Module manager: Dr Seunghoon Chae
Email: s.chae@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
PIED2711 Analysing Data in Politics, Development and International Relations OR LUBS 2570 Introduction to Econometrics
This module is not approved as a discovery module
This module builds on the foundational knowledge acquired in introductory statistics modules, including multiple linear regressions, and advances students’ understanding of statistical analysis and its application in the social sciences. It introduces key modelling approaches commonly used in empirical research, as well as the tools to effectively communicate and present quantitative findings. It also promotes critical reflection on the assumptions, strengths, and limitations of quantitative data and methods. Through lectures and hands-on workshops, the module develops both the analytical and practical skills necessary for independent research, while also introducing more advanced methods encountered in modern social science research and further study.
This module introduces students to advanced statistical models used in the social sciences and extends the skills developed in introductory statistics modules for the preparation, analysis, and interpretation of quantitative data. It focuses on the application of regression models for different types of outcome variables and data structures, and consolidates proficiency in using the Stata software to implement, interpret, and present these analyses effectively.
On successful completion of the module students will be able to:
1. Demonstrate a rigorous grasp of the theoretical foundations of key statistical models;
2. Recognize and address complex data structures and related methodological challenges;
3. Evaluate and critically assess the appropriateness, assumptions, and limitations of different statistical methods and tools for specific research applications.
On successful completion of the module students will be able to:
1 Choose and apply appropriate statistical methods for specific research questions, data types, and analytical objectives;
2. Process and analyse quantitative data using statistical software;
3. Interpret statistical results and communicate quantitative findings effectively to both specialist and non-specialist audiences;
Applied statistical analysis
Data analysis
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 2 | 20 |
| Practical | 10 | 2 | 20 |
| Independent online learning hours | 160 | ||
| Private study hours | 0 | ||
| Total Contact hours | 40 | ||
| Total hours (100hr per 10 credits) | 200 | ||
A formal formative assessment opportunity will be provided for the summative assessment task, which is specifically pedagogically aligned to that task. As part of this, each student will receive feedback designed to support the development of knowledge and skills that will be later assessed in the summative task.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | - | 60 |
| Coursework | - | 40 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 07/05/2026
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