Week Title Topics covered Essential reading
1 Introduction to Quantitative Methods (and R) Introduction to quantitative methods; description, prediction and causality; research design; R basics Chapter 1, “Introduction”, in Imai
2 Causality Observational and experimental studies; the logic of counterfactuals; randomization; confounding; difference-in-differences Chapter 2, “Causality”, in Imai
3 Describing quantitative data Descriptive statistics; visualising data Chapter 3, “Measurement”, in Imai
4 Regression I (Prediction) Prediction using quantitative data; simple linear regression; multiple linear regression Chapter 4, “Prediction”, in Imai
5 Regression II (Model specification) Modelling non-linear relationships; Interaction terms; Statistics for model fit Chapter 4, “Prediction”, in Imai
6 Regression III (Causality) Linear regression as a tool for analysing experiments; regression and confounding; regression-discontinuity-designs; difference-in-differences (again); heterogeneous treatment effects Chapter 4, “Prediction”, in Imai
7 Regression IV (Panel data) Data with repeated observations of the same units over time; fixed-effect models Chapter 10, “Regression with Panel Data”, in Stock and Watson, available here
8 Sampling, Uncertainty, and Confidence intervals Unbiasedness and consistency; standard errors; confidence intervals for difference-in-means Chapter 7.1-7.2, “Uncertainty”, in Imai
9 Hypothesis Testing and Uncertainty in Regression Hypothesis tests and confidence intervals for regression Chapter 7.3, “Uncertainty”, in Imai
10 Additional Topics Non-standard standard errors; logistic regression To be determined

The main textbook we will use on the course is: Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton University Press