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      Multiple-Group Factor Analysis Alignment

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      Structural Equation Modeling: A Multidisciplinary Journal
      Informa UK Limited

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          Bayesian structural equation modeling: a more flexible representation of substantive theory.

          This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
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            Model modification

            Dag Sorbom (1989)
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              Bayesian Item Response Modeling

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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 16 2014
                October 02 2014
                July 16 2014
                October 02 2014
                : 21
                : 4
                : 495-508
                Article
                10.1080/10705511.2014.919210
                36796989
                00dd84ff-8fd0-48fe-ada1-9423f5956335
                © 2014
                History

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