Not available in 2022/23
Research Design in Political Science
This information is for the 2022/23 session.
Dr Florian Foos
This course is available on the BSc in Philosophy, Politics and Economics, BSc in Politics, BSc in Politics and Economics, BSc in Politics and History, BSc in Politics and International Relations and BSc in Politics and Philosophy. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.
The course will introduce students to the design, conduct and analysis of research in empirical Political Science spanning different subfields. The first term covers the formulation of research questions, and the development of theory and empirically testable hypotheses. From there, we will discuss different types of data, measurement, the distinction between description and inference, as well as correlation and causation, and basic quantitative and qualitative data collection and analysis strategies. Moreover, students will learn about research ethics, and some of the major methodological challenges that we face as a discipline including p-hacking, the file-drawer problem, issues of statistical power, as well as potential solutions such as pre-registration and results-blind review. The second term introduces students to specific research designs including ethnographic research, comparative case studies, as well as experimental and quasi-experimental designs. Throughout the year, there is an emphasis on the importance of good research design, and a solid understanding of the assumptions underlying the design that have implications for data analysis. The ultimate goal of the course is to equip students with the knowledge and skills to conduct their own research projects such as their BA thesis, and to allow them to evaluate published and unpublished work, as well as scientific and journalistic claims, based on the quality of the underlying research design.
In MT, the course includes an introduction to the statistical programming language R, where students will learn practical skills of basic data management and analysis.
This course is delivered through a combination of classes and lectures totalling a minimum of 51 and a half hours across Michaelmas Term, Lent Term and Summer Term. Some or all of this teaching will be delivered through a combination of online and on-campus lectures and classes. There will be a Week 6 reading week in both the MT and LT terms.
Students will complete three formative problem sets throughout the course, two in MT and one in LT, that allow them to apply material from the course to concrete political science examples (e.g., identifying design elements of a published research paper; proposing strategies for answering a given research question, etc.). Some of these problem sets will involve applied problems in R.
Bueno de Mesquita, E. & Fowler, A. 2019. Thinking Clearly in a Data-Driven Age.
Geddes, B. 2003. Paradigms and Sand Castles: Theory building and research design in comparative politics. Ann Arbor, Michigan: University of Michigan Press.
Gerber, A. S., and D. P. Green. 2008. Field experiments and natural experiments. The Oxford Handbook of Political Science. Oxford: Oxford University Press.
Healy, K. 2017. Fuck nuance, Sociological Theory 35(2): 118–127.
Kellstedt, P.M. and Whitten, G.D., 2018. The fundamentals of political science research. Cambridge University Press.
King, G.; Keohane, R. O. & Verba, S. 1994. Designing Social Inquiry. Princeton University Press.
Mill, J.S. 1882. A System of Logic, Chapter VIII. On the four methods of experimental inquiry. 8th edition. Harper and Brothers.
Wedeen, L. 2010. Reflections on ethnographic work in political science. Annual Review of Political Science 13: 255-272.
Exam (40%, duration: 2 hours) in the summer exam period.
Coursework (30%, 2000 words) in the MT.
Coursework (30%, 2000 words) in the LT.
The coursework in the Michaelmas Term will consist of a Summative Problem Set, and the coursework in the Lent Term will consist of a Research Design Proposal.
The Class Summary Grade for General Course students will be calculated as follows: 25% Problem Set 1, 25% Problem Set 2, 25% Problem Set 3, and 25% Problem Set 4.
Student performance results
(2019/20 - 2021/22 combined)
|Classification||% of students|
Total students 2021/22: 88
Average class size 2021/22: 18
Capped 2021/22: Yes (75)
Lecture capture used 2021/22: Yes (MT & LT)
Value: One Unit
Course selection videos
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Personal development skills
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Commercial awareness
- Specialist skills