<p><b>A VALUABLE NEW EDITION OF A STANDARD REFERENCE</b> <p>The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. <i>An Introduction to Categorical Data Analysis, Third Edition</i> summarizes these methods and shows readers how to use them with 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. <p>Adding to the value in the new edition is: <ul> <li>Illustrations of the use of R software to perform all the analyses in the book</li> <li>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</li> <li>New sections in many chapters introducing the Bayesian approach for the methods of that chapter</li> <li>More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets</li> <li>An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to odd-numbered exercises</li> <li>A companion website of other material, including all data sets analyzed in the book and some extra exercises</li> </ul> <p>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. <p><i>An Introduction to Categorical Data Analysis, Third Edition</i> 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.