An Introduction to Political and Social Data Analysis (With R)
1st Edition
Published on 7. November 2024
Book
Paperback/Softback
480 pages
978-1-0719-2942-1 (ISBN)
Description
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 the R programming environment. Author Thomas M. Holbrook patiently explains each step in statistical analysis with R, avoiding complicated tools or packages.
Reviews / Votes
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 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 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 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 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 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 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 LiebertzMore details
Edition
1. Auflage
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 232 mm
Width: 187 mm
Thickness: 30 mm
Weight
955 gr
ISBN-13
978-1-0719-2942-1 (9781071929421)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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 for the past several years.
Content
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
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