Textbook
An Introduction to Categorical Data Analysis, 2nd EditionMarch 2007, ©2007

Description
Table of Contents
Preface to the Second Edition xv
1. Introduction 1
1.1 Categorical Response Data 1
1.2 Probability Distributions for Categorical Data 3
1.3 Statistical Inference for a Proportion 6
1.4 More on Statistical Inference for Discrete Data 11
Problems 16
2. Contingency Tables 21
2.1 Probability Structure for Contingency Tables 21
2.2 Comparing Proportions in TwobyTwo Tables 25
2.3 The Odds Ratio 28
2.4 ChiSquared Tests of Independence 34
2.5 Testing Independence for Ordinal Data 41
2.6 Exact Inference for Small Samples 45
2.7 Association in ThreeWay Tables 49
Problems 55
3. Generalized Linear Models 65
3.1 Components of a Generalized Linear Model 66
3.2 Generalized Linear Models for Binary Data 68
3.3 Generalized Linear Models for Count Data 74
3.4 Statistical Inference and Model Checking 84
3.5 Fitting Generalized Linear Models 88
Problems 90
4. Logistic Regression 99
4.1 Interpreting the Logistic Regression Model 99
4.2 Inference for Logistic Regression 106
4.3 Logistic Regression with Categorical Predictors 110
4.4 Multiple Logistic Regression 115
4.5 Summarizing Effects in Logistic Regression 120
Problems 121
5. Building and Applying Logistic Regression Models 137
5.1 Strategies in Model Selection 137
5.2 Model Checking 144
5.3 Effects of Sparse Data 152
5.4 Conditional Logistic Regression and Exact Inference 157
5.5 Sample Size and Power for Logistic Regression 160
Problems 163
6. Multicategory Logit Models 173
6.1 Logit Models for Nominal Responses 173
6.2 Cumulative Logit Models for Ordinal Responses 180
6.3 PairedCategory Ordinal Logits 189
6.4 Tests of Conditional Independence 193
Problems 196
7. Loglinear Models for Contingency Tables 204
7.1 Loglinear Models for TwoWay and ThreeWay Tables 204
7.2 Inference for Loglinear Models 212
7.3 The Loglinear–Logistic Connection 219
7.4 Independence Graphs and Collapsibility 223
7.5 Modeling Ordinal Associations 228
Problems 232
8. Models for Matched Pairs 244
8.1 Comparing Dependent Proportions 245
8.2 Logistic Regression for Matched Pairs 247
8.3 Comparing Margins of Square Contingency Tables 252
8.4 Symmetry and QuasiSymmetry Models for Square Tables 256
8.5 Analyzing Rater Agreement 260
8.6 Bradley–Terry Model for Paired Preferences 264
Problems 266
9. Modeling Correlated Clustered Responses 276
9.1 Marginal Models Versus Conditional Models 277
9.2 Marginal Modeling: The GEE Approach 279
9.3 Extending GEE: Multinomial Responses 285
9.4 Transitional Modeling Given the Past 288
Problems 290
10. Random Effects: Generalized Linear Mixed Models 297
10.1 Random Effects Modeling of Clustered Categorical Data 297
10.2 Examples of Random Effects Models for Binary Data 302
10.3 Extensions to Multinomial Responses or Multiple Random Effect Terms 310
10.4 Multilevel (Hierarchical) Models 313
10.5 Model Fitting and Inference for GLMMS 316
Problems 318
11. A Historical Tour of Categorical Data Analysis 325
11.1 The Pearson–Yule Association Controversy 325
11.2 R. A. Fisher’s Contributions 326
11.3 Logistic Regression 328
11.4 Multiway Contingency Tables and Loglinear Models 329
11.5 Final Comments 331
Appendix A: Software for Categorical Data Analysis 332
Appendix B: ChiSquared Distribution Values 343
Bibliography 344
Index of Examples 346
Subject Index 350
Brief Solutions to Some OddNumbered Problems 357
Author Information
New To This Edition

Second edition of one of the bestselling books on categorical data analysis, from one of the most authoritative authors in the field.

Features new chapters on marginal models, including the generalized estimating equations (GEE) approach and random effects models.

Already existing material, including SAS and SPSS data sets, is updated to reflect technical advances since the publication of the first edition.

Introductory material on generalized linear models will now include information on negative binomial regression.

Written on a relatively low technical level and does not require familiarity with advanced mathematics such as calculus or matrix algebra.
Reviews
"This text does a good job of achieving its state goal, and we enthusiastically recommend it." (Journal of the American Statistical Association, September 2008)
"This book is very wellwritten and it is obvious that the author knows the subject inside out." (Journal of Applied Statistics, April 2008)
"Provides an applied introduction to the most important methods for analyzing categorical data, such as chisquared tests and logical regression." (Statistica 2008)
"This is an introductory book and as such it is marvelous...essential for a novice..." (MAA Reviews, June 26, 2007)
 Wiley ETexts are powered by VitalSource and accessed via the VitalSource Bookshelf reader, available online and via a downloadable app.
 Wiley ETexts are accessible online and offline, and can be read on a variety of devices, including smartphones and tablets.
 Wiley ETexts are nonreturnable and nonrefundable.
 Wiley ETexts are protected by DRM. For specific DRM policies, please refer to our FAQ.
 WileyPLUS registration codes are NOT included with any Wiley EText. For informationon WileyPLUS, click here .
 To learn more about Wiley ETexts, please refer to our FAQ.
 Ebooks are offered as ePubs or PDFs. To download and read them, users must install Adobe Digital Editions (ADE) on their PC.
 Ebooks have DRM protection on them, which means only the person who purchases and downloads the ebook can access it.
 Ebooks are nonreturnable and nonrefundable.
 To learn more about our ebooks, please refer to our FAQ.