This module explores how and why voters make their choices using a data-driven approach. Students will gain dual proficiency in the study of electoral behaviour by thoroughly examining the main theoretical and empirical approaches to voting while simultaneously developing data analytics skills through practical, hands-on sessions based on real-world survey data.
The course is uniquely structured to blend theory and practice, with students engaging in a learning cycle that combines theoretical approaches, empirical literature, R coding, statistics, and practical tasks. Students will be trained to use the statistical programming language R to clean, manage and prepare survey data, and apply regression techniques appropriate for the analysis of vote choice, such as binary logistic regression.
This module is strongly recommended for students interested in elections, comparative politics, political sociology, and political psychology, and who are also looking to acquire highly valuable, marketable skills in data analysis, statistical modelling, and R coding.
Previous training in statistics is not required. Foundational instruction in statistics will be provided as an integral part of the module.