An R Companion to Political Analysis 3rd Edition by Philip Pollock, Barry Edwards 1071862413 9781071862414

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ISBN 10: 1071862413

ISBN 13: 9781071862414

Author: Philip H Pollock, Barry C Edwards

The Third Edition of An R Companion to Political Analysis by Philip H. Pollock III and Barry C. Edwards teaches your students to conduct political research with R, the open-source programming language and software environment for statistical computing and graphics. This workbook offers the same easy-to-use and effective style as the other software companions to the Essentials of Political Analysis, tailored for R. With this comprehensive workbook, students analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (such as cross-tabulations and mean comparisons), controlled comparisons, correlation and bivariate regression, interaction effects, and logistic regression. The clear explanations and instructions are aided by the use of many annotated and labeled screen shots, as well as QR codes linking to demonstration videos. The many end-of-chapter exercises allow students to apply their new skills. The Third Edition includes new and revised exercises, along with new and updated datasets from the 2020 American National Election Study, an experiment dataset, and two aggregate datasets, one on 50 U.S. states and one based on countries of the world. A new structure helps break up individual elements of political analysis for deeper explanation while an updated suite of R functions makes using R even easier. Students will gain valuable skills learning to analyze political data in R.

An R Companion to Political Analysis 3rd Table of contents:

I.1 About R

I.2 Installing R

I.3 A Quick Tour of the R Environment

I.4 Installing the “RCPA3” Package

• The Companion Datasets

I.5 Troubleshooting Installation Problems

I.6 Installing R Studio

I.7 Instant Access to Tutorials and Resources

Chapter 1 • Using R for Data Analysis

1.1 Interacting with R

1.2 Objects

1.2.1 Examples of Objects

1.2.2 Creating Objects

• A Closer Look: R Environments

1.2.3 Accessing Some of an Object’s Values

1.3 Functions

1.3.1 Using a Function’s Arguments

1.3.2 The Widget Factory Needs Your Help!

1.4 Writing and Running Scripts

1.5 Managing R Output: Graphics and Text

1.6 Getting Help

Chapter 1 Exercises

Chapter 2 • Descriptive Statistics

2.1 Identifying Levels of Measurement

2.2 Describing Nominal Variables

2.2.1 Generating Tables of Descriptive Statistics

2.2.2 Using Tables and Figures to Describe Distributions of Values

2.3 Describing Ordinal Variables

2.3.1 Describing Two Variables in One Table

2.3.2 Assessing the Dispersion of Ordinal Variables

• A Closer Look: Weighted and Unweighted Analysis: What’s the Difference?

