Multilevel Modeling

3.0 credits

This course introduces students to advanced statistical approaches for analyzing complex, multilevel data, including: · Hierarchical Linear Modeling (HLM) · Multilevel Structural Equation Modeling (M-SEM) · Dynamic Structural Equation Modeling (DSEM) · Random Intercept Cross-Lagged Panel Modeling (RI-CLPM) Students will apply one of these methods to their own multilevel dataset as part of a hands-on class project. The course culminates in a professional 20-minute research presentation in which students share their analytic approach, results, and substantive conclusions with the class.