The University of Southampton
Social Sciences: Social Statistics & DemographyPart of Social SciencesPostgraduate study

STAT6108 Analysis of Hierarchical (Multilevel and Longitudinal) Data

Module Overview

The aim of this module is to introduce students to modelling approaches that can handle hierarchical data structures and enable students to apply these methods critically to both multilevel and longitudinal repeated measures data. The unit will present the modelling techniques in an applied way, with theory introduced to ensure understanding. A further aim is to familiarise the participants with the software appropriate for the analysis of hierarchical data. MLwiN will be the main computing tool with some reference to use of R.

Aims and Objectives

Aim

Having successfully completed this module, you will be able to:

  • Understand and apply different methods for the analysis of hierarchical data
  • Appreciate the basic underlying theory
  • Interpret in non-technical language the results from a hierarchical analysis of a large dataset
  • Use more advanced modelling techniques, including contextual effects, residual analysis and random slopes and marginal models for longitudinal data analysis
  • Conduct hierarchical data analyses using the statistical software package MLwiN

Syllabus

  • Hierarchical multilevel data structure
  • Longitudinal data structures and how these fit within a multilevel framework
  • Growth curve models for repeated measurements for continuous and discrete response data
  • Random intercepts models
  • Estimation and testing, contextual effects and cross-level interactions
  • Random slope models for more complex error structures
  • Models for binary response data
  • Diagnostic checking (specification issues and residual analysis)
  • Model selection
  • Marginal models and Generalised Estimation Equations as alternative approaches

The module will integrate the theory with practical application using MLwiN.

Learning and Teaching

Teaching and learning methods

This unit is taught via one double lecture per week and supported by computer workshops where appropriate. All materials will be available via Blackboard.

Resources and reading list

Students require access to computer lab with R and MLwiN.

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.  (1995).  Multilevel Statistical Models.  London: Edward Arnold. 

Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change and Event Occurrence. New York: Oxford University Press.

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

Assessment

Assessment methods

Assessment Method Hours % contribution to final mark Feedback
Coursework 4000 word assignment   100% after 2 weeks individual feedback sheets will be returned

Referral Method: By set coursework assignment(s)

Method of Repeat Year: Repeat year internally. Repeat year externally.

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