HIERARCHICAL LINEAR MODELING
Introduction to the statistical methodology of hierarchical linear modeling, including random intercept and random slope and intercept models; models for longitudinal data; and multilevel generalized linear models.
Prerequisites & course information
Requisites: ED PSYCH 763 Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify the properties of clustered data and explain statistical treatments of multilevel data Audience: Graduate 2. Summarize and visualize multilevel data within and across levels Audience: Graduate 3. Select and apply multilevel models that are appropriate for various forms of clustered data Audience: Graduate 4. Interpret multilevel analysis results and communicate statistical findings Audience: Graduate 5. Write a project report for multilevel analysis including program code Audience: Graduate Learning Outcomes: 1. Identify the properties of clustered data and explain statistical treatments of multilevel data Audience: Graduate 2. Summarize and visualize multilevel data within and across levels Audience: Graduate 3. Select and apply multilevel models that are appropriate for various forms of clustered data Audience: Graduate 4. Interpret multilevel analysis results and communicate statistical findings Audience: Graduate 5. Write a project report for multilevel analysis including program code Audience: Graduate
Sources
- Course catalogue ↗2026-2027