Date: Mon, 19 Sep 2005 18:26:14 -0500
Reply-To: "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
Subject: Re: PCA and Rotation
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Principal components is a data reduction procedure, not a way to identify interpretable factors. To do the latter, use principal axes analysis or some other factor algorithm that targets common factors. It makes sense to rotate these since you are intreseted in inpretable factors. However, I suggest an oblique rotation to ensure that the factors are no correlated before forcing them to be.
Paul R. Swank, Ph.D.
Professor, Developmental Pediatrics
Director of Research, Center for Improving the Readiness of Children for Learning and Education (C.I.R.C.L.E.)
UT Health Science Center at Houston
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Luis O.
Sent: Monday, September 19, 2005 5:14 PM
Subject: PCA and Rotation
Dear List Members,
I am new in the list and want to ask a very basic question regarding the principal component analysis. I was doing some analysis by using principal component method and VArimax rotation. However, one of my friends told me that the stat book says that we should not rotate principal components. That is, the principal component analysis should not rotate the soluations, because, by theory, it produces a unique soluation. On the other hand, when I read some SPSS manuals, they usually tell you to use the principal component method with some rotation method. Which is correct?
Luis O. Benavent
Benavnet Talps Research