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From |
Kai M�hleck <[email protected]> |

To |
<[email protected]> |

Subject |
st: questions towards pcf (principal components factor method) |

Date |
Mon, 10 Jan 2005 17:20:33 +0100 |

I have some questions with respect to Stata's factor analysis method pcf: Telling from my experience pcf always (!) yields clearly 'better' solutions than the other factor methods pf, ipf, and ml (e.g. higher factor loadings, lower uniqueness, and sometimes a more stable factor structure). Any ideas why is that? Would you interpret pcf results in the same way you interpret results of other methods? Or, putting it another way, is pcf in this sense superior to the other methods? The Reference Guide states that the pcf method assumes the communalities