Agresti A. An Introduction to Categorical Data Analysis

Second Edition, John Wiley & Sons, 2007. — 372 p. — ISBN: 0471226181, 978-0471226185.The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses.This Second Edition features:
1) Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models.
2) A unified perspective based on generalized linear models.
3) An emphasis on logistic regression modeling.
4) An appendix that demonstrates the use of SAS for all methods.
5) An entertaining historical perspective on the development of the methods.
6) Specialized methods for ordinal data, small samples, multicategory data, and matched pairs.
7) More than 100 analyses of real data sets and nearly 300 exercises.Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.Categorical Response Data, Probability Distributions for Categorical Data, Statistical Inference for a Proportion, More on Statistical Inference for Discrete Data
Contingency Tables
Probability Structure for Contingency Tables, Comparing Proportions in Two-by-Two Tables, The Odds Ratio,
Chi-Squared Tests of Independence, Testing Independence for Ordinal Data, Exact Inference for Small Samples, Association in Three-Way Tables
Generalized Linear Models
Components of a Generalized Linear Model, Generalized Linear Models for Binary Data, Generalized Linear Models for Count Data,
Statistical Inference and Model Checking, Fitting Generalized Linear Models
Logistic Regression
Interpreting the Logistic Regression Model, Inference for Logistic Regression, Logistic Regression with Categorical Predictors,
Multiple Logistic Regression, Summarizing Effects in Logistic Regression
Building and Applying Logistic Regression Models
Strategies in Model Selection, Model Checking, Effects of Sparse Data,
Conditional Logistic Regression and Exact Inference, Sample Size and Power for Logistic Regression
Multicategory Logit Models
Logit Models for Nominal Responses, Cumulative Logit Models for Ordinal Responses, Paired-Category Ordinal Logits,
Tests of Conditional Independence
Loglinear Models for Contingency Tables
Loglinear Models for Two-Way and Three-Way Tables, Inference for Loglinear Models, The Loglinear–Logistic Connection,
Independence Graphs and Collapsibility, Modeling Ordinal Associations
Models for Matched Pairs
Comparing Dependent Proportions, Logistic Regression for Matched Pairs, Comparing Margins of Square Contingency Tables,
Symmetry and Quasi-Symmetry Models for Square Tables, Analyzing Rater Agreement, Bradley–Terry Model for Paired Preferences
Modeling Correlated, Clustered Responses
Marginal Models Versus Conditional Models, Marginal Modeling: The GEE Approach, Extending GEE: Multinomial Responses,
Transitional Modeling, Given the Past
Random Effects: Generalized Linear Mixed Models
Random Effects Modeling of Clustered Categorical Data, Examples of Random Effects Models for Binary Data,
Extensions to Multinomial Responses or Multiple Random Effect Terms, Multilevel (Hierarchical) Models, Model Fitting and Inference for GLMMS
A Historical Tour of Categorical Data Analysis
Appendix A: Software for Categorical Data Analysis
Appendix B: Chi-Squared Distribution ValuesIndex of ExamplesBrief Solutions to Some Odd-Numbered Problems