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Applied Ordinal Logistic Regression Using Stata
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Applied Ordinal Logistic Regression Using Stata
From Single-Level to Multilevel Modeling

  • Xing Liu - Eastern Connecticut State University
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552 pages | SAGE Publications, Inc
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software. 

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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

 

Supplements

Instructor Resource Site
Datasets and Stata Code

Answers to In-Text Questions

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
University of Houston

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
University of La Verne

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Sample Materials & Chapters

Chapter 1


Xing Liu

Xing Liu, Ph.D., is an associate 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, and educational assessment. 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 ... More About Author

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