Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization

Addictive Behaviors
Kevin A HallgrenDavid C Atkins

Abstract

Structural equation modeling (SEM) is a multivariate data analytic technique used in many domains of addictive behaviors research. SEM results are usually summarized and communicated through statistical tables and path diagrams, which emphasize path coefficients and global fit without showing specific quantitative values of data points that underlie the model results. Data visualization methods are often absent in SEM research, which may limit the quality and impact of SEM research by reducing data transparency, obscuring unexpected data anomalies and unmodeled heterogeneity, and inhibiting the communication of SEM research findings to research stakeholders who do not have advanced statistical training in SEM. In this report, we show how data visualization methods can address these limitations and improve the quality of SEM-based addictive behaviors research. We first introduce SEM and data visualization methodologies and differentiate data visualizations from model visualizations that are commonly used in SEM, such as path diagrams. We then discuss ways researchers may utilize data visualization in SEM research, including by obtaining estimates of latent variables and by visualizing multivariate relations in two-dimensional fi...Continue Reading

Citations

Aug 6, 2019·Joint Commission Journal on Quality and Patient Safety·Celia FiordalisiJeanne-Marie Guise
Feb 27, 2021·Behavior Research Methods·Dustin A FifePatrice D Tremoulet

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