So long as you have correctly reverse scored your items, as far as I understand the sign of the factor loading in factor space is somewhat arbitrary and I think different factor methods can in fact produce different signs. If you intend to use the resulting factors as scale scores, I think people usually deal with this by creating summated scale scores rather than the saving factor scores method(i.e., after reflecting those conceptually negative items, simply sum the items on each factor to create subscales) and the items will be in the correct direction here.
Hope this helps.
Kathryn
Dr. Kathryn Gardner
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>>> Caroline Wilson <[log in to unmask]> 12/08/09 8:47 AM >>>
Hi,
I've just done an exploratory factor analysis on some data (using principal axis factoring and oblique rotation) and all is well except that three of the factors are made up of items which are ALL showing loadings with negative values where there should be positive values. Apart from the negative sign the loadings are pretty much as I would have expected.
I'm scouring my codebook to see if I've done something silly like re-code positive items instead of negative (so that they're all now negative) but am otherwise at a loss for why the negative sign.
Has anyone come up against another reason for negative loadings?
Many thanks
Caroline Wilson
PhD Research Student
Institute of Energy and Sustainable Development
De Montfort University
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