Although generalizability theory (GT) designs typically are analyzed using analysis of variance (ANOVA) procedures, they also can be integrated into structural equation models (SEMs). In this tutorial, we review basic concepts for conducting univariate and multivariate GT analyses and demonstrate advantages of doing such analyses within SEM frameworks using multi-occasion data from the Rosenberg Self-Esteem Scale. We show how GT-SEMs can reproduce variance components for both relative and absolute error obtained from ANOVA models, estimate effects of changes made to measurement procedures and universes of generalization, incorporate estimation methods to correct for scale coarseness, represent essential tau-equivalent or congeneric relationships, include additional method factors for negatively and positively worded items, incorporate bifactor designs, allow for formal tests of model fit when warranted, and derive Monte Carlo confidence intervals for key parameters of interest. We provide code for conducting the demonstrated analyses using several statistical packages in extended online Supplemental Material.