Government 61: Research Practive in Quantitive Methods
Wednesday 2-4, CGIS North K109
- 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:
- 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.
- Incompletes: 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.
- Topics (subject to minor change):
- January 24: Introduction to regression models for political science data, slides
- Reading: Gelman & Hill, Chapter 1.
- Assignment: Download, install, and start R and Rstudio.
Go here CRAN for R, and
go here CRAN for RStudio, both free.
Read the USGS instructions and perform the simple exercises provided by the USGS
- January 31, February 7, February 14:
Learning the R language for data processing and statistical analysis
- February 21: The linear regression model, slides
- Reading: Gelman & Hill, Chapter 3
- Assignment: Gelman & Hill, Section 3.9, Exercises 1, 2, 3.
- Chapter Data: kidiq.dta
- February 28: Probability and inference review, slides
- Reading: Gelman & Hill, Chapter 2
- Assignment: Gelman & Hill, Section 2.8, Exercises 3, 4, 5.
- Chapter Data: height.dta
- March 7: Regression diagnostics and summary, slides
- Reading: Gelman & Hill, Chapter 4
- Assignment: Gelman & Hill, Section 4.9, Exercises 2, 5, 7.
- March 21: Logistic Regression and Related Forms, slides
- Reading: Handout, Chapter 5
- Assignment: Gelman & Hill, Section 5.10, Exercises 1, 5, 8.
- March 28: Nonlinear regression models (GLMs), part 1, slides
- Reading: Gelman & Hill, Chapter 6, 6.1-6.4
- Assignment: Gelman & Hill, Section 6.10, Exercises 1, 3.
- April 4: Nonlinear regression models (GLMs), part 2, slides
- Reading: Gelman & Hill, Chapter 6, 6.5-6.9
- Assignment: Gelman & Hill, Section 6.10, Exercises 8, 9.
- April 11: Simulation for inference and fit
- Reading: Gelman & Hill, Chapter 7, Chapter 8
- Assignment: Gelman & Hill, Section 7.6, Exercises 2, 5.
- April 18: Multilevel regression models, slides
- Reading: Gleman & Hill, Chapter 11, Chapter 12
- Assignment: Gelman & Hill, Section 11.6, Exercise 4, Section 12.11, Exercise 2.
- April 25: Treatment of datasets with missing values
- Reading: Gelman & Hill, Chapter 25
- Assignment: Gelman & Hill, Section 25.9, Exercise 2