Multivariate Linear to Logistic Regression

Module fact file

Masters Level
Summer Term
10 credits
Contact Hours:
Over two days
Pre-requisite: Available to postgraduate students who have taken Fundamentals of Quantitative Research Methods or equivalent.

Contact details

Module lead

Laurence Lessard-Phillips


Module description

Intended to serve as a `bridging' course between the basic data collection and analysis modules and a wider range of short courses dealing with particular data analysis and statistical approaches. It would be taken between such courses, and would need to be scheduled accordingly.

This is envisaged as a key course to discuss the key assumptions of the multiple linear regression model and the kinds of diagnostic tools available. It will provide a grounding in the statistical approach to analysing social science data.

A key objective is to provide a brief survey of the range of other statistical methods available, to enable informed choices about other courses.

A variety of software packages would suffice, but (given current site licences) it is envisaged that SPSS would be the main package used.

Learning outcomes

On completing this course, you will be able to:

  • learning & understanding the assumptions required for the linear regression model to have the best linear unbiased estimators
  • the importance of looking at a range of diagnostic information (particularly relating to examining the residuals) and the dangers of over-reliance on some popular summary statistics
  • critique existing research, and produce regression results of your own
  • know how this model may be extended to logistic regression where the dependent variable is categorical, how the approach differs, and alternative model specifications (`probit') and extensions to multi-category dependent variables with and without an `order'
  • show awareness of other statistical approaches that draw on the techniques


A 2000-word data analysis report.

The optional modules listed on the website for this programme may unfortunately occasionally be subject to change. As you will appreciate key members of staff may leave the University and this necessitates a review of the modules that are offered. Where the module is no longer available we will let you know as soon as we can and help you make other choices.