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Full Description
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.
New to the second edition:
The addition of R syntax for all analyses and an update of SPSS and SAS syntax.
The addition of a new chapter on GLMMs.
Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters.
Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book's accessible approach.
Contents
Preface
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
References
