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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)



September 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
 
Index

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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

Thomas M. Holbrook

Thomas M. Holbrook is Emeritus Professor at the University of Wisconsin-Milwaukee, where he was a Distinguished Professor and the Wilder Crane Professor of Government in the political science department. He is a former editor of American Politics Research and the author of Do Campaigns Matter (Sage, 1996), Altered States (Oxford, 2016), and dozens of articles on various aspects of voting behavior and elections in the United States, most recently focusing on local politics. Professor Holbrook has taught undergraduate courses on data analysis and survey research for the past three decades and has integrated R into his data analysis courses... More About Author

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ISBN: 9781071929421
$142.00