Hi everyone.
I have nonnormal data. I want to do confirmatory factor analysis. So I'm using a robust technique (MLM) with R. When I use MLM estimator without deletion anything, it results bad fit indices. If I firstly delete influential outliers according to mahalanobis distance(p < 0.001) then use MLM, I get acceptable fit indices. But I am confused about whether is true method or overfitting.
On the other hand, as I understand I should report results with and without deletion outliers. I want to ask, if I report with/without, can I say the study provides validation?
I'll be glad to hear your advice.
Best regards!
I would recommend looking at this book which is available in the z-library, the second author is a world class expert on such things. The software is available in the R cluster package.. BTW Peter will probably answer questions by email. He is on Researchgate. Best wishes, David Booth
David Eugene Booth Great source! It will be very helpful. Thank you!
Zhenwei Dai
Your answer made me more clear. Thank you!İsmail Yazıcı
Deleting data because they do not fit what you want is ethically questionable. Further, if you remove items just because they are extreme, the way to calculate things like confidence intervals changes. Further (2), you don't say what robust method you are using. What is loss function? This is likely what you will want to adjust. See what you wrote in your ethics committee proposal for this.
Here is a paper that you may find useful as well. Best wishes, David Booth
Following Dan's Advice here is another paper for you. David Booth
Good evening,
Before doing your confirmatory factor analysis, I recommend that you transform your variables that do not have the normal distribution. The transformation of these variables is done in SPSS in the transformation tab. In short, this is how you will obtain a normal distribution. Follow the link below and you will understand the steps.
https://www.youtube.com/watch?v=8JM6D5p2cdc&ab_channel=OliversTube
Good luck.
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