st: Retaining factors of a principal axis analysis using eigenvalues

[Andrés Cardona Jaramillo <andres.cardona@uni-bielefeld.de>]

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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