Meta-analytic criterion profile analysis.

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. Translational Abstract: Patterns or configurations of predictors are popular ways for researchers and science consumers to understand phenomena. Criterion profile analysis (CPA; Davison & Davenport, 2002) is a regression-based pattern matching procedure that identifies patterns of predictors that are maximally related to a criterion of interest. This technique allows researchers to consider a new perspective on the relationship between a set of predictors and a criterion—that the criterion is associated most with a specific pattern or configuration of predictors. Existing CPA methods require individual-level data, limiting opportunities for research synthesis via meta-analysis or reanalysis of published research. In this article, we develop methods for meta-analytic criterion profile analysis (MACPA), including new methods for estimating cross-validation performance and sensitivity analyses. We also review methodological considerations and caveats for MACPA, including homogeneity of studies in meta-analyses, correction 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. Application of MACPA in both new meta-analyses and reanalysis of existing correlational research can open new avenues of inquiry into the potential mechanisms driving important outcomes in psychological research.