Mixed models
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Linear mixed models
- Hierarchical data and random effects motivation
- LMM specification and interpretation
- Estimation: REML and ML
- BLUPs and shrinkage
- Crossed and nested random effects
Generalized LMMs
- GLMMs for binary and count outcomes
- Laplace approximation and quadrature
- Convergence issues and identifiability
- Ordinal mixed models (introduction)
- Bayesian mixed models (overview)
Extensions
- Multilevel models for longitudinal data
- Random slopes for treatment effect heterogeneity
- ICC and variance partitioning
- Simulation-based power for mixed models
STEM / applied
Applied mixed modeling
- Workflow in R (lme4, glmmTMB) or SAS PROC MIXED
- Diagnostics: QQ plots for random effects
- Crossed random effects in psycholinguistics data
- Meta-analysis as mixed models (intro)
- Communicating fixed vs random effects
- Handling convergence failures in practice
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Focused graduate course on mixed models beyond introductory longitudinal analysis. Covers crossed random effects and GLMMs.