A Stata Companion to Political Analysis, Fourth Edition |
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Comment from the Stata technical groupThe 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. |
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Table of contentsView table of contents >> Figures
Preface
Introduction: Getting Started
About Companion Datasets
Chapter 1: 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 Chapter 2: 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 Chapter 3: 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 Chapter 4: 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 Chapter 5: 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
Visualized Controlled Mean Comparisons An Example of an Additive Relationship Exercises Chapter 6: 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 Chapter 7: Chi-square and Measures 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 Chapter 8: 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 Chapter 9: 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 Chapter 10: 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
Exercises The margins Command with the over Option Combining atmeans and over Options Chapter 11: Doing Your Own Political Analysis
Seven Doable Ideas
Political Knowledge and Interest
Importing Data into Stata 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
Stata Formatted Datasets
Writing It Up Microsoft Excel Datasets HTML Table Data
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 |
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