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
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.
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. (2011). Multilevel Statistical Models.
Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence.
Goldstein, H. (1995). Multilevel Statistical Models.
Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods.
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.
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
Costs
Costs associated with this module
Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.
In addition to this, students registered for this module typically also have to pay for:
Books and Stationery equipment
Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase reading texts as appropriate.
Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.