Longitudinal Structural Equation Modeling is a five-day workshop focused on the application and interpretation of structural equation models fitted to repeated measures data. The analysis of longitudinal data (i.e., the repeated measurement of the same cases over time) has become fundamental in most areas of social and behavioral science research. There are many structural equation models available for analyzing repeated measures data, ranging from two time-point autoregressive models to complex multivariate latent curve models spanning multiple time periods. The goal of this workshop is to present a variety of longitudinal SEMs ranging from traditional methods to recent advances in latent curve and latent change score modeling. Topics include longitudinal factor analysis, autoregressive cross-lagged models, latent change score models, latent curve models, and observed and latent multiple group growth models (a.k.a. mixture models). This course offers a comprehensive examination of the SEM as a foundation for the estimation of a variety of models for testing hypotheses about stability and change in repeated measures data, as described below.