Multistage selection strategies: Simulating the effects on adverse impact and expected performance for various predictor combinations.

Abstract

Examination of the trade-off between mean performance and adverse impact has received empirical attention for single-stage selection strategies; however, research for multistage selection strategies is almost nonexistent. The authors used Monte Carlo simulation to explore the trade-off between expected mean performance and minority hiring in multistage selection strategies and to identify those strategies most effective in balancing the trade-off. In total, 43 different multistage selection strategies were modeled; they reflected combinations of predictors with a wide range of validity, subgroup differences, and predictor intercorrelations. These selection models were examined across a variety of net and stage-specific selection ratios. Though it was still the case that an increase in minority hiring was associated with a decrease in predicted performance for many scenarios, the current results revealed that certain multistage strategies are much more effective than others for managing the performance and adverse impact trade-offs. The current study identified several multistage strategies that are clearly more desirable than those strategies previously suggested in the literature for practitioners who seek a practical selection system that will yield a high-performing and highly representative workforce.