MSc International Relations and Diplomacy students.
Should diplomats and experts in international relations use data-driven analysis to better understand pressing global issues and multilateral diplomacy? If so, how could this be done?
This course will shed light on how novel models for causal inference and computationally-intensive methodological developments, which have lapped at the shores of social science for several years now, can be productively used in research on IR and comparative politics, and for policy analysis (e.g. by looking at countries’ votes coincidences and anomalies, effects of regional institutions, causes and duration of wars, etc.).
This course will consist of five applied seminars (3 hours each), which will be built around the following topics:
1. Interaction models (conditional hypotheses),
2. Natural and survey experiments
3. Synthetic control method,
4. Text analysis,
5. Network analysis,
6. Basic machine learning techniques (if time permits).
By the end of this course, the learners will be able to (1) discuss advantages and disadvantages of novel quantitative research methods, (2) apply several novel empirical methods, and (3) create original datasets.
On the right-hand side of the programme front page of the E-Prospectus you will find a link to the online timetables.
Mode of instruction
Seminars, class discussion.
Study load: 140 hours
Final grades are calculated based on four components:
Two in-class challenges (20%),
Draft proposal of an applied paper (20%)
Applied paper (40%)
You can find more information about assessments and the timetable exams on the website.
Details for submitting papers (deadlines) are posted on Brightspace.
Failed partial grades or components should be compensated by passed partial grades or components. The calculated grade must be at least 5,5 to pass the course. It is not possible to re-sit a partial grade or component once you have passed the course.
Bachner, J., Ginsberg B., & Wagner Hill, K. (2017). Analytics, Policy and Governance. Yale University Press.
Imai, K. (2018). Quantitative Social Science: An Introduction. Princeton University Press.
Academic articles and book excerpts announced before the lectures.
Use both uSis and Brightspace to register for every course.
Dr. J.J. Kantorowicz firstname.lastname@example.org
This course is an elective course designed for second year MIRD students.
This elective is conditional on at least 5 students registering for this course.