@article{ref1,
title="Meta-analytic criterion profile analysis",
journal="Psychological methods",
year="2020",
author="Wiernik, Brenton M. and Wilmot, Michael P. and Davison, Mark L. and Ones, Deniz S.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Intraindividual patterns or configurations are intuitive explanations for phenomena, and popular in both lay and research contexts. Criterion profile analysis (CPA; Davison & Davenport, 2002) is a well-established, regression-based pattern matching procedure that identifies a pattern of predictors that optimally relate to a criterion of interest and quantifies the strength of that association. Existing CPA methods require individual-level data, limiting opportunities for reanalysis of published work, including research synthesis via meta-analysis and associated corrections for psychometric artifacts. In this article, we develop methods for meta-analytic criterion profile analysis (MACPA), including new methods for estimating cross-validity and fungibility of criterion patterns. We also review key methodological considerations for applying MACPA, including homogeneity of studies in meta-analyses, corrections for statistical artifacts, and second-order sampling error. Finally, we present example applications of MACPA to published meta-analyses from organizational, educational, personality, and clinical psychological literatures. R code implementing these methods is provided in the configural package, available at https://cran.r-project.org/package=configural and at https://doi.org/10.17605/osf.io/aqmpc. (PsycInfo Database Record (c) 2020 APA, all rights reserved).<p /> <p>Language: en</p>",
language="en",
issn="1082-989X",
doi="10.1037/met0000305",
url="http://dx.doi.org/10.1037/met0000305"
}