Advanced Quantitative Methods for Social Science

Code

POLI3125

School

Politics and International Relations

Level

3

Credits

20

Semesters

Autumn UK

Summary

This module provides the final year of training on the QM-SPIR pathway and adds to the quantitative methods skills that students have acquired through previous modules.

Target Students

Available to Final Year 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 POLI2054.

Co-requisites

Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:

• Analysing and Interpreting Political Data (POLI2054)

Classes

This module is taught through a combination of lectures and computing sessions.

Assessment

• 50% Coursework 1: 2,000-word take home practical
• 50% Report: 2,000-word research report.

Assessed by end of autumn semester

Educational Aims

Learning Outcomes

Knowledge and Understanding  
• This module is part of the QM pathway within SPIR. As such, part of the purpose of this module is to satisfy a requirement of said pathway.  
• The aim is to train students in multiple quantitative methods (QM) useful for the analysis of various modern social science data-sets, beyond the scope of Y1 and Y2 QM training.

Intellectual and Transferable Skills  
By the end of the module, students will know and understand:  
• The basic principles of the advanced quantitative methods covered; 
• How to identify appropriate methods for analysis;  
• Issues in the quantitative analysis of data.  

By the end of the module, students will be able to:  
• Prepare real-world secondary data for statistical analysis;  
• Apply appropriate quantitative techniques to address a substantive research question;  
• Interpret and communicate results from analysis;  
• Visualize and represent data;  
• Critically analyse and disseminate information;

Professional/ Practical Skills  
By the end of the module, students will be able to:  
• Initiate projects;  
• Communicate research findings;  
• Independently use advanced quantitative methods to analyse real-world complex data:  
• language and environment for statistical computing.

Digital Capabilities  
By the end of the module, students will:  
• Be able to find, evaluate, and manage digital information;  
• Have had experience with statistical software;  
• Be able to use digital evidence to solve problems and answer questions.

Conveners

• Dr Scott Moser