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Because the data departed from multivariate normality (see Table 2) we used full information maximum likelihood parameter estimates with standard errors that are robust to non-normality (i.e., MLR).
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The third assumption made by MEM is the multivariate normality of the model coefficient vector (see Review of Mixed Effects Model, Supporting Information).
We conducted Mardia's normalized coefficient of multivariate kurtosis to examine multivariate normality.
A test of multivariate normality was used to determine the skewness of data to be used during CFA (41).
The multivariate normality assumption was confirmed by normal quantile-quantile plots of residuals.
Multivariate normality was assessed by calculating the normalized estimate of Mardia's multivariate kurtosis coefficient [ 24].
Rotation theoretically depends on multivariate normality, but simulations with grossly non-normal data shows that ROAST is insensitive to departures from normality, agreeing with analogous results of Dørum et al. (2009).
The results reveal that the proposed method gives better results depending on whether or not the data set is multivariate normal even though multivariate analyses require checking the multivariate normality.
Datascreening was then carried out in order to check for univariate and multivariate normality and missing cases.
Examination of univariate and multivariate normality for each questionnaire item was highly suggestive of non-normal distributions in the population.
Using the R package MVN, we find that Mardia's test does not reject multivariate normality of the three datasets from each species of iris.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com