2. Edition - April 2007
152.- Euro
2007. 400 Pages, Hardcover
- Handbook/Reference Book -
ISBN-10: 0-471-22618-1
ISBN-13: 978-0-471-22618-5 - John Wiley & Sons






Sample Chapter

Short description
An Introduction to Categorical Data Analysis, Second Edition presents an introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role such as chi-squared tests and measures of association. It provides special emphasis, however, to logistic regression and loglinear modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition presents new methods for clustered data, which are increasingly common in longitudinal studies, for example. Two new chapters discuss these methods along with improvements in major software. Chapter 10 deals with marginal models, including the generalized estimating equations (GEE) approach. Chapter 11 deals with random effects models through generalized linear models. Earlier chapters and appendices are updated.

From the contents
Preface to the Second Edition.

1. Introduction.

1.1 Categorical Response Data.

1.2 Probability Distributions for Categorical Data.

1.3 Statistical Inference for a Proportion.

1.4 More on Statistical Inference for Discrete Data.

Problems.

2. Contingency Tables.

2.1 Probability Structure for Contingency Tables.

2.2 Comparing Proportions in Two-by-Two Tables.

2.3 The Odds Ratio.

2.4 Chi-Squared Tests of Independence.

2.5 Testing Independence for Ordinal Data.

2.6 Exact Inference for Small Samples.

2.7 Association in Three-Way Tables.

Problems.

3. Generalized Linear Models.

3.1 Components of a Generalized Linear Model.

3.2 Generalized Linear Models for Binary Data.

3.3 Generalized Linear Models for Count Data.

3.4 Statistical Inference and Model Checking.

3.5 Fitting Generalized Linear Models.

Problems.

4. Logistic Regression.

4.1 Interpreting the Logistic Regression Model.

4.2 Inference for Logistic Regression.

4.3 Logistic Regression with Categorical Predictors.

4.4 Multiple Logistic Regression.

4.5 Summarizing Effects in Logistic Regression.

Problems.

5. Building and Applying Logistic Regression Models.

5.1 Strategies in Model Selection.

5.2 Model Checking.

5.3 Effects of Sparse Data.

5.4 Conditional Logistic Regression and Exact Inference.

5.5 Sample Size and Power for Logistic Regression.

Problems.

6. Multicategory Logit Models.

6.1 Logit Models for Nominal Responses.

6.2 Cumulative Logit Models for Ordinal Responses.

6.3 Paired-Category Ordinal Logits.

6.4 Tests of Conditional Independence.

Problems.

7. Loglinear Models for Contingency Tables.

7.1 Loglinear Models for Two-Way and Three-Way Tables.

7.2 Inference for Loglinear Models.

7.3 The Loglinear-Logistic Connection.

7.4 Independence Graphs and Collapsibility.

7.5 Modeling Ordinal Associations.

Problems.

8. Models for Matched Pairs.

8.1 Comparing Dependent Proportions.

8.2 Logistic Regression for Matched Pairs.

8.3 Comparing Margins of Square Contingency Tables.

8.4 Symmetry and Quasi-Symmetry Models for Square Tables.

8.5 Analyzing Rater Agreement.

8.6 Bradley-Terry Model for Paired Preferences.

Problems.

9. Modeling Correlated, Clustered Responses.

9.1 Marginal Models Versus Conditional Models.

9.2 Marginal Modeling: The GEE Approach.

9.3 Extending GEE: Multinomial Responses.

9.4 Transitional Modeling, Given the Past.

Problems.

10. Random Effects: Generalized Linear Mixed Models.

10.1 Random Effects Modeling of Clustered Categorical Data.

10.2 Examples of Random Effects Models for Binary Data.

10.3 Extensions to Multinomial Responses or Multiple Random Effect Terms.

10.4 Multilevel (Hierarchical) Models.

10.5 Model Fitting and Inference for GLMMS.

Problems.

11. A Historical Tour of Categorical Data Analysis.

11.1 The Pearson-Yule Association Controversy.

11.2 R. A. Fisher's Contributions.

11.3 Logistic Regression.

11.4 Multiway Contingency Tables and Loglinear Models.

11.5 Final Comments.

Appendix A: Software for Categorical Data Analysis.

Appendix B: Chi-Squared Distribution Values.

Bibliography.

Index of Examples.

Subject Index.

Brief Solutions to Some Odd-Numbered Problems.