2nd Edition
Categorical Data Analysis for the Behavioral and Social Sciences
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.
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
Biography
Razia Azen is Professor at the University of Wisconsin–Milwaukee, USA, where she teaches basic and advanced statistics courses. Her research focuses on methods that compare the relative importance of predictors in linear models. She received her MS in statistics and PhD in quantitative psychology from the University of Illinois, USA.
Cindy M. Walker is President and CEO of Research Analytics Consulting, LLC. Previously, she was a professor at the University of Wisconsin–Milwaukee, where she taught basic and advanced measurement and statistics courses. Her research focuses on applied issues in psychometrics. She received her PhD in quantitative research methodologies from the University of Illinois at Urbana–Champaign, USA.
Praise for a Previous Edition:
"An accessible walkthrough of the world of Bernoulli, Poisson, log-linear, multinomial, logistic, and all things non-interval. ... I enjoyed reading this book and I will come back to it both as a reference and to digest some of the weightier chapters." - Chris Beeley, Institute of Mental Health, Nottingham, UK, in The Psychologist
"This book fills an important need for a practitioner-oriented book on categorical data analyses. It not only could serve as an excellent resource for researchers working with categorical data, but would also make an excellent text for a graduate course in categorical data analysis." - Terry Ackerman, University of North Carolina, Greensboro, USA
"A much needed book… it fills a significant gap in the market for a user-friendly categorical data analysis book…Anyone wishing to learn categorical data analysis can read this book…The integration of both SPSS and SAS… increases the usability of this book." - Sara Templin, University of Alabama, USA
"An accessible treatment of an important topic. ... Through practical examples, data, and ... SPSS and SAS code, the Azen and Walker text promises to put these topics within reach of a much wider range of students. ... The applied nature of the book promises to be quite attractive for classroom use." - Scott L. Thomas, Claremont Graduate University, USA."Many social science students do not have the mathematical background to tackle the material covered in categorical data analysis books. What these students need is an understanding of what method to apply to what data, how to use software to analyze the data, and most importantly, how to interpret the results. ...The book would be very useful for a graduate level course ... for Sociologists and Psychologists. It might also be appropriate for Survey Methodologists." - Timothy D. Johnson, University of Michigan, USA
"This book does an admirable job of combining an intuitive approach to categorical data analysis with statistical rigor. It has definitely set a new standard for textbooks on the topic for the behavioral and social sciences." – Wim J. van der Linden, Chief Research Scientist, CTB/McGraw-Hill
