An undergraduate-level course that introduces students to modern quantitative social science with a focus on computing with R. This is a required course for students in our department. We cover quantitative approaches to causal inference, measurement, prediction, and inference, drawing on recent social science empirical applications.

• Syllabus

Lecture Notes (2018):

• Introduction: Course Introduction and Welcome Slides Printable Slides
• Introduction: Introduction to R and R Markdown Slides Printable Slides
• Causality: Introduction Slides Printable Slides
• Causality: Randomized Experiments Slides Printable Slides
• Causality: Observational Studies Slides Printable Slides
• Measurement: Descriptive Statistics Slides Printable Slides
• Measurement: Visualizing One Variable Slides Printable Slides
• Measurement: Survey Sampling Slides Printable Slides
• Measurement: Bivariate Relationships Slides Printable Slides
• Prediction: Election Prediction Slides Printable Slides
• Prediction: Regression (I) Slides Printable Slides
• Prediction: Regression (II) Slides Printable Slides
• Prediction: Causality and Regression Slides Printable Slides
• Prediction: Causality and Regression (II) Slides Printable Slides
• Probability: Introduction Slides Printable Slides
• Probability: Random Variables Slides Printable Slides
• Probability: Sums and Means in Large Samples Slides Printable Slides
• Inference: Survey Estimation Slides Printable Slides
• Inference: Experimental Estimation Slides Printable Slides
• Inference: Hypothesis Testing (I) Slides Printable Slides
• Inference: Hypothesis Testing (II) Slides Printable Slides
• Inference: Uncertainty in Regression Slides Printable Slides