结构方程建模
验证性因素分析
探索性因素分析
心理学
潜变量
拟合优度
路径分析(统计学)
判别效度
因子分析
心理测量学
测量不变性
统计
计量经济学
发展心理学
数学
内部一致性
作者
Herbert W. Marsh,Alexandre J. S. Morin,Philip D. Parker,Gurvinder Kaur
标识
DOI:10.1146/annurev-clinpsy-032813-153700
摘要
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 Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential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.
科研通智能强力驱动
Strongly Powered by AbleSci AI