Pub. Date:
SAGE Publications
R Companion to Political Analysis / Edition 1

R Companion to Political Analysis / Edition 1

by Philip H. Pollock


View All Available Formats & Editions
Current price is , Original price is $64.0. You
Select a Purchase Option
  • purchase options
    $57.60 $64.00 Save 10% Current price is $57.6, Original price is $64. You Save 10%.
  • purchase options
    $31.68 $64.00 Save 50% Current price is $31.68, Original price is $64. You Save 50%.
    Note: Access code and/or supplemental material are not guaranteed to be included with textbook rental or used textbook.

Product Details

ISBN-13: 2901452287316
Publisher: SAGE Publications
Publication date: 11/04/2013
Pages: 224
Product dimensions: 10.90(w) x 14.00(h) x 0.80(d)

About the Author

Philip H. Pollock III is professor of political science at the University of Central Florida. He has taught courses in research methods at the undergraduate and graduate levels for nearly 30 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 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.

Table of Contents

List of Boxes and Figures
A Quick Reference Guide to R Companion Functions
Introduction: Getting Acquainted with R
About R
Installing R
A Quick Tour of the R Environment
Getting Help
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
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
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
Chapter 4: Making Comparisons
Cross-Tabulations and Mosaic Plots
Line Charts
Mean Comparison Analysis
Box Plots
Strip Charts
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
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
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
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
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
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
Chapter 11: Doing Your Own Political Analysis
Seven Doable Ideas
Importing Data
Writing It Up
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

Customer Reviews

Most Helpful Customer Reviews

See All Customer Reviews