Includes bibliographical references and index.

Introduction and overview -- Probability distributions -- Proportions, estimation, and goodness-of-fit -- Association between two categorical variables -- Associations between three categorical variables -- Modeling and the generalized linear model -- Log-linear models -- Logistic regression with continuous predictors -- Logistic regression with categorical predictors -- Logistic regression for multicategory outcomes -- Generalized linear mixed models -- Appendix -- References -- Index.

"Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a 'Look Ahead' section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge"--