scores. In six cases the cut‐score for course qualification was within the Johnson‐Neyman region, and in every case the bias was negative for blacks. It is noted that if the cut score had been set substantially higher the bias would have been positive for blacks in all cases. This analysis may explain why earlier studies that averaged bias across the predictor distribution yielded mixed results. It is hypothesized that the consistent results obtained here are a consequence of the lower predictability found in the black subpopulation.