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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.
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.