@article{DahlkeNotrestrictedselection2019,
  langid = {english},
  title = {Not Restricted to Selection Research: Accounting for Indirect Range Restriction in Organizational Research},
  issn = {1094-4281, 1552-7425},
  url = {http://journals.sagepub.com/doi/10.1177/1094428119859398},
  doi = {10/gf9crt},
  shorttitle = {Not Restricted to Selection Research},
  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.},
  journaltitle = {Organizational Research Methods},
  shortjournal = {Org. Res. Meth.},
  urldate = {2019-10-03},
  date = {2019-07-24},
  author = {Dahlke, Jeffrey A. and Wiernik, Brenton M.},
  pubstate = {Advance online publication}
}