Department of Education, University of Western Sydney, Penrith NSW 2751, Australia; email:
Annu Rev Clin Psychol. 2014;10:85-110. doi: 10.1146/annurev-clinpsy-032813-153700. Epub 2013 Dec 2.
Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM) have long histories in clinical research. Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors in support of discriminant validity. Part of the problem is undue reliance on overly restrictive CFAs in which each item loads on only one factor. Exploratory SEM (ESEM), an overarching integration of the best aspects of CFA/SEM and traditional EFA, provides confirmatory tests of a priori factor structures, relations between latent factors and multigroup/multioccasion tests of full (mean structure) measurement invariance. It incorporates all combinations of CFA factors, ESEM factors, covariates, grouping/multiple-indicator multiple-cause (MIMIC) variables, latent growth, and complex structures that typically have required CFA/SEM. ESEM has broad applicability to clinical studies that are not appropriately addressed either by traditional EFA or CFA/SEM.
探索性因子分析(EFA)和验证性因子分析(CFA)、路径分析和结构方程模型(SEM)在临床研究中有着悠久的历史。尽管 CFA 在很大程度上已经取代了 EFA,但多维结构的 CFAs 通常不符合良好测量的标准:拟合优度、测量不变性、缺乏差异项目功能以及支持判别有效性的因素差异。部分问题是过度依赖于过于严格的 CFA,其中每个项目仅加载在一个因素上。探索性 SEM(ESEM)是 CFA/SEM 和传统 EFA 的最佳方面的综合,它提供了对先验因子结构、潜在因子与潜在因子之间关系的确认性检验,以及全(均值结构)测量不变性的多组/多场合检验。它结合了 CFA 因子、ESEM 因子、协变量、分组/多指标多原因(MIMIC)变量、潜在增长和复杂结构的所有组合,这些通常需要 CFA/SEM。ESEM 广泛适用于传统的 EFA 或 CFA/SEM 都无法很好地解决的临床研究。