Statement from Government Department:

In fractious times, we reaffirm core principles. The Government Department at Cornell studies and teaches about intolerance, but will not practice it. We write about xenophobia and bigotry, but will not pretend they only exist elsewhere. We research racism, homophobia, and misogyny, but will not permit them to pass unchallenged. Our role as academics has never been inconsistent with our duty as citizens, and we pledge to honor the rich diversity of our students, staff, and faculty. Those who come to us—as students, co-workers, and colleagues—must be free to learn and live without fear, and we urge the administration to declare—promptly and forcefully—that Cornell is a sanctuary campus.

Teaching Team

Professor Sergio I Garcia-Rios
TA Kevin Foley

Class Meetings

Class Monday 11:20AM - 2:20PM Zoom link

Office Hours

Sergio Garcia-Rios Click here to find available office hours Zoom link
Kevin Foley Wednesdays 12:30-1:30pm - Click here to find available office hours Zoom link

Overview and Class Goals

This course continues the graduate sequence in quantitative political methodology, focused particularly on fitting, interpreting, and refining the linear regression model.

Our agenda includes gaining familiarity with statistical programming via the popular R environment, developing clear and informative graphical representations of regression results, and understanding regression models in matrix form.


It is desirable for students to have taken the introductory course in the sequence (Government 6019), but any prior course on basic social statistics and linear regression should suffice.

Assessment and Evaluation

Problem Sets

Problem sets assigned weekly or bi-weekly. These problem sets will include programming problems with an emphasis on writing understandable, reproducible code.

The assignments and due dates will be distributed during the semester

Assignments will be submitted digitally through canvas at the due date.

Poster Presentation

You will use all (relevant) techniques learned in this class to finalize your research project and present your findings to your classmates in a poster presentation session joined by the 2021 cohort. We will invite other faculty and grad students, our chance to show off!

Tentative date for poster presentation session: TBA

Final Paper

A 15–20 page original report on an original quantitative analysis or replication-and-extension of a published article. The quantitative analysis should be conducted in R and reproducible.

The final paper is due on May 21, 2021 11:59 pm EST.

Further details on the projects will be provided as due dates approach

Email, Canvas, and Slack:

You can find all the relevant information for this course on this website.

Canvas will be be primarily used as a dropbox for assignments submissions. All of your assignments will be submitted through Canvas. You have to submit a HTML document and RMarkdown file. Canvas has trust issues with html files so you might have to submit a zipped folder with both files. More on this later.

It is often more efficient to answer questions in person, so try to ask them in class. However, if you want to reach the teaching team outside of class time the preferred method of communication will through Slack. If you run into coding issues you can send direct messages to us but I encourage you use the #ProblemSets channel where we will try to troubleshoot the issues collectively (class and teaching team). My experience is that if you have a question or are running into coding issues at least someone else in the class might have the same question.

Of course you can always email us but try to limit problem-set and coding questions to Slack


We will be using a selection of readings available online through this website


Creative Commons License

Science should be open, here at Cornell and everywhere else, all materials for this class are licensed under a Creative Commons Attribution 4.0 International License.

The source for the materials of this course including the source for this website is on GitHub at


This course has benefited from numerous open materials, which are listed here.