  A framework for assessing the matrix variate normality of three-way data is
developed. The framework comprises a visual method and a goodness of fit test
based on the Mahalanobis squared distance (MSD). The MSD of multivariate and
matrix variate normal estimators, respectively, are used as an assessment tool
for matrix variate normality. Specifically, these are used in the form of a
distance-distance (DD) plot as a graphical method for visualizing matrix
variate normality. In addition, we employ the popular Kolmogorov-Smirnov
goodness of fit test in the context of assessing matrix variate normality for
three-way data. Finally, an appropriate simulation study spanning a large range
of dimensions and data sizes shows that for various settings, the test proves
itself highly robust.
