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.
Pre-requisite: STAT6095
Having successfully completed this module you will be able to:
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.
| Type | Hours |
|---|---|
| Independent Study | 73 |
| Teaching | 27 |
| Total study time | 100 |
Singer, J.D., Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modelling Change and Event Occurrence. New York: OUP.
Goldstein, H. (2011). Multilevel Statistical Models. Chichester: John Wiley & Sons.
Hox, J. (2010). Multilevel analysis. Techniques and applications. New York: Routledge.
Raudenbush, S.W. and Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks: Sage Publications.
Diggle, P. J., Heagerty, P., Liang, K.Y. and Zeger, S.L. (2013). The Analysis of Longitudinal Data. Oxford: Clarendon Press.
Snijders, T.A.B. and Bosker, R.J. (1999). Multilevel Analysis. London: Sage.
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat type: Internal & External