This course introduces students to the most common quantitative empirical research design in political science and international relations: the linear model. The goal of the course is for students to understand when and how to apply the linear model to data, and to be able to do so appropriately. This means that we will learn about the underlying assumptions of the linear model, how most “social” data breaks these assumptions, and what we can do about that to still learn from our data using linear regression. The course does not involve intensive discussion of mathematics or probability theory, but will review these concepts to ensure students understand conceptually what linear regression is doing.
The course is focused on hands-on applications of linear regression and data processing to make data ready for analysis. Students will spend a significant portion of class time coding using the R software package, but no coding experience is expected or required.
For MS INTA:
- Students will be able to apply basic statistical skills to include quantitative and qualitative methodologies in academic and professional contexts within the field of international affairs.
- Students will develop research skills in order to produce a research or policy paper on specific issues in international affairs.
For INTA PhD:
- Students will be able to apply advanced statistical skills and quantitative and qualitative research methodologies in the study of international affairs.
- Students will be able to apply advanced research skills in producing (eventually) publishable research that contributes to the body of scholarly work in international affairs.
There are no required textbooks for this course. I will provide PDFs for all articles that are assigned, which are available through the Georgia Tech library.
The course will consist of the following:
- 4 Homework Assignments (45% total)
- Research Project (40%)
- Participation (15%)
A: 90-100
Superior performance
B: 80-89
Above-average, high-quality performance
C: 70-79
Average (not inferior) performance
D: 60-69
Below-average performance
F: 0-59
Unacceptable performance
This is an in-person course and as such there will not be remote attendance options nor recorded lectures (with some scheduled exceptions). We will have in-depth class discussions throughout the semester about research design and tools, as well as the specifics of individual articles that we read. I will provide multiple avenues to participate in these discussions. Students are expected to have completed the readings prior to class. Preparedness and level of engagement are both factors of the participation grade. I reserve the right to call on individuals at random or administer reading quizzes, although I prefer not to. Additionally, attendance is part of the participation grade. You are allowed three unexcused absences; after this, failure to attend will negatively impact your participation grade.
Students are expected to maintain the highest standards of academic integrity. All work submitted must be original and properly cited. Plagiarism, cheating, or any form of academic dishonesty will result in immediate consequences as outlined in the university's academic integrity policy.