R Companion to Political Analysis / Edition 1 available in Paperback
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 by Philip H. Pollock III and Barry C. Edwards 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.
|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 FiguresPrefaceA Quick Reference Guide to R Companion FunctionsIntroduction: Getting Acquainted with R About R Installing R A Quick Tour of the R Environment Objects Functions Getting Help ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 4: Making Comparisons Cross-Tabulations and Mosaic Plots Line Charts Mean Comparison Analysis Box Plots Strip Charts ExercisesChapter 5: Making Controlled Comparisons Cross-Tabulation Analysis with a Control Variable Multiple Line Charts The legend Function Mean Comparison Analysis with a Control Variable ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 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 ExercisesChapter 11: Doing Your Own Political Analysis Seven Doable Ideas Importing Data Writing It UpAppendix 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 DatasetAbout the Authors