Multistage Selection Strategies: Simulating the Effects on Adverse Impact and Expected Performance for Various Predictor Combinations

• 成果类型: Article
• 署名作者: Finch, David M.; Edwards, Bryan D.; Wallace, J. Craig
• 署名单位: University System of Georgia; University of Georgia; Auburn University System; Auburn University; Oklahoma State University System; Oklahoma State University - Stillwater
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/a0013775
• 发表日期: 2009
• 页码: 318-340
• 关键词: adverse impact; Monte Carlo simulation; multistage; multiple hurdle; selection

摘要

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.