An Introduction To Categorical Data Analysis, 3rd Edition

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:

• 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

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.

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.

Preface ix

About the Companion Website xiii

1 Introduction 1

1.1 Categorical Response Data 1

1.2 Probability Distributions for Categorical Data 3

1.3 Statistical Inference for a Proportion 5

1.4 Statistical Inference for Discrete Data 10

1.5 Bayesian Inference for Proportions * 13

1.6 Using R Software for Statistical Inference about Proportions * 17

Exercises 21

2 Analyzing Contingency Tables 25

2.1 Probability Structure for Contingency Tables 26

2.2 Comparing Proportions in 2 × 2 Contingency Tables 29

2.3 The Odds Ratio 31

2.4 Chi-Squared Tests of Independence 36

2.5 Testing Independence for Ordinal Variables 42

2.6 Exact Frequentist and Bayesian Inference * 46

2.7 Association in Three-Way Tables 52

Exercises 56

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 Counts and Rates 72

3.4 Statistical Inference and Model Checking 76

3.5 Fitting Generalized Linear Models 82

Exercises 84

4 Logistic Regression 89

4.1 The Logistic Regression Model 89

4.2 Statistical Inference for Logistic Regression 94

4.3 Logistic Regression with Categorical Predictors 98

4.4 Multiple Logistic Regression 102

4.5 Summarizing Effects in Logistic Regression 107

4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation 110

Exercises 113

5 Building and Applying Logistic Regression Models 123

5.1 Strategies in Model Selection 123

5.2 Model Checking 130

5.3 Infinite Estimates in Logistic Regression 136

5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * 140

5.5 Alternative Link Functions: Linear Probability and Probit Models * 145