title: 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.
authors.affiliation: Department of Psychology, University of Georgia, USA. dfinch@aptmetrics.com
authors.forename: David M
authors.initials: DM
authors.lastname: Finch
authors.forename: Bryan D
authors.initials: BD
authors.lastname: Edwards
authors.forename: J Craig
authors.initials: JC
authors.lastname: Wallace
mesh_terms: D001076:Aptitude; D018864:Cultural Diversity; D003661:Decision Support Techniques; D004650:Employee Performance Appraisal / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009937:Organizational Objectives; D010560:Personnel Selection / Q000706:statistics & numerical data*