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Multilevel Modelling for Health Research

Course Description

This course aims to provide students with a solid grounding in the theory of multilevel models and enable students to perform and interpret multilevel model analyses using the package Stata.

Clustered or hierarchical data is common in health and social sciences research. For example, individuals may be nested within a geographical area or within schools, hospitals or workplaces. Hierarchical data also arises in longitudinal studies where repeated measures on the same individual are collected over time. This course will cover concepts of multilevel models and how to analyse continuous and binary data outcomes in a multilevel framework. Examples will be taken from health and social science research.

This three day advanced level course is designed to give participants a good understanding of the basics of multilevel modelling. The course will be a mixture of theoretical sessions and practical sessions to illustrate the theoretical concepts. The practical sessions will use the Stata software package.

Topics to be covered will include: Introduction to multilevel models, random intercept and random coefficient (slope) models, examining residuals, multilevel models for binary data and an introduction to multilevel models for repeated measures/longitudinal data.

Topics to be covered will include: Introduction to multilevel models, random intercept and random coefficient (slope) models, examining residuals, multilevel models for binary data and an introduction to multilevel models for repeated measures/longitudinal data.

This course will run in Term 3 

Participants with an interest in repeated measures/longitudinal data may note complimentary course on Longitudinal Data Analysis.

Prerequisites:
This is an advanced level course and participants will be expected to have a good understanding and experience of applying and interpreting multiple linear regression models and logistic regression models. Participants should have prior experience of using a statistical package to analyse data such as STATA, SAS or SPSS.

Students will need a UCL computer ID to access course materials and take part in the computer practicals.

Researcher Development Framework Categories

A1) Knowledge base

Course Recommended for

This course is particularly relevant to the following groups:

  • 2nd Year Research Students
  • 3rd Year Research Students
  • 4th Year Research Students
  • Students in Social & Historical Sciences
  • Students in Medical Sciences
  • Students in Population Health Sciences

Course Organisers

  • Course Tutor - Prof Jenny Head - (Research Department of Epidemiology & Public Health)
  • Course Tutor - Dr Mai Stafford - (MRC Unit for Life Long Health and Ageing)
  • Course Tutor - Dr Owen Nicholas - (Research Department of Epidemiology & Public Health)
  • Course Tutor - Dr Ewan Carr - (Research Department of Epidemiology & Public Health)
  • Course Tutor - Dr William Johnson - (External Institution)
  • Administrator - Ms Kasia Bronk - (Organisational & Staff Development)
  • Course Tutor - Dr Shaun Scholes - (Research Department of Epidemiology & Public Health)

 

30 Mar-1 Apr 2015 expand

Page last updated: 23rd April 2015