I have a quick question re: the possibility of overfactoring. I have been running a few EFA's where the model fails to converge at higher levels of factors (i.e., the model is fine until say, 5 or 6 factors). In examining the output, this does not appear due to negative residual variances (in fact, I eliminated one manifest indicator due to this issue which had poor internal consistency anyway). Is it likely that if, say, a four-factor model appears to account for the lions share of the variance (i.e., strong model fit - RMSEA, CFI, etc.) that the failure to converge at higher factor levels is simply a product of the fact that no higher number of factors can explain the data. I realize there is no 'simple' answer here, but rather am looking for the most likely explanation (as an aside, my sample N = 259, 13 manifest indicators, and am proposing a lower and upper bound of factors at 2 and 6).
Thanks, Linda. Relevant to the above, am I correct in my understanding that if a failure to converge at higher order factor solutions is due to a negative residual variance of a given manifest indicator, then this indicates that all of the variance in that indicator has been exhausted via the latent factors (i.e., a higher order solution can not be obtained because there is no variance left in a particular indicator?). Or is that only one possible reason?
I think that is only one possible reason. I think I can imagine cases where with m factors you have a negative residual variance, whereas with m+1 factors you don't because the factor loadings arrange themselves differently.