STAT6102 Multilevel Modelling
This module introduces students to multilevel methods for analysing hierarchical structures in the context of cross-sectional and longitudinal data. It also provides students with an understanding of some key methods of hierarchical data analysis, how to apply these methods and how to interpret the results using suitable statistical software.
Aims and Objectives
The main aim is to introduce the ideas and methods relating to the analysis of hierarchical (multilevel and longitudinal data). The unit will present the modelling techniques in an applied way, with theory introduced to ensure understanding. A further aim is to familiarise the participants with the software appropriate for the analysis of hierarchical data. MLwiN will be the main computing tool.
Having successfully completed this module you will be able to:
- Understand and apply different methods for the analysis of hierarchical data
- Follow the basic underlying theory of multilevel models, and relate the theory to the practice of model fitting
- Interpret in non-technical language the results from a hierarchical analysis of a large dataset
- Use and know when to use, more advanced modelling techniques, including contextual effects, residual analysis and random slopes, multivariate models and logistic models, also for longitudinal data analysis
- Conduct hierarchical data analyses using the statistical software package MLwiN
The module covers the following topics: Hierarchical data, Intercept only models, models with fixed effects, models with random effects, three level models, residual analysis, contextual effects and cross-level interactions, multilevel logistic modelling, longitudinal data structures.
This module is run as a week-long short course, a component of the MSc in Official Statistics
Learning and Teaching
|Total study time||100|
Resources & Reading list
Goldstein, H. (2011). Multilevel Statistical Models.
Snijders, T.A.B. and Bosker, R.J. (1999). Multilevel Analysis.
Raudenbush, S.W. and Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods.
Hox, J. (2010). Multilevel analysis. Techniques and applications.
Diggle, P. J., Heagerty, P., Liang, K.Y. and Zeger, S.L. (2013). The Analysis of Longitudinal Data.
Singer, J.D., Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modelling Change and Event Occurrence.
Repeat type: Internal & External
To study this module, you will need to have studied the following module(s):
Costs associated with this module
Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.
In addition to this, students registered for this module typically also have to pay for:
Candidates may use calculators in the examination room only as specified by the University and as permitted by the rubric of individual examination papers. The University approved model is Casio FX-570 This may be purchased from any source and no longer needs to carry the University logo.
You will be expected to provide your own day-to-day stationery items, e.g. pens, pencils, notebooks, etc.
Where a module specifies core texts these should generally be available on the reserve list in the library. However due to demand, students may prefer to buy their own copies. These can be purchased from any source. Some modules suggest reading texts as optional background reading. The library may hold copies of such texts, or alternatively you may wish to purchase your own copies. Although not essential reading, you may benefit from the additional reading materials for the module.
Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.