770—Categorical Data Analysis. (3) (Prereq: STAT 704 and consent of instructor, or BIOS 759) Advanced methods for analysis of discrete data. Higher-order contingency tables, log-linear and other generalized linear models. Multivariate methods for matched pairs and longitudinal data.
Sample Course Homepage: Recent Semester
Usually Offered: Fall Semesters
Purpose: To develop expertise in description and statistical inference for contingency tables. To extend expertise in constructing and interpreting models for binary response data. To develop expertise in constructing and interpreting log-linear and other generalized linear models for categorical data.
Current Textbook: Categorical Data Analysis, 2nd ed. by A. Agresti. Wiley, 2002.
Topics Covered | Chapters | Time |
Description and inference for two-dimensional contingency tables: Categorical response data, sampling schemes and distributions, summary measures of association, large sample inference, exact test for small samples |
1-3 | 2 weeks |
Models for binary response variables and generalized linear models: Logistic regression, logit models, probit models, model diagnostics |
4-6 | 3 weeks |
Ordinal and Polytomous Response data: Logit models for ordinal variables, RC models,
Generalized logit models, cumulative and baseline category logit models |
7 | 2 weeks |
Log-linear models:Log-linear models for two dimensions, log-linear models for three or more dimensions, testing goodness of fit, estimation model parameters, iterative MLEs, hierarchical model fitting, diagnostics |
8 | 2 weeks |
Dependent samples: Symmetry models, marginal homogeneity, measuring agreement, case-control models |
10 | 2 weeks |
Repeated measures:Marginal homogeneity, modeling a repeated categorical response, modeling a repeated ordinal response, generalized linear models and quasi-likelihood |
11 | 2 weeks |
Notes: Projects to reinforce the concepts will be typical.
Contact Faculty: Joshua Tebbs