Categorical Data Analysis and Multilevel Modeling Using R

Xing Liu, Ph.D., is a Distinguished 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 two statistics books, Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling and Categorical Data Analysis and Multilevel Modeling Using R. His major publications focus on advanced statistical models. His articles were recognized among the most popular papers published in the Journal of Modern Applied Statistical Methods (JMASM). Dr. Liu is the recipient of the Distinguished Professor Award and the Excellence Award in Creativity/Scholarship at Eastern Connecticut State 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.

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