Dear Bengt or Linda, We conducted a multilevel MIMIC model with kids nested within families and also a single level model randomly selecting one child. A reviewer is asking us to report an assessment of multivariate normality. I ran Tech 13 for a one class model but we are using missing data. Is there a way to get a Mardia's coefficient for missing data? Thank you in advance
No, I don't think so. But why don't you instead analyze with ML versus MLR and see how different the SEs and the chi-2 test of model fit are - there you would see the real effect of lack of multivariate normality.
Thank you for this suggestion. For a typical FIML ML model would you consider a chi-2 test of model improvement for MLR a general good practice step to take in choosing MLR over ML? Or mainly check if the ML model has poor fit. I assumed that among the consequences of multivariate kurtosis for ML there would be poor model fit, reviewer preferred a test.
You can't do a difference test between ML and MLR. You should just look at the two tests and see if there is a difference in fit and also look at the standard errors. If they are different, this suggests that non-normality exists.
I have checked my dataset for multivariate normality and I have found out that there are positive skewness and kurtosis in all variables. I tried to logtransform my dataset using stata. Then I tried to work with my LGC model with both datasets.
But: 1. the logtransformed dataset did perform worse than the non-transformed interms of the chi-square and the other model fit indices
2. when I compared the ML and MLR for the non-transformed dataset, I can see that there is a massive difference in the chi-square values( MLR=589 and ML=1064; p-values <0.0001) which according to your explanations above is the sign of significant effect of the non-normality
So, how do I deal with such situation? Can I simply pick the non-transformed data under MLR?
I have also "Scaling Correction Factor 1.805 for MLR" in my output. What is this telling me?