Categorical Data Analysis and Multilevel Modeling Using R
1st Edition
Published on 10. May 2022
Book
Paperback/Softback
744 pages
978-1-5443-2490-6 (ISBN)
Description
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 datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
Reviews / Votes
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 ClarkMore details
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 232 mm
Width: 187 mm
Weight
1172 gr
ISBN-13
978-1-5443-2490-6 (9781544324906)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
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
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