Outliers and model fit PreviousNext
Mplus Discussion > Confirmatory Factor Analysis >
 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,

 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,
 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.
 Paulo Alexandre Ferreira Martins posted on Wednesday, October 19, 2016 - 9:11 am
I've been trying hard to manage outliers from MPlus, unsuccessfully...

Even though i detected them from data file/raw data (i.e., observing the last column who presented "outmahap" <; 0.001), i still can't figure out how to select these outliers and write them (delete them) in MPlus syntax.

I tried another way by exporting all this data to an excel file, deleted these outliers and exported new data to a csv format file so that i could run MPlus syntax...but, it didn't work...

Thank you!
 Linda K. Muthen posted on Wednesday, October 19, 2016 - 3:01 pm
For the Mac, plots are done using R. Go to http://www.statmodel.com/platforms.shtm where there is a link to plot information using R. On the Mac, you cannot left-click on the plot to see the ID and value. You can read the value off of the plot and exclude the person based on the value.
 Paulo Alexandre Ferreira Martins posted on Saturday, October 22, 2016 - 2:29 pm
Ok, i've already download HDF5 package, but i still didn't succeed to run my "gh5 file" in RStudio...

In the RStudio script window, i just can see the input and output Mplus files, but not the new created file: ....newdata.gh5...

Thank you!
 Paulo Alexandre Ferreira Martins posted on Sunday, October 23, 2016 - 11:38 am
Sorry, but i think in your MPlus.R tutorial, you refer to the "R source code", "mplus.r" for a windows user:

"Open R. In Windows, go to Start -> Programs -> R.
Under the File menu, choose the Source R code... option. Browse to the folder with the mplus.R source code...."

Do you possible know which commands for 'os x users' to download mplus.R?...
I've been trying with "R" and "RStudio" but didn't succeed..

Thank you!
 Linda K. Muthen posted on Monday, October 24, 2016 - 9:32 am
You don’t use R to download mplus.R. Just use your browser to go to our website. Then download mplus.R to your computer. Make sure you save it as plain text. In R, go to the File menu and choose “Source File…”. Locate the mplus.R file you have downloaded. Then click Open.
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