I am developing a scale which consists of six scale dimensions, six items per dimension, with hypothesized positive relationships between all factors. All scale dimensions have positive correlations between each other. However, in an EFA of the scale which gives a working six-factor solution (based on Kaiser's K1 rule and scree plot; principal axis factoring, direct oblimin rotation with deltas =0), even though almost all items load well on their "expected" factors, one of the factors correlates negatively to other factors at the factor-level correlations. Let's call this scale dimension "dimension A" below for clarity. So, at the manifest level, "dimension A" correlates positively with all other dimensions, but at latent level it correlates negatively to other factors.
I further tested "dimension A" in separate EFAs with all other scale dimensions, looking for whether the two-factor solutions all worked. Turns out that in one of these EFAs, there are two factors where on the first factor all items from both scale dimensions load positively in the .7 to .8 range, and on the second factor the items from "factor A" load positively and items from the other factor load negatively in the .3 to .5/ -.3 to -.5 range. There is no clear theoretical explanation for this, so I was wondering if someone had an idea what might be happening here. Low sample size (n=100) could affect the results.