MESA 8430 General Linear Models
Addresses the construction, interpretation, and application of linear statistical models. Specifically, lectures and computer exercises cover ordinary least squares regression models; matrix algebra operations; parameter estimation techniques; missing data options; power transformations; exploratory versus confirmatory model building; linear-model diagnostics, sources of multicollinearity; diagnostic residual analysis techniques; variance partitioning procedures; dummy, effect, and orthogonal coding procedures; moderation and mediation analysis; regularization tecniques, and an introduction to structural equation modeling.
Course overview
- Department
- Measurement, Evaluation, Statistics, and Assessment
- School
- Lynch
- Credits
- 3
- Level
- Graduate
- Offered
- Every Fall, Every Spring
Catalog details
- Prerequisites
- MESA7469 or APSY7469
Course evaluations
- Course rating
- 4.20 / 5 · Very Good
- Instructor rating
- 4.31 / 5 · Very Good
- Terms evaluated
- 3
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Kayla Woputz | 4.1 | 4.7 | 4.3 | 4.1 | 4.8 | 4.6 | 4.8 | 4.1 | 10+ hrs/week | 10 of 14 |
| Spring 2026 | Zhushan Li | 4.3 | 4.4 | 4.8 | 4.7 | 4.7 | 4.8 | 4.6 | 4.5 | 7–9 hrs/week | 20 of 23 |
| Spring 2025 | Zhushan Li | 4.0 | 4.0 | 4.5 | 4.6 | 4.3 | 4.5 | 4.6 | 4.0 | 4–6 hrs/week | 24 of 28 |
| Summer 2024 | Zhushan Li | 4.4 | 4.4 | 4.9 | 4.9 | 4.6 | 4.7 | 4.9 | 4.7 | 10+ hrs/week | 7 of 9 |