2.4 Describing Interval-Level Variables

2.4.1 Using Statistics to Describe Interval Variables

• A Closer Look: Additional Math and Statistics Functions

2.4.2 Visualizing Dispersion with Histograms

• A Closer Look: Editing Plots with Purpose

2.5 Obtaining Case-Level Information

Chapter 2 Exercises

Chapter 3 • Creating and Transforming Variables

3.1 Applying Mathematical and Logical Operators to Variables

3.1.1 Mathematical Operators

3.1.2 Logical Operators

3.2 Creating Dummy Variables

3.3 Adding or Modifying Variable Attributes

3.3.1 Labels

3.3.2 Class Attributes

3.3.3 Levels of a Variable’s Values

3.4 Collapsing Variables into Simplified Categories

3.5 Centering, Standardizing, and Ranking Numeric Variables

3.6 Creating an Additive Index

Chapter 3 Exercises

Chapter 4 • Making Comparisons

• Analysis Guide

4.1 Creating Cross-Tabulations to Make Comparisons

• A Closer Look: Debugging R Code

4.2 Mean Comparison Analysis

4.3 Making Comparisons with Interval-Level Independent Variables

• A Closer Look: Creating Plots from Summary Data

Chapter 4 Exercises

Chapter 5 • Graphing Relationships and Describing Patterns

5.1 Graphs for Binary Dependent Variables

5.1.1 Simple Bar Charts with Nominal-Level Independent Variables

5.1.2 Simple Line Plots with Ordinal-Level Independent Variables

5.1.3 Graphs with Interval-Level Independent Variables

5.2 Graphs for Nominal Dependent Variables

5.2.1 Clustered Bar Charts with Nominal Independent Variables

5.2.2 Multiple Line Plots with Ordinal Independent Variables

5.2.3 Graphs with Interval-Level Independent Variables

5.3 Graphs for Ordinal-Level Dependent Variables

5.3.1 Using Bars or Lines to Represent Select Values

5.3.2 Mosaic Plots for an Ordinal–Ordinal Relationship

5.3.3 Graphs with Interval-Level Independent Variables

5.4 Graphs for Interval-Level Dependent Variables

5.4.1 Plotting Means with Bars or Lines

5.4.2 Box Plots

5.4.3 Scatterplots

• A Closer Look: Creating Custom Graphs with R

Chapter 5 Exercises

Chapter 6 • Random Assignment and Sampling

6.1 Random Assignment

6.1.1 Two Groups with Equal Probability

6.1.2 Multiple Groups with Varying Probabilities

6.2 Analyzing the Results of an Experiment

6.2.1 Assessing Random Assignment

6.2.2 Evaluating the Effect of Treatment

6.3 Random Sampling

6.3.1 Simple Random Samples with Replacement

6.3.2 Simple Random Samples without Replacement

6.3.3 Systematic Random Samples

6.3.4 Clustered and Stratified Random Samples

6.4 Selecting Cases for Qualitative Analysis

6.4.1 Most Similar Systems

6.4.2 Most Different Systems

6.5 Analyzing Data Ethically: Clarity, Replication, and Transparency

6.5.1 Ethical Issues in Data Analysis

6.5.2 Ten Tips for Writing R Scripts

Chapter 6 Exercises

Chapter 7 • Making Controlled Comparisons

7.1 Cross-Tabulation Analysis with Control Variables

7.1.1 Start with a Basic Cross-Tabulation

7.1.2 Adding Control Variables

7.1.3 Interpreting Controlled Cross-Tabulations

7.1.4 Options for Graphing Controlled Cross-Tabulations

• A Closer Look: The Legend Function

7.2 Mean Comparison Analysis with Control Variables

7.2.1 Start with a Basic Mean Comparison Table

7.2.2 Adding Control Variables

7.2.3 Interpreting a Controlled Mean Comparison

7.2.4 Options for Graphing Controlled Mean Comparisons

7.3 Controlled Comparisons with Interval-Level Control Variables

Chapter 7 Exercises

Chapter 8 • Foundations of Statistical Inference

8.1 Estimating a Population Proportion with R Simulation

8.2 Estimating a Population Mean with R Simulation

• A Closer Look: Using Probability Distributions to Simulate Raw Data

8.3 Expected Shape of Sampling Distributions

8.3.1 Central Limit Theorem and the Normal Distribution

8.3.2 Normal Distribution of Sample Proportions

8.3.3 Normal Distribution of Sample Means

8.3.4 The Standard Normal Distribution

8.3.5 The Empirical Rule (68-95-99 Rule)

8.4 Confidence Intervals and Margins of Error

8.4.1 Critical Values for Confidence Intervals

8.4.2 Reporting the Confidence Interval for a Sample Proportion

8.4.3 Reporting the Confidence Interval for a Sample Mean

8.5 Student’s t-Distribution: When You’re Not Completely Normal

8.5.1 The t-Distribution’s Role in Inferential Statistics

8.5.2 Critical Values of t-Distributions

Chapter 8 Exercises

Chapter 9 • Hypothesis Tests with One or Two Samples

9.1 Role of the Null Hypothesis

9.2 Testing a Hypothesis about One Sample Proportion

9.3 Testing the Difference between Two Sample Proportions

9.3.1 Testing the Difference of Proportions with z Scores and P-Values

9.3.2 Confidence Interval for the Difference of Proportions

• A Closer Look: Changing the Order of Groups 1 and 2

9.3.3 Comparing Two Similarly Coded Variables (x1 vs. x2)

9.4 Testing a Hypothesis about One Sample Mean

• A Closer Look: Treating Census as a Sample

9.5 Testing the Difference between Two Sample Means

9.5.1 Variants of the Difference of Means Test

9.5.2 Testing the Difference of Means with the testmeansC Function

9.5.3 Confidence Intervals for the Difference of Means

9.5.4 Testing the Difference of Means Assuming Equal Variances

9.5.5 Testing the Difference of Means with Two Similarly Coded Variables

9.5.6 Paired Difference of Means Test

Chapter 9 Exercises

Chapter 10 • Chi-Square Test and Analysis of Variance

10.1 The Chi-Square Test of Independence

10.1.1 How the Chi-Square Test Works

10.1.2 Conducting a Chi-Square Test

10.1.3 Example with a Nominal Independent Variable

10.1.4 R’s Chi-Square Distribution Functions

• A Closer Look: Other Applications of Chi-Square Tests

10.2 Measuring the Strength of Association between Categorical Variables

10.2.1 Lambda

10.2.2 Somers’ d

10.2.3 Cramer’s V

10.3 Chi-Square Test and Measures of Association in Controlled Comparisons

10.3.1 Analyzing an Ordinal-Level Relationship with a Control Variable

10.3.2 Analyzing a Nominal-Level Relationship with a Control Variable

10.4 Analysis of Variance

10.4.1 How ANOVA Works

10.4.2 Single-Factor ANOVA

10.4.3 Two-Factor ANOVA

10.4.4 R’s F-Distribution Functions

Chapter 10 Exercises

Chapter 11 • Correlation and Bivariate Regression

11.1 Correlation Analysis

11.1.1 Correlation between Two Variables

11.1.2 Correlation among More Than Two Variables

• A Closer Look: Other Types of Applications of Correlation Analysis

11.2 Bivariate Regression Analysis

11.2.1 Are Democratic Countries More Peaceful?

• A Closer Look: R-Squared and Adjusted R-Squared: What’s the Difference?

