Descriptive Discriminant Analysis of Multivariate Repeated Measures Data: A Use Case

TL;DR

This paper introduces a robust multivariate technique called descriptive discriminant analysis (DDA) for analyzing multivariate repeated measures data, demonstrated through a case study on traumatic brain injury outcomes.

Contribution

It proposes applying DDA to multivariate repeated measures data, filling a gap in current analysis methods and providing a tutorial with R code.

Findings

DDA effectively analyzes multivariate repeated measures data.

Application to mTBI data shows significant group and time effects.

Provides a practical tutorial for researchers.

Abstract

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.