For Harvard students: Class: 2–4pm Tuesdays (CGIS S020 Belfer Case Study Room); Section: 6-8pm Wednesday (CGIS K354). For others: register here for online access (for credit or auditing).
The iSite for the course, which contains the dropbox, quizzes, and other password-protected material is here.
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Adam Glynn, Associate Professor |
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Andy Hall, Teaching Fellow |
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Konstantin Kashin, Teaching Fellow |
How can we detect voting irregularities? What causes individuals to vote? How do electoral institutions affect the number of political parties? Quantitative political scientists address these questions and many others by using and developing statistical methods that are informed by theories in political science. In this course, we provide an introduction to the tools used in basic quantitative political methodology. The first half of the course covers descriptive inference with univariate statistics and linear regression from a sampling perspective, with some discussion of missing data. The second half of the course covers causal inference with linear regression and standardization, also from a sampling and missing data perspective. The principles learned in this course provide a foundation for the future study of more advanced topics in quantitative political methodology. While the tools of statistical inference are worth studying in their own right, the primary goal of this course is to provide graduate students (and some undergraduates) with the necessary skills to critically read, interpret,and replicate the quantitative content of many political science articles.
The tentative outline for the course is available here.
The 1000, 2000, 2000e, and E-2000 Course Numbers
GOV 2000 is designed for students who already have some background in statistics/mathematics/computing, or for beginners who are looking for a challenge. Students taking this section of the course will learn to be flexible data analysts, capable of tailoring standard methods to the unique specifications of each task. As such, these students will be asked in the problem sets to write/adjust the code necessary to replicate and critique results from the literature. This section of the course will be taught within the R statistical com- puting environment.
GOV 2000e is designed for students with a limited background in statistics/mathematics/computing. Students in this section of the course will focus on the analysis and critique portions of the assignments. This section of the course will be taught with the Stata statistical software, and the students will be provided with the additional code necessary to replicate and critique results from the literature.
GOV 1000 is intended for undergraduate students and will be taught with the Stata statistical software as the default option. Undergraduate students may choose to use R instead, but will be responsible for some additional questions on problem sets.
GOV E-2000 is designed for Harvard Extension School students. This section of the course will be taught with the R statistical computing environment as the default with the belief that concepts such as statistical simulation, which are heavily used in GOV E-2001, are important skills that students should take away from the course. However, we will consider accommodating requests to complete the problem sets using Stata statistical software.
Weekly Schedule
