Preface
About the Author
Chapter 1. Stata Basics
1.1 Introduction to Stata
1.1.1. Do You Still Need to Use Commands?
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 Data Management
1.2.1. Creating a New Variable
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 Graphs
1.3.1. Histograms
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
1.4 Summary of Stata Commands in This Chapter
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?
3.1.2. 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
3.2 Research Example and Description of the Data and Sample
3.3 Logistic Regression With Stata: Commands and Output
3.3.1. Simple Logistic Regression Using Stata
3.3.2. Multiple Logistic Regression
3.4 Making Publication-Quality Tables
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
4.1.2. Brant Test of the PO Assumption
4.1.3. Goodness of Fit
4.1.4. Interpretation of Model Parameter Estimates
4.2 Research Example and Description of the Data and Sample
4.3 Proportional Odds Models With Stata: Commands and Output
4.3.1. The PO Model: One-Predictor Model
4.3.2. The PO Model: Multiple-Predictor Model
4.3.3. Model Comparisons Using the Log Likelihood Ratio Test and Other Fit Statistics
4.4 Making Publication-Quality Tables
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
5.1.2. Goodness of Fit
5.1.3. Interpretation of Model Parameter Estimates
5.2 Research Example and Description of the Data and Sample
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
6.1.2. Goodness-of-Fit Statistics
6.1.3. Interpretation of Model Parameter Estimates
6.2 Research Example and Description of the Data and Sample
6.3 Continuation Ratio Models With Stata: Commands and Output
6.3.1. The CR Model With the logit Link: One-Predictor Model
6.3.2. The CR Model With the logit Link: Multiple-Predictor Model
6.3.3. Fitting Continuation Ratio Models Using the seqlogit Command
6.4 Making Publication-Quality Tables
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
7.1.2. Goodness-of-Fit Statistics
7.1.3. Interpretation of Model Parameter Estimates
7.1.4. From the Multinomial Logistic Model to the AC model
7.2 Research Example and Description of the Data and Sample
7.3 Adjacent Categories Models With Stata: Commands and Output
7.3.1. Multinomial Logistic Regression Using Stata
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
7.4 Reporting the Results
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
8.1.2. Model Fit Statistics
8.1.3. Interpretation of Model Parameter Estimates
8.2 Research Example and Description of the Data and Sample
8.3 Stereotype Logistic Regression Models With Stata: Commands and Output
8.3.1. The SL Model: One-Predictor Model
8.3.2. The SL Model: Multiple-Predictor Model
8.3.3. Model Comparisons Using the Log Likelihood Ratio Test
8.4 Making Publication-Quality Tables
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
9.1.2. Variance Estimation in Complex Survey Sampling
9.2 Research Example and Description of the Data and Sample
9.3 Data Analysis With Stata: Commands and Output
9.3.1. Proportional Odds Model With Four Explanatory Variables Without Weights
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
9.4 Making Publication-Quality Tables
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
10.1.2. 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 Multilevel Modeling for Continuous Outcome Variables
10.2.1. Research Example and Research Questions
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 Multilevel Modeling for Binary Outcome Variables
10.3.1. Odds and Odds Ratios in Multilevel Logistic Regression Models
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
10.4 Summary of Stata Commands in This Chapter
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
11.1.2. Odds and Odds Ratios in Multilevel PO Models
11.1.3. Likelihood Ratio Test
11.2 Research Example: Research Problem and Questions
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)
11.3.2. 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
11.4 Making Publication-Quality Tables
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
12.1.2. 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 Multinomial Logistic Regression Models
12.2.1. Multinomial Logistic Regression Models: An Introduction
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
12.3 Summary of Stata Commands in This Chapter
12.4 Exercises
Key Formulas for Statistical Models
Appendix: List of Stata User-Written Commands
References
Index
