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Applied Ordinal Logistic Regression Using Stata |
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Comment from the Stata technical groupApplied Ordinal Logistic Regression Using Stata by Xing Liu is an approachable introduction to ordinal logistic regression for students and applied researchers in education, the behavioral sciences, the social sciences, and related fields. This book is a practical guide to understanding and implementing a variety of models for ordinal data. Liu first focuses on the use of Stata, including an overview of Stata's interface features, command syntax, and help system. Readers are also introduced to commands for data management, graphics, and basic statistics. Then the discussion turns to logistic regression for binary outcomes and ordinal logistic regression for ordered outcomes. Beyond this, Liu covers more advanced models such as generalized ordinal logit, continuation ratio, adjacent categories logit, stereotype logit, and multilevel ordinal logit models. For each type of model, Liu provides worked examples and the corresponding Stata commands. For easy reference, each chapter concludes with the complete set of Stata commands necessary to reproduce all examples in the chapter. |
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Table of contentsView table of contents >> Preface
About the Author
Chapter 1. Stata Basics
1.1 Introduction to Stata
1.1.1. Do You Still Need to Use Commands?
1.2 Data Management
1.1.2. Stata at First Sight: Interface, Menus, and Toolbar 1.1.3. Creating a File and Entering Data 1.1.4. How to Open an Existing Data File 1.1.5. The Structure of Stata Commands 1.1.6. Do-Files 1.1.7. How to Save Stata Results 1.1.8. What If I have a Question? How Do I Get Help?
1.2.1. Creating a New Variable
1.3 Graphs
1.2.2. Recoding a Variable 1.2.3. Labeling a Variable 1.2.4. Labeling Values 1.2.5. The egen Command 1.2.6. How to Deal With Missing Values When Recoding Variables 1.2.7. Other Useful Data Management Commands
1.3.1. Histograms
1.4 Summary of Stata Commands in This Chapter 1.3.2. Bar Charts 1.3.3. Box Plots 1.3.4. Scatter Plots 1.3.5. How to Save Graphs 1.3.6. Stata Graph Editor 1.5 Exercises Chapter 2. Review of Basic Statistics
2.1 Understand Your Data Using Descriptive Statistics
2.2 Descriptive Statistics for Continuous Variables Using Stata 2.3 Frequency Distribution for Categorical Variables Using Stata 2.4 Independent Samples t Test Using Stata 2.5 Paired-Samples t Test 2.6 Analysis of Variance (ANOVA) 2.7 Correlation 2.8 Simple Linear Regression 2.9 Multiple Linear Regression 2.10 Chi-Square Test 2.11 Making Publication-Quality Tables Using Stata 2.12 General Guidelines for Reporting Results 2.13 Summary of Stata Commands in This Chapter 2.14 Exercises 3. Logistic Regression for Binary Data
