Dr Melissa Sands

This course is available on the MSc in Development Management, MSc in Development Studies, MSc in Political Science (Global Politics), MSc in Political Science (Political Science and Political Economy) and MSc in Public Policy and Administration. This course is not available as an outside option.

This course is capped at 1 group. Priority will be given to students enrolled on programmes in the Department of Government.

Quantitative Analysis for Political Science (GV481), Introduction to Quantitative Analysis (MY451), or equivalent.

This course introduces students to the latest empirical research and covers different applications of novel and “big" data in political science. Themes include causality and credibility, administrative and open data, media, social media, and search data, and text and image data. Students will be introduced to the set of questions that each type of data can help answer. The course situates the “big data” revolution within the broader context of political science and policy research and discusses some of the promises and pitfalls of digital innovations and new data science methods, with an emphasis on the importance of ensuring the integrity of the research process from start to finish.

15 hours of lectures and 15 hours of seminars in the WT.

There will be a reading week in Week 6 of the Winter Term.

Students will be expected to produce 1 presentation and 1 problem sets in the WT.

Presentation is a brief (10-15) overview and critique of one published research paper of the student's choice, selected from a menu of options.

• Brady, Henry E. "The challenge of big data and data science." Annual Review of Political Science 22 (2019): 297-323.
• Titiunik, Rocío. "Can big data solve the fundamental problem of causal inference?." PS: Political Science & Politics 48, no. 1 (2015): 75-79.
• Carlitz, Ruth D., and Rachael McLellan. "Open Data from Authoritarian Regimes: New Opportunities, New Challenges." Perspectives on Politics 19, no. 1 (2021): 160-170.
• King, Gary, Jennifer Pan, and Margaret E. Roberts. "How the Chinese government fabricates social media posts for strategic distraction, not engaged argument." American political science review 111.3 (2017): 484-501.
• Chen, M. Keith, and Ryne Rohla. "The effect of partisanship and political advertising on close family ties." Science 360, no. 6392 (2018): 1020-1024.
• Nickerson DW, Rogers T. 2014. Political campaigns and big data. Journal of Economic Perspectives 28(2): 51–73
• Lerman, Amy E., and Vesla Weaver. "Staying out of sight? Concentrated policing and local political action." The ANNALS of the American Academy of Political and Social Science 651, no. 1 (2014): 202-219.
• Vomfell, L., Stewart, N. Officer bias, over-patrolling and ethnic disparities in stop and search. Nat Hum Behav 5, 566–575 (2021).
• Law, Tina, and Joscha Legewie. "Urban data science." Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource (2015): 1-12.

Coursework (80%, 3000 words) in the ST.
Problem sets (20%) in the WT.

The coursework would comprise a replication exercise, where students replicate and extend the analysis of one published research paper.