In this course, you will learn the basics of data science as applied to the social sciences. This involves two broad skill sets: (1) learning the computing and programming tools to both manage and analyze data; and (2) understanding the conceptual foundations of why we might manage or analyze data in one way versus another.
Our goal is to give you the ability to understand, explain, and perform social science research, with a special focus on data analysis and causal reasoning. You will be able to read and understand the methodology of most academic articles in the social sciences, but more importantly you will have a foot in the door of the data science world. The ability to collect and analyze data in a sophisticated manner is becoming a crucial skill set for the modern job market across industries. Finally, you will obtain data literacy that will help you be a critical consumer of evidence for the rest of your life.
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.
Data analysis has become a key part of many fields including politics, business, law, and public policy. This undergraduate course covers the fundamentals of data analysis, giving students the necessary statistical skills to understand and critically analyze contemporary political, legal, and policy puzzles. Lectures focus on the theory and practice of quantitative analysis and weekly lab sessions guide students through the particulars of statistical software. No prior knowledge of statistics or data analysis is required. Taught with R.
Quantitative methods in the social sciences are exploding. Each year researchers are deploying new and exciting methods to answer important substantive questions. But to use (and not misuse) these novel methods, it is crucial to have a firm understanding of the basic building blocks of quantitative methods in the social sciences: probability, statistical inference, and (more often than not) the linear model. This course, the second in the four-course quantitative methods sequence for PhD students in the Government department, provides this rigorous foundation necessary for the rest of the sequence and the rest of your careers. After reviewing the basic probability and statistical inference, we offer a systematic introduction to the linear model and its variants – the workhorse models for social scientists. We will cover the material with enough mathematical rigor to understand the intuition and concepts, but also cover how to use statistical computing to apply the methods.
This is the third course in a PhD-level quantitative methods sequence and introduces students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course draws upon examples from political science, economics, sociology, public health, and public policy. Taught with R.
This is the first course in a PhD-level quantitative methods sequence and provides an introduction to the tools used in basic quantitative social science research. The first four weeks of the course cover introductory univariate statistics, while the remainder of the course focuses on linear regression models. Furthermore, the principles learned in this course provide a foundation for the future study of more advanced topics in quantitative political methodology.
A topics class in statistical methods for PhD students. Topics covered includes Bayesian methods, causal inference, text analysis, item-response models, and others. Taught with a mix of lecture, discussion, and student presentations. Designed for second-year graduate students. Taught with Arthur Spirling.
Substantive questions in empirical social science research are often causal. Does voter outreach increase turnout? Do political institutions affect economic development? Are job training programs effective? This graduate-level class will introduce students to both the theory and the practice behind making these kinds of causal inferences. We will cover causal identification, potential outcomes, experiments, matching, regression, difference-in-differences, instrumental variables estimation, regression discontinuity designs, sensitivity analysis, dynamic causal inference, and more. The course will draw upon examples from political science, economics, sociology, public health, and public policy. Taught with R, with a mix of lectures, discussion, and in-class computing.