Outliers and model fit PreviousNext
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 Christian S posted on Wednesday, September 01, 2010 - 1:47 pm
Dear Drs. Muthen,

I am running a CFA. Multivariate outliers were identified with Mahalanobis D. If OUTMAHAP was 0.001 or below, a case was considered an outlier (rel. conservative approach).

When comparing model fits (Chi2, CFI etc.), the fit is better with the outliers included than after the elimination of outliers.

Is that an indication for a problem? What could be the reason for that?

Thanks in advance.

Best Regards,

Christian
 Linda K. Muthen posted on Wednesday, September 01, 2010 - 4:27 pm
Using outlier detectopm based on the Mplus loglikelihood outlier detection processes should yield a better fitting model when outliers are removed. I don't think this is necessarily the case with Mahalanobis D.
 Elizabeth Barrett-Cheetham posted on Sunday, April 07, 2013 - 12:10 am
Dear Linda and Bengt,

I am currently trying to improve the fit of my model by removing outliers. I understand that mplus offers 4 different ways of detecting outliers.

I have read a Mplus discussion (CFA>Outliers and model fit>01 Sep, 2010) where Linda suggested that “Using outlier detectopm based on the Mplus loglikelihood outlier detection processes should yield a better fitting model when outliers are removed. I don't think this is necessarily the case with Mahalanobis D”.

Would you suggest that I use the Mahalanobis, Logliklihood, Influence or Cooks analysis to try and improve my model fit? Also, for this suggestion that you provide, could you please explain the relevant criterion that I should be using to determine what outlier cases should be deleted/modified? I have searched the user guides and mplus discussions but can’t seem to find anything.

Many thanks for your assistance,
Elizabeth
 Linda K. Muthen posted on Monday, April 08, 2013 - 11:46 am
I would plot the loglikelihood on the y-axis and an important dependent variable on the x-axis and examine the outlier. If you use an IDVARIABLE, you can hold the mouse on the point and see the id of the outlier. There are references for the other outliers in the SAVEDATA command.
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