Bibliografía: p. 363-364.

Preface -- About the Companion Website -- 1 Introduction: Categorical Response Data. Probability Distributions for Categorical Data. Statistical Inference for a Proportion. Statistical Inference for Discrete Data. Bayesian Inference for Proportions. Using R Software for Statistical Inference about Proportions. Exercises -- 2. Analyzing Contingency Tables: Probability Structure for Contingency Tables. Comparing Proportions in 2 × 2 Contingency Tables. The Odds Ratio. Chi-Squared Tests of Independence.
Testing Independence for Ordinal Variables. Exact Frequentist and Bayesian Inference. Association in Three-Way Tables. Exercises -- 3. Generalized Linear Models: Components of a Generalized Linear Model. Generalized Linear Models for Binary Data. Generalized Linear Models for Counts and Rates. Statistical Inference and Model Checking. Fitting Generalized Linear Models.
Exercises -- 4. Logistic Regression: The Logistic Regression Model. Statistical Inference for Logistic Regression. Logistic Regression with Categorical Predictors. Multiple Logistic Regression. Summarizing Effects in Logistic Regression. Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation. Exercises -- 5. Building and Applying Logistic Regression Models: Strategies in Model Selection. Model Checking. Infinite Estimates in Logistic Regression. Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression. Alternative Link Functions: Linear Probability and Probit Models. Sample Size and Power for Logistic Regression. Exercises -- 6. Multicategory Logit Models: Baseline-Category Logit Models for Nominal Responses. Cumulative Logit Models for Ordinal Responses. Cumulative Link Models: Model Checking and Extensions. Paired-Category Logit Modeling of Ordinal Responses. Exercises -- 7. Loglinear Models for Contingency Tables and Counts: Loglinear Models for Counts in Contingency Tables. Statistical Inference for Loglinear Models. The Loglinear – Logistic Model Connection. Independence Graphs and Collapsibility. Modeling Ordinal Associations in Contingency Tables. Loglinear Modeling of Count Response Variables. Exercises -- 8. Models for Matched Pairs: Comparing Dependent Proportions for Binary Matched Pairs. Marginal Models and Subject-Specific Models for Matched Pairs. Comparing Proportions for Nominal Matched-Pairs Responses. Comparing Proportions for Ordinal Matched-Pairs Responses. Analyzing Rater Agreement. Bradley–Terry Model for Paired Preferences -- 9. Marginal Modeling of Correlated, Clustered Responses: Marginal Models Versus Subject-Specific Models.
Marginal Modeling: The Generalized Estimating Equations (GEE) Approach. Marginal Modeling for Clustered Multinomial Responses. Transitional Modeling, Given the Past. Dealing with Missing Data. 10. Random Effects: Generalized Linear Mixed Models: Random Effects Modeling of Clustered Categorical Data. Examples: Random Effects Models for Binary Data. Extensions to Multinomial Responses and Multiple Random Effect Terms. Multilevel (Hierarchical) Models. Latent Class Models. Classification and Smoothing. Classification: Linear Discriminant Analysis. Classification: Tree-Based Prediction. Cluster Analysis for Categorical. Smoothing: Generalized Additive Models. Regularization for High-Dimensional Categorical Data (Large p) -- 12 A Historical Tour of Categorical Data Analysis -- Appendix: Software for Categorical Data Analysis -- R for Categorical Data Analysis -- SAS for Categorical Data Analysis -- Stata for Categorical Data Analysis -- SPSS for Categorical Data Analysis -- Brief Solutions to Odd-Numbered Exercises -- Bibliography.

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