Schedule

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