Basic and advanced Structural Equation Modeling (SEM) with and without latent variables. Topics include statistical theory underlying multivariate statistical modeling specific to SEM, path analysis, confirmatory (and exploratory) factor analysis, multiple group analysis, multiple indicator multiple cause (MIMIC) modeling, full SEM, and contemporary extensions to growth modeling and latent class analysis. Homework involves applying SEM software to real and simulated social science data. Recommended prerequisite: a basic statistics course and a course covering linear regression modeling.
Department of Sociology