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I like to see the unrotated eigenvalues reported, because that can tell the reader about the extraction decision, but I don't think post-rotation eigenvalues are much use. Jeremy On 08/06/07, Kathryn Jane Gardner <[log in to unmask]> wrote: > Your response was really useful - thanks Jeremy. I "finally" know the > answer and it seems I was on the right track. In reporting factor > analysis results for publication in a journal then, I am reporting the > rotated eigenvalues for the varimax FAs, but for the oblique ones shall > i report the unrotated eigenvalues or not report any? > > Glad to hear someone read my article, and liked it :-) > Kathryn > > >>> "Jeremy Miles" <[log in to unmask]> 06/08/07 3:49 PM >>> > It's a bit analogous with multiple regression. When you have a > multiple regression with uncorrelated predictors, you can take the > standardized beta, and square, it, and that is the proportion of > variance that each predictor explains in the outcome variable. You > can sum those proportions of variance, and you'll get R^2. > > When your predictors are correlated, you can't do that. They don't sum > to R^2. You can enter them hierarchically, and get the unique > variance accounted for by each predictor, but that's the unique > variance, and they won't sum to R^2, because some variance is shared > between predictors, you don't know which one it 'belongs' to. > > Same thing with factors, except the outcome is now the > variance/covariance matrix, and the predictors are the factors. When > you do an orthogonal rotation, the factors are uncorrelated. That > means you can uniquely identify how much (co)variance is associated > with each factor - that's like its variance accounted for, and is the > eigenvalue. When the factors are correlated, that doesn't work any > more, because they share some variance, and although you can know the > total of the eigenvalues, you don't know which factor it belongs to, > because that total variance is shared. > > Jeremy > > P.S. Nice piece in The Psychologist. > > On 08/06/07, Kathryn Jane Gardner <[log in to unmask]> wrote: > > Hi all, > > > > Is there a reason why oblique (i.e., correlated) factor analysis such > > as direct oblim doesn't produce rotated eigenvalues? Is there a way I > > can get SPSS to produce them or are they not appropriate for oblique > > rotations? > > > > When components are correlated like in oblim, sums of squared loadings > > isn't computed to get a total variance and the rotated eigenvalues > > output usually comes off with this part of the output (in orthogonal > > varimax rotation). Perhaps I am missing something (occupational hazard > > of fixating my eyes on a PC until midnight) but I can't find the > answer > > in any textbook and no-one else seems to know the answer. > > > > Hopefully someone can shed some light as I need to find the answer out > > for this by next week! > > > > Many thanks > > Kathryn > > > > > -- > Jeremy Miles > Learning statistics blog: www.jeremymiles.co.uk/learningstats > Psychology Research Methods Wiki: www.researchmethodsinpsychology.com > > -- Jeremy Miles Learning statistics blog: www.jeremymiles.co.uk/learningstats Psychology Research Methods Wiki: www.researchmethodsinpsychology.com