Module Description

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: 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.

Module Objectives

To familiarise students with the key issues in the craft of applied work so that they become careful, considered and thoughtful researchers in quantitative social sciences.

Learning Outcomes

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, and;
• Have had practical experience of applying these methods to real-world data, using R language and environment for statistical computing.

Structure

1 hour lecture + 2 hour computer laboratory.

Assessment

The assessments consists of 1 final essay of 4,000 words.

Illustrative Bibliography

• Bartholomew D. J. 2008. Analysis of multivariate social science data. CRC Press
• Crawley M. J. 2013. The R book. Wiley
• Faraway, J. 2016. Extending the Linear Model with R. Chapman & Hall/CRC
• Fox J. 2016. Applied regression analysis and generalized linear models. Sage
• Fox J. and Weisberg S. 2019. An R companion to applied regression. Sage
• Gelman, A., ed. 2009. A Quantitative Tour of the Social Sciences. Cambridge University Press
• Hardy M. A. and Bryman A. 2009. Handbook of data analysis. Sage
• Hox J. J., Moerbeek M. and van de Schoot R. 2017. Multilevel analysis: techniques and applications. Routledge

Indicative Syllabus

• Week 1: Introduction to the module, data and software
• Weeks 2-3: Advanced linear modelling
• Weeks 4-5: Logistic and multinomial modelling
• Week 6: Reading week
• Weeks 7-8: Multilevel analysis
• Weeks 9-10: Factor analysis