STAT6108 Analysis of Hierarchical (Multilevel & Longitudinal) Data
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.
Aims and Objectives
Module Aims
The aims of the module are to introduce students to the main statistical models for hierarchical and longitudinal data, with emphasis on multilevel and marginal models.
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.
- Apply multilevel and marginal models for the analysis of hierarchical and longitudinal data.
- Interpret the results from these statistical analyses in a non-technical language.
- Understand the potential of more advanced elements, such as contextual variables and heterogeneous covariance structures for the analysis of hierarchical and longitudinal data.
- 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 • Growth curve models • Models for hierarchical and longitudinal binary response data The module will integrate the theory with practical application using MLwiN and R.
Special Features
This module combines theory and practice, via lectures and computer sessions.
Learning and Teaching
Teaching and learning methods
This unit is taught via one double lecture per week and supported by practical computer sessions. Additional reading material and formative tests are provided where appropriate.
| Type | Hours |
|---|---|
| Independent Study | 80 |
| Teaching | 20 |
| Total study time | 100 |
Resources & Reading list
Goldstein, H. (1995). Multilevel Statistical Models.
Goldstein, H. (2011). Multilevel Statistical Models.
Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data.
Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.
Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis.
Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods.
Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Coursework (4000 words) | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Coursework (4000 words) | 100% |
Repeat Information
Repeat type: Internal & External
Linked modules
Prerequisites: STAT6083 or RESM6004 or RESM6104