Analysing and Interpreting Political Data
| Code | School | Level | Credits | Semesters |
| POLI2054 | Politics and International Relations | 2 | 20 | Autumn UK |
- Code
- POLI2054
- School
- Politics and International Relations
- Level
- 2
- Credits
- 20
- Semesters
- Autumn UK
Summary
This is the Year 2 module in the SPIR’s Quantitative Methods (QM) pathway, building on the Year 1 offering and focusing on the theory and practice of regression analysis. Participants will understand what output regression models generate and how to interpret it. They will learn how to test hypotheses of single and multiple restrictions. They will then advance to correct for situations in which classical assumptions do not hold, including heteroskedasticity, multi-collinearity, and serial correlation. They will also consider models for non-continuous dependent variables. Throughout, participants will continue learning how to work with and present data in an innovative and easily interpretable manner.
Target Students
Available to Year 2 UG students in the School of Politics and International Relations on the Politics and International Relations plan. Only available to students who have progressed from POLI1031.
Classes
This module is taught through a combination of lectures and computing sessions.
Assessment
- 50% Coursework 1: 2,000-word research project
- 50% Coursework 2: 2,000-word data project
Assessed by end of autumn semester
Educational Aims
This module will provide students with:i) an understanding of the theory behind the standard Linear Regression modelii) how to manage and manipulate data to develop theoretically-driven variablesiii) how to estimate such models using statistical softwareiv) how to generate and interpret statistical analysis. Regression models suitable for different types of dependent variables will be presented as well.Learning Outcomes
Knowledge and Understanding
How statistical analysis are generated;
How to generate these yourself;
How to interpret statistical results.
Intellectual Skills
Theory of regression analysis;
Interpretation of quantitative academic literature;
Use of statistical software
Professional and Practical skills
Practical use of statistical analysis in academic, commercial and government settings;
Data management and manipulation.
Transferable skills
Analytical, critical thinking and problem solving;
Use of statistical software