Meta-analytic criterion profile analysis.

Brenton M. Wiernik, Michael P. Wilmot, Mark L. Davison, Deniz S. Ones

Research output: Contribution to journalArticlepeer-review

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. 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. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Original languageEnglish (US)
Pages (from-to)186-209
Number of pages24
JournalPsychological Methods
Volume26
Issue number2
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2020 American Psychological Association

Keywords

  • configural
  • criterion profile analysis
  • fungible regression weights
  • meta-analysis
  • pattern analysis

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Meta-analytic criterion profile analysis.'. Together they form a unique fingerprint.

Cite this