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Hi, I am using Mplus for an EFA on 68 items. The higher the number of factors, the better the fit gets and, particularly, the Chisquare difference is always significant for each addition of one factor I tried (until now 12). Parallel analysis run in spss suggested 6 factors and MAPanalysis suggested 8 factors. So my question is: when should one stop adding factors in Mplus? Which are the best indices? I fear that if I only base my argumentation on the interpretability of the factor solution this may sound to a reviewer like chosing the result that fulfil my expections. Thank you. 


What you are seeing could come about if there are many minor factors or a very large sample size or both. Parallel analysis is a reasonable way to find the major factors. 


Hello, I'm new to Mplus and have got very much the same problem. My questionnaire contains 53 Items, n is 282. Parallel analysis and MAPTest in SPSS indicate a 5factormodel but the fit in Mplus for this model is bad. Consequently I tried more factors. Though the fit gets better the more factors I add, the additional factors contain only 12 items or consist of doubleloadingitems only (though rotation is geomin). In my case, is it useful to use RMSEA, CFI, TLI and SRMR to determine the number of factors or should I rely on MAPtest and parallel analysis? Thank you very much! 


I don't see you mention the substantive theory behind the questionnaire which should guide your item creation and the expected number of factors. Applying factor analysis to a set of items without that background is not likely to be successful  not all data can be well fitted by a factor model. Perhaps you are in the early stages of a questionnaire development in which case you should consider deleting and adding items to measure the hypothesized factors better. I think it is always useful to consider fit statistics like chisquare and RMSEA in addition to more descriptivelyoriented checks like parallel analysis. You may also ask this general (not Mplusspecific) modeling question on SEMNET. 


Dear Mr. and Mrs. Muthen, in your Topic 1 video of short courses you recommend to determine the number of factors in an EFA by picking the solution with lowest amount of factors which has an unsignificant chisquare and other acceptable fitmeasures. Do you know any references which i could citate to justify this procedure? Thank you very much! 


I don't know that this needs a reference given that it is like "a first principle" of modeling  finding the best fitting but still parsimonious model (so in line with the BIC criterion). I can't offhand point to an EFA book on ML  I think LawleyMaxwell's classic factor analysis text would cover it but also other modern factor analysis books. 

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