A Stata Companion to Political Analysis

The fourth edition of Philip Pollock’s A Stata Companion to Political Analysis is an excellent guide, whether you are taking your first political science course or teaching one. The new edition was updated for Stata 15. Like the previous editions, this book provides instructional insights and focuses on how to present results effectively.

 

Each chapter is a tutorial with a rich set of exercises. The book surveys the statistical methods that professional political scientists use; its treatment of research methods deftly incorporates data management, graphical analysis, and statistics in the political science domain. In this edition, the authors use Stata’s factor variable notation, which simplifies working with categorical variables and interactions. This complements the authors’ discussion of margins and marginsplot as essential tools to analyze estimation results. The thorough examples show how to complete each task with Stata while giving firsthand experience in political research.

Figures
Preface
Introduction: Getting Started
Datasets

 

INTRODUCTION TO STATA

Information about a Dataset
Information about Variables
General Syntax of Stata Commands
Do-Files
Printing Results and Copying Output
Log Files
Getting Help
Customizing Your Display
Exercises

 

DESCRIPTIVE STATISTICS

Interpreting Measures of Central Tendency and Variation
Describing Nominal Variables
A CLOSER LOOK: Weighting the GSS and NES Datasets
Describing Ordinal Variables
Describing Interval Variables
A CLOSER LOOK: Stata’s Graphics Editor
Histograms for Interval Variables
Obtaining Case-Level Information with sort and list
Exercises

 

TRANSFORMING VARIABLES

Creating Indicator Variables
Working With Variable Labels
Collapsing Variables Into Simplified Categories
Centering or Standardizing a Numeric Variable
Creating an Additive Index
Exercises

 

MAKING COMPARISONS

Cross-Tabulation Analysis
Visualizing Comparisons With Nominal or Ordinal Dependent Variables
A CLOSER LOOK: The replace Command
Mean Comparison Analysis
A CLOSER LOOK: The format Command
Strip Charts: Graphs for Small N Datasets
Exercises

 

MAKING CONTROLLED COMPARISONS

Cross-Tabulation Analysis with a Control Variable
A CLOSER LOOK: The If Qualifier
Visualizing Controlled Comparisons With Categorical Dependent Variables
Mean Comparison Analysis with a Control Variable

An Example of Interaction
An Example of an Additive Relationship

Visualized Controlled Mean Comparisons
Exercises

 

MAKING INFERENCES ABOUT SAMPLE MEANS

Finding the 95 Percent Confidence Interval of a Sample Mean
Testing a Hypothetical Claim About the Population Mean
Testing the Difference between Two Sample Means
A CLOSER LOOK: Inferences About Means with Unweighted Data
Extending the mean and lincom Commands to Other Situations
Making Inferences About Sample Proportions
A CLOSER LOOK: Inferences About Proportions With Unweighted Data
Exercises

 

CHI-SQUARE AND MASURES OF ASSOCIATION

Analyzing Ordinal-Level Relationships
A CLOSER LOOK: Analyzing Unweighted Data with the tabulate Command

Summary: Reporting and Interpreting Results

Analyzing an Ordinal-Level Relationship with a Control Variable

Analyzing Nominal-Level Relationships
Exercises

 

CORRELATION AND LINEAR REGRESSION

Correlation Analysis
Regression Analysis
A CLOSER LOOK: Treating Census as a Sample
A CLOSER LOOK: R-Squared and Adjusted R-Squared: What’s the Difference?
Creating a Scatterplot with a Linear Prediction Line
Multiple Regression
A CLOSER LOOK: Bubble Plots
Correlation and Regression with Weighted Data
Exercises

 

DUMMY VARIABLES AND INTERACTION EFFECTS

Regression with Dummy Variables
Interaction Effects in Multiple Regression
Graphing Linear Prediction Lines for Interaction Relationships
Changing the Reference Category
Exercises

 

LOGISTIC REGRESSION

Thinking About Odds, Logged Odds, and Probabilities
Estimated Logistic Regression Models
Logistic Regression With Multiple Independent Variables
A CLOSER LOOK: Comparing Logistic Regression Models With the estimates Command and the lrtest Command
Graphing Predicted Probabilities With One Independent Variable
Graphing Predicted Probabilities With Multiple Independent Variablees

The margins Command with the atmeans Option
The margins Command with the over Option
Combining atmeans and over Options

Exercises

 

DOING YOUR OWN POLITICAL ANALYSIS

Seven Doable Ideas

Political Knowledge and Interest
Self-Interest and Policy Preferences
Economic Performance and Election Outcomes
Electoral Turnout in Comparative Perspective
Interviewer Effects on Public Opinion Surveys
Religion and Politics
Race and Politics

Importing Data into Stata

Stata Formatted Datasets
Microsoft Excel Datasets
HTML Table Data

Writing It Up

The Research Question
Previous Research
Data, Hypotheses, and Analysis
Conclusions and Implications
APPENDIX
Table A-1: Variables in the GSS Dataset in Alphabetical Order
Table A-2: Variables in the NES Dataset in Alphabetical Order
Table A-3: Variables in the States Dataset by Topic
Table A-4: Variables in the World Dataset by Topic
Author: Philip H. Pollock III and Barry C. Edwards
Edition: Fourth Edition
ISBNISBN-13: 978-1-50637-970-8
©Copyright: 2019

The book surveys the statistical methods that professional political scientists use; its treatment of research methods deftly incorporates data management, graphical analysis, and statistics in the political science domain. In this edition, the authors use Stata’s factor variable notation, which simplifies working with categorical variables and interactions.