Longitudinal data analysis
Graduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Longitudinal data analysis — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Longitudinal data structure
- Balanced vs unbalanced panels — Balanced vs unbalanced panels
- Missing data patterns: MCAR, MAR, MNAR — Missing data patterns: MCAR, MAR, MNAR
- Exploratory analysis of trajectories — Exploratory analysis of trajectories
- Correlation structures over time — Correlation structures over time
- Time-varying vs time-invariant covariates — Time-varying vs time-invariant covariates
Modeling approaches
- Mixed-effects (multilevel) models — Mixed-effects (multilevel) models
- Random intercepts and random slopes — Random intercepts and random slopes
- Generalized estimating equations (GEE) — Generalized estimating equations (GEE)
- Autoregressive — Autoregressive
- Toeplitz correlation models — Toeplitz correlation models
- Growth curve models — Growth curve models
Inference and diagnostics
- ML and REML estimation — ML and REML estimation
- Kenward–Roger corrections (introduction) — Kenward–Roger corrections (introduction)
- Model comparison for nested mixed models — Model comparison for nested mixed models
- Diagnostics for longitudinal residuals — Diagnostics for longitudinal residuals
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Longitudinal data structure
Balanced vs unbalanced panels
Coming soon - Modeling approaches
Mixed-effects (multilevel) models
Coming soon - Inference and diagnostics
ML and REML estimation
Coming soon
Notes
Graduate Longitudinal data analysis — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/graduate/longitudinal.json`.