Bayesian Multivariate Linear Mixed-Effects Models for Speech Research: A Tutorial Using brms

What is it about?

Many things and phenomena in the world have multiple dimensions or aspects, and researchers are often interested in how certain factors affect these different dimensions. A common approach is to analyse each dimension separately. However, this ignores the fact that the dimensions may be related to each other. As a result, the estimates may be less accurate, and it can be harder to understand how the effects on different dimensions are connected. A multivariate approach solves this problem by analysing all dimensions together in a single model. This tutorial shows how to do this using Bayesian multivariate linear mixed-effects models in the R package brms.

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Photo by Leiada Krözjhen on Unsplash

Photo by Leiada Krözjhen on Unsplash

Why is it important?

The multivariate approach reduced uncertainty in population-level effect estimates compared to univariate models and provided a convenient way to examine correlations among effects across outcomes.