Multivariate Behavioral Research · 2019 · 409 citations · 21 references
Social SciencesEducational PsychologyPsychologyPsychometricsScale DevelopmentConfigural InvarianceFactor AnalysisFactor Loading InvarianceModel Fit AssessmentStructural Equation ModelingTest TheoryCross-group Factor ComparisonsFactor Loading Equality TestingFactor Loading EquivalenceGoodness-of-fit IndicesLoading InvarianceMeasurement Model IdentificationMulti-group Measurement ModelsSecond-order Factor Models
CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis' (EFA) flexibility, and CFA/SEM's rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse "real" data applications with accessible, heuristic explanations of best practice.
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Herbert W. Marsh, Kit‐Tai Hau, Zhonglin Wen · Structural Equation Modeling A Multidisciplinary Journal · 2004 · 6.5K citations
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Personnel Psychology · 2007 · 2.8K citations