Research Topic

Recent Advancements in Structural Equation Modeling (SEM): From Both Methodological and Application Perspectives

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Structural equation modeling (SEM) is becoming the central and arguably most popular analytical tool in social sciences. Many classical and modern statistical techniques such as regression analysis, path analysis, confirmatory factor analysis, and models with both measurement and structural components have ...

Structural equation modeling (SEM) is becoming the central and arguably most popular analytical tool in social sciences. Many classical and modern statistical techniques such as regression analysis, path analysis, confirmatory factor analysis, and models with both measurement and structural components have been shown to fall under the umbrella of SEM. The flexibility of SEM makes it applicable to many research designs, including experimental and non-experimental data, cross-sectional and longitudinal data, and multiple-group and multilevel problems.

Recently, there have been exciting advancements in SEM, from fundamental issues like alternative estimation methods that are robust to often violated assumptions to the expansions of SEM to incorporate complex multilevel and cross-classified data that are common in the social sciences. This Research Topic aims to bring in a collection of SEM papers which not only tackle technical estimation issues, but also examine and demonstrate the applications of SEM to more complex settings. The proposed topics include (but are not limited to): applying robust estimation method, testing interaction effect, examining measurement invariance, and specifying and evaluating models applied to more complex data including meta-analytic data, multilevel and longitudinal data.


Keywords: Structural Equation Modeling, Measurement Invariance, Meta Analysis, Multilevel Modeling, Robust Estimation Method


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