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 Anonymous posted on Wednesday, October 13, 2004 - 2:01 am
Since "higher" Residuals (observed-expected in an EFA; and Correlations in a CFA) between Variables can be seen as responsible for the height of RSMSR respectively SRMR, can you tell me something about a cutoff or boundary for the values in "Residual Observed-expected" or in the "Residual Correlations"? So that i can perhaps say: Ok, this or that variable is quite often involved, so I should take it out of the analysis to get lower RMSR or SRMR.
 bmuthen posted on Wednesday, October 13, 2004 - 4:51 pm
It is hard to say, but if you consider < 0.05 as a reasonable decent RSMSR/SRMR cutoff, and noting that this is the average residual in a correlation metric, then it seems a good idea to keep an eye on residuals in the correlation metric that exceed 0.05.
 Anonymous posted on Wednesday, December 29, 2004 - 12:43 am
I have got a question regarding the residual correlations. I have run several models and the modification indices output tells me to add a residual correlation between two items of one of my latent variables. The fit indices grow significantly when adding this residual correlation. But how can I theoretically explain this? Is a residual correlation a sign of a misspecified model, of ommitted variables or just of very similar scales? Is it quite normal to add residual correlations or is it seen as a weekness of a model?
 Linda K. Muthen posted on Wednesday, December 29, 2004 - 8:30 am
Residual covariances are legitimate parameters but should only be added if they make some substantive sense. In factor analysis, they might represent a minor methods factor, for example, due to similar wording of items or another minor factor that was not well-represented and therefore was not found in an EFA.
 Anonymous posted on Tuesday, April 26, 2005 - 6:48 am
Residual covariance

I wonder if it seems appropriate to make a residual covariance between the items " I am in good health" and "times a week I am doing sports"?
Althought, they are not similar in wording, they somehow include each other.
 Linda K. Muthen posted on Tuesday, April 26, 2005 - 8:24 am
If you can support adding such a residual covariance, then it should be okay to do so.
 Emanuela Botta posted on Wednesday, March 21, 2018 - 5:51 am
Hi, I'm a phd student at Uniroma1. I'm using Mplus v.4.1 to do an EFA with categorical variables and estimator wlsmv. In my version of Mplus I have in output only RMSR index. Is it the same that SRMSR? The cutoff value of .08 is correct?
 Bengt O. Muthen posted on Thursday, March 22, 2018 - 12:17 pm
Yes and yes.
 Samuel Abplanalp posted on Wednesday, March 11, 2020 - 1:41 pm

I have read that one type of ESEM is using EFA with correlated residuals. However, I haven't really seen this done in practice. Would running an EFA while letting residuals correlate technically be classified as ESEM?
 Bengt O. Muthen posted on Wednesday, March 11, 2020 - 3:52 pm
You could classify it this way.
 Samuel Abplanalp posted on Thursday, March 12, 2020 - 12:50 pm
Thanks for your quick reply. I am having somewhat of a hard time understanding conceptually what a correlation between residuals means while having EFA cross-loading. Is there an easy way to understand this?
 Bengt O. Muthen posted on Thursday, March 12, 2020 - 2:41 pm
Cross-loadings means that a factor indicator is influenced by more than one factor. A correlation between 2 factor indicator residuals means that they correlate not only because they are influenced by the same factor(s) but also beyond that (directly); for instance, they may have similar wordings or both draw on a minor factor that is not included.
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