How do I make sense between difference in Eigenvalue variance and extracted some of squares variance? I'm doing an EFA on a scale that was designed to quantify LSD's effects. However we have a applied it in patients receiving Ketamine. We are doing the EFA because we wan't to see which items of the scale are relevant when applied for this purpose.
What I'm trying to understand is that when I run the EFA in SPSS, the first factor (Which contains only 3 items) has an Eigenvalue of 6.898, accounting for 29.9% variance. The next factor which contains most of the items has an Eigenvalue of 1.902, explaining 8.269% of variance.
BUT in the Extraction Sums of Squared Loadings, the first factor explains on 9.437% of variance and the second 24.015% of variance. What is the difference between these two measures? I've seen cases where they are slightly different, but here the second factor accounts for much more variance in the sums of squared analysis. Does this mean that this factor is indeed the one which accounts for more variance, despite initially having a much lower Eigenvalue?
 A: The (unrotated) eigenvalues in factor analysis do not explain portions of total variance, they explain portions of common variance (i.e. variance not unique to each variable). These SPSS labels "Extraction Sums of Squared Loadings."  The "Eigenvalues" in the first column result from an eigendecomposition of the correlation matrix: so these are eigenvalues from a PCA. The former will always be smaller than the latter.
A: In the following link, search for
f.  Extraction Sums of Squared Loadings
As you can see in the table above it, the "Extraction" is merely copying over of the major contributory factors such that 
f.  Extraction Sums of Squared Loadings - The number of rows in this panel of the table correspond to the number of factors retained.  In this example, we requested that three factors be retained, so there are three rows, one for each retained factor.  The values in this panel of the table are calculated in the same way as the values in the left panel, except that here the values are based on the common variance.  The values in this panel of the table will always be lower than the values in the left panel of the table, because they are based on the common variance, which is always smaller than the total variance.
The next part of the table refers to rotation of coordinates, which coordinates solve for independent conditions of transformed components. Described thus,
g.  Rotation Sums of Squared Loadings - The values in this panel of the table represent the distribution of the variance after the varimax rotation.  Varimax rotation tries to maximize the variance of each of the factors, so the total amount of variance accounted for is redistributed over the three extracted factors.
This changes the relative contribution of each component to the total variance, and may be where the confusion lies. 
Now can you reproduce the corresponding table you have, please, so that we can see what information you are referring to.
