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?

  • $\begingroup$ "Rotation failed to converge in 25 iterations" - what rotation? $\endgroup$ – amoeba Oct 11 '16 at 16:26
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    $\begingroup$ stats.stackexchange.com/q/166799/3277 $\endgroup$ – ttnphns Oct 13 '16 at 23:43
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    $\begingroup$ 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. $\endgroup$ – Mr. Threepwood Oct 14 '16 at 16:08
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    $\begingroup$ @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 $\endgroup$ – ttnphns Oct 14 '16 at 19:34
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    $\begingroup$ @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. $\endgroup$ – Mr. Threepwood Nov 7 '16 at 10:22

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.


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