Linear Mixed Effects Modelling
In person
This course will introduce you to linear mixed-effects models (LMMs). It will help develop your theoretical understanding and practical skills for running such models in R.
Linear mixed-effects models are powerful and flexible statistical tools that help us understand the world. This is particularly useful in cases where we need to account for individual differences as well as group tendencies. For example, in language sciences, we might want to investigate how time spent on the internet per week influences people’s tendency to use internet slang in face-to-face communication, whilst acknowledging that this effect might differ from person to person. Linear mixed models allow us to do this and more!
This course includes three 2-hour taught sessions:
Session 1 is mainly conceptual. We will discuss how linear mixed-effects regressions differ from univariate regression, and when/why we might want to use mixed-effects models.
Sessions 2 and 3 are more practical in that we will go through a real dataset from a sociolinguistic study to demonstrate how to run LMMs in R using the lme4 package (Bates, Mächler, Bolker, and Walker, 2015). You will also have the opportunity to try it out yourself by analysing a made-up dataset with extended exercises in the Friday drop-in sessions.
Those who have registered to take part will receive an email with full details on how to get ready for the course.
This course will be taught by Fang Yang.
After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows.
More details about Data Surgeries.
Those who have registered to take part will receive an email with full details on how to get ready for this course.
If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event.
Level
This workshop requires the following pre-knowledge:
- Familiarity with working with R
- Familiarity with handling and wrangling data and installing packages
- Familiarity with statistical analysis concepts such as regression and correlation
Learning Outcomes
- Understand the concept of model structure (fixed and random effects) and how coefficients work (intercepts and slopes)
- Understand how to perform model fitting and selection and interpret the model assumptions
- Know how to construct LMMs in R
Skills
- Building and running mixed-effects models in R using the lme4 package
- Evaluating and selecting models
- Applying LMMs to research data
Explore More Training
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- Digital Method of the Month. Statistics: How to pick the right method
- Explainable Machine Learning (XAI)
- Foundations of Machine Learning
- Getting Started with Bayesian Statistics
- Getting Started with Data Analysis in Python
- Getting Started with Regression in R
Return to the Training Homepage to see other available events
Room 3.35, Edinburgh Future Institute
This room is on Level 3, in the North East side of the building.
When you enter via the level 2 East entrance on Middle Meadow Walk, the room will be on the 3rd floor straight ahead.
When you enter via the level 2 North entrance on Lauriston Place underneath the clock tower, the room will be on the 3rd floor to your left.
When you enter via the level 0 South entrance on Porters Walk (opposite Tribe Yoga), the room will be on the 3rd floor to your right.
