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

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

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.

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.

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

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

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

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