Categorical data analysis
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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Topics typically covered
Standard syllabus
Contingency tables
- Two-way tables: independence and association
- Chi-square tests and expected counts
- Odds ratios and relative risk for 2×2 tables
- Exact tests for small samples
- Stratified tables and Mantel–Haenszel methods
Logistic and log-linear models
- Logistic regression for binary outcomes
- Multinomial and ordinal logistic models (intro)
- Log-linear models for count data
- Model selection and deviance
- Overdispersion and quasi-likelihood (intro)
Advanced categorical methods
- Repeated categorical data: GEE preview
- Matched-pair designs and McNemar's test
- Agresti-style interpretation of odds ratios
- Residual analysis for GLMs
STEM / applied
Applied categorical data analysis
- Fitting GLMs in R (glm) or Python (statsmodels)
- Visualization of categorical associations
- Case studies in epidemiology and social science
- Handling sparse tables and separation problems
- Reporting effect sizes for categorical outcomes
- Survey-weighted logistic regression (introduction)
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Standard follow-on to regression for statistics majors. Covers log-linear models and generalized linear models for categorical outcomes.