3.1 Logistic Regression Models: An Introduction
3.1.1. Why Do We Need a Logistic Transformation?
3.2 Research Example and Description of the Data and Sample 3.1.2. Probabilities, Odds, and Odds Ratios 3.1.3. Transformation Among Probabilities, Odds, and Log Odds in Logistic Regression 3.1.4. Goodness-of-Fit Statistics 3.1.5. Testing Significance of Predictors 3.1.6. Interpretation of Model Parameter Estimates in Logistic Regression 3.3 Logistic Regression With Stata: Commands and Output
3.3.1. Simple Logistic Regression Using Stata
3.4 Making Publication-Quality Tables 3.3.2. Multiple Logistic Regression 3.5 Reporting the Results 3.6 Summary of Stata Commands in This Chapter 3.7 Exercises 4. Proportional Odds Models for Ordinal Response Variables
4.1 Proportional Odds Models: An Introduction
4.1.1. Odds and Odds Ratios in PO Models
4.2 Research Example and Description of the Data and Sample 4.1.2. Brant Test of the PO Assumption 4.1.3. Goodness of Fit 4.1.4. Interpretation of Model Parameter Estimates 4.3 Proportional Odds Models With Stata: Commands and Output
4.3.1. The PO Model: One-Predictor Model
4.4 Making Publication-Quality Tables 4.3.2. The PO Model: Multiple-Predictor Model 4.3.3. Model Comparisons Using the Log Likelihood Ratio Test and Other Fit Statistics 4.5 Reporting the Results 4.6 Summary of Stata Commands in This Chapter 4.7 Exercises 5. Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models
5.1 Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models: An Introduction
5.1.1. Odds and Odds Ratios
5.2 Research Example and Description of the Data and Sample 5.1.2. Goodness of Fit 5.1.3. Interpretation of Model Parameter Estimates 5.3 Partial Proportional Odds Models and Generalized Ordinal Logistic Models With Stata: Commands and Output
5.3.1. Stata Commands and Output
5.4 Generalized Ordinal Logistic Regression Models With Stata: An Example
5.4.1. Stata Commands and Output
5.5 Making Publication-Quality Tables 5.6 Reporting the Results 5.7 Summary of Stata Commands in This Chapter 5.8 Exercises 6. Continuation Ratio Models
6.1 Continuation Ratio Models: An Introduction
6.1.1. Conditional Probabilities, Odds, and Odds Ratios
6.2 Research Example and Description of the Data and Sample 6.1.2. Goodness-of-Fit Statistics 6.1.3. Interpretation of Model Parameter Estimates 6.3 Continuation Ratio Models With Stata: Commands and Output
6.3.1. The CR Model With the logit Link: One-Predictor Model
6.4 Making Publication-Quality Tables 6.3.2. The CR Model With the logit Link: Multiple-Predictor Model 6.3.3. Fitting Continuation Ratio Models Using the seqlogit Command 6.5 Reporting the Results 6.6 Summary of Stata Commands in This Chapter 6.7 Exercises 7. Adjacent Categories Logistic Regression Models
7.1 Adjacent Categories Models: An Introduction
7.1.1. Odds and Odds Ratios in AC Models
7.2 Research Example and Description of the Data and Sample 7.1.2. Goodness-of-Fit Statistics 7.1.3. Interpretation of Model Parameter Estimates 7.1.4. From the Multinomial Logistic Model to the AC model 7.3 Adjacent Categories Models With Stata: Commands and Output
7.3.1. Multinomial Logistic Regression Using Stata
7.4 Reporting the Results 7.3.2. Single-Predictor AC Model Using Stata 7.3.3. Making Publication-Quality Tables for the Single-Predictor AC Model 7.3.4. Adjacent Categories Models With Stata: Multiple-Predictor Model 7.3.5. Making Publication-Quality Tables for the Multiple-Predictor Model 7.5 Summary of Stata Commands in This Chapter 7.6 Exercises 8. Stereotype Logistic Regression Models
8.1 Stereotype Logistic Regression Models: An Introduction
8.1.1. Odds and Odds Ratios in Stereotype Logistic Regression Models
8.2 Research Example and Description of the Data and Sample 8.1.2. Model Fit Statistics 8.1.3. Interpretation of Model Parameter Estimates 8.3 Stereotype Logistic Regression Models With Stata: Commands and Output
8.3.1. The SL Model: One-Predictor Model
8.4 Making Publication-Quality Tables 8.3.2. The SL Model: Multiple-Predictor Model 8.3.3. Model Comparisons Using the Log Likelihood Ratio Test 8.5 Reporting the Results 8.6 Summary of Stata Commands in This Chapter 8.7 Exercises 9. Ordinal Logistic Regression With Complex Survey Sampling Designs
9.1 Proportional Odds Models With Complex Survey Sampling Designs: An Introduction
9.1.1. Features of Complex Surveys
