Applied Ordinal Logistic Regression Using Stata

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.

1. Stata Basics Introduction to Stata Data Management Graphs A Summary of Stata Commands in this Chapter Exercises 2. Review of Basic Statistics Understand Your Data Using Descriptive Statistics Descriptive Statistics for Continuous Variables Using Stata Frequency Distribution for Categorical Variables Using Stata Independent Samples t-test Using Stata Paired Samples t-test Analysis of Variance (ANOVA) Correlation Simple Linear Regression Multiple Linear Regression Chi-Square Test Making Publication-Quality Tables Using Stata General Guidelines for Reporting Resutls A Summary of Stata Commands in this Chapter Exercises 3. Logistic Regression for Binary Data Logistic Regression Models: An Introduction Research Example and Description of the Data and Sample Logistic Regression with Stata: Commands and Output Summary of Stata Commands in this Chapter Exercises 4. Proportional Odds Models for Ordinal Response Variables Proportional Odds Models: An Introduction Research Example and Description of the Data and Sample Proportional Odds Models with Stata: Commands and Output Summary of Stata Commands in this Chapter Exercises 5. Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models Introduction Research Example and Description of the Data and Sample Partial Proportional Odds Models with Stata: Commands and Output Generalized Ordinal Logistic Regression Models with Stata: An Example Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 6. Continuation Ratio Models Continuation Ratio Models: An Introduction Research Example and Description of the Data and Sample Continuation Ratio Models with Stata: Commands and Output Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 7. Adjacent Categories Logistic Regression Models Adjacent Categories Models: An Introduction Research Example and Description of the Data and Sample Adjacent Categories Models with Stata: Commands and Output Presenting the Results Summary of Stata Commands in this Chapter 8. Stereotype Logistic Regression Models Stereotype Logistic Regression Models: An Introduction Research Example and Description of Data and Sample Stereotype Logistic Regression with Stata: Commands and Output Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 9. Ordinal Logistic Regression with Complex Survey Sampling Designs Ordinal Logistic Regression with Complex Survey Sampling Designs: An Introduction Research Example and the Description of Data and Variables Data Analysis with Stata: Commands and Output Making Publication-Quality Tables Summary of Stata Commands in this Chapter Exercises 10. Multilevel Modeling for Continuous and Binary Response Variables Multilevel Modeling: An Introduction Multilevel Modeling for Continuous Outcome Variables Multilevel Modeling for Binary Outcome Variables Multilevel Modeling for Binary Outcome Variables with Stata: Commands and Output Making Publication-Quality Tables Reporting the Results 11. Multilevel Modeling for Ordinal Response Variables Multilevel Modeling for Ordinal Response Variables: An Introduction Research Example: Research Problem and Questions Building a Two-Level Model for Ordinal Response Variables with Stata: Commands and Output Making Publication-Quality Tables Presenting the Results Summary of Stata Commands in this Chapter Exercises 12. Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models Ordinal Probit Models Multinomial Logistic Regression Models Summary of Stata Commands in this Chapter Exercises

In this book, Xing Liu offers a well-crafted book focused on the application of ordinal response models across fields. Readers will be equipped to competently handle a variety of statistical techniques from basic correlations to more advanced generalized ordered logistic regression models. This is an excellent resource for both new consumers of these statistical applications to seasoned veterans working on more complex issues related to ordinal response models. -- Jennifer Hayes Clark Logistic regression can be difficult to understand. Without a book explaining the test in a plain and easy-to-understand matter, learners will feel lost and get frustrated. However, Applied Ordinal Logistic Regression Using Stata explains the concept clearly and provides practical codes and output. Learners will find this book approachable and easy to follow. -- Lu Liu