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Governing by Numbers (Level 5)

Overview

  • Credit value: 30 credits at Level 5
  • Convenors: Deborah Mabbett, Dr Laszlo Horath
  • Assessment: six data exercises (30%), an annotated notebook (30%), a collaborative notebook (30%) and a reflective mini-essay (10%)

Module description

Information is power, and statistical information is often more powerful than other types of information, even though it is often presented misleadingly. This module is ideal if you do not have a background in mathematics or statistics and would like to be able to understand the statistical data used in public debate, and find data relevant to questions you are interested in.

You will find out how to look up data relevant to politics and international relations from authoritative sources, and learn how to present and interpret data using spreadsheets and graphs. Some basic statistical concepts (e.g. correlation) will be discussed but no grasp of advanced statistics will be required.

This module is a great opportunity to improve your numeracy skills for other studies in politics.

Indicative syllabus

  • GDP: what it is; comparing GDP across countries; introducing PPP
  • Alternative measures of welfare; Human Development Index
  • Measuring democracy and autocracy; autocracy and the resource curse
  • Poverty and inequality; global inequality; gender inequality
  • Voting behaviour
  • Introduction to social science computing
  • Data justice; doing data science for social good
  • Lab work: analysing the far-right vote

Learning objectives

By the end of this module, you will:

  • have enhanced knowledge about the availability of authoritative statistical information on a range of social, economic and political subjects, including administrative, survey and experimental data
  • recognise the role of data and computational methods in social science
  • have confidence in engaging with computational tools, including using spreadsheets, creating charts, interpreting code in R and adapting existing code to answer new questions
  • be able to interpret empirical findings, including tables and charts relating to political phenomena, and to draw on data to illustrate and develop an argument or test a hypothesis
  • understand and be able to apply correctly core concepts in descriptive and inferential statistics, including causal inference
  • be accustomed to working in teams using collaborative programming environments
  • be able to reflect on critical and ethical issues around data, including bias, representation and data justice.