ST3MSD-Modelling Structured Data

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Spring term module
Pre-requisites: ST2LM Linear Models or ST2LMD Linear Models and Data Analysis
Non-modular pre-requisites:
Co-requisites:
Modules excluded: ST4MSD Modelling Structured Data
Current from: 2023/4

Module Convenor: Dr Fazil Baksh
Email: m.f.baksh@reading.ac.uk

Type of module:

Summary module description:
This module will consider traditional and modern methods for analysing repeated measurement data.

Aims:

• to give students the ability to recognise and appreciate the issues associated with analysing repeated measurement data


• to describe a range of statistical methods, both traditional and modern, for the analysis of repeated measurement data


• to train students to identify and apply appropriate techniques, using statistical software, and to interpret the results.

Assessable learning outcomes:
By the end of the module it is expected that the student will have:
• an awareness of repeated measurements and methods for analysing data in this form
• the ability to compare and contrast different approaches for analysing repeated measurements
• the ability to perform common types of analysis and interpret the results.

Additional outcomes:

Outline content:

Synopsis: Many statistical techniques are only applicable when observations are independent. When successive observations on quantities, such as weight or a measure of lung function, are made the repeated measurements will usually be correlated. Traditional statistical methods used in the analysis of this form of data will be described, such as the summary statistics approach, split-plot analysis of variance and repeated measures multivariate analysis of variance. More modern approaches utilise mixed models, which have become popular for analysing repeated measurement data. Such models will be considered in detail. Syllabus: Summary statistics Split-plot analysis of variance Repeated measures multivariate analysis of variance Mixed models - marginal and random coefficient models Maximum likelihood and REML fitting methodologies Use of SAS PROC GLM and PROC MIXED.

Brief description of teaching and learning methods:
Lectures supported by problem sheets and practicals.