Multilevel Modelling: A robust analytical method for randomised controlled educational trials

Course Code

HUB-19-20/21-P-R

Organised by

NCRM, University of Southampton and Durham Research Methods Centre

Presenter

Professor Adetayo Kasim, Dr Akansha Singh, Dr Janelle Wagnild and Dr Ehsan Kharati

Date

25/10/2021 - 28/10/2021

Venue

Online run by University of Southampton and University of Durham

Map

View in Google Maps  (SO17 1BJ)

Contact

Jacqui Thorp, Training and Capacity Building Co-Ordinator, National Centre for Research Methods, University of Southampton
Email: jmh6@soton.ac.uk

Description

This four day course (running from 10am - 4pm each day) will focus on the conceptual understanding of multilevel modelling and its relevance for robust analysis of evidence from randomised controlled trials, with case studies from educational trials. It will focus on ‘meaning’ and application of multilevel models instead of computations. The course will run for four days with the first day focusing on the transition from linear regression models to multilevel models. Practical examples with simple exercises will be used to motivate the need for a more robust approach than t-tests or linear regressions in educational trials. The different sources of variability will be discussed as well as their implications on effect size. The course will primarily be taught in R, but we would also be able to support individual exercises in SAS and STATA. This is an intermediate course that requires good understanding of linear regression model as a prerequisite.

The course covers:

  • Overview of multisite and cluster randomised controlled trials
  • Hierarchical or correlated data structure
  • Linear regression with structure covariances
  • Random intercepts models
  • Random site by intervention models

By the end of the course participants will:

  • Gain practical skills in converting data to long form
  • Make a link between study design and analytical choice
  • Gain practical skills in applying multilevel models and interpreting results
  • Acquire necessary skills to check robustness of results from educational trials

This course is suitable for postgraduate students, researchers, trial statisticians and methodologists. A basic understanding of statistical methods including analysis of variance and linear regression model is required. Participants will require access to a computer with R enabled software.

Preparatory Reading

Discovering Statistics Using R by Andy Field, Jeremy Miles and Zoë Field 

Multilevel statistical models by Harvey Goldstein

 

 

 

 

 

Level

Intermediate (some prior knowledge)

Cost

The fee per teaching day is: • £30 per day for students registered at UK/EU University. • £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions. • £100 per day for all other participants In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration

Region

South East

Keywords

Multilevel Modelling , Hierarchical models, Mixed models, Random effects, R, Randomized Control Trials , , , Educational Interventions , ,

Related publications and presentations

Multilevel Modelling
Hierarchical models
Mixed models
Random effects
R

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