Linear mixed effects models (ONLINE LIVE TRAINING)

Prerequisites

This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies.

We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets.

After completing the course, you should have:

• A conceptual understanding of what mixed effects models are, and when they should be used
• Familiarity with fitting and interpreting mixed effects models using the lme4 package in R

Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.

If you do not have a University of Cambridge Raven account please book or register your interest here.

If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry.

• Our courses are only free for registered University of Cambridge students. All other participants will be charged according to our charging policy.
• Attendance will be taken on all courses and a charge is applied for non-attendance. After you have booked a place, if you are unable to attend any of the live sessions, please email the Research Informatics Training Team.
• Further details regarding eligibility criteria are available here.

• Everyone is welcome to attend the courses, please review the relevant policies.

Though this course is aimed a non-specialist audience, it assumes that all attendees already have:

• Knowledge of core statistical concepts, in particular linear modelling
• A working knowledge of R/RStudio

Please do not sign up to attend this course unless you have these prerequisite skills and knowledge.

We strongly recommend first attending the Core Statistics and/or Introduction to R course, if you do not already have equivalent statistical and basic R training.

If you would like to check your own knowledge, you can use our quick pre-requisites quiz.

Number of sessions: 3

• Random effects (intercepts & slopes)
• The syntax of the lme4 package
• Visualising/plotting mixed effects models
• Significance testing
• Checking assumptions & quality of mixed effects model
• Nested and crossed random effects
• Fitting mixed effects models to complex experimental designs

The course also gives a brief introduction to maximum likelihood estimation and mixed effects models notation for those who are interested in these topics.

The course is delivered via a mix of lectures and self-paced practicals with worked examples and support from trainers.

Participants must have their own computers to work on and a stable internet connection for the duration of the course. This course will require you to have an up-to-date installation of R and RStudio on your computer beforehand. Brief installation guides will be provided, and support will be available from the tutors during the sessions.

• Free for registered University of Cambridge students
• £ 60/day for all University of Cambridge staff, including postdocs, temporary visitors (students and researchers) and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
• It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
• £ 60/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
• £ 120/day for all Industry participants. These charges must be paid at registration
• Further details regarding the charging policy are available here

1.5 days

Several times a year

• Core Statistics using R or Python (ONLINE LIVE TRAINING)
• Introduction to R (ONLINE LIVE TRAINING)
• Generalised linear models (IN-PERSON)