An Introduction to Categorical Data Analysis, 3rd Edition

A valuable new edition of a standard reference

The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data.

Adding to the value in the new edition is:

• Illustrations of the use of R software to perform all the analyses in the book
• A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis
• New sections in many chapters introducing the Bayesian approach for the methods of that chapter
• More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets
• An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises

Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more.

An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

Table of Contents

Chapter 1 Introduction
Chapter 2 Analyzing Contingency Tables
Chapter 3 Generalized Linear Models
Chapter 4 Logistic Regression
Chapter 5 Building And Applying Logistic Regression Models
Chapter 6 Multicategory Logit Models
Chapter 7 Loglinear Models For Contingency Tables And Counts
Chapter 8 Models For Matched Pairs
Chapter 9 Marginal Modeling Of Correlated, Clustered Responses
Chapter 10 Random Effects: Generalized Linear Mixed Models
Chapter 11 Classification And Smoothing
Chapter 12 A Historical Tour Of Categorical Data Analysis
Appendix: Software for Categorical Data Analysis