  Psychological research often focuses on examining group differences in a set
of numeric variables for which normality is doubtful. Longitudinal studies
enable the investigation of developmental trends. For instance, a recent study
(Voormolen et al (2020), https://doi.org/10.3390/jcm9051525) examined the
relation of complicated and uncomplicated mild traumatic brain injury (mTBI)
with multidimensional outcomes measured at three- and six-months after mTBI.
The data were analyzed using robust repeated measures multivariate analysis of
variance (MANOVA), resulting in significant differences between groups and
across time points, then followed up by univariate ANOVAs per variable as is
typically done. However, this approach ignores the multivariate aspect of the
original analyses. We propose descriptive discriminant analysis (DDA) as an
alternative, which is a robust multivariate technique recommended for examining
significant MANOVA results and has not yet been applied to multivariate
repeated measures data. We provide a tutorial with annotated R code
demonstrating its application to these empirical data.
