Meta-analysis is frequently combined with multiple regression or path analysis to examine how the Big Five/Five-Factor Model (FFM) personality traits relate to work outcomes. A common approach in such studies is to construct a synthetic correlation matrix by combining new meta-analyses of FFM-criterion correlations with previously published meta-analytic FFM intercorrelations. Many meta-analytic FFM intercorrelation matrices exist in the literature, with 3 matrices being frequently used in industrial-organizational (I-O) psychology and related fields (i.e., Mount, Barrick, Scullen, & Rounds, 2005; Ones, 1993; van der Linden, te Nijenhuis, & Bakker, 2010). However, it is unknown how the choice of FFM matrix influences study conclusions, why we observe such differences in the matrices, and which matrix researchers and practitioners should use for their specific studies. We conducted 3 studies to answer these questions. In Study 1, we demonstrate that researchers' choice of FFM matrix can substantively alter conclusions from meta-analytic regressions or path analyses. In Study 2, we present a new meta-analysis of FFM intercorrelations using measures explicitly constructed around the FFM and based on employee samples. In Study 3, we systematically explore the sources of differences in FFM intercorrelations using second-order meta-analyses of 44 meta-analytic FFM matrices. We find that personality rating source (self vs. other) and inventory-specific substantive and methodological features are the primary moderators of meta-analytic FFM intercorrelations. Based on the findings from these studies, we provide a framework to guide future researchers in choosing a meta-analytic FFM matrix that is most appropriate for their specific studies, research questions, and contexts. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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元分析五因素模型人格相互关联：Eeny、meeny、miney、moe、how、which、why 和去哪里。

元分析经常与多元回归或路径分析相结合，以检查大五/五因素模型 (FFM) 人格特质与工作成果的关系。此类研究中的一种常见方法是通过将 FFM 标准相关性的新元分析与先前发布的元分析 FFM 相关性相结合来构建合成相关矩阵。文献中存在许多元分析 FFM 相关矩阵，其中 3 个矩阵经常用于工业组织 (IO) 心理学和相关领域（即 Mount、Barrick、Scullen 和 Rounds，2005 年；Ones，1993 年；van der Linden , te Nijenhuis, & Bakker, 2010)。然而，未知 FFM 矩阵的选择如何影响研究结论，为什么我们观察到矩阵中的这种差异，以及哪些矩阵研究人员和从业者应该在他们的具体研究中使用。我们进行了 3 项研究来回答这些问题。在研究 1 中，我们证明研究人员对 FFM 矩阵的选择可以从根本上改变元分析回归或路径分析的结论。在研究 2 中，我们使用围绕 FFM 并基于员工样本明确构建的度量，对 FFM 相关性进行了新的元分析。在研究 3 中，我们使用 44 个元分析 FFM 矩阵的二阶元分析系统地探索了 FFM 相关性差异的来源。我们发现个性评级来源（自我与其他）和特定于库存的实质性和方法论特征是元分析 FFM 相互关联的主要调节因素。根据这些研究的结果，我们提供了一个框架来指导未来的研究人员选择最适合他们的特定研究、研究问题和背景的元分析 FFM 矩阵。（PsycINFO 数据库记录 (c) 2020 APA，保留所有权利）。