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