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

by Xing Liu

Overview

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. 

An open-access website for the book contains data sets, Stata code, and answers to in-text questions.

Product Details

ISBN-13:	9781483319766
Publisher:	SAGE Publications
Publication date:	09/30/2015
Sold by:	Barnes & Noble
Format:	eBook
Pages:	552
File size:	13 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

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

Editorial Reviews

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

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