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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Retaining factors of a principal axis analysis using eigenvalues |

Date |
Tue, 17 Feb 2009 11:25:54 -0000 |

I'd add a perhaps unfashionable view that the principal components or factors to work with are likely to be those that can be interpreted scientifically or practically. But clearly much depends on the problem and on approved tribal behaviour. Nick n.j.cox@durham.ac.uk Philip Ender My understanding is that the Kaiser criterion is, in fact, based on the unreduced correlation matrix. So yes, you should use the number of eigenvalues greater than or equal to one from the -pca-. Selecting the number of factors using this approach is not recommended much these days. Goodness of fit, RMSEA and the Tucker-Lewis Index, which are not available in Stata -ipf-, are more up-to-date methods. Andrés Cardona Jaramillo wrote: 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) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Retaining factors of a principal axis analysis using eigenvalues***From:*Philip Ender <ender97@gmail.com>

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