How can we detect voting irregularities? What causes individuals to vote? In what sense (if any) does democracy (or trade) reduce the probability of war? Quantitative political scientists address these questions and many others by using and developing statistical methods that are informed by theories in political sci- ence. In this course, we provide an introduction to the tools used in basic quantitative political methodology. 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.
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. As such, the statistical methods covered in this course will be presented within the context of a number of articles. Throughout the term, we will reanalyze the data and revisit the conclusions from
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.
The prerequisites differ across type of student. For graduate students in the Government Department, there are no prerequisites. For other graduate students, undergraduate students, and Extension School students, the prerequisite is GOV 50, GOV E-1005, or the equivalent.
For any student who meets the prerequisites yet is concerned with his or her preparedness for the course, we strongly encourage the following in advance of the semester. First, we recommend reading and working through the exercises in David Freedman, Robert Pisani, and Roger Purves, Statistics, 2007 (any of the older editions should suffice as well). Next, we encourage familiarization with the appropriate statistical package - R or Stata - for the section of the course the student intends on taking. Moreover, if the student plans on typesetting problem set answers in LaTeX, familiarity with the LaTeX markup language would be helpful. Resources on R, Stata, and LaTeX are available here.
Grades will be based on
I will not give incompletes in this course.
The weekly homework assignments will consist of analytical problems, computer work, and data analysis. The 2000 section of the course will have additional problems. For all sections, the homework will be assigned on Tuesday night and due the following Tuesday at 1:00pm. Solutions will be posted on Tuesday night, and students will have one week to “self correct” their homework on the basis of the solution key (due the following Tuesday at 1:00pm). These corrections should take the form of comments added to the original homework that indicate where mistakes were made and that demonstrate an understanding of those mistakes.
The homework write-up must be word processed (MS Word is fine), with tables and figures incorporated in the document. No late homework will be accepted except in the case of a documented emergency. All sufficiently attempted homework will be graded on a (+,✓,-) scale, and all sufficiently student corrected homework will recover half credit (e.g., homework that receives a ✓ and is sufficiently corrected with receive a final grade of ✓/+). All sufficiently attempted homework will be typed and well organized with all problems attempted, and all sufficiently corrected homework will include typed and well organized comments integrated into the original homework. The instructor will determine sufficiency in borderline cases.
We encourage students to work together on the homework assignments, but you must write your own solutions (this includes computer work), and you must write the names of your collaborators on your assignment. We also strongly suggest that you make a solo effort at all the problems before consulting others. The midterm and the final will be very difficult if you have no experience working on your own.
The course mailing list is firstname.lastname@example.org. Please subscribe to the list here. If you have trouble subscribing to the list, please email Andy and Konstantin within the first week of the course. This an ideal forum for posting questions regarding the course material and/or computing. We encourage students to reply to each other’s questions, and a student’s respectful and constructive participation on the mailing list will count toward his/her class participation grade.
Adam Glynn's office hours will immediately follow lecture on Tuesdays from 4:00 - 6:00pm.
The office hours for Andy Hall and Konstantin Kashin will be determined during the first week of the course. Both will be held in the HMDC computer lab. If you have questions about the course material, computational issues, or other course-related issues please do not hesitate to set up an appointment with either Adam, Andy, or Konstantin.
If you have a general question, you can also send it to the course mailing list. This is almost always the fastest way to get an answer. However, you can also email Adam directly at email@example.com. If the question is of general interest, he will forward the question and the answer to the list. Make sure to mention explicitly in your email if you would like to stay anonymous.
In addition to several articles that will be distributed in pdf form on the course website, the required books for the course are:
Note: Most of the required material from ALZ can also be found in the Wooldridge text listed first in the optional books section.
The following books are optional but may prove useful to students looking for additional coverage of some of the course topics.