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

- Xing Liu - Eastern Connecticut State University

**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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Introduction to Stata |

Data Management |

Graphs |

A Summary of Stata Commands in this Chapter |

Exercises |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

Ordinal Probit Models |

Multinomial Logistic Regression Models |

Summary of Stata Commands in this Chapter |

Exercises |

### Supplements

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.

**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.

**University of La Verne**