Title

Not Restricted to Selection Research: Accounting for Indirect Range Restriction in Organizational Research

Document Type

Article

Publication Date

2019

Keywords

meta-analysis, reliability and validity, construct validation procedures

Digital Object Identifier (DOI)

https://doi.org/10.1177/1094428119859398

Abstract

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 corrections 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 correlation between two predictor variables). Existing meta-analytic methods incorporating bivariate range-restriction 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 using the bivariate indirect range restriction (BVIRR; “Case V”) correction and describe improved methods 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.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Organizational Research Methods, v. 23, issue 4, p. 717-749

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