Praise for the Second Edition: "A must-have book for anyone expecting to do research and/or applications in categorical data analysis". ("Statistics in Medicine"). "It is a total delight reading this book". ("Pharmaceutical Research"). "If you do any analysis of categorical data, this is an essential desktop reference". ("Technometrics"). The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. "Categorical Data Analysis, Third 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 loglinear models for discrete data with normal regression for continuous data. This edition also features: an emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models; two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis; new sections introducing the Bayesian approach for methods in that chapter; more than 100 analyses of data sets and over 600 exercises; notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources; and, a supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions. "Categorical Data Analysis, Third Edition" is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Preface 1. Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 1.2 Distributions for Categorical Data 1.3 Statistical Inference for Categorical Data 1.4 Statistical Inference for Binomial Parameters 1.5 Statistical Inference for Multinomial Parameters 1.6 Bayesian Inference for Binomial and Multinomial Parameters Notes Exercises 2. Describing Contingency Tables 2.1 Probability Structure for Contingency Tables 2.2 Comparing Two Proportions 2.3 Conditional Association in Stratified 2x2 Tables 2.4 Measuring Association in I x J Tables Notes Exercises 3. Inference for Two-Way Contingency Tables 3.1 Confidence Intervals for Association Parameters 3.2 Testing Independence in Two-Way Contingency Tables 3.3 Following-Up Chi-Squared Tests 3.4 Two-Way Tables with Ordered Classifications 3.5 Small-Sample Inference for Contingency Tables 3.6 Bayesian Inference for Two-Way Contingency Tables 3.7 Extensions for Multiway Tables and Nontabulated Responses Notes Exercises 4. Introduction to Generalized Linear Models 4.1 The Generalized Linear Model 4.2 Generalized Linear Models for Binary Data 4.3 Generalized Linear Models for Counts and Rates 4.4 Moments and Likelihood for Generalized Linear Models 4.5 Inference and Model Checking for Generalized Linear Models 4.6 Fitting Generalized Linear Models 4.7 Quasi-Likelihood and Generalized Linear Models Notes Exercises 5. Logistic Regression 5.1 Interpreting Parameters in Logistic Regression 5.2 Inference for Logistic Regression 5.3 Logistic Models with Categorical Predictors 5.4 Multiple Logistic Regression 5.5 Fitting Logistic Regression Models Notes Exercises 6. Building, Checking, and Applying Logistic Regression Models 6.1 Strategies in Model Selection 6.2 Logistic Regression Diagnostics 6.3 Summarizing the Predictive Power of a Model 6.3 Mantel-Haenszel and Related Methods for Multiple 2x2 Tables 6.4 Detecting and Dealing with Infinite Estimates 6.5 Sample Size and Power Considerations Notes Exercises 7. Alternative Modeling of Binary Response Data 7.1 Probit and Complementary Log-Log Models 7.2 Bayesian Inference for Binary Regression 7.3 Conditional Logistic Regression 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models 7.5 Issues in Analyzing High-Dimensional Categorical Data Notes Exercises 8. Models for Multinomial Responses 8.1 Nominal Responses: Baseline-Category Logit Models 8.2 Ordinal Responses: Cumulative Logit Models 8.3 Ordinal Responses: Alternative Models 8.4 Testing Conditional Independence in I ? J ? K Tables 8.5 Discrete-Choice Models 8.6 Bayesian Modeling of Multinomial Responses Notes Exercises 9. Loglinear Models for Contingency Tables 9.1 Loglinear Models for Two-Way Tables 9.2 Loglinear Models for Independence and Interaction in Three-Way Tables 9.3 Inference for Loglinear Models 9.4 Loglinear Models for Higher Dimensions 9.5 The Loglinear?Logistic Model Connection 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions 9.7 Loglinear Model Fitting: Iterative Methods and their Application Notes Exercises 10. Building and Extending Loglinear Models 10.1 Conditional Independence Graphs and Collapsibility 10.2 Model Selection and Comparison 10.3 Residuals for Detecting Cell-Specific Lack of Fit 10.4 Modeling Ordinal Associations 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis 10.6 Empty Cells and Sparseness in Modeling Contingency Tables 10.7 Bayesian Loglinear Modeling Notes Exercises 11. Models for Matched Pairs 11.1 Comparing Dependent Proportions 11.2 Conditional Logistic Regression for Binary Matched Pairs 11.3 Marginal Models for Square Contingency Tables 11.4 Symmetry, Quasi-symmetry, and Quasi-independence 11.5 Measuring Agreement Between Observers 11.6 Bradley-Terry Model for Paired Preferences 11.7 Marginal Models and Quasi-symmetry Models for Matched Sets Notes Exercises 12. Clustered Categorical
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