Standard syllabus
Longitudinal data analysis · Graduate · Statistics
Topics
Longitudinal data structure
- Balanced vs unbalanced panels
- Missing data patterns: MCAR, MAR, MNAR
- Exploratory analysis of trajectories
- Correlation structures over time
- Time-varying vs time-invariant covariates
Modeling approaches
- Mixed-effects (multilevel) models
- Random intercepts and random slopes
- Generalized estimating equations (GEE)
- Autoregressive and Toeplitz correlation models
- Growth curve models
Inference and diagnostics
- ML and REML estimation
- Kenward–Roger corrections (introduction)
- Model comparison for nested mixed models
- Diagnostics for longitudinal residuals
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.