DATA ANALYSIS FOR SOCIAL SCIENCE

CHAPTER 1: INTRODUCTION

In chapter 1, we start from the very beginning by installing and familiarizing ourselves with the two programs we use―R and RStudio―and by laying the groundwork for forthcoming analyses.

• Code and Datasets Used in DSS:

  • Introduction.R
  • STAR.csv

• Self-Graded Review Exercises (run code in linked R script in RStudio):

  • Chapter 1

• Additional Readings:

  • How to Use Posit Cloud for Data Analysis

CHAPTER 2: ESTIMATING CAUSAL EFFECTS WITH RANDOMIZED EXPERIMENTS

In chapter 2, we learn what causal effects are and how to estimate them using randomized experiments. We analyze data from Project STAR to answer: What is the effect of small classes on student performance?

• Code and Datasets Used in DSS:

  • Experimental.R
  • STAR.csv

• Interactive Graphs:

  • Random Treatment Assignment

• Self-Graded Review Exercises (run code in linked R script in RStudio):

  • Chapter 2

• Videos:

  • How to Run a Randomized Experiment

CHAPTER 3: INFERRING POPULATION CHARACTERISTICS VIA SURVEY RESEARCH

In chapter 3, we learn about surveys and how to visualize and summarize the distribution of single variables as well as the relationship between two variables. We analyze data on the 2016 British referendum to answer: Who Supported Brexit?

• Code and Datasets Used in DSS:

  • Population.R
  • BES.csv & UK_districts.csv

• Interactive Graphs:

  • Random Sampling
  • Mean and Standard Deviation
  • Correlation Coefficient

CHAPTER 4: PREDICTING OUTCOMES USING LINEAR REGRESSION

In chapter 4, we learn how to predict outcomes using simple linear regression models. We analyze data from 170 countries to predict GDP growth using night-time light emissions as measured from space.

• Code and Datasets Used in DSS:

  • Prediction.R
  • countries.csv

• Interactive Graphs:

  • Intercept and Slope
  • Least Squares Method

• Videos:

  • How to Fit a Line to Predict Y Based on X

CHAPTER 5: ESTIMATING CAUSAL EFFECTS WITH OBSERVATIONAL DATA

In chapter 5, we learn how to estimate causal effects using observational data. We analyze survey and electoral data to answer: What was the effect of Russian TV propaganda on the 2014 Ukrainian elections?

• Code and Datasets Used in DSS:

  • Observational.R
  • UA_survey.csv & UA_precincts.csv

CHAPTER 6: PROBABILITY

In chapter 6, we cover basic probability. We learn about random variables and their distributions, the distinction between population parameters and sample statistics, and the two large sample theorems that enable us to measure statistical uncertainty.

• Code and Datasets Used in DSS:

  • Probability.R

• Interactive Graphs:

  • Law of Large Numbers (coming soon)
  • Central Limit Theorem (coming soon)

CHAPTER 7: QUANTIFYING UNCERTAINTY

In chapter 7, we learn how to quantify the uncertainty in our empirical findings in order to draw conclusions at the population level. We complete some of the analyses we started in chapters 2 through 5.

• Code and Datasets Used in DSS:

  • Uncertainty.R
  • STAR.csv, BES.csv, countries.csv & UA_survey.csv