Module overview
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.
Linked modules
Prerequisites: STAT6083 or STAT6123 or STAT6117
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Contrast the main statistical models available for the analysis of hierarchical and longitudinal data.
- Interpret the results from these statistical analyses in a non-technical language.
- Recognise the basic statistical theory underpinning multilevel and marginal models.
- 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.
Syllabus
- 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
This module is taught via one double lecture per week and supported by practical computer sessions. Additional reading material and formative tests will be provided where appropriate.
| Type | Hours |
|---|---|
| Teaching | 30 |
| Independent Study | 70 |
| Total study time | 100 |
Resources & Reading list
General Resources
Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.
Textbooks
Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence. New York: Oxford University Press.
Goldstein, H. (1995). Multilevel Statistical Models. London: Eduard Arnold.
Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage.
Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data. Oxford: Clarendon Press.
Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis. London: Sage.
Goldstein, H. (2011). Multilevel Statistical Models. London: Wiley.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat Information
Repeat type: Internal & External