PCA with oblimin rotation: should I interpret component matrix, pattern matrix or structure matrix?

I conducted a principal component analysis (PCA) with direct oblimin factor rotation in SPSS.

Because by that time I didn't know any better, I used the COMPONENT MATRIX for interpretation. I added the items that loaded highest on factor one to form a scale, than I added the items that loaded highest on factor 2 and formed a scale of these items... After that, I tested for internal consistency with Cronbach's alpha and tested for correlations between sociodemographic data and my scales.

Now I found out that normally you interpret pattern or structure matrix. Interestingly both of them were NOT computed, only an error saying: Rotation failed to converge in 25 iterations. (Convergence = ,000).

Was my approach wrong? Is there something defendable about it or do I have to discard everything build on my (maybe wrong) assumption?

• "Rotation failed to converge in 25 iterations" - what rotation? Oct 11, 2016 at 16:26
• stats.stackexchange.com/q/166799/3277 Oct 13, 2016 at 23:43
• In the answers posted in the presented link, I couldn't find anything about the component matrix (thats a specific matrix outputted by spss next to the pattern and structure matrix) and their potential interpretation. Oct 14, 2016 at 16:08
• @Mr.Threepwood, By "component" or "factor" matrix SPSS mean a matrix of loadings prior a rotation of factors (or components). So, you are asking if it is reasonable to interpret factors/compnents unrotated, right? One thread on this is here, and actually the link to it is present under the link "Q/A" in the first sentence of my answer stats.stackexchange.com/a/166823/3277 Oct 14, 2016 at 19:34
• @amoeba The oblimin rotation was not outputted in my case, because it failed to converge (see first poste). So if I understand the answers posted by ttnphns right, I interpreted an unrotated PCA while analyzing the component matrix. Nov 7, 2016 at 10:22