Categorical Data Analysis and Multilevel Modeling Using R
eBook - ePub

Categorical Data Analysis and Multilevel Modeling Using R

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Categorical Data Analysis and Multilevel Modeling Using R

About this book

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.

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Yes, you can access Categorical Data Analysis and Multilevel Modeling Using R by Xing Liu in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title Page
  3. Acknowledgements
  4. Copyright Page
  5. Brief Contents
  6. Detailed Contents
  7. Acknowledgments
  8. About the Author
  9. Preface
  10. 1 R Basics
  11. 2 Review of Basic Statistics
  12. 3 Logistic Regression for Binary Data
  13. 4 Proportional Odds Models for Ordinal Response Variables
  14. 5 Generalized Ordinal Logistic Regression Models and Partial Proportional Odds Models
  15. 6 Other Ordinal Logistic Regression Models
  16. 7 Multinomial Logistic Regression Models
  17. 8 Poisson Regression Models
  18. 9 Negative Binomial Regression Models and Zero-Inflated Models
  19. 10 Multilevel Modeling for Continuous Response Variables
  20. 11 Multilevel Modeling for Binary Response Variables
  21. 12 Multilevel Modeling for Ordinal Response Variables
  22. 13 Multilevel Modeling for Count Response Variables
  23. 14 Multilevel Modeling for Nominal Response Variables
  24. 15 Bayesian Generalized Linear Models
  25. 16 Bayesian Multilevel Modeling of Categorical Response Variables
  26. References
  27. Index