MIT — Graduate Courses
17.800: Quantitative Research Methods I: Probability, Statistics and Regression Analysis
This is the first course in the four-course sequence on quantitative research methods in the Ph.D. program at the MIT political science department. This course provides a graduate-level introduction to regression models, along with the basic principles of probability and statistics which are essential for understanding how regression works. Regression models are routinely used in political science, policy research, and other disciplines in social science. The principles learned in this course also provide a foundation for the general understanding of quantitative political methodology.
17.802: Quantitative Research Methods II: Causal Inference
This is the second course in the quantitative research methods sequence at the MIT political science department. Building on the first course (17.800) which covered probability, statistics, and linear regression analysis, this second class provides a survey of more advanced empirical tools, with a particular focus on causal inference. We cover a variety of research designs and statistical methods for causal inference, including experiments, matching, regression, panel methods, difference-in-differences, synthetic control methods, instrumental variable estimation, regression discontinuity designs, causal mediation analysis, nonparametric bounds, and sensitivity analysis. We will analyze the strengths and weaknesses of these methods. Applications are drawn from various fields including political science, public policy, economics, and sociology.
17.804: Quantitative Research Methods III: Generalized Linear Models and Extensions
This course is the third course in the quantitative research methods sequence at the MIT political science department. Building on the first two courses of the sequence (17.800 and 17.802), this class covers advanced statistical tools for empirical analysis in modern political science. Our focus in this course will be on techniques for model-based inference, including various regression models for cross-section data (e.g., binary outcome models, discrete choice models, sample selection models, event count models, survival outcome models, etc.) as well as grouped data (e.g., mixed effects models and hierarchical models). This complements the methods for design-based inference primarily covered in the previous course of the sequence. This course also covers basics of the fundamental statistical principles underlying these models (e.g., maximum likelihood theory, theory of generalized linear models, Bayesian statistics) as well as a variety of estimation techniques (e.g., numerical optimization, bootstrap, Markov chain Monte Carlo). The ultimate goal of this course is to provide students with adequate methodological skills for conducting cutting-edge empirical research in their own fields of substantive interest.
17.806: Quantitative Research Methods IV: Advanced Topics
This course is the fourth and final course in the quantitative methods sequence at the MIT political science department. The course covers various advanced topics in applied statistics, including those that have only recently been developed in the methodological literature and are yet to be widely applied in political science. The topics for this year are organized into three broad areas: (1) Advanced causal inference, where we build on the basic materials covered earlier in the sequence (17.802) and study more advanced topics; (2) statistical learning, where we provide an overview of machine learning, one of the most active subfields in applied statistics in the past decade; and (3) Bayesian inference and statistical computing, where we extend the model-based inference techniques covered in the previous course of the sequence (17.804) and study more technically sophisticated materials as well as applications in political science.
Princeton — Graduate Courses
POL572: Quantitative Analysis II
This is the second course in the quantitative analysis sequence offered in the Department of Politics at Princeton University. The covered topics are: basics of causal inference, multiple regression, structural equation modeling, instrumental variables, maximum likelihood theory, binary outcome models, and discrete choice models. I taught this course as Assistant Instructor (for Kosuke Imai) in Spring 2009. The syllabus can be found here.
POL573: Quantitative Analysis III
This is the third course in the quantitative analysis sequence. The covered topics include: event count models, generalized linear models, survival data analysis, causal inference (matching and weighting methods), mixed effects models, hierarchical models, panel data methods, and Bayesian data analysis. I taught this course as Assistant Instructor in Fall 2008 (for John Londregan) and also in Fall 2009 (for Kosuke Imai). The syllabus for the latter can be found here.
Statistical Software Camp: Introduction to R
This is a short course designed to prepare students for homework assignments (computational exercises and empirical analyses) in the quantitative analysis sequence. The course covers basics of the R language. I taught this course twice as Instructor.
Princeton — Undergraduate Courses
POL345: Quantitative Analysis and Politics
This is an undergraduate course for Politics majors at Princeton. The course covers basic principles of probability and statistics as well as introductory programming skills for data analysis. It is primarily aimed at sophomores and juniors who wish to conduct quantitative analyses for their junior and/or senior theses. I taught this course as Assistant Instructor in Fall 2008. More information can be found at Prof. Imai's website.