ABSTRACT
Multivariate multilevel regression models are multilevel regression models that contain
more than one response variable. As such, they are comparable to classical multivariate
analysis of variance (MANOVA) models, where we also have several outcome measures.
The reason to use a multivariate model is usually because the researchers have decided to
use multiple measurements of one underlying construct, to achieve a better construct
validity. A classic example is in medical research when diseases manifest themselves in a
syndrome that leads to a pattern of related effects (Sammel, Lin & Ryan, 1999). By using
several outcome measures, researchers can obtain a better and more complete description
of what is affected by changes in the predictor variables (cf. Tabachnick & Fidell, 1996).
Simply carrying out a series of univariate analyses, one for each response measure, seems
inadequate. One manifest advantage of multivariate analysis is that carrying out a series
