Categorical Data Analysis

Front Cover
John Wiley & Sons, Mar 31, 2003 - Mathematics - 734 pages

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.
 

Selected pages

Contents

1 Introduction Distributions and Inference for Categorical Data
1
2 Describing Contingency Tables
36
3 Inference for Contingency Tables
70
4 Introduction to Generalized Linear Models
115
5 Logistic Regression
165
6 Building and Applying Logistic Regression Models
211
7 Logit Models for Multinomial Responses
267
8 Loglinear Models for Contingency Tables
314
13 Other Mixture Models for Categorical Data
538
14 Asymptotic Theory for Parametric Models
576
15 Alternative Estimation Theory for Parametric Models
600
16 Historical Tour of Categorical Data Analysis
619
Appendix A Using Computer Software to Analyze Categorical Data
632
Appendix B ChiSquared Distribution Values
654
References
655
Examples Index
689

9 Building and Extending LoglinearLogit Models
357
10 Models for Matched Pairs
409
11 Analyzing Repeated Categorical Response Data
455
12 Random Effects Generalized Linear Mixed Models for Categorical Responses
491
Author Index
693
Subject Index
701
Copyright

Common terms and phrases

ˆ ˆ ˆ Ä4 Ž analysis approximate association asymptotic binary response categorical data cell counts chi-squared column comparing conditional independence confidence interval contingency table correlation covariance matrix denote deviance equals explanatory variables Fisher fitted values gender GLMM ij ij inference instance Interpret large-sample latent class latent class model likelihood equations likelihood function likelihood-ratio likelihood-ratio test linear log likelihood log odds ratio logistic regression logit model logitPY loglinear model marginal distributions marginal homogeneity marginal model methods ML estimates model fits model Ž multinomial multivariate normal observations odds ratio ordinal overdispersion P-value pairs parameter estimates Pearson residuals predictor probability probit quasi-likelihood quasi-symmetry random effects model regression model response variable sample proportion saturated model scores Section standard errors sufficient statistics tion variance Wald width Ý Ý Ž Ž

References to this book

About the author (2003)

ALAN AGRESTI, PhD, 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.

Bibliographic information

TitleCategorical Data Analysis
Wiley Series in Probability and Statistics
AuthorAlan Agresti
Edition2, illustrated
PublisherJohn Wiley & Sons, 2003
ISBN0471458767, 9780471458760
Length734 pages
Subjects ›  › 

Mathematics / General
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
  
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