Dr Melissa Sands

This course is compulsory on the BSc in Politics and Data Science. This course is available on the BA in Social Anthropology, BSc in History and Politics, BSc in Philosophy, Politics and Economics, BSc in Politics, BSc in Politics and Economics, BSc in Politics and International Relations, BSc in Politics and Philosophy and BSc in Social Anthropology. This course is available with permission as an outside option to students on other programmes where regulations permit and to General Course students.

Students must have completed Introduction to Political Science (GV101) and Research Design in Political Science (GV249).

The goal of this course is to introduce students to the latest empirical research using big data in political science. This course will cover different applications of big data in political science. For each, students will be introduced to the type of questions that each type of data can help answer and learn to apply the methods needed to analyse each type of data.

15 hours of lectures and 15 hours of classes in the LT.

There will be a reading week in LT Week 6.

Students will be expected to produce 1 problem set and 1 presentation in the LT.

Brady, Henry E. "The challenge of big data and data science." Annual Review of Political Science 22 (2019): 297-323.

Gohdes, Anita R. "Repression technology: Internet accessibility and state violence." American Journal of Political Science (2020).

King, Gary, Jennifer Pan, and Margaret E. Roberts. "How censorship in China allows government criticism but silences collective expression." American Political Science Review 107, no. 2 (2013): 326-343.

Krupenkin, Masha. "Does partisanship affect compliance with government recommendations?." Political behavior 43, no. 1 (2021): 451-472.

Titiunik, Rocío. "Can big data solve the fundamental problem of causal inference?." PS: Political Science & Politics 48, no. 1 (2015): 75-79.

Coursework (80%) in the ST.
Problem sets (20%) in the LT.

The coursework would comprise a replication exercise, where students would replicate and extend the analysis of one paper of their choice, discussed in class.