Designing Pareto-Optimal Selection Systems: Formalizing the Decisions Required for Selection System Development

• 成果类型: Article
• 署名作者: De Corte, Wilfried; Sackett, Paul R.; Lievens, Filip
• 署名单位: Ghent University; University of Minnesota System; University of Minnesota Twin Cities; Ghent University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/a0023298
• 发表日期: 2011
• 页码: 907-926
• 关键词: adverse impact; personnel selection; Pareto-optimal; selection design

摘要

The article presents an analytic method for designing Pareto-optimal selection systems where the applicants belong to a mixture of candidate populations. The method is useful in both applied and research settings. In an applied context, the present method is the first to assist the selection practitioner when deciding on 6 major selection design issues: (1) the predictor subset, (2) the selection rule, (3) the selection staging, (4) the predictor sequencing, (5) the predictor weighting, and (6) the stage retention decision issue. From a research perspective, the method offers a unique opportunity for studying the impact and relative importance of different strategies for reducing adverse impact.