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Structural equation modeling (SEM) is a multivariate technique suited for testing proposed relations between variables. In this article, the authors discuss the potential for SEM as a tool to advance health communication research both statistically and conceptually. Specifically, the authors discuss the advantages that latent variable modeling in SEM affords researchers by extracting measurement error. In addition, they argue that SEM is useful in understanding communication as a complex set of relations between variables. Moreover, the authors articulate the possibility for examining communication as an agent, mediator, and an outcome. Finally, they review the application of SEM to recursive models, interactions, and confirmatory factor analysis.