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

Oi-Man Kwok, Mike W. L. Cheung, Suzanne Jak, Ehri Ryu, Jiun-Yu Wu
2018 Frontiers in Psychology  
Structural equation modeling (SEM) is becoming the central and most popular analytical tool in the 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. Thus, the flexibility of SEM makes it applicable to many research designs, including experimental and non-experimental data, cross-sectional and
more » ... udinal data, and multiple-group and multilevel data. Further enhancing the popularity and widespread use of SEM, it has recently experienced exciting advancements-from fundamental issues like alternative estimation methods that are robust to often violated assumptions to the expansion of SEM to incorporate multilevel and crossclassified data that are common in the social sciences. This Special Research Topic aims to bring in a collection of SEM papers that not only tackle technical estimation issues but also examine and demonstrate application of SEM to more complex settings, such as applying robust estimation method, testing interaction effect, examining measurement invariance, and specifying and evaluating models applied to different types of data, including meta-analytic data, multilevel, and longitudinal data. We are presenting 19 cutting-edge papers covering a wide variety of topics related to SEM. The papers have been grouped into three main themes: (a) analysis of different types of data (from crosssectional data with floor effects to complex survey data and longitudinal data); (b) measurementrelated issues (from the development of new scale to the evaluation of person fit and new ways to test measurement invariance); and (c) technical advancement and software development. Below you will find a summary of the three themes and corresponding papers for each. ANALYSIS OF DIFFERENT TYPES OF DATA One of the major advantages of SEM is its flexibility for analyzing different types of data. On this research topic, Zhu and Gonzalez have demonstrated how to analyze multilevel data with strong floor effects using multilevel SEM and examined the impact of ignoring these floor effects when using regular multilevel analysis via a Monte Carlo study. Similarly, Wu et al. have demonstrated
doi:10.3389/fpsyg.2018.01936 pmid:30356842 pmcid:PMC6190731 fatcat:k4mfhttchzgtzigxm7pzkgskd4