AMS 573: Categorical Data Analysis

Measuring the strength of association between pairs of categorical variables. Methods for evaluating classification procedures and inter-rater agreement. Analysis of the associations among three or more categorical variables using log linear models. Logistic regression.

Required Textbook: Categorical Data Analysis by Alan Agresti, 3rd edition, 2013, Wiley

Supplementary Textbooks:

1. Categorical Data Analysis Using SAS^® by Maura E. Stokes, Charles S. Davis and Gary G. Koch, 3rd edition, 2012, SAS Institute
2. An Introduction to Categorical Data Analysis by Alan Agresti, 3rd edition, 2018, Wiley
3. The Little SAS^® Book: A Primer by Lora D. Delwiche and Susan J. Slaughter, 6th edition, 2019, SAS Institute

Learning Outcomes

1. Demonstrate skills of working with various categorical data, including binary, nominal, ordinal and count data:
   • Expectation, variance, covariance and probability density function;
   • Point estimation with maximal likelihood method;
   • Hypothesis testing with Wald, score and likelihood ratio tests;
   • Constructing confidence intervals based on Wald, score and likelihood ratio test statistics.
2. Demonstrate skills with statistical inference for contingency tables (joint distribution of categorical variables):
   • Difference of proportions, relative risk and odds ratio;
   • Chi-squared tests;
   • Fisher’s exact test;
   • McNemar test for matched pairs.
3. Demonstrate skills with statistical modeling for binary/nominal/ordinal response:
   • Build and apply logistic regression, baseline category and cumulative logit models;
   • Maximal likelihood fitting and goodness of fit tests;
   • Model diagnostic and model selection;
   • Other link functions: log-log, complementary log-log.
4. Demonstrate skills with statistical modeling for count data:
   • Build and apply log-linear models;
   • Connection between log-linear and logit models;
   • Model fitting and goodness of fit tests;
   • Association graphs and collapsibility.
5. Demonstrate skills with proficient usage of standard statistical software tools for categorical data analysis:
   • Understanding of the assumptions, derivation and interpretation of results from statistical analysis;
   • Proficient in SAS^® procedures: FREQ, GENMOD, GLM and LOGISTIC.