MA Politics

Year of entry: 2024

Course unit details:
Lies, damned lies and statistics: politics and data science

Course unit fact file
Unit code POLI71212
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No


The course will start by looking at some problems of measurement. It will then focus in on the question of causal inference; how can we tell that one thing causes another? For instance, can we tell whether all the activity that political parties put into campaigning actually affects the election result – or do the efforts of one party cancel out the efforts of their rivals? This is not only a philosophical discussion, but rather a practical problem to be solved. We will then look at the problems of data that has a multilevel structure (such as voters within constituencies), research that compares different countries, and how to understand the historical legacies of political or social phenomena over a long period of time. In addition, we will spend time discussing the how published research handles different methodological and conceptual problems, and proposing alternative solutions to those problems. We will also discuss the best way to explain and present the conclusions of statistical research methods. This course builds on an initial understanding of data description and regression. It goes further that its prerequisites in three main respects; (i) we will look at methodological tools that allow us to have some confidence that a relationship is causal, (ii) we will use more advanced methods, as they are applied in published cutting-edge research, and (iii) you will have the opportunity to go beyond what is published in the replication report.


Unit title Unit code Requirement type Description
SOCS70511 Pre-Requisite Compulsory
Understanding Political Choice in Britain POLI31041 Pre-Requisite Recommended
Tools and techniques of applied quantitative analysis POLI60341 Pre-Requisite Compulsory


This course aims to help you see world differently through statistics and data science. By the end of the course, students will be able to use advanced statistical tools to answer important questions about politics. Each week we will use data from published articles at the forefront of recent research to examine the methodological problems that arise when studying politics, and how to overcome them. The methodological topics covered will include causal inference (e.g. how can we tell whether election campaigning works?), estimating quantities (e.g. how many students are eligible to claim Free School Meals but do not?), and measurement (e.g. how can we tell what people really think about sexism?). The political questions covered will be wide-ranging, and there is the opportunity for examples to be tailored to students’ particular interests. However, indicative readings are given below on topics such as young voters (the ‘youthquake’ in 2017), social media censorship, and racial disparities in policing. It will be of interest to students considering a career involving research, such as in government or the civil service, consultancy, market research and political polling, third sector organisations, or academia.

Teaching and learning methods

This course will rely on replication as its main teaching method. We will take data and code from published research articles in different subfields of political science, and use these to explore and then apply research methods ourselves in computer lab sessions. In addition to this, students will need to complete introductory readings from a textbook and other published material. Formative quizzes will be provided that allow students to check on their own progress each week, and the course convenor will use these quizzes to ensure that students are not either left behind or insufficiently challenged.

Knowledge and understanding

  • Understand some important reasons why it is hard to provide convincing evidence of causal processes
  • Understand how different methods (like difference-in-differences, regression discontinuity, or experiments) try to overcome these challenges
  • Recognise some common problems in accurate measurement of political phenomena

Intellectual skills

  • Identify the most important barriers to answering a particular research question
  • Choose between different available statistical methods to answer a research question
  • Evaluate the methods and concepts used in published research

Practical skills

  • Carry out analysis using some of the methods covered above
  • Visualise the results of research
  • Communicate the results of complex statistical procedures to lay audiences

Transferable skills and personal qualities

  • Programming skills in statistical software
  • Critical thinking and statistical literacy

Assessment methods

Method Weight
Other 25%
Written assignment (inc essay) 75%

Assessment task (Please include opportunities for formative feedback)

Length required

Weighting within unit (if relevant)


Review of published article, taken from a list provided by the course convenor



Formative and summative

Replication report; students will firstly replicate the results of, and secondly extend the analysis of a published piece of research. In addition to the report, they will need to submit the code and data used, which will not count towards the word limit.


NB. The course convenor will suggest a number of papers which are suitable for this assessment, but students will also be able to suggest a different paper on the approval of the course convenor.


Where appropriate, advice will be given to students of excellent reports on how to improve these reports so that they can be submitted to academic journals to be considered for publication.




Feedback methods

weekly quiz - formative

Article Review - formative and summative

Replication report - summative

Recommended reading

Methodological content: NB. The first of these is an accessible introduction to the issues covered in the course, whilst the second is an advanced textbook. Neither will be used alone in this course.

  • Chivers, T. and Chivers, D. (2021). How to Read Numbers: A Guide to Stats in the News (and Knowing When to Trust Them). Orion Publishing Group Ltd: London.
  • Gelman, Andrew, Jennifer Hill, and Aki Vehtari. Regression and other stories. Cambridge University Press, 2020.

Politics content: NB. The exact substantive topics covered each year will very according to the interests of students, but these pieces are research are given as examples of the type of studies we will look at.

  • Achen, Christopher, and Larry Bartels. Chapter 4 Blind Retrospection: Electoral Responses to Droughts, Floods, and Shark Attacks.Democracy for Realists. Princeton: Princeton University Press, 2018. . Available at:
  • Adena, Maja, Enikolopov, Ruben, Petrova, Maria, Santarosa, Veronica, and Zhuravskaya, Ekaterina. (2015) Radio and the Rise of The Nazis in Prewar Germany, The Quarterly Journal of Economics, 130(4) pp.1885–1939
  • Gelman, Andrew, Jeffrey Fagan, and Alex Kiss. An analysis of the New York City police department policy in the context of claims of racial bias. Journal of the American statistical association 102, no. 479 (2007): 813-823.
  • Glynn, Adam N., and Maya Sen. Identifying judicial empathy: does having daughters cause judges to rule for womens issues?. American Journal of Political Science 59, no. 1 (2015): 37-54.
  • 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.
  • Prosser, Chris, Ed Fieldhouse, Jane Green, Jonathan Mellon, and Geoff Evans. (2018). The myth of the 2017 youthquake election. Blog post available at
  • Dahlum, Sirianne, and Tore Wig. Chaos on Campus: Universities and Mass Political Protest. Comparative Political Studies 54, no. 1 (2021): 3-32.

Study hours

Scheduled activity hours
Seminars 20
Independent study hours
Independent study 130

Teaching staff

Staff member Role
Nicole Martin Unit coordinator

Return to course details