I have conducted an exploratory factor analysis using principal axis factoring, with direct oblimin rotation. The results indicate two factors, in that there are two factors with eigenvalues above 1, and when I ran a parallel analysis the eigenvalues for these two factors were greater in my data than in the parallel analysis dummy data. The scree plot also seemed to back up this assumption. However, when I've come to look at the factor loadings, all items load on the first factor at >.4 and the only item that loads on the second factor at >.4 also cross-loads on the first factor so will have to be removed anyway.

So I was wondering whether anyone had any advice as to how to interpret this? Eigenvalues seem to suggest two factors, but the factor loadings suggest something else entirely. Below is the output from SPSS that I'm talking about.

Screenshot of SPSS output


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