Categorical Data Analysis and Multilevel Modeling Using R

Xing Liu Ph.D., is a professor of educational research and assessment at Eastern Connecticut State University. He received his Ph.D. in measurement, evaluation, and assessment in the field of educational psychology from the University of Connecticut, Storrs. His interests include categorical data analysis, multilevel modeling, longitudinal data analysis, structural equation modeling, educational assessment, propensity score methods, data science, and Bayesian methods. He is the author of Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling (2016). His major publications focus on advanced statistical models. His articles have been recognized among the most popular papers published in the Journal of Modern Applied Statistical Methods (JMASM). Dr. Liu is the recipient of the Excellence Award in Creativity/Scholarship at Eastern Connecticut State University.

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 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 This is an excellent book that covers many topics that are given just slight attention in many other books. -- Ahmed Ibrahim I would highly recommend this book, especially if readers are beginners. -- Man-Kit Lei This book provides an engaging and intuitive introduction to maximum likelihood estimation through contemporary examples. -- Jennifer Hayes Clark