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st: Retaining factors of a principal axis analysis using eigenvalues


From   Andrés Cardona Jaramillo <andres.cardona@uni-bielefeld.de>
To   statalist@hsphsun2.harvard.edu
Subject   st: Retaining factors of a principal axis analysis using eigenvalues
Date   Mon, 16 Feb 2009 20:14:18 +0100

Hi,

I'm having some doubts in choosing the number of factors to retain after an iterated principal axis analysis (factor, ipf). I normally use the Kaiser Criterion (eigenvalues > 1) to solve this issue with the command line "factor, ipf mineigen(1)". However I’ve seen that other statistical packages like SPSS figure out the number of factor to be extracted in a principal axis analysis based on the eigenvalues of a principal component analysis an not the eigenvalues of the former (which seems to be more intuitive). The latter would be obtainer in Stata through the following 3 command lines: factor, pc // write down the number of extracted factor "x" // factor, ipf factors(x)

My question is: are there any theoretical (or maybe practical) reason to first estimate a principal component analysis (factor, pc) and to use these eigenvalues in choosing the number of factors to retain in an iterated principal axis analysis (factor, ipf)?

Thanks for your help.

Andrés Cardona.
Department of Sociology
Bielefeld University
Germany

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