Beyond path diagrams: Enhancing applied structural equation modeling research through data visualization. (July 2019)

• Record Type: Journal Article
• Authors: Hallgren, Kevin A.; McCabe, Connor J.; King, Kevin M.; Atkins, David C.
• Is Part Of: Addictive behaviors. Volume 94(2019)
• Page Start: 74
• Page End: 82
• Publication Date: 2019-07
• Subjects: Applied data analysis -- Data visualization -- Latent variable modeling -- Mediation -- Moderation -- Structural equation model; Substance abuse -- Periodicals; Alcoholism -- Periodicals; Drug addiction -- Periodicals; Nicotine addiction -- Periodicals; Smoking -- Periodicals; Gambling -- Psychological aspects -- Periodicals; Electronic journals; 362.29
• DOI: 10.1016/j.addbeh.2018.08.030
• Languages: English
• ISSNs: 0306-4603

Abstract

Abstract: Introduction: 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. Methods and results: 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 figures. R syntax is provided to help others generate data visualizations for several types of effects commonly modeled in SEM, including correlation, regression, moderation, and simple mediation. Discussion: The techniques outlined here may help spur the use of data visualization in SEM-based addictive behaviors research. Using data visualization in SEM may enhance methodological transparency and improve communication of research findings. Graphical abstract: Unlabelled Image Highlights: Data visualization is rarely used in SEM-based addictive behaviors research. We discuss challenges, benefits, and approaches for visualizing SEM data. Visualization may improve research transparency and communication to stakeholders.