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Anonymous posted on Wednesday, October 13, 2004  8:01 am



Since "higher" Residuals (observedexpected 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 Observedexpected" 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  10:51 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  6:43 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? 


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 wellrepresented and therefore was not found in an EFA. 

Anonymous posted on Tuesday, April 26, 2005  12:48 pm



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. 


If you can support adding such a residual covariance, then it should be okay to do so. 

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