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An R Companion to Political Analysis
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An R Companion to Political Analysis

Second Edition


© 2018 | 248 pages | CQ Press
Teach your students to conduct political research using R, the open source programming language and software environment for statistical computing and graphics. An R Companion to Political Analysis offers the same easy-to-use and effective style as the best-selling SPSS and Stata Companions. The all-new Second Edition includes new and revised exercises and datasets showing students how to analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (cross-tabulations and mean comparisons), controlled comparisons, statistical inference, linear correlation and regression, dummy variables and interaction effects, and logistic regression. The clear explanation and instruction is accompanied by annotated and labeled screen shots and end-of-chapter exercises to help students apply what they have learned.

“Students will love this book, as will their teachers.”
 – Courtney Brown, Emory University
 
List of Boxes and Figures
 
Preface
 
A Quick Reference Guide to R Companion Functions
 
Introduction: Getting Acquainted with R
About R  
Installing R  
A Quick Tour of the R Environment  
Objects  
Functions  
Getting Help  
Exercises  
 
Chapter 1: The R Companion Package
Running Scripts  
Ten Tips for Writing Good R Scripts  
Managing R Output: Graphics and Text  
Additional Software for Working with R  
Debugging R Code  
Exercises  
 
Chapter 2: Descriptive Statistics
Interpreting Measures of Central Tendency and Variation  
Describing Nominal Variables  
Describing Ordinal Variables  
Describing the Central Tendency of Interval Variables  
Describing the Dispersion of Interval Variables  
Obtaining Case-Level Information  
Exercises  
 
Chapter 3: Transforming Variables
Applying Mathematical and Logical Operators to Variables  
Creating Indicator Variables  
Changing Variable Classes  
Adding or Modifying Variable Labels  
Collapsing Variables into Simplified Categories  
Centering or Standardizing a Numeric Variable  
Creating an Additive Index  
Exercises  
 
Chapter 4: Making Comparisons
Cross-Tabulations and Mosaic Plots  
Line Charts  
Mean Comparison Analysis  
Box Plots  
Strip Charts  
Exercises  
 
Chapter 5: Making Controlled Comparisons
Cross-Tabulation Analysis with a Control Variable  
Multiple Line Charts  
The legend Function  
Mean Comparison Analysis with a Control Variable  
Exercises  
 
Chapter 6: Making Inferences about Sample Means
Finding the 95 Percent Confidence Interval of the Population Mean  
Testing Hypothetical Claims about the Population Mean  
Making Inferences about Two Sample Means  
Making Inferences about Two Sample Proportions  
Exercises  
 
Chapter 7: Chi-Square and Measures of Association
Analyzing an Ordinal-Level Relationship  
Analyzing an Ordinal-Level Relationship with a Control Variable  
Analyzing a Nominal-Level Relationship with a Control Variable  
Exercises  
 
Chapter 8: Correlation and Linear Regression
Correlation Analysis  
Bivariate Regression with a Dummy Variable  
Bivariate Regression with an Interval-Level Independent Variable  
Multiple Regression Analysis  
Multiple Regression with Ordinal or Categorical Variables  
Weighted Regression with a Dummy Variable  
Multiple Regression Analysis with Weighted Data  
Weighted Regression with Ordinal or Categorical Independent Variables  
Creating Tables of Regression Results  
Exercises  
 
Chapter 9: Visualizing Correlation and Regression Analysis
Visualizing Correlation  
General Comments about Visualizing Regression Results  
Plotting Multiple Regression Results  
Interaction Effects in Multiple Regression  
Visualizing Regression Results with Weighted Data  
Special Issues When Plotting Observations with Limited Unique Values  
Exercises  
 
Chapter 10: Logistic Regression
Thinking about Odds, Logged Odds, and Probabilities  
Estimating Logistic Regression Models  
Interpreting Logistic Regression Results with Odds Ratios  
Visualizing Results with Predicted Probabilities Curves  
Probability Profiles for Discrete Cases  
Model Fit Statistics for Logistic Regressions  
An Additional Example of Multivariable Logistic Regression  
Exercises  
 
Chapter 11: Doing Your Own Political Analysis
Seven Doable Ideas  
Importing Data  
Writing It Up  
 
Appendix
Table A.1 Alphabetical List of Variables in the GSS Dataset  
Table A.2 Alphabetical List of Variables in the NES Dataset  
Table A.3 Alphabetical List of Variables in the States Dataset  
Table A.4 Alphabetical List of Variables in the World Dataset  
 
About the Authors

Supplements

Instructor Resources
  • Downloadable R datasets used in the text. 
  • A set of all the graphics from the text, including all of the maps, tables, and figures, in PowerPoint, .pdf, and .jpg formats for class presentations.

"R and its application continues to expand worldwide, replacing both its less flexible and less available alternatives and offering new opportunities. R Companion helps quickly climb the frequently steep learning curve of the 'program library of program libraries'. The book has a deserved good record as a path-breaker in teaching R with concerns towards political analysis. Highly recommended."

Pertti Ahonen
University of Helsinki

“Phillip H. Pollock has written a timely, useful, and well-written book to accompany his popular text The Essentials of Political Analysis. The use of R in the classroom is increasing each year, and the need for user-friendly books to help integrate methodological training with this powerful statistical language has reached a critical stage. Professor Pollock’s book fills this gap superbly. It takes the student from the elements of installing R on their own computer or laptop through the use of R to solve both simple and complex problems in social and political analysis. Students will love this book, as will their teachers.”

Courtney Brown
Emory University
Key features
NEW TO THIS EDITION:
  • Featured datasets and functions are bundled into the R package for quick and easy set-up.
  • Updated examples reflect new data and contemporary political issues.
  • Greater emphasis is placed on data visualization throughout.
  • A new Introduction to R section emphasizes good coding practices and troubleshooting.
  • A new chapter on visualizing correlations and regression results is included.
  • Updates have been made to end of chapter exercises.
KEY FEATURES:
  • Step-by-step instructions and labeled screen shots offer students clear guidance and visual explanations.
  • Students can quickly and easily install the R package that bundles all the featured datasets and functions.
  • Engaging exercises and research-quality datasets provide students with hands-on practice and skill application.
  • Concepts are reinforced by requiring students to write and run R scripts from the script window.
  • A Closer Look boxes teach students how to write R commands and are integrated throughout the workbook for easy reference.
  • Students are shown how to create stunning data visualizations.
  • Students are taught how to troubleshoot errors and use functions, not just copy R codes.

Sample Materials & Chapters

Chapter 1

Chapter 2


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