This module introduces students to a selected set of advanced statistical methods that are commonly used in quantitative social research.

You will cover three advanced methods such as regression diagnostics and interactions, logistic and multinomial regression modelling, multilevel modelling, cluster analysis and factor analysis. These methods allow you to answer questions such as: What are the limitations of linear models and can they be fixed? Why do some people support a given public policy (e.g. the death penalty, Brexit or the GAFA tax), and others not? What are the main nuances and cleavages within a party (e.g. the Greens) or an ideological orientation (e.g. the populists)?

To gain hands-on experience with answering these questions with social and political science data of varying complexity, you will apply these methods to existing small- and large-scale data sets. The expectation is that by the end of the module you will understand the basic principles of the advanced statistical methods covered, appreciate the context in which the methods are best applied, and have had practical experience of applying these methods to real-world data.

By the end of the course, students will:

• Understand the basic principles of the advanced statistical methods covered;
• Appreciate the context in which the methods are best applied;
• Have used R to apply these methods to real-world data.