Correcting for range restriction in meta-analysis: A reply to Oh et al. (2023)

• Main Authors: SACKETT, Paul R., BERRY, Christopher M., LIEVENS, Filip, ZHANG, Charlene
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2023
• 主題: meta-analysis; range restriction; validation; Management Sciences and Quantitative Methods; Organizational Behavior and Theory
• 在線閱讀: https://ink.library.smu.edu.sg/lkcsb_research/7268; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8267/viewcontent/correcting_for_range.pdf
• 機構: Singapore Management University

Oh et al. (2023) question a number of choices made in our article (Sackett et al., 2022); here we respond. They interpret our article as recommending against correcting for range restriction in general in concurrent validation studies; yet, we emphasize that we endorse correction when one has access to the information needed to do so. Our focus was on making range restriction corrections when conducting meta-analyses, where it is common for primary studies to be silent as to the prior basis for selection of the employees later participating in the concurrent validation study. As such, the applicant pool information needed for correction is typically not available. Sackett et al. (2022) highlighted that in many situations, range restriction will be small; so, the inability to correct for it results in only a modest underestimate of validity. Oh et al. mention settings that would result in substantial range restriction; here, we present our rationale as to why we view such settings as uncommon rather than as making up the bulk of the studies contributing to meta-analyses.