A Stata® Companion to Political Analysis

Philip H. Pollock III is a professor of political science at the University of Central Florida. He has taught courses in research methods at the undergraduate and graduate levels for more than thirty years. His main research interests are American public opinion, voting behavior, techniques of quantitative analysis, and the scholarship of teaching and learning. His recent research has been on the effectiveness of Internet-based instruction. Pollock’s research has appeared in the American Journal of Political Science, Social Science Quarterly, and the British Journal of Political Science. Recent scholarly publications include articles in Political Research Quarterly, the Journal of Political Science Education, and PS: Political Science and Politics. Barry C. Edwards writes textbooks and works for Fair Trial Analysis, LLC, a company that conducts research on juries and jurors for civil and criminal litigation. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. He taught survey design and analysis, research methods, and prelaw courses at the University of Central Florida and continues to teach occasional courses for the University of Georgia. His political science interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com website. His research has been published in American Politics Research, Congress & the Presidency, Election Law Journal, Emory Law Journal, Georgia Bar Journal, Harvard Negotiation Law Review, Journal of Politics, NYU Journal of Legislation and Public Policy, Political Research Quarterly, Presidential Studies Quarterly, Public Management Review, State Politics and Policy Quarterly, and UCLA Criminal Justice Law Review.

Figures and Tables
Preface
Introduction: Getting Started with Stata
I.1Datasets for Stata Companion
I.2A Quick Tour of Stata
I.3Running Commands in Stata
I.4Quick Access to Tutorials and Resources
Chapter 1 Using Stata for Data Analysis
1.1General Syntax of Stata Commands
1.2Using Stata’s Graphic User Interface Effectively
1.3Do-files
1.4Printing Results and Copying Output
1.5Customizing Your Display
1.6Log Files
1.7Getting Help
Chapter 1 Exercises
Chapter 2 Descriptive Statistics
2.1Identifying Levels of Measurement
2.2Describing Nominal Variables
A Closer Look: Weighted and Unweighted Analysis: What’s the Difference?
2.3Describing Ordinal Variables
2.4Bar Charts for Nominal and Ordinal Variables
2.5Describing Interval Variables
A Closer Look: Stata’s Graphics Editor
2.6Histograms for Interval Variables
2.7Obtaining Case-Level Information
Chapter 2 Exercises
Chapter 3 Transforming Variables
3.1Creating Dummy Variables
3.2Applying Math Operators to Variables
3.3Managing Variable Descriptions and Labels
3.4Collapsing Variables into Simplified Categories
3.5Centering or Standardizing a Numeric Variable
3.6Creating an Additive Index
Chapter 3 Exercises
Chapter 4 Making Comparisons
4.1Cross-Tabulation Analysis
A Closer Look: The replace Command
4.2Mean Comparison Analysis
A Closer Look: The format Command
4.3Making Comparisons with Interval-Level Independent Variables
Chapter 4 Exercises
Chapter 5 Graphing Relationships and Describing Patterns
5.1Graphs for Binary Dependent Variables
5.1.1Simple Bar Charts with Nominal-Level Independent Variables
5.1.2 Area Chart with Ordinal-Level Independent Variables
5.1.3Graphs with Interval-Level Independent Variables
5.2Graphs for Nominal-Level Dependent Variables
5.2.1Clustered Bar Charts with Nominal-Level Independent Variables
5.2.2 Multiple Line Plots with Ordinal-Level Independent Variables
5.2.3Graphs with Interval-Level Independent Variables
5.3Graphs for Ordinal-Level Dependent Variables
5.3.1Using Bars to Represent Select Values
5.3.2Stacked Bar Chart for Ordinal-Ordinal Relationship
5.3.3Graphs with Interval-Level Independent Variables
5.4Graphs for Interval-Level Dependent Variables
5.4.1Plotting Means with Bars or Lines
5.4.2Box Plots
5.4.3Scatterplots
Chapter 5 Exercises
Chapter 6 Random Assignment and Sampling
