An Introduction to Categorical Data Analysis 3/e (H)
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:
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.
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
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
2 Analyzing Contingency Tables
3 Generalized Linear Models
4 Logistic Regression
5 Building and Applying Logistic Regression Models
6 Multicategory Logit Models
7 Loglinear Models for Contingency Tables and Counts
8 Models for Matched Pairs
9 Marginal Modeling of Correlated, Clustered Responses
10 Random Effects: Generalized Linear Mixed Models
11 Classification and Smoothing
12 A Historical Tour of Categorical Data Analysis
Appendix: Software for Categorical Data Analysis
Brief Solutions to Odd-Numbered Exercises
ALAN AGRESTI is Distinguished Professor Emeritus at the University of Florida. He has presented short courses on categorical data methods in 35 countries. He is the author of seven books, including the bestselling Categorical Data Analysis (Wiley), Foundations of Linear and Generalized Linear Models (Wiley), Statistics: The Art and Science of Learning from Data (Pearson), and Statistical Methods for the Social Sciences (Pearson).