Many published articles in leading social sciences journals make use of statistical analysis techniques applied to social survey data. Since such articles stretch across a very wide range of substantive sub-fields, it is important to have a sufficiently good technical understanding to critique the content of articles in your field effectively. And, of course, developing the practical skills needed to carry out such analyses yourself is a valuable way of enhancing your abilities as a research practitioner.

This module works up from the basics of quantitative social research, to a point where you are able to carry out reasonably sophisticated analyses that allow you to address the kinds of important research questions that require the use of multivariate analysis techniques, i.e. forms of statistical analysis which involve three or more measures simultaneously. These techniques, such as logistic regression and log-linear models, have been used in many important sociological studies, such as the classic studies of social class mobility in Britain. So, for example, if you want to understand the reasons for differences between the educational achievements of children from different family types, and whether these reflect the differing material resources of such households, multivariate statistical models are essential. However, the module first provides a useful grounding in a range of standard statistical approaches to describing and analysing patterns and relationships in quantitative data, which serves to act as a platform for a sound understanding of how the more sophisticated techniques work.

Students taking the module gain familiarity with powerful but user-friendly statistical software, which they apply within some of the portfolio of assessment components, and they also have the opportunity to familiarise themselves with important quantitative data sources and critique published quantitative articles. Not all quantitative social research is free from flaws, and the process of doing quantitative research is in itself a lot more messy and challenging than you would imagine from some social statistics textbooks!