Biostatistics Seminar: From Regression to Structural Equation Modeling

Structural equation modeling (SEM) extends classical linear regression. This workshop begins with a quick review of regression and its assumptions, followed by path analysis to capture direct and indirect effects under the SEM view that all variables are sampled (random). We then extend to latent variables via confirmatory factor analysis (CFA). 

Using public datasets, we’ll demonstrate step-by-step implementations in R, from specification and estimation to diagnostics and clear reporting—giving you a practical on-ramp from regression to SEM.