Harvard University: Government 61, Research Practice in Quantitative Methods





Course Description
This class introduces students to a variety of statistical methods used to investigate political phenomena. We will address the principles behind these methods, their application, and their limitations. The course will be useful to those undertaking a quantitative methods thesis in Government, but not solely. Indeed, the course aims to provide anyone interested in political science with a proficient understanding of the intuitions behind several of the methods used to analyze political data and identify causal paths. By the end of the course, students will have acquired important analytical and practical skills and will be able to evaluate the quality and reliability of scholarly and journalistic work done using quantitative methods. Students will learn statistical software skills (R). For the course's final assignment, students will be given the choice between writing a research paper using data and writing a different kind of project more suitable to those who are not interested in writing a quantitative research paper. 

Prerequisite Details: 
Government 50. 

Course Grade: 
The final grade will be based on weekly problem sets (50%) and the final assignment (50%). The problem sets are outlined below. The final assignment is designed to take a question and dataset that you want to use for other purposes, or one that I can suggest, and produce a defensible and polished analysis. This includes a descriptoin of the data, an outline of the statistical model, and a writeup of the results. 

Students are free to work together on the assignments, but the work that is turned in must be original. 

Late Submission: 
Assignments must be turned in by the class session following their assignment on the syllabus. Exceptions will be only for documented extraordinary circumstances discussed with the teaching staff. 

Office Hours: 
Wednesday 10-12, in the CGIS South building, Room S407.

Due to the scheduled nature of the course, no incompletes will be given.

Teaching Fellow: 
Mayya Komisarchik. Office Hours: Thursdays 4pm-5pm, CGIS K401.

Required Text: 
Gelman and Hill, "Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge University Press 2007). Other readings will be papers will made available at jstor.org or distributed by the instructor on this syllabus/webpage. Readings should be completed before class listed on the syllabus.

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