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).
Human Resources Management | Organizational Behavior and Theory
Organisational Behaviour and Human Resources
Adverse Impact: Implications for Organizational Staffing and High Stakes Selection
OUTTZ, James L.
SACKETT, Paul R.; DE CORTE, Wilfried; and LIEVENS, Filip.
Decision aids for addressing the validity-adverse impact trade-off. (2009). Adverse Impact: Implications for Organizational Staffing and High Stakes Selection. 459-478. Research Collection Lee Kong Chian School Of Business.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5821
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