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?
Best Answer
I would look at the component matrix for any variables that load in one component .500 or higher. Eliminate the others from your analysis variables and try again. Basically, some of the variables aren't loading strongly into any of the components and SPSS is trying to find a way to make them fit. Remove them, and you should be good to go.