An Introduction to Categorical Data Analysis

Concise, complete, nontechnical—the ideal introduction to an increasingly important topi

In recent years, the use of statistical methods for categorical data has increased dramatically in a variety of areas and applications. This book provides an applied introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role, such as chi-squared tests, but places special emphasis on logistic regression and loglinear modeling techniques

Special features of the book include:

• Emphasis on logistic regression modeling of binary data and Poisson regression modeling of count data
• A unified perspective, based on generalized linear models, that connects these methods with standard regression methods for normally-distributed data
• An appendix showing the use of a new SAS procedure (GENMOD) for generalized linear modeling that can conduct nearly all methods presented in the book
• An entertaining historical perspective of the development of the methods
• Specialized methods for ordinal data, small samples, multicategory data, and matched pairs
• More than 100 examples of real data sets and more than 200 exercises

Writing in an applied, nontechnical style, Alan Agresti illustrates methods using a wide variety of real data, including alcohol, cigarette, and marijuana use by teenagers; AZT use and delay of AIDS; space shuttle launches and O-ring failure; passive smoking and lung cancer; and much more. An Introduction to Categorical Data Analysis is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control

ALAN AGRESTI is a professor in the Department of Statistics at the University of Florida. He has published extensively and has served on the editorial boards of several journals. He is the author of two advanced texts, including the bestselling Categorical Data Analysis, and the coauthor of Statistical Methods for the Social Sciences.