Latent Growth Models


Latent growth models (LGMs) allow for an analysis of dynamic processes that unfold over time. Rooted in the structural equation modeling framework, LGMs can be used to characterize trajectories of social processes across cases, examine predictors of the trajectories, and connect different trajectories to outcomes. This course introduces LGMs with a focus on the intuition underlying the models and applications of the models. Examples, exercises, and code will be provided in R and Stata. Knowledge of R or Stata and linear regression is recommended. 

Agenda (Request syllabus)

Day 1: Longitudinal data structures and unconditional latent growth models

Day 2: Nonlinear and conditional growth models

Day 3: Effect decomposition and outcomes of growth processes


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.


“I absolutely loved this course. I learned so much. I felt prepared from the class material, yet still challenged to not only apply what we learned but extend my learnings and explore during analysis. I also especially appreciated the combination of equations and conceptual examples. This is a fantastic course and instructor.”


“His explanations and command over the subject and how he incorporates it in the lectures + the way he builds his slides + his class really help me understand the material.”

“Shawn is a great professor and truly cares about the success of his students. He goes above and beyond to make sure we are understanding the material. I also enjoyed reading papers that employed the methods we were learning.”