One more thing I would like to add is that the negative eigenvalues are the initial ones calculated from the reduced correlation matrix (the diagonals are not 1). The 11th eigenvalue is the first of these initially calculated eigenvalues to turn negative.   Coincidentally, SAS will use no more than 10 factors, even if I specify 12 or 20 or whatever. My gut is saying this is because it might lead to a heywood or ultraheywood case, so SAS prohibits this from happening. I did try specifying HEYWOOD and ULTRAHEYWOOD in the PROC FACTOR statement, but it still would only retain a max of 10 factors regardless of what value I put in for nfactors.   This leads to me to believe that the negative values are not necessarily an issue, but instead provide me an upperbound for the number of factors I could potentially have (in my case 10). I have not yet found any literature to support this, but I will do some digging.   Now, after it gets through the initial estimates, I've found that with some values of nfactors, I will end up with a quasi heywood case where a communality for a variable is greater than its estimated reliability. I read that this should be met with just as much skepticism as an ultraheywood case so I figured there were a few options: remove the offending variable(s) or find the subset of factor structures where the quasi heywood case does not occur. Ultimately, all any model with a number of factors >4 ended up having this quasi heywood case.   This then suggests to me that any model with 5 or more factors is not plausible given my data, and any hypothetical models I explore moving forward will be with 4 or fewer factors.   As a quick note, I did try removing the offending variable, but then another one exceeded its reliability upon re-running the program. I also figured changing the model structure was generally more favorable than dropping data points. ... View more