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An Introduction to Political and Social Data Analysis (With R)
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An Introduction to Political and Social Data Analysis (With R)



October 2024 | 480 pages | SAGE Publications, Inc
An Introduction to Political and Social Data Analysis (With R) provides students with an accessible overview of practical data analysis while also providing a gentle introduction to R. By starting with statistics first and using just enough R code to generate results, this text helps students focus on learning how to do data analysis while slowly gaining confidence in using R as they progress through the material. This book is structured around learning by doing. Students can follow along in each chapter by reading about statistics and their applications in R, and then running the R code on their own as they work through contemporary political science and social science examples. Author Thomas M. Holbrook patiently explains each step in in the process, avoiding overly complicated jargon and commands. Exercises at the end of chapters feature both conceptual and calculation-based questions so students can check their understanding of data analysis and practice using R. At the end of the semester, students can confidently add skills in data analysis with R to their resumes.
 
Chapter 1: Introduction to Research and Data
Political and Social Data Analysis

 
Data Analysis or Statistics?

 
Uses of Data Analysis

 
The Research Process

 
Other Data-Related Issues

 
Causal Language

 
Next Steps

 
Exercises

 
 
Chapter 2: Using R to Do Data Analysis
Accessing R

 
Opening RStudio

 
Understanding Where R (or Any Program) Fits In

 
Time to Use R

 
Some R Terminology

 
Managing Files and Output

 
Next Steps

 
Exercises

 
 
Chapter 3: Frequencies and Basic Graphs
Get Ready

 
Introduction

 
Frequencies

 
Graphing Outcomes

 
Next Steps

 
Exercises

 
 
Chapter 4: Data Preparation
Get Ready

 
Introduction

 
Data Transformations

 
Collapsing and Reordering Categories

 
Combining Variables

 
Save Your Changes

 
Next Steps

 
Exercises

 
 
Chapter 5: Measures of Central Tendency
Get Ready

 
Central Tendency

 
Mode

 
Median

 
The Mean

 
Mean, Median, and the Distribution of Variables

 
Skewness Statistic

 
Adding Legends to Graphs

 
Next Steps

 
Exercises

 
 
Chapter 6: Measures of Dispersion
Get Ready

 
Introduction

 
Measures of Spread

 
Dispersion Around the Mean

 
Dichotomous Variables

 
Dispersion in Categorical Variables?

 
The Standard Deviation and the Normal Curve

 
Calculating Area Under a Normal Curve

 
One Last Thing

 
Next Steps

 
Exercises

 
 
Chapter 7: Probability
Get Ready

 
Probability

 
Theoretical Probabilities

 
Empirical Probabilities

 
The Normal Curve and Probability

 
Next Steps

 
Exercises

 
 
Chapter 8: Sampling and Inference
Get Ready

 
Statistics and Parameters

 
Sampling Error

 
Sampling Distributions

 
Proportions

 
Confidence Intervals

 
Next Steps

 
Exercises

 
 
Chapter 9: Hypothesis Testing
Get Ready

 
The Logic of Hypothesis Testing

 
Direct Hypothesis Tests

 
Proportions

 
T-Distribution

 
Types of Error

 
t-test in R

 
Next Steps

 
Exercises

 
 
Chapter 10: Hypothesis Testing with Two Groups
Get Ready

 
Testing Hypotheses About Two Means

 
Hypothesis Testing With Two Means

 
Difference in Proportions

 
Plotting Mean Differences

 
What’s Next?

 
Exercises

 
 
Chapter 11: Hypothesis Testing With Multiple Groups (ANOVA)
Get Ready

 
Internet Access as an Indicator of Development

 
The Relationship Between Wealth and Internet Access

 
Analysis of Variance

 
Anova in R

 
Effect Size

 
Connecting the t-score and F-ratio

 
Next Steps

 
Exercises

 
 
Chapter 12: Hypothesis Testing with Non-Numeric Variables (Crosstabs)
Get Ready

 
Crosstabs

 
Sampling Error

 
Hypothesis Testing With Crosstabs (Chi-square)

 
Get Ready

 
Directional Patterns in Crosstabs

 
Limitations of Chi-square

 
Next Steps

 
Exercises

 
 
Chapter 13: Measures of Association
Get Ready

 
Going Beyond Chi-squared

 
Measures of Association for Crosstabs

 
Ordinal Measures of Association

 
Revisiting the Gender Gap in Abortion Attitudes

 
Next Steps

 
Exercises

 
 
Chapter 14: Correlation and Scatterplots
Get Ready

 
Relationships Between Numeric Variables

 
Scatterplots

 
Pearson’s r

 
Variation in Strength of Relationships

 
Proportional Reduction in Error

 
Correlation and Scatterplot Matrices

 
Overlapping Explanations

 
Next Steps

 
Exercises

 
 
Chapter 15: Simple Regression
Get Ready

 
Linear Relationships

 
Ordinary Least Squares Regression

 
How Well Does the Model Fit the Data?

 
Proportional Reduction in Error

 
Getting Regression Results in R

 
Understanding the Constant

 
Organizing the Regression Output

 
Revisiting Life Expectancy

 
Important Caveat

 
Adding Regression Information to Scatterplots

 
Next Steps

 
Exercises

 
 
Chapter 16: Multiple Regression
Get Ready

 
Multiple Regression

 
Model Accuracy

 
Predicted Outcomes

 
Revisiting Presidential Votes in the States

 
Next Steps

 
Exercises

 
 
Chapter 17: Advanced Regression Topics
Get Ready

 
Incorporating Access to Health Care

 
Multicollinearity

 
Checking on Linearity

 
Which Variables Have the Greatest Impact?

 
Statistics Versus Substance

 
Next Steps

 
Exercises

 
 
Chapter 18: Regression Assumptions
Get Ready

 
Regression Assumptions

 
Next Steps

 
Exercises

 
 
Appendix A: Codebooks
 
Appendix B: Quarto Tutorial
 
Appendix C: Hidden R Code
 
Endnotes
 
Index

Supplements

Instructor Resources
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Clarity in communication is absolutely essentialin introductory methodology and data science courses. Holbrook's way with words makes complicated statistical and computational language easy to understand and instills confidence in students.

Jeffrey M. Glas
University of Georgia

The book introduces many useful concepts without getting too bogged down in any individual concept. Students get a huge, wide exposure to content. This will help facilitate class conversations on day one, which is a real leverage.

Soren Jordan
Auburn University

Holbrook introduces complex and technically challenging concepts in a way that, for those new to the world of R, is approachable and easy to understand. Fantastic introductory text for undergraduate study.

Daniel Ashwood
Miami University

Chapter 1 provides a strong overview of the research process. While it talks a lot about data, it does so in a non-technical way that I think most undergraduates would be able to get through reasonably well. The examples provided are relevant and broadly interesting.

Aaron Sparks
Elon University

This text is structured well for taking students through Data Analysis for political science. Students are walked through both the meaning of the statistics examined and how the computer can be made to generate them. Each section builds on the preceding sections in a clear manner. The only problem with this book is that I wish I had written it. I can't wait to use it.

Brad Lockerbie
East Carolina University

Professor Holbrook's book provides an accessible entry-point for students of all levels to use data for political and social research, while offering clear and easy-to-understand guidance to the use of the R software.

Renato Corbetta
University of Alabama at Birmingham

This is a highly engaging and practical introduction to social research using R. All the essentials are here with little to no distracting material that might confuse already anxious students. I highly recommend this text.

Scott Liebertz
University of South Alabama

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