Programme And Module Handbook
Course Details in 2018/19 Session

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Module Title LM Multivariate Linear + Logistic Regression
SchoolCOS - College Hub
Department COS - College Hub
Module Code 08 21873
Module Lead Prof Stephen Gorard, Dr Richard Gale, Dr Sin Yi Cheung
Level Masters Level
Credits 10
Semester Full Term
Pre-requisites LM Social Research Methods II - (08 21878)
Restrictions MA Social Research
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 Students will develop skills in:
  • 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
  • be able to critique existing research, and produce regression results of their own
  • 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
Assessment 21873-01 : 2,000 word data analysis report : Coursework (100%)
Assessment Methods & Exceptions Assessment: Assignment: 2000 words
Other None
Reading List