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The most popular measures 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 that are not so highly correlated as to detract from their discriminant validity. Part of the problem, the authors argue, is undue reliance on overly restrictive independent cluster models of confirmatory factor analysis (ICM-CFA) in which each item loads on one, and only one, factor. Here the authors demonstrate exploratory structural equation modeling (ESEM), an integration of the best aspects of CFA and traditional exploratory factor analyses (EFA). On the basis of responses to the 11-factor Motivation and Engagement Scale ( n = 7,420, M age = 14.22), we demonstrate that ESEM fits the data much better and results in substantially more differentiated (less correlated) factors than corresponding CFA models. Guided by a 13-model taxonomy of ESEM full-measurement (mean structure) invariance, the authors then demonstrate invariance of factor loadings, item intercepts, item uniquenesses, and factor variancescovariances, across gender and over time. ESEM has broad applicability to other areas of research that cannot be appropriately addressed with either traditional EFA or CFA and should become a standard tool for use in psychometric tests of psychological assessment instruments.
Marsh et al. (Tue,) studied this question.