6.1Random Assignment
6.1.1Two Groups with Equal Probability
6.1.2Multiple Groups with Varying Probabilities
6.1.3Random Assignment to Predetermined-Size Groups
6.2Analyzing the Results of an Experiment
6.2.1Assessing Random Assignment
6.2.2Evaluating the Effect of Treatment
6.3Random Sampling
6.3.1Simple Random Sampling with Replacement
6.3.2Simple Random Sampling without Replacement
6.3.3Systematic Random Samples
6.3.4Clustered and Stratified Random Samples
6.4Selecting Cases for Qualitative Analysis
6.4.1Most Similar Systems
6.4.2Most Different Systems
6.5Analyzing Data Ethically
6.5.1Ethical Issues in Data Analysis
6.5.2Ten Tips for Writing Replication Code
Chapter 6 Exercises
Chapter 7 Making Controlled Comparisons
7.1Cross-Tabulation Analysis with a Control Variable
7.1.1Start with a Basic Cross-Tabulation
7.1.2Controlling for Another Variable
7.1.3Interpreting Controlled Cross-Tabulations
A Closer Look: The If Qualifier
7.2Visualizing Controlled Comparisons with Categorical Dependent Variables
7.3Mean Comparison Analysis with a Control Variable
7.3.1Start with Basic Mean Comparison Table
7.3.2Adding Control Variables
7.3.3Interpreting a Controlled Mean Comparison
7.4Visualizing Controlled Mean Comparisons
Chapter 7 Exercises
Chapter 8 Foundations of Inference
8.1Estimating Population Parameters with Simulations
8.2Expected Shape of Sampling Distributions
8.2.1Central Limit Theorem and the Normal Distribution
8.2.2Normal Distribution of Sample Proportions
8.2.3Normal Distribution of Sample Means
8.2.4The Standard Normal Distribution
8.2.5The Empirical Rule (68-95-99 Rule)
8.3Confidence Interval and Margins of Error
8.3.1Critical Values for Confidence Intervals
8.3.2Reporting the Confidence Interval for a Sample Proportions
8.3.2Reporting the Confidence Interval for a Sample Means
8.4Student’s t-Distribution: When You’re Not Completely Normal
8.4.1The t-Distribution’s Role in Inferential Statistics
8.4.1Critical Values of t-Distributions
Chapter 8 Exercises
Chapter 9 Hypothesis Tests with One and Two Samples
9.1Role of the Null Hypothesis
9.2Testing Hypotheses with Sample Proportions
9.2.1Testing One Sample Proportion Against Hypothesized Value
9.2.2Testing Difference Between Two Sample Proportions Using Groups
9.2.3 Testing Difference Between Two Sample Proportions Using Variables
9.2.4Testing Hypotheses about Proportions with Weighted Data
9.3Testing Hypotheses with Sample Means
9.3.1Testing One Sample Mean Against Hypothesized Value
9.3.2Testing the Difference Between Two Sample Means Using Groups
9.3.3Testing the Difference Between Two Sample Means Using Variables
9.3.4T-Test Variations from Assumptions about Variance
9.3.5Testing Hypotheses about Means with Weighted Data
Chapter 9 Exercises
Chapter 10 Chi-Square Test and Analysis of Variance
10.1The Chi-Square Test of Independence
10.1.1How the Chi-Square Test Works
10.1.2Conducting a Chi-Square Test
10.1.3Example with Nominal-Level Independent Variable
A Closer Look: Chi-Square Test with Weighted Data
10.1.4Reporting and Interpreting Results
A Closer Look: Other Applications of Chi-Square Tests
10.2Measuring the Strength of Association between Categorical Variables
10.2.1Lambda
10.2.2Somers’ D
10.2.3Cramer’s V
10.3Chi-Square Test and Measures of Association in Controlled Comparisons
10.3.1Analyzing an Ordinal-Level Relationship with a Control Variable
10.3.2 Analyzing a Nominal-Level Relationship with a Control Variable (and Weighted Observations)
10.4Analysis of Variance (ANOVA)
10.4.1How ANOVA Works
10.4.2 Single Factor ANOVA
10.4.3 Two Factor ANOVA
10.4.4 Stata’s F-Distribution Functions
Chapter 10 Exercises
Chapter 11 Correlation and Bivariate Regression
11.1Correlation Analysis
11.1.1Correlation between Two Variables
11.1.2Correlation Among More than Two Variables
A Closer Look: Other Types and Application of Correlation Analysis
11.2Bivariate Regression Analysis
A Closer Look: Treating Census as a Sample
A Closer Look: R-Squared and Adjusted R-Squared: What’s the Difference?
