Department of Education, University of Oxford, Oxford, England.
Psychol Assess. 2010 Sep;22(3):471-91. doi: 10.1037/a0019227.
NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to the overly restrictive CFA assumptions. We demonstrate that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis (EFA), overcomes these problems with responses (N = 3,390) to the 60-item NEO-Five-Factor Inventory: (a) ESEM fits the data better and results in substantially more differentiated (less correlated) factors than does CFA; (b) tests of gender invariance with the 13-model ESEM taxonomy of full measurement invariance of factor loadings, factor variances-covariances, item uniquenesses, correlated uniquenesses, item intercepts, differential item functioning, and latent means show that women score higher on all NEO Big Five factors; (c) longitudinal analyses support measurement invariance over time and the maturity principle (decreases in Neuroticism and increases in Agreeableness, Openness, and Conscientiousness). Using ESEM, we addressed substantively important questions with broad applicability to personality research that could not be appropriately addressed with the traditional approaches of either EFA or CFA.
NEO 量表被广泛用于评估大五人格因素，但由于过于严格的 CFA 假设，项目层面的验证性因子分析（CFA）并不支持其先验结构。我们证明，探索性结构方程模型（ESEM），即 CFA 和探索性因子分析（EFA）的整合，克服了对 NEO-五因素量表 60 项的反应（N=3390）的这些问题：（a）ESEM 更适合数据，并且与 CFA 相比，结果产生的因素更具区分性（相关性更低）；（b）使用 13 模型 ESEM 分类法对全测量不变性的因子负荷、因子方差协方差、项目独特性、相关独特性、项目截距、差异项目功能和潜在均值进行性别不变性检验表明，女性在所有 NEO 大五因素上的得分都更高；（c）纵向分析支持随着时间的推移测量不变性和成熟原则（神经质降低，宜人性、开放性和尽责性增加）。我们使用 ESEM 解决了具有广泛适用性的重要实质性问题，这些问题无法通过 EFA 或 CFA 的传统方法得到适当解决。
