Standard syllabus
Mixed models · Graduate · Statistics
Topics
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
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.