Mixed models
Graduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Mixed models — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Linear mixed models
- Hierarchical data — Hierarchical data
- Random effects motivation — Random effects motivation
- LMM specification — LMM specification
- Interpretation — Interpretation
- Estimation: REML and ML — Estimation: REML and ML
- BLUPs and shrinkage — BLUPs and shrinkage
- Crossed and nested random effects — Crossed and nested random effects
Generalized LMMs
- GLMMs for binary and count outcomes — GLMMs for binary and count outcomes
- Laplace approximation — Laplace approximation
- Quadrature — Quadrature
- Convergence issues — Convergence issues
- Identifiability — Identifiability
- Ordinal mixed models (introduction) — Ordinal mixed models (introduction)
- Bayesian mixed models (overview) — Bayesian mixed models (overview)
Extensions
- Multilevel models for longitudinal data — Multilevel models for longitudinal data
- Random slopes for treatment effect heterogeneity — Random slopes for treatment effect heterogeneity
- ICC and variance partitioning — ICC and variance partitioning
- Simulation-based power for mixed models — Simulation-based power for mixed models
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Linear mixed models
Hierarchical data
Coming soon - Generalized LMMs
GLMMs for binary and count outcomes
Coming soon - Extensions
Multilevel models for longitudinal data
Coming soon
Notes
Graduate Mixed models — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/graduate/mixed_models.json`.