Methodology

Methodology is the newest field at UCSD, but by no means a new area of research. Indeed, our commitment to rigorous research and tool development has always been a part of our brand and a central part of our graduate training, and is present in all the other major fields. Indeed, faculty in every field are using advanced methodological tools.

Our focus at UCSD in methodology is on formal and quantitative tools. Coursework covers classical regression tools, linear models, maximum likelihood, Bayesian methods, experimental and causal inference techniques, network analysis, graphical models, nonparametric tools, and text analysis - and those are just the courses in the department. Students may go outside the department and take courses in other topics of interest. Our students regularly enroll in courses in computer science, economics, mathematics, public policy, and other fields.

Almost every faculty member in the department uses advanced methods; in this sense the methods field is only weakly distinct from the other fields and overlaps with every other field.

Our goal in graduate training is empowerment- to empower students to conduct rigorous and appropriate research with any advanced toolset, to develop their own methods, and to engage in debates about inference and tools.

Courses of Study

To this end, we have designed a course sequence to enable students to use and study advanced methods as early as possible in their graduate careers, indeed, before the end of their first year. The field requirements consist of two required and two elective courses. The first required course is POL270, which covers calculus, linear algebra, and probability. This course is offered as a three-week math pre-course in September of each year before the quarter starts. The course is open to all of our students. The second required course is POL271, which focuses on maximum likelihood applied to general linear models, with some exposure to Bayesian and nonparametric tools.

Most students attend the math bootcamp (POL270), take our core regression course (204b) in the fall, and POL271 in the winter. Most students take one or more advanced methods electives in the spring quarter of their first year.

We regularly offer electives in Bayesian methods, causal inference, graphical models, network analysis, measurement theory, and text analysis. In addition, students have taken courses for credit outside the department in advanced game theory, econometrics, computational statistics, nonparametric statistics, and other topics appropriate for their interests. ECON 250A-Labor Economics, for example, has been approved as an elective for the methodology field. We encourage all our students to take advantage of the wide variety of high quality course offerings on campus.

Methodology Faculty

• Kirk Bansak, Assistant Professor, PhD, Stanford. Methodology, Political Economy
• Scott W. Desposato, Professor, PhD, UCLA. Comparative Politics, Latin America, Methodology
• James H. Fowler, Professor, PhD, Harvard University. American Politics, Methodology
• Erik A. Gartzke, Professor, PhD, University of Iowa. International relations, formal/quantitative theory.
• Seth J. Hill, Associate Professor. PhD, UCLA. American politics, political methodology, voting behavior, and campaigns and elections
• Federica Izzo, Ph.D., London School of Economics, 2019. Methodology.
• Thaddeus B. Kousser, Professor. PhD, UC Berkeley. Legislatures and Legislative Elections, California Politics
• Agustina Paglayan, Assistant Professor. PhD, Stanford University. Comparative Politics, American Politics, Quantitative Methods, Public Policy
• Margaret E. Roberts, Associate Professor, PhD, Harvard. Methodology.
• Branislav L. Slantchev, Professor. PhD, University of Rochester. International relations, game theory
• David Wiens, Associate Professor. PhD, University of Michigan. Contemporary political philosophy, philosophy of social science, applied ethics.