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 LeaveVote 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, andPrediction 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 LightEmissions
        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 Modelsin 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 2014Ukrainian Aﬀairs
        5.2 Challenges of Estimating Causal Eﬀects withObservational 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’ VotingBehavior
        5.3.1 Using the Simple Linear Model to Computethe Diﬀerence-in-Means Estimator
        5.3.2 Controlling for Confounders Using aMultiple Linear Regression Model
        5.4 The Eﬀect of Russian TV on Ukrainian ElectoralOutcomes
        5.4.1 Using the Simple Linear Model to Computethe Diﬀerence-in-Means Estimator
        5.4.2 Controlling for Confounders Using aMultiple 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 Analysesin This Chapter?
        5.5.4 How Good Was the Causal Analysis inChapter 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 ProbabilityDistributions
        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

Data Analysis for Social Science

A Friendly and Practical Introduction