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进化心理学中的高阶构念是否归因于遗漏的交叉载荷偏差?一种探索性结构方程建模方法。

Are Higher-Order Constructs in Evolutionary Psychology Attributable to Omitted Cross-Loading Bias? An Exploratory Structural Equation Modeling Approach.

作者信息

Richardson George B, Bates Daniel G, McLaughlin Laura E, McGee Nathan, Tse Winnie W-Y, Lai Mark H C

机构信息

School of Human Services, University of Cincinnati, Cincinnati, OH, USA.

Department of Psychology, University of Southern California, Los Angeles, CA, USA.

出版信息

Hum Nat. 2025 Jun;36(2):257-280. doi: 10.1007/s12110-025-09497-7. Epub 2025 Jul 22.

Abstract

Global constructs such as the general factor of personality (GFP), trait emotional intelligence (TEI), and the K-factor have generated considerable interest as well as controversy in evolutionary psychology. Research employing exploratory structural equation modeling (ESEM) suggests higher-order factors may be attributable to the omission of cross-loadings from confirmatory factor models and scale score computation, which can upwardly bias first-order factor and scale score correlations. In the current project, we conducted two studies to determine if GFP and TEI are method artifacts using national random-digit-dialing (n = 1,805) and teacher (n = 331) samples, respectively. We also conducted a study examining the possibility that K is an artifact using a sample of college students (n = 661). Using ESEM and bifactor ESEM to allow cross-loadings, we found evidence suggesting a general factor did not subsume all the Big Five personality traits and concluded that GFP is likely an artifact of omitted cross-loading bias. Evidence of global K and TEI factors survived free estimation of cross-loadings, and findings suggest total TEI scores may be sufficient; however, model-based reliability was too low to warrant the use of total Mini-K scores. Researchers should consider using ESEM to examine the internal structures of their scales at the item level before computing total scale scores.

摘要

诸如人格一般因素(GFP)、特质情绪智力(TEI)和K因素等全局结构在进化心理学中引发了相当大的兴趣和争议。采用探索性结构方程建模(ESEM)的研究表明,高阶因素可能归因于验证性因素模型中交叉载荷的遗漏以及量表分数计算,这可能会使一阶因素和量表分数相关性产生向上偏差。在当前项目中,我们分别使用全国随机数字拨号样本(n = 1805)和教师样本(n = 331)进行了两项研究,以确定GFP和TEI是否为方法假象。我们还进行了一项研究,使用大学生样本(n = 661)检验K是一种假象的可能性。通过使用ESEM和双因素ESEM来允许交叉载荷,我们发现有证据表明一般因素并未涵盖所有大五人格特质,并得出结论,GFP可能是遗漏交叉载荷偏差的假象。全局K和TEI因素的证据在交叉载荷的自由估计中仍然存在,研究结果表明TEI总分可能就足够了;然而,基于模型的信度太低,无法保证使用Mini-K总分。研究人员在计算总量表分数之前,应考虑使用ESEM在项目层面检查其量表的内部结构。

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