9.2 Research Example and Description of the Data and Sample 9.1.2. Variance Estimation in Complex Survey Sampling 9.3 Data Analysis With Stata: Commands and Output
9.3.1. Proportional Odds Model With Four Explanatory Variables Without Weights
9.4 Making Publication-Quality Tables 9.3.2. Proportional Odds Model With Weights 9.3.3. Proportional Odds Model for Complex Survey Data Using the Stata svy Command 9.3.4. How to Deal With Singleton Strata 9.5 Reporting the Results 9.6 Summary of Stata Commands in This Chapter 9.7 Exercises 10. Multilevel Modeling for Continuous and Binary Response Variables
10.1 Multilevel Modeling: An Introduction
10.1.1. Multilevel Data Structure
10.2 Multilevel Modeling for Continuous Outcome Variables
10.1.2. Intraclass Correlation 10.1.3. Overview of a Basic Two-Level Model 10.1.4. Model-Building Strategies 10.1.5. Model Fit Statistics 10.1.6. Centering 10.1.7. Data Structure for Model Fitting
10.2.1. Research Example and Research Questions
10.3 Multilevel Modeling for Binary Outcome Variables
10.2.2. Description of the Data and Sample 10.2.3. Multilevel Modeling for Continuous Outcome Variables With Stata: Commands and Output 10.2.4. Making Publication-Quality Tables 10.2.5. Reporting the Results
10.3.1. Odds and Odds Ratios in Multilevel Logistic Regression Models
10.4 Summary of Stata Commands in This Chapter 10.3.2. Research Example and Research Questions 10.3.3. Description of the Data and Sample 10.3.4. Multilevel Modeling for Binary Outcome Variables With Stata: Commands and Output 10.3.5. Making Publication-Quality Tables 10.3.6. Reporting the Results 10.5 Exercises 11. Multilevel Modeling for Ordinal Response Variables
11.1 Multilevel Modeling for Ordinal Response Variables: An
Introduction
11.1.1. Model Specification
11.2 Research Example: Research Problem and Questions
11.1.2. Odds and Odds Ratios in Multilevel PO Models 11.1.3. Likelihood Ratio Test
11.2.1. Description of the Data and Sample
11.3 Multilevel Modeling for Ordinal Response Variables With Stata: Commands and Output
11.3.1. Unconditional Model or Null Model (Model 1)
11.4 Making Publication-Quality Tables 11.3.2. Random-Intercept Model (Model 2) 11.3.3. Random-Coefficient Model With a Level 1 Variable (Model 3) 11.3.4. Contextual Model With Both Level 1 and Level 2 Variables (Model 4) 11.3.5. Contextual Model With Cross-Level Interactions (Model 5) 11.3.6. Model Comparisons Using the AIC and BIC Statistics 11.3.7. Computing the Estimated Probabilities With the margins Command 11.3.8. Fitting Multilevel PO Models Using the meglm Command 11.3.9. Fitting Multilevel PO Models Using the gllamm Command 11.5 Reporting the Results 11.6 Summary of Stata Commands in This Chapter 11.7 Exercises 12. Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models
12.1 Ordinal Probit Regression Models
12.1.1. Ordinal Probit Regression Models: An Introduction
12.2 Multinomial Logistic Regression Models
12.1.2. Description of the Research Example, Data, and Sample 12.1.3. Ordinal Probit Models With Stata: Commands and Output 12.1.4. Interpreting the Output 12.1.5. Interpreting the Marginal Effects With the margins Command 12.1.6. Computing the Marginal Effects With the Improved margins Command in Stata 14 12.1.7. Interpreting the Estimated Probabilities With the margins Command 12.1.8. Model Comparison Using the Log Likelihood Ratio Test 12.1.9. Making Publication-Quality Tables Comparing the Probit Model and Proportional Odds Model 12.1.10. Reporting the Results
12.2.1. Multinomial Logistic Regression Models: An Introduction
12.3 Summary of Stata Commands in This Chapter 12.2.2. Odds in Multinomial Logistic Models 12.2.3. Odds Ratios or Relative Risk Ratios in Multinomial Logistic Regression Models 12.2.4. Description of the Research Example, Data, and Sample 12.2.5. Multinomial Logistic Regression Models With Stata: Commands and Output 12.2.6. Interpreting the Output 12.2.7. Interpreting the Odds Ratios of Being in a Category j Versus the Base Category 1 12.2.8. Interpreting the Estimated Probabilities With the margins Command 12.2.9. Independence of Irrelevant Alternatives (IIA) Tests 12.2.10. Making Publication-Quality Tables 12.2.11. Reporting the Results 12.4 Exercises Key Formulas for Statistical Models
Appendix: List of Stata User-Written Commands
References
Index
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