Standard syllabus
Longitudinal data analysis · Graduate · Math
Learning objectives from the Longitudinal data analysis syllabus, grouped by unit. Click an objective for study materials.
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
Learning objectives
Click an objective for study materials.
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
Definitions and 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
Proofs and reasoning
- 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
Abstraction and generalization
- Growth curve models — Growth curve models
- 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
Modeling and computation
- Apply balanced vs unbalanced panels in engineering contexts — Apply balanced vs unbalanced panels in engineering contexts
- Apply missing data patterns: mcar, mar, mnar in engineering contexts — Apply missing data patterns: mcar, mar, mnar in engineering contexts
- Apply exploratory analysis of trajectories in engineering contexts — Apply exploratory analysis of trajectories in engineering contexts
- Apply correlation structures over time in engineering contexts — Apply correlation structures over time in engineering contexts
- Apply time-varying vs time-invariant covariates in engineering contexts — Apply time-varying vs time-invariant covariates in engineering contexts
Data and technology
- Use software to explore longitudinal data analysis problems numerically — Use software to explore longitudinal data analysis problems numerically
- Interpret computational results against analytic predictions — Interpret computational results against analytic predictions
- Build spreadsheets or scripts for routine calculations — Build spreadsheets or scripts for routine calculations
- Visualize functions, fields, or datasets tied to course topics — Visualize functions, fields, or datasets tied to course topics
- Connect course methods to lab, industry, or research workflows — Connect course methods to lab, industry, or research workflows
Problem-solving practice
- 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
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
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
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$1,162 · Longitudinal data analysis · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.