An Introduction to Categorical Data Analysis, 2nd Edition
March 2007, ©2007
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
2. Contingency Tables 21
2.1 Probability Structure for Contingency Tables 21
2.2 Comparing Proportions in Two-by-Two Tables 25
2.3 The Odds Ratio 28
2.4 Chi-Squared Tests of Independence 34
2.5 Testing Independence for Ordinal Data 41
2.6 Exact Inference for Small Samples 45
2.7 Association in Three-Way Tables 49
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
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
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
6. Multicategory Logit Models 173
6.1 Logit Models for Nominal Responses 173
6.2 Cumulative Logit Models for Ordinal Responses 180
6.3 Paired-Category Ordinal Logits 189
6.4 Tests of Conditional Independence 193
7. Loglinear Models for Contingency Tables 204
7.1 Loglinear Models for Two-Way and Three-Way 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
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 Quasi-Symmetry Models for Square Tables 256
8.5 Analyzing Rater Agreement 260
8.6 Bradley–Terry Model for Paired Preferences 264
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
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
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: Chi-Squared Distribution Values 343
Index of Examples 346
Subject Index 350
Brief Solutions to Some Odd-Numbered Problems 357
Second edition of one of the best-selling 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.
"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 well-written 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 chi-squared 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 E-Texts are powered by VitalSource and accessed via the VitalSource Bookshelf reader, available online and via a downloadable app.
- Wiley E-Texts are accessible online and offline, and can be read on a variety of devices, including smartphones and tablets.
- Wiley E-Texts are non-returnable and non-refundable.
- Wiley E-Texts are protected by DRM. For specific DRM policies, please refer to our FAQ.
- WileyPLUS registration codes are NOT included with any Wiley E-Text. For informationon WileyPLUS, click here .
- To learn more about Wiley E-Texts, please refer to our FAQ.
- E-books are offered as e-Pubs or PDFs. To download and read them, users must install Adobe Digital Editions (ADE) on their PC.
- E-books have DRM protection on them, which means only the person who purchases and downloads the e-book can access it.
- E-books are non-returnable and non-refundable.
- To learn more about our e-books, please refer to our FAQ.