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).

中文翻译：

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荟萃分析标准概况分析。


个体内部模式或配置是对现象的直观解释，并且在外行和研究环境中都很流行。标准概况分析（CPA；Davison & Davenport，2002）是一种完善的、基于回归的模式匹配程序，可识别与感兴趣的标准最佳相关的预测变量模式，并量化该关联的强度。现有的 CPA 方法需要个人层面的数据，限制了对已发表作品进行重新分析的机会，包括通过荟萃分析进行研究综合以及对心理测量工件的相关校正。在本文中，我们开发了元分析标准概况分析（MACPA）的方法，包括估计标准模式的交叉有效性和可替代性的新方法。我们还回顾了应用 MACPA 的关键方法学考虑因素，包括荟萃分析中研究的同质性、统计伪影的校正和二阶抽样误差。最后，我们展示了 MACPA 在组织、教育、人格和临床心理学文献中已发表的荟萃分析中的应用示例。配置包中提供了实现这些方法的 R 代码，可从 https://cran.r-project.org/package=configural 和 https://doi.org/10.17605/osf.io/aqmpc 获取。 （PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。