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Steffi posted on Tuesday, June 03, 2008  6:43 am



Hi everybody, I hope you can help me. I ran an exploratory factor analysis with 15 variables and got out 4 factors. These 4 factors explain 58% of the overall variance. Is this too little variance? What are good values for the variance explained? I could extract another factor which has an eigenvalue of 0.979 and could argue that the eigenvalue is close to 1. This would increase the total variance explained to 65%. What's your opinion on that? Thanks a lot for any help, Steffi 


EFA attempts to explain the correlations not the variance. I would decide on the factors based on model fit statistics and substantive reasoning. The reason to select a model with five factors rather than four should be motivated by the number of factors expected from the way the items were constructed and the meaningfulness of the factors. Factors should not be extracted for the purpose of explaining variance. 

Steffi posted on Tuesday, June 03, 2008  12:49 pm



Linda, thanks a lot for your opinion. I'm glad you're arguing from a logical rather than from a statistical point of view. The four factors that were originally extracted make perfect sense and I'm happy to have found such meaningful and distinct factors. I was just worried that somebody could argue that those factors do not explain enough of the overall variance of all my variables. So instead of the overall variance, would you look at criteria such as Bartlett's test and KMO? Thanks, Steffi 


I would look at the fit statistics provided by Mplus and also residuals. 

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