Education Commons^™

Modeling Student Outcomes In A General Education Course With Hierarchical Linear Models, Christine E. Demars

Department of Graduate Psychology - Faculty Scholarship

When students are nested within course sections, the assumption of independence of residuals is unlikely to be met, unless the course section is explicitly included in the model. Hierarchical linear modeling (HLM) allows for modeling the course section as a random effect, leading to more accurate standard errors. In this study, students chose one of four themes for a communications course, with multiple sections and instructors within each theme. HLM was used to test for differences by theme in scores on a final exam; the differences were not significant when SAT scores were controlled.