PIED3701 Analysing Data in Politics, Development and International Relations

Reading List
  • Taught: Semester 1
  • Credits: 20
  • Class Size: 45
  • Module Manager: Dr Susan Gaines
  • Email: s.gaines@leeds.ac.uk
  • This Module is approved as a Discovery Module

Discovery module overview

Module Summary

Module Summary

What is the relationship between social inequality and support for human rights? Which groups in society are most likely to support terrorism? What are the main reasons for abstention in elections? What factors influence individuals’ opinions on the UK’s EU membership? Such questions are core to the social scientific study, but are not easily answered without the use of statistical methods. Often, statistics are seen as inaccessible and ‘difficult’ by many people, but the basic concepts and their use are, in fact, simple and accessible. Using a hands-on, applied approach rather than just mathematics, this module will introduce you to basic statistical analysis and provide you the tools not only to answer questions such as the ones above, but also give you a range of analytical skills which will be invaluable in many of the careers chosen by POLIS graduates. How you use numerical data, apply it and explain the results of this analysis to non-experts is a core skill required by the majority of employers. Being able to use such approaches will also be invaluable to your final year dissertation, giving you a much wider range of possible topics and approaches.

Objectives

The module is designed with the goal of introducing students to different quantitative methodologies used by social scientists and policy researchers. Using datasets relevant to Politics, International Relations and Development, students are taken from a level of standard numeracy to understanding and applying technically advanced statistical methods including regression analysis. The module moves from basic descriptive analysis through measures of association, to multivariate techniques. The module will also introduce students to rigorous use of the survey technique and best practice in data collection, cleaning and analysis. The skills developed on the module should be applicable both for academic research, and in subsequent careers where data analysis is applicable.

Syllabus

The module will cover topics such as:

  • - Introduction to Quantitative Analysis and STATA
  • - Descriptive Statistics
  • - Statistical Inference
  • - Measures of Association and Difference
  • - Correlation and Bivariate Regression
  • - Multiple Regression
  • - Logistic Regression

Learning Outcomes

By the end of the module, students will be able to:

  • - understand the quantitative methodologies employed in political, social, and economic research;
  • - perform data analysis using standard statistical software packages;
  • - interpret output using clear, simple language accessible to non-specialists as well as statically trained audiences;
  • - evaluate the use of these methods in answering questions of a political nature;
  • - use these methodologies (if appropriate) in their Level 3 dissertation research;
  • - transfer these methodologies for use in a working environment, as well as for research.

Assessment and teaching

Assessment and teaching

Coursework

Assesment type Notes % of formal assesment
Essay 1 x 1000 word Essay (Midterm) 35
Report 1 x 3000 Project Report (End of Term) 50
In-course MCQ 1 x 25 Questions 15
Total percentages (Assessment Coursework) 100

Private Study

Students will be expected to practise the techniques studied in class, to ensure that they become familiar with the techniques themselves, and familiarise themselves with the statistical software they are using. At the end of each practical session, students will be given a simple task to carry out for the following weak, which should reinforce the learning outcomes from the week’s session. These will not be formally assessed, but students will be strongly encouraged to engage with these. Though there will be required readings for lecture, given the applied nature of the module, students will not be required to undertake specific reading before a seminar. However, to understand the topics covered by the lectures and seminars, and to work through the assignments, students will find it helpful to read the relevant chapter(s) of the recommended texts. As students become familiar with the different approaches and techniques, their capacity to apply these and associated techniques they encounter elsewhere will reinforce their ability to apply such approaches independently

Progress Monitoring

Weekly informal assignments will allow staff to monitor engagement and understanding of the different techniques. An early multiple choice assessment (15%) will provide staff with evidence as to understanding of the technical concepts in basic descriptive statistics. A short, mid-term essay (35%) will focus on interpretation of basic bivariate tests. In this essay, the students will not be asked to produce results, but simply to interpret results given to them. The final project will demonstrate students’ capacity not only to carry out statistical analysis, but also to write this up clearly and simply, to ensure understanding for a wide range of audiences. More broadly, small seminar groups will allow staff to work individually with students to ensure that they engage with the techniques, and can discuss any issues they have with using quantitative methods.

Teaching methods

Delivery type Number Length hours Student hours
Lecture 11 2 22
Practical 11 2 22
Private Study Hours 156
Total Contact Hours 44
Total hours (100hr per 10 credits) 200

Reading List

Reading List
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