Amstat News asked three review editors to rate their top
five favorite books in the September 2003 issue. Categorical
Data Analysis was among those chosen.
A valuable new edition of a standard reference
‘A ‘must-have’ book for anyone expecting to do research and/or
applications in categorical data analysis.’
-Statistics in Medicine on Categorical Data Analysis,
First Edition
The use of statistical methods for categorical data has
increased dramatically, particularly for applications in the
biomedical and social sciences. Responding to new developments in
the field as well as to the needs of a new generation of
professionals and students, this new edition of the classic
Categorical Data Analysis offers a comprehensive
introduction to the most important methods for categorical data
analysis.
Designed for statisticians and biostatisticians as well as
scientists and graduate students practicing statistics,
Categorical Data Analysis, Second Edition summarizes the
latest methods for univariate and correlated multivariate
categorical responses. Readers will find a unified generalized
linear models approach that connects logistic regression and
Poisson and negative binomial regression for discrete data with
normal regression for continuous data. Adding to the value in the
new edition is coverage of:
* Three new chapters on methods for repeated measurement and
other forms of clustered categorical data, including marginal
models and associated generalized estimating equations (GEE)
methods, and mixed models with random effects
* Stronger emphasis on logistic regression modeling of binary
and multicategory data
* An appendix showing the use of SAS for conducting nearly all
analyses in the book
* Prescriptions for how ordinal variables should be treated
differently than nominal variables
* Discussion of exact small-sample procedures
* More than 100 analyses of real data sets to illustrate
application of the methods, and more than 600 exercises
* An Instructor’s Manual presenting detailed solutions to all
the problems in the book is available from the Wiley editorial
department.
Table of Content
Preface.
1. Introduction: Distributions and Inference for Categorical
Data.
2. Describing Contingency Tables.
3. Inference for Contingency Tables.
4. Introduction to Generalized Linear Models.
5. Logistic Regression.
6. Building and Applying Logistic Regression Models.
7. Logit Models for Multinomial Responses.
8. Loglinear Models for Contingency Tables.
9. Building and Extending Loglinear/Logit Models.
10. Models for Matched Pairs.
11. Analyzing Repeated Categorical Response Data.
12. Random Effects: Generalized Linear Mixed Models for Categorical
Responses.
13. Other Mixture Models for Categorical Data*.
14. Asymptotic Theory for Parametric Models.
15. Alternative Estimation Theory for Parametric Models.
16. Historical Tour of Categorical Data Analysis*.
Appendix A. Using Computer Software to Analyze Categorical
Data.
Appendix B. Chi-Squared Distribution Values.
References.
Examples Index.
Author Index.
Subject Index.
About the author
ALAN AGRESTI, Ph D, is Distinguished Professor in the Department of Statistics at the University of Florida. He has published extensively on categorical data methods and has presented courses on the topic for universities, companies, and professional organizations worldwide. A Fellow of the American Statistical Association, he is also the author of two other Wiley texts on categorical data analysis and coauthor of Statistical Methods for the Social Sciences.
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