11.3Creating a Scatterplot with a Linear Prediction Line
A Closer Look: Creating Graphs with Multiple Layered Elements
A Closer Look: What If a Scatterplot Doesn’t Show a Linear Relationship?
11.4Correlation and Bivariate Regression Analysis with Weighted Data
A Closer Look: Creating Tables of Regression Results
Chapter 11 Exercises
Chapter 12 Multiple Regression
12.1Multiple Regression Analysis
12.1.1Estimating and Interpreting a Multiple Regression Model
12.1.2Visualizing Multiple Regression with Bubble Plots
12.1.3Multiple Regression with Weighted Observations
12.2Regression with Multiple Dummy Variables
12.2.1Estimating and Interpreting Regression with Multiple Dummy Variables
12.2.2Changing the Reference Category
12.2.3Visualizing Regression with Multiple Dummy Variables
12.3Interaction Effects in Multiple Regression
12.3.1Estimating Regression Model with Interaction Term
12.3.2Graphing Linear Prediction Lines for Interaction Relationships
Chapter 12 Exercises
Chapter 13 Analyzing Regression Residuals
13.1Expected Values, Observed Values, and Regression Residuals
13.1.1Example from Bivariate Regression Analysis
13.1.2Residuals from Multiple Regression Analysis
13.2Squared and Standarized Residuals
13.2.1Squared Residuals
13.2.2Standardized Residuals
13.3Assumptions about Regression Residuals
13.4Analyzing Graphs of Regression Residuals
13.4.1Histogram of Regression Residuals
13.4.2Residual Diagnostic Plots
13.5Testing Regression Assumptions with Residuals
13.5.1 Testing Assumption that Residuals are Normally Distributed
13.5.2Testing the Constant Variance Assumption
15.3.3 Regression Diagnostics for Multiple Regression Analysis
A Closer Look: Other Regression Diagnostic Tests
13.6What If You Diagnose Problems with Residuals?
Chapter 13 Exercises
Chapter 14 Logistic Regression
14.1Odds, Logged Odds, and Probabilities
14.2Estimating Logistic Regression Models
14.2.1Logistic Regression with One Independent Variable
14.2.2Reporting and Interpreting Odds Ratios
14.2.3Evaluating Model Fit
A Closer Look: Logistic Regression Analysis with Weighted Observations
14.3Logistic Regression with Multiple Independent Variables
14.4Graphing Predicted Probabilities with One Independent Variable
14.4.1Interval-Level Independent Variable
14.4.2Categorical Independent Variable
A Closer Look: Marginal Effects and Expected Changes in Probability
14.5Graphing Predicted Probabilities with Multiple Independent Variables
14.5.1One Categorical and One Interval-Level Independent Variable
14.5.2Two Categorical Independent Variables
A Closer Look: Stata’s Quiet Mode
14.5.3 Plotting Predicted Probabilities with atmeans Option
14.5.4 Combining atmeans and over Options
Chapter 14 Exercises
Chapter 15 Doing Your Own Political Analysis
15.1Doable Research Ideas
15.1.1Political Knowledge and Interest
15.1.2 Self-Interest and Policy Preferences
15.1.3 Economic Performance and Election Outcomes
15.1.4Electoral Turnout in Comparative Perspective
15.1.5 Correlates of State Policies
15.1.6 Religion and Politics
15.1.7 Race and Politics
15.2Getting Data into Stata
15.2.1 Opening Stata Formatted Datasets
15.2.2 Importing Microsoft Excel Datasets
15.2.3 Using HTML Table Data
15.2.4Entering Data with Stata’s Data Editor
15.3Writing It Up
15.3.1The Research Question
15.3.2 Previous Research
15.3.3Data, Hypotheses, and Analysis
15.3.4Conclusions and Implications
Chapter 15 Exercises
Appendix
Table A-1: Variables in the Debate Dataset in Alphabetical Order
Table A-2: Variables in the GSS Dataset in Alphabetical Order
Table A-3: Variables in the NES Dataset in Alphabetical Order
Table A-4: Variables in the States Dataset by Topic
Table A-5: Variables in the World Dataset by Topic