Skip to main content

Unit information: Spatial Modelling 3: Multilevel Modelling in 2016/17

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Spatial Modelling 3: Multilevel Modelling
Unit code GEOG35260
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Jones
Open unit status Not open

GEOG25010 Spatial Modelling 2


Available to year-three Geography and year- four Geography with Study Aboard/Continental Europe students only.

School/department School of Geographical Sciences
Faculty Faculty of Science


This unit aims to provide students with a theoretical and practical understanding of a major strand in current quantitative social science: multilevel modelling. Populations commonly exhibit complex structure with many levels, so that individuals (at level 1) may learn their health-related behaviour in the context of households (2) and local cultures (3). By using multilevel models we can model simultaneously at several levels, gaining the potential for improved estimation, valid inference, and a better substantive understanding of the realities of social life. This Unit requires a prior understanding single level regression models.

Intended learning outcomes

On completion of this Unit students should be able to:

  • to recognise a multilevel structure, and understand the need to apply multilevel models in geographical research
  • specify a multilevel model with complex variation at a number of levels;
  • fit and interpret a range of multilevel models

The following transferable skills are developed in this Unit:

  • Numeracy, computer and problem solving;
  • Analytical and quantitative skills and project management;

Written and verbal communication

Teaching details

A common pattern of lecture/practical is generally adopted throughout the course:

  • Introduction of concept, usually through graphs with a specific example
  • Turning the graphs into multilevel equations
  • Specifying the equations in the MLwiN software
  • Interpreting the results
  • Hands-on experience of fitting and interpreting the results using MLWin
  • Directed reading of research papers applying the methodology

Assessment Details

Nature (See Footnote C) Contribution to Overall Mark: Unseen Examination of 1 hour 30 mins duration: 30% ; Computer-based Project report 60%; Reading assignment reviewing an empirical application 10%

Total for unit 100%

The assessment comprises:

An unseen examination that will test the synoptic understanding of the entirety of the course

a 1,000-word critical review of a paper that provides an empirical study using multilevel modelling. This provides assessment of a student’s understanding of the techniques, command of relevant literature and critical skills;

a computer-based individual project, using the MLwiN programming environment, to analyse and model a data-set using the multilevel modelling techniques taught in the Unit. The report is 3,000 words and has to include not only the student’s analysis and modelling work, but a linkage of this to other empirical studies in the literature;

Reading and References

The reading for this unit mainly comprises recent papers in academic journals. Detailed reading lists and handouts are provided during the unit; the following are indicative:

  • Jones, K., and Duncan, C. (1998). ‘Modelling context and heterogeneity: Applying multilevel models’, in E. Scarbrough and E. Tanenbaum (Eds.), Research Strategies in the Social Sciences. Oxford University Press.
  • Johnston, R.J., Jones, K., Propper, C & Burgess, S.M. (2007) 'Region, Local Context, and Voting at the 1997 General Election in England', American Journal of Political Science, 51 (3), (pp. 641-655), ISSN: 0092-5853
  • Moon, G., Subramanian, S.V., Jones, K., Duncan, C. & Twigg, L. (2005) 'Area-based studies and the evaluation of multilevel influences on health outcomes', in Ann Bowling and Shah Ebrahim (Eds.), Handbook of Health Research Methods, (pp. 266-292), Open University Press, ISBN: 0335214606
  • Jones, K. (2011) 'An introduction to statistical modelling', in Somekh,B Lewin,C (Eds.), Research methods in the social sciences, (pp. 236-250), Sage, 2011. ISBN: 0761944028
  • Jones, K, Subramanian, S.V. & Duncan, C. (2003) 'Multilevel methods for public health research', in Kawachi, I and Berkman L F (Eds.), Neighbourhoods and Health, (pp. 65-111), Oxford University Press, ISBN: 0195138384
  • Jones, K & Duncan, C. (2001) 'Using multilevel models to model heterogeneity: potential and pitfalls', Geographical Analysis, 32, (pp. 279-305) ISSN: 0016-736