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:
- Apply multilevel and marginal models for the analysis of hierarchical and longitudinal data.
- 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.
- Interpret the results from these statistical analyses in a non-technical language.
- Recognise the basic statistical theory underpinning multilevel and marginal models.
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
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.
| Type | Hours |
|---|---|
| Independent Study | 80 |
| Teaching | 20 |
| 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
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.
Goldstein, H. (2011). Multilevel Statistical Models. London: Wiley.
Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis. London: Sage.
Goldstein, H. (1995). Multilevel Statistical Models. London: Eduard Arnold.
Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence. New York: Oxford University Press.
Assessment
Assessment strategy
There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one
online assignment.
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 Information
Repeat type: Internal & External