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      MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

      1 , 2
      Structural Equation Modeling: A Multidisciplinary Journal
      Informa UK Limited

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          Abstract

          <p class="first" id="P1"> <i>MplusAutomation</i> is a package for <i>R</i> that facilitates complex latent variable analyses in <i>Mplus</i> involving comparisons among many models and parameters. More specifically, <i>MplusAutomation</i> provides tools to accomplish three objectives: to create and manage <i>Mplus</i> syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of <i>MplusAutomation</i> is to support methodological developments in structural equation modeling using <i>Mplus</i>. </p>

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          Most cited references12

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          How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

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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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              OpenMx 2.0: Extended Structural Equation and Statistical Modeling.

              The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.
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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
                January 16 2018
                July 04 2018
                January 19 2018
                July 04 2018
                : 25
                : 4
                : 621-638
                Affiliations
                [1 ] The Pennsylvania State University
                [2 ] Monash University
                Article
                10.1080/10705511.2017.1402334
                6075832
                30083048
                4da110ee-6174-4b76-b6e1-7cfd51333e74
                © 2018
                History

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