Structural Equation Modeling (SEM) is designed to apply general statistical modeling techniques to establish relationships among variables. SEM provides researchers with powerful techniques that takes into account the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, multiple latent independents each measured by multiple indicators, and one or more latent dependents also each with multiple indicators. Empirical research articles that use structural equation modeling as a major analytic tool appear regularly in leading academic journals. The purpose of this course is to train doctoral students in both the conceptual and applied uses of SEM.
Topics covered include regression models, path analysis models, exploratory and confirmatory factor analyses, latent variables, basic steps in structural equation modeling, multiple indicators and multiple causes (MIMIC) models, Multilevel Models, latent growth curve models, and dynamic factor models.