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      A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality

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      Structural Equation Modeling: A Multidisciplinary Journal
      Informa UK Limited

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          Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis.

          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 Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential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.
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            Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching

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              The problem of equivalent models in applications of covariance structure analysis.

              For any given covariance structure model, there will often be alternative models that are indistinguishable from the original model in terms of goodness of fit to data. The existence of such equivalent models is almost universally ignored in empirical studies. A study of 53 published applications showed that equivalent models exist routinely, often in large numbers. Detailed study of three applications showed that equivalent models may often offer substantively meaningful alternative explanations of data. The importance of the equivalent model phenomenon and recommendations for managing and confronting the problem in practice are discussed.
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                Author and article information

                Journal
                Structural Equation Modeling: A Multidisciplinary Journal
                Structural Equation Modeling: A Multidisciplinary Journal
                Informa UK Limited
                1070-5511
                1532-8007
                July 28 2015
                January 02 2016
                June 18 2015
                January 02 2016
                : 23
                : 1
                : 116-139
                Article
                10.1080/10705511.2014.961800
                65f8216f-0a3e-4675-be8c-7f52cde08b73
                © 2016
                History

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