Not restricted to selection research: Accounting for indirect range restriction in organizational research

Range restriction is a common problem in organizational research and is an important statistical artifact to correct for in meta-analysis. Historically, researchers have had to rely on range-restriction correc-tions that only make use of range-restriction information for one variable, but it is not uncommon for researchers to have such information for both variables in a correlation (e.g., when studying the cor-relation between two predictor variables). However, existing meta-analytic methods incorporating these corrections overlook their unique implications for estimating the sampling variance of corrected correlations and for accurately assigning weights to studies in individual-correction meta-analyses. We introduce new methods for computing individual-correction and artifact-distribution meta-analyses us-ing the bivariate indirect range-restriction (BVIRR; “Case V”) correction and describe improved meth-ods for applying BVIRR corrections that substantially reduce bias in parameter estimation. We illustrate the effectiveness of these methods in a large-scale simulation and in meta-analyses of expatriate data. We provide R code to implement the methods described in this article; more comprehensive and robust functions for applying these methods are available in the psychmeta package for R (Dahlke & Wiernik, 2018, 2017/2019).