MultiLevel Modelling

Optional for SRMS

Lecturer: Dr Nikos Tzavidis & Dr Mark Tranmer

Timetable: Tuesday 10 – 12.30, Venue Dover Street BS2 & Ellen Wilkinson B.3.3


Description of the Unit: This unit will teach the theory and applications of multilevel models.
Having introduced the basic statistical concepts and modelling tools in Semester 1, in this
module, students will be introduced to more advanced modelling techniques. The unit will
cover basic and more advanced multilevel models including random intercepts models, random
slopes models, inference for multilevel models, the use of contextual variables in multilevel
analysis, modelling complex variance structures, generalised linear mixed models and
multivariate response linear and generalized linear mixed models. All students will gain
familiarity with and hands-on experience. Typically this will be managed by having both
lectures and practical workshops in each session. Statistical software such as SPSS, MLwiN will
be used. A range of prepared data sets will be used, including large-scale surveys, longitudinal
studies and experiments. Students will achieve, as a minimum, a level of competence that
enables them to use more advanced modelling techniques.

Aim: The aim of this unit is to teach students the theory of multilevel models and present
applications of multilevel models as well as software for fitting such models.

Objectives: Students should be able to:
• Recognise when there is a need for more advanced modelling techniques
• Apply multilevel techniques to normal response data, discrete data and repeated
measures data
• Acquire knowledge on how to use the MLwiN software for fitting multilevel models
• Understand why multilevel analysis may be more appropriate for certain data designs
such as clustered designs
• Discuss the basic underlying theory of multilevel models
• Interpret in non-technical language the results from a multilevel analysis of a large
dataset
• Use MLwiN software for multilevel analysis
• Students will develop skills for using multilevel models for their own research and for
reading journal papers that very often employ multilevel analysis

Teaching and Learning: The course will consist of lecture-based sessions and practical
sessions (MLwiN workshops).

Assessment: The assessment for this module will be based on one piece of coursework.

Preliminary Reading
Dobson, A. (2002). An introduction to generalized linear models. Chapman and Hall
Goldstein, H. (1995). Multilevel Statistical Models. London: Edward Arnold.
Snijders, T.A.B. and Bosker, R.J. (1999). Multilevel Analysis. London: Sage.