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?