Dear colleagues, I'm working with Path analyses in lavaan and MVN packages. There are some results that for me are confusing and features from the MVN that I do not know how to set.
My dataset is composed of 130 rows and 7 variables. Using the MVN package to run Mardia, hz and Royston, results indicate that my data is multivariate normal. However, the Mahalanobis distance at different alpha levels give me different results: alpha 0.5 = 14 alpha 0.6 = 8 alpha 0.65 = 5 alpha 0.7 = 0
I have different questions: 1. what is the meaning of 'candidate' outliers? if MVN and Q-Q plot indicates good fit to MVN, should I not consider the outliers? based on what?
2. if outliers are the main concern to get multivariate normality, how can it be that I get multivariate normality by 3 different methods while I have 0-14 candidate outliers?
3. Most important, where can I read which alpha and tolerance values I have to use in my case? Is there any way I can see any tutorial, or recommendations of the alpha according to sample size and other data characteristics?
I was looking on the web and I have found no answer to this. So, any literature recommendation or advice will be welcome.
Thanks and sorry for taking your time. Sincerely,
he data often contain outliers, which are the values that may be far from the real values or are unfamiliar. The statistical methods used to treat them are nonparametric estimation methods which depend on estimating the median using the smallest Mahalanobis distance .
Hello Mario,
If your check of multivariate normality suggests a satisfactory conformance of your data set to the target distribution, I'd forget about jettisoning any cases as outliers unless there was some more compelling reason (such as, impossible values, coding errors, etc.) for doing so.
Good luck with your work.
Mario Garrido, I agree with David Morse. You can additionally consider estimating your path model in lavaan using the estimator="MLR" option.
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