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Mplus Discussion > Exploratory Factor Analysis >
 ClaudiaBergomi posted on Wednesday, May 04, 2011 - 4:46 am

I am using Mplus for an EFA on 68 items. The higher the number of factors, the better the fit gets and, particularly, the Chi-square difference is always significant for each addition of one factor I tried (until now 12).

Parallel analysis run in spss suggested 6 factors and MAP-analysis 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.
 Linda K. Muthen posted on Wednesday, May 04, 2011 - 11:17 am
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.
 Natalie Enders posted on Wednesday, January 01, 2014 - 2:33 pm

I'm new to Mplus and have got very much the same problem. My questionnaire contains 53 Items, n is 282. Parallel analysis and MAP-Test in SPSS indicate a 5-factor-model 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 1-2 items or consist of double-loading-items 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 MAP-test and parallel analysis?

Thank you very much!
 Bengt O. Muthen posted on Thursday, January 02, 2014 - 10:15 am
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 chi-square and RMSEA in addition to more descriptively-oriented checks like parallel analysis.

You may also ask this general (not Mplus-specific) modeling question on SEMNET.
 Benjamin Strothmann posted on Tuesday, June 27, 2017 - 9:17 am
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 chi-square and other acceptable fit-measures. Do you know any references which i could citate to justify this procedure?

Thank you very much!
 Bengt O. Muthen posted on Tuesday, June 27, 2017 - 5:36 pm
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 off-hand point to an EFA book on ML - I think Lawley-Maxwell's classic factor analysis text would cover it but also other modern factor analysis books.
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