This module introduces students to the main statistical modelling approaches that can handle hierarchical data structures. The module has mainly an applied scope where basic theory is introduced to ensure understanding. Practical computer sessions using MLwiN and R are conducted where appropriate.
Prerequisites: STAT6083 or RESM6004 or RESM6117
Aims and Objectives
Having successfully completed this module you will be able to:
- Interpret the results from these statistical analyses in a non-technical language.
- Recognise the basic statistical theory underpinning multilevel and marginal models.
- Contrast the main statistical models available for the analysis of hierarchical and longitudinal data.
- Understand the potential of more advanced elements, such as contextual variables and heterogeneous covariance structures for the analysis of hierarchical and longitudinal data.
- Apply multilevel and marginal models for the analysis of hierarchical and longitudinal data.
- Hierarchical data structures
- Multilevel models: random intercepts and random slopes; contextual variables, cross-level interactions and heterogeneous variance structures
- Model building: estimation; testing; diagnostic checking (specification issues and residual analysis); model selection
- Longitudinal data structures
- Multilevel and Marginal models for longitudinal data
- Models for hierarchical and longitudinal binary response data
The module will integrate the theory with practical application using MLwiN and R.
Learning and Teaching
Teaching and learning methods
Teaching will be delivered by a mixture of synchronous and asynchronous online methods, which may include lectures, quizzes, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study. Face-to-face teaching opportunities will be explored depending on circumstances and feasbility.
|Total study time||100|
Resources & Reading list
Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.
Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence. New York: Oxford University Press.
Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data. Oxford: Clarendon Press.
Goldstein, H. (1995). Multilevel Statistical Models. London: Eduard Arnold.
Goldstein, H. (2011). Multilevel Statistical Models. London: Wiley.
Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis. London: Sage.
There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External