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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R

  • Xing Liu - Eastern Connecticut State University

April 2022 | 456 pages | SAGE Publications, Inc

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains PowerPoint slides and solutions for the end-of-chapter exercises on the instructor site, and datasets and R commands used in the book on the student site. 

Chapter 1. R Basics
Chapter 2. Review of Basic Statistics
Chapter 3. Logistic Regression for Binary Data
Chapter 4. Proportional Odds Models for Ordinal Response Variables
Chapter 5. Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models
Chapter 6. Other Ordinal Logistic Regression Models
Chapter 7. Multinomial Logistic Regression Models
Chapter 8. Poisson Regression Models
Chapter 9. Negative Binomial Regression Models and Zero-Inflated Models
Chapter 10. Multilevel Modeling for Continuous Response Variables
Chapter 11. Multilevel Modeling for Binary Response Variables
Chapter 12. Multilevel Modeling for Ordinal Response Variables
Chapter 13. Multilevel Modeling for Count Response Variables
Chapter 14. Multilevel Modeling for Nominal Response Variables
Chapter 15. Bayesian Generalized Linear Models
Chapter 16. Bayesian Multilevel Modeling of Categorical Response Variables

This is an excellent book that covers many topics that are given just slight attention in many other books.

Ahmed Ibrahim
Johns Hopkins University

This book provides a highly accessible and practical introduction to some of the most useful regression models in social science research. Most students and applied researchers will find it valuable.

Yang Cao
University of North Carolina at Charlotte

I would highly recommend this book, especially if readers are beginners.

Man-Kit Lei
University of Georgia

This book provides an engaging and intuitive introduction to maximum likelihood estimation through contemporary examples.

Jennifer Hayes Clark
University of Houston

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ISBN: 9781544324906