October 21-23, 2025 | Face-to-face
Mixed models are a powerful, flexible tool that empower biologists to control for various sources of non-independence (“pseudoreplication”) without losing (hardly any) statistical power to detect a significant pattern in their variable of interest. In this three-day course, students, who must already be familiar with linear models and GLMs in R, will learn how to code and implement the next natural step: mixed models. Students will learn the difference between fixed effects and random effects, will learn how to ditch clunky transformations and instead embrace the native distribution of the data, and will be forced to think critically about experimental design. Students will also analyze their own data, so this course is particularly well suited to those with data in hand (datasets will also be provided for those without their own data). This course is practical, hands on and workshop style with very little mathematics per se.
Application deadline: 10 October 2025
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