The explosion in the development of methods for analyzing categorical data that began in the 1960s has continued apace in recent years. This book provides an overview of these methods, as well as older, now standard, methods. It gives special emphasis to generalized linear modeling techniques, which extend linear model methods for continuous variables, and their extensions for multivariate responses.

Today, because of this development and the ubiquity of categorical data in applications, most statistics and biostatistics departments offer courses on categorical data analysis. This book can be used as a text for such courses.

The material in Chapters 1_7 forms the heart of most courses. Chapters 1_3 cover distributions for categorical responses and traditional methods for two-way contingency tables. Chapters 4_7 introduce logistic regression and related logit models for binary and multicategory response variables. Chapters 8 and 9 cover loglinear models for contingency tables. Over time, this model class seems to have lost importance, and this edition reduces somewhat its discussion of them and expands its focus on logistic regression.

In the past decade, the major area of new research has been the development of methods for repeated measurement and other forms of clustered categorical data. Chapters 10_13 present these methods, including marginal models and generalized linear mixed models with random effects. Chapters 14 and 15 present theoretical foundations as well as alternatives to the maximum likelihood paradigm that this text adopts. Chapter 16 is devoted to a historical overview of the development of the methods. It examines contributions of noted statisticians, such as Pearson and Fisher, whose pioneering efforts and sometimes vocal debates broke the ground for this evolution.

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