Featuring a practical approach with numerous examples, this book focuses on helping the reader develop a conceptual, rather than technical, understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analyses and emphasize specific research questions that can be addressed by each analytic procedure so that readers are able to address the research questions they wish to answer. To achieve this goal, the authors:
- Review the theoretical implications and assumptions underlying each of the procedures
- Present each concept in general terms and illustrate each with a practical example
- Demonstrate the analyses using SPSS and SAS and show the interpretation of the results provided by these programs.
A "Look Ahead" section at the beginning of each chapter provides an overview of the material covered so that the reader knows what to expect. This is followed by one or more research questions that can be addressed using the procedure(s) covered in the chapter. A theoretical presentation of the material is provided and illustrated using realistic examples from the behavioral and social sciences. To further enhance accessibility, the new procedures introduced in the book are explicitly related to analytic procedures covered in earlier statistics courses, such as ANOVA and linear regression. Throughout each chapter the authors use practical examples to demonstrate how to obtain and interpret statistical output in both SPSS and SAS. Their emphasis on the relationship between the initial research question, the use of the software to carry out the analysis, and the interpretation of the output as it relates to the initial research question, allows readers to easily apply the material to their own research. The data sets for executing chapter examples using SAS Version 9.1.3 and/or IBM SPSS Version 18 are available on a book specific web site. These data sets and syntax allow readers to quickly run the programs and obtain the appropriate output. The book also includes both conceptual and analytic end-of-chapter exercises to assist instructors and students in evaluating the understanding of the material covered in each chapter.
This book covers the most commonly used categorical data analysis procedures. It is written for those without an extensive mathematical background, and is ideal for graduate courses in categorical data analysis or cross-classified data analysis taught in departments of psychology, human development & family studies, sociology, education, and business. Researchers in these disciplines interested in applying these procedures to their own research will appreciate this book’s accessible approach.
Table of Contents
1. Introduction and Overview. 2. Probability Distributions. 3. Proportions, Estimation and Goodness-of-Fit. 4. Association Between Two Categorical Variables. 5. Association Between Three Categorical Variables. 6. Modeling and the Generalized Linear Model. 7. Log-Linear Models. 8. Logistic Regression with Continuous Predictors. 9. Logistic Regression with Categorical Predictors. 10. Logistic Regression for Multicategory Outcomes. Appendix.
Razia Azen is an Associate Professor in the Department of Educational Psychology at the University of Wisconsin – Milwaukee. She received her Ph.D. in Quantitative Psychology from the University of Illinois, Urbana – Champaign. She teaches graduate-level statistics courses, including a course in categorical data analysis, at the University of Wisconsin – Milwaukee.
Cindy M. Walker is an Associate Professor in the Department of Educational Psychology at the University of Wisconsin – Milwaukee. She received her Ph.D. in Educational Psychology from the University of Illinois, Champaign – Urbana. She teaches graduate-level measurement and statistics courses, including a course in categorical data analysis, at the University of Wisconsin – Milwaukee.
"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