Centre for Research Informatics Training
Linear mixed effects models
Event details
Sessions
- 8 December 2026 — 09:30 to 17:00 — Zoom
- 9 December 2026 — 09:30 to 13:00 — Zoom
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About the course
An applied introduction to linear mixed effects models, also known as multi-level or hierarchical models, for researchers who want to use these methods in their own research or studies.
This course focuses on the practical skills and key concepts needed to work with mixed effects models using R and the lme4 package. The emphasis is on applied examples, real datasets, interpretation, and model checking rather than mathematical derivation.
Topics include random intercepts and slopes, the syntax of the lme4 package, model visualisation, significance testing, model comparison, checking assumptions, nested random effects, crossed random effects, and complex experimental designs.
By the end of the course, participants should be able to:
- understand what mixed effects models are and when they should be used
- fit mixed effects models using the lme4 package in R
- interpret and visualise mixed effects model outputs confidently
- assess model assumptions and evaluate model quality
- understand how mixed models handle grouped and non-independent data
Please note that this course builds on knowledge of linear modelling and should not be considered a general introduction to statistical modelling.
Intended audience
This course is suitable for:
- postgraduate students, postdoctoral researchers, and other researchers working with experimental or observational data
- participants whose data contain repeated measures, grouped observations, hierarchical structure, or other forms of non-independence
- researchers who already understand linear models and want to extend their statistical toolkit
- participants with a working knowledge of R and RStudio
Prerequisites
Although this course is aimed at a non-specialist audience, it assumes that all attendees already have knowledge of core statistical concepts, especially linear modelling.
A working knowledge of R and RStudio is required. Please do not sign up for this course unless you have these prerequisite skills and knowledge.
We strongly recommend first attending Core Statistics and/or Data Analysis in R if you do not already have equivalent statistical and basic R training.
Course fees
All fees are per full training day.
| Category | Fee |
|---|---|
| Industry full charge | £130.00 |
| Academic / Government / charity concessionary | £65.00 |
| Cambridge University staff members / postdocs / visitors | £65.00 |
| Cambridge University registered students | Free |
| Cambridge University registered students non-attendance | £22.00 |
| Special events | Per event |
Payment options will be provided in booking confirmation emails sent after registration.
General information
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