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Dear MPlusteam, would you say, it's possible to use the RMSR fit index to make a decision on how many factors one should include in a model? I'm conducting an EFA (estim. ULS) and according to Eigenvalue criteria I should assume 2 factors but the RMSR is much (seems to be significant) better when assuming 3 factors. So I'm thinking about comparing the fit indices with something like a X² difference test  of course without X²s but perhaps based on RMSR. Would you say that's a feasible way? Thanks a lot for your support and advice. 


I'm not sure if your indicators are continuous or categorical but if you use ML or WLSMV, you will obtain chisquare and RMSEA in addition to RMSR. 


Dear Linda, that sounds good to me. I'm (still) following Grilli and Rampichini (2004). Step 2 would be an EFA on the polychoric correlation matrix. > WLSMV instead of ULS? Step 3 requires seperate analysis of the between and pooledwithin matrices. My indicators are actually ordinal, but the seperated matrices are only available when I treat them as being continuous ... So I've to conduct the EFA on the two matrices based on continuous data. > ML instead of ULS? Can I use the obtained RMSEA directly or would you recommend doing two CFAs with DIFFTEST? 


Yes. Yes. If your outcomes do not have strong floor or ceiling effects, it you have 5 categories you may want to treat them as continuous at least for these preliminary studies. I'm not clear on your last question. 


To make a decision in favour of a certain number of factors I want take the fit into account as well as an Eigenvalue criterion. So I'd have to compare two factor solutions. The EFA outputs provides fit indices as well so I thought about comparing them. I just talked to my mentor and we decided to model two CFAs according to the EFA outcome and comparing the CFA fit indices afterwards. So the question is answered in a way. I'll get fit indices for the CFA models and only take those from the EFA with a one factorsolution. Thank you very much  for further comments on our model selection strategy, too. 

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