Decision aids for addressing the validity-adverse impact trade-off

• Main Authors: SACKETT, Paul R., DE CORTE, Wilfried, LIEVENS, Filip
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Human Resources Management; Organizational Behavior and Theory
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/5821; https://ink.library.smu.edu.sg/context/lkcsb_research/article/6820/viewcontent/aids.pdf
• Institution: Singapore Management University

Typically, adverse impact (AI) is an after-the-fact analysis: Once predictor scores for a pool of applicants are available, AI is evaluated. Sometimes the analysis is made in real time, as predictor scores are obtained on a set of applicants, and AI calculations are done on a “what if” basis as input to decisions about features such as where to set a cutoff score. The focus of this chapter, however, is on attempts to estimate in advance the likely impact of a given selection system. Here, estimates are made based on available information about the features such as the expected magnitude of subgroup differences, expected interpredictor correlations, and expected predictor-criterion correlations. Such information may be local (e.g., group differences observed the last time a predictor was used) or based on a more general research literature (e.g., group differences reported in publisher manuals or in the published literature for a given predictor type and a given job category).