Structural Equation Models


Widely used in social and behavioral sciences, structural equation models (SEM) provide a highly valuable framework for integrating measurement and mediation analysis into statistical modeling. An SEM allows linking latent (or unobserved) variables to observed ones in order to address measurement error. It also permits the specification of multiple dependent variables simultaneously and an assessment of mediation based on the decomposition of total effects into direct and indirect effects. This course emphasizes the intuition behind SEMs and their applications. Examples, exercises, and code (R or Stata) will be provided. Knowledge of R (or Stata) and linear regression is required. 

Agenda (Request syllabus)

Day 1: Introduction, Effect Decomposition, and Mediation Analysis

Day 2: Latent Variables and Measurement Models

Day 3: General SEMs and Extensions 


Dr. Shawn Bauldry is an Associate Professor of Sociology and a Core Faculty in Advanced Methods at Purdue University. He received a Ph.D. in Sociology and an M.S. in Statistics from the University of North Carolina at Chapel Hill. He has published papers on a range of topics related to Structural Equation Models and has co-authored a book on Confirmatory Factor Analysis in the Sage QASS series. He has taught graduate-level statistics courses for over 10 years and has developed numerous workshops and courses on SEMs, causal inference, and multilevel and longitudinal analyses. He is a recipient of the Purdue Sociology Department Graduate Mentoring Award.

Class Time

On-demand. Lecture and lab materials and recordings are available to view at your convenience.


“This was a great course and Dr. Bauldry did an excellent job of explaining challenging concepts and answering all questions.”

“The slides were self-contained, which made studying the material on your own possible. The material covered was difficult; however, the instructor's lectures were organized into easily digestible chunks of information.”

“Absolutely amazing! I really didn't think I would be able to do so much so fast.”