An introduction to categorical data analysis

Abstract

This book 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.