11.2.2 Turnout in Battleground States Example

11.2.3 Using Regression Results to Make Informed Predictions

11.3 Visualizing Bivariate Regression Analysis

11.3.1 Scatterplots with Regression Lines

11.3.2 Visualizing Regression Analysis with Categorical Independent Variables

11.3.3 Alternatives to the Plot Function

• A Closer Look: What If a Scatterplot Doesn’t Show a Linear Relationship?

11.4 Regression Analysis with Weighted Observations

• A Closer Look: Creating Tables of Regression Results

Chapter 11 Exercises

Chapter 12 • Multiple Regression

12.1 Estimating a Multiple Regression Model

12.1.1 Voter Turnout Revisited

12.1.2 Another Look at Democratic Peace

12.2 Regression with Multiple Dummy Variables

12.2.1 Estimating a Regression Model with Multiple Dummy Variables

12.2.2 Interpreting Regression Results with Multiple Dummy Variables

• A Closer Look: Changing the Reference Category

12.2.3 Visualizing Regression Analysis with Multiple Dummy Variables

12.3 Interaction Effects in Multiple Regression

12.3.1 Regression Equation with an Interaction Term

12.3.2 Estimating and Interpreting a Linear Model with an Interaction Term

12.3.3 Visualizing Interactive Relationships

12.4 Visualizing Multiple Regression Analysis

12.4.1 One Categorical and One Interval Independent Variable

12.4.2 Visualizing Regression with Two Interval Independent Variables

12.4.3 Visualizing Regression with Two Categorical Independent Variables

• A Closer Look: Visualizing Multiple Regression with Many Independent Variables

Chapter 12 Exercises

Chapter 13 • Analyzing Regression Residuals

13.1 Expected Values, Observed Values, and Regression Residuals

13.1.1 Example from Bivariate Regression Analysis

13.1.2 Residuals from Multiple Regression Analysis

13.2 Squared and Standardized Residuals

13.2.1 Squared Residuals

13.2.2 Standardized Residuals

13.3 Assumptions about Regression Residuals

13.4 Analyzing Graphs of Regression Residuals

13.4.1 Histogram of Regression Residuals

13.4.2 Residual Diagnostic Plots

13.5 Testing Regression Assumptions with Residuals

13.5.1 Testing the Assumption That Residuals Are Normally Distributed

13.5.2 Testing the Constant Variance Assumption

13.5.3 Regression Diagnostics for Multiple Regression Analysis

• A Closer Look: Other Regression Diagnostic Tests

13.6 What If You Diagnose Problems with Residuals?

Chapter 13 Exercises

Chapter 14 • Logistic Regression

14.1 Odds, Logged Odds, and Probabilities

14.2 Estimating Logistic Regression Models

14.2.1 Logistic Regression with One Independent Variable

14.2.2 Reporting and Interpreting Odds Ratios

14.2.3 Evaluating Model Fit

14.3 Logistic Regression with Multiple Independent Variables

14.3.1 Estimating Model with Multiple Independent Variables

14.3.2 Odds Ratios and Model Fit Statistics

14.3.3 Logistic Regression Analysis with Weighted Observations

14.4 Plotting Predicted Probabilities with One Independent Variable

14.4.1 Interval-Level Independent Variables

14.4.2 Categorical Independent Variables

• A Closer Look: Marginal Effects and Expected Changes in Probability

14.5 Plotting Predicted Probabilities with Multiple Independent Variables

14.5.1 One Categorical and One Interval-Level Independent Variable

14.5.2 Multiple Categorical Independent Variables

14.5.3 Two Interval-Level Independent Variables

14.5.4 Plotting Predicted Probabilities with Many Independent Variables

Chapter 14 Exercises

Chapter 15 • Doing Your Own Political Analysis

15.1 Doable Research Ideas

15.1.1 Political Knowledge and Interest

15.1.2 Self-Interest and Policy Preferences

15.1.3 Economic Performance and Electronic Outcomes

15.1.4 Electoral Turnout in Comparative Perspective

15.1.5 Correlates of State Policies

15.1.6 Religion and Politics

15.1.7 Race and Politics

15.2 Reading R-Format Datasets

15.3 Importing Data into R

15.3.1 SPSS- and Stata-Formatted Datasets

15.3.2 Microsoft Excel Datasets

15.3.3 Data from HTML Tables

15.4 Writing It Up

15.4.1 The Research Question

15.4.2 Previous Research

15.4.3 Data, Hypotheses, and Analysis

15.4.4 Conclusions and Implications

Chapter 15 Exercises

Appendix, Table A-1: Variables in the Debate Experiment Dataset

Appendix, Table A-2: Variables in the NES Dataset

Appendix, Table A-3: Variables in the States Dataset by Topic

Appendix, Table A-4: Variables in the World Dataset by Topic

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