Data Analysis for Social Science: A Friendly and Practical Introduction by Elena Llaudet, ISBN-13: 978-0691199429
[PDF eBook eTextbook]
- Publisher: Princeton University Press (November 29, 2022)
- Language: English
- 256 pages
- ISBN-10: 0691199426
- ISBN-13: 978-0691199429
An ideal textbook for complete beginners—teaches from scratch R, statistics, and the fundamentals of quantitative social science.
Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses’ strengths and limitations.
- Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.
- Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.
- Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.
- Assumes no prior knowledge of statistics or coding.
- Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.
- Provides cheatsheets of statistical concepts and R code.
- Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed.
Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Table of Contents:
Contents
Preface
1. Introduction
1.1 Book Overview
1.2 Chapter Summaries
1.3 How to Use This Book
1.4 Why Learn to Analyze Data?
1.4.1 Learning to Code
1.5 Getting Ready
1.6 Introduction to R
1.6.1 Doing Calculations in R
1.6.2 Creating Objects in R
1.6.3 Using Functions in R
1.7 Loading and Making Sense of Data
1.7.1 Setting the Working Directory
1.7.2 Loading the Dataset
1.7.3 Understanding the Data
1.7.4 Identifying the Types of Variables Included
1.7.5 Identifying the Number of Observations
1.8 Computing and Interpreting Means
1.8.1 Accessing Variables inside Dataframes
1.8.2 Means
1.9 Summary
1.10 Cheatsheets
1.10.1 Concepts and Notation
1.10.2 R Symbols and Operators
1.10.3 R Functions
2. Estimating Causal Effects with Randomized Experiments
2.1 Project STAR
2.2 Treatment and Outcome Variables
2.2.1 Treatment Variables
2.2.2 Outcome Variables
2.3 Individual Causal Effects
2.4 Average Causal Effects
2.4.1 Randomized Experiments and the Difference-in-Means Estimator
2.5 Do Small Classes Improve Student Performance?
2.5.1 Relational Operators in R
2.5.2 Creating New Variables
2.5.3 Subsetting Variables
2.6 Summary
2.7 Cheatsheets
2.7.1 Concepts and Notation
2.7.2 R Symbols and Operators
2.7.3 R Functions
3. Inferring Population Characteristics via Survey Research
3.1 The EU Referendum in the UK
3.2 Survey Research
3.2.1 Random Sampling
3.2.2 Potential Challenges
3.3 Measuring Support for Brexit
3.3.1 Predicting the Referendum Outcome
3.3.2 Frequency Tables
3.3.3 Tables of Proportions
3.4 Who Supported Brexit?
3.4.1 Handling Missing Data
3.4.2 Two-Way Frequency Tables
3.4.3 Two-Way Tables of Proportions
3.4.4 Histograms
3.4.5 Density Histograms
3.4.6 Descriptive Statistics
3.5 Relationship between Education and the Leave Vote in the Entire UK
3.5.1 Scatter Plots
3.5.2 Correlation
3.6 Summary
3.7 Cheatsheets
3.7.1 Concepts and Notation
3.7.2 R Symbols and Operators
3.7.3 R Functions
4. Predicting Outcomes Using Linear Regression
4.1 GDP and Night-Time Light Emissions
4.2 Predictors, Observed vs. Predicted Outcomes, and Prediction Errors
4.3 Summarizing the Relationship between Two Variables with a Line
4.3.1 The Linear Regression Model
4.3.2 The Intercept Coefficient
4.3.3 The Slope Coefficient
4.3.4 The Least Squares Method
4.4 Predicting GDP Using Prior GDP
4.4.1 Relationship between GDP and Prior GDP
4.4.2 With Natural Logarithm Transformations
4.5 Predicting GDP Growth Using Night-Time Light Emissions
4.6 Measuring How Well the Model Fits the Data with the Coefficient of Determination, R2
4.6.1 How Well Do the Three Predictive Models in This Chapter Fit the Data?
4.7 Summary
4.8 Appendix: Interpretation of the Slope in the Log-Log Linear Model
4.9 Cheatsheets
4.9.1 Concepts and Notation
4.9.2 R Functions
5. Estimating Causal Effects with Observational Data
5.1 Russian State-Controlled TV Coverage of 2014 Ukrainian Affairs
5.2 Challenges of Estimating Causal Effects with Observational Data
5.2.1 Confounding Variables
5.2.2 Why Are Confounders a Problem?
5.2.3 Confounders in Randomized Experiments
5.3 The Effect of Russian TV on Ukrainians’ Voting Behavior
5.3.1 Using the Simple Linear Model to Compute the Difference-in-Means Estimator
5.3.2 Controlling for Confounders Using a Multiple Linear Regression Model
5.4 The Effect of Russian TV on Ukrainian Electoral Outcomes
5.4.1 Using the Simple Linear Model to Compute the Difference-in-Means Estimator
5.4.2 Controlling for Confounders Using a Multiple Linear Regression Model
5.5 Internal and External Validity
5.5.1 Randomized Experiments vs. Observational Studies
5.5.2 The Role of Randomization
5.5.3 How Good Are the Two Causal Analyses in This Chapter?
5.5.4 How Good Was the Causal Analysis in Chapter 2?
5.5.5 The Coefficient of Determination, R2
5.6 Summary
5.7 Cheatsheets
5.7.1 Concepts and Notation
5.7.2 R Functions
6. Probability
6.1 What Is Probability?
6.2 Axioms of Probability
6.3 Events, Random Variables, and Probability Distributions
6.4 Probability Distributions
6.4.1 The Bernoulli Distribution
6.4.2 The Normal Distribution
6.4.3 The Standard Normal Distribution
6.4.4 Recap
6.5 Population Parameters vs. Sample Statistics
6.5.1 The Law of Large Numbers
6.5.2 The Central Limit Theorem
6.5.3 Sampling Distribution of the Sample Mean
6.6 Summary
6.7 Appendix: For Loops
6.8 Cheatsheets
6.8.1 Concepts and Notation
6.8.2 R Symbols and Operators
6.8.3 R Functions
7. Quantifying Uncertainty
7.1 Estimators and Their Sampling Distributions
7.2 Confidence Intervals
7.2.1 For the Sample Mean
7.2.2 For the Difference-in-Means Estimator
7.2.3 For Predicted Outcomes
7.3 Hypothesis Testing
7.3.1 With the Difference-in-Means Estimator
7.3.2 With Estimated Regression Coefficients
7.4 Statistical vs. Scientific Significance
7.5 Summary
7.6 Cheatsheets
7.6.1 Concepts and Notation
7.6.2 R Symbols and Operators
7.6.3 R Functions
Index of Concepts
Index of Mathematical Notation
Index of R and RStudio
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