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The University of Southampton

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.


• 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.

Independent Study80
Total study time100

Resources & Reading list

Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence. 

Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods. 

Goldstein, H. (2011). Multilevel Statistical Models. 

Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis. 

Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.

Goldstein, H. (1995). Multilevel Statistical Models. 

Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data. 


Assessment Strategy

There will be opportunities to evaluate your progress through formative assessment, with summative assessment based on one online assignment.


MethodPercentage contribution
Coursework  (4000 words) 100%


MethodPercentage contribution
Coursework  (4000 words) 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: STAT6083 or RESM6004 or RESM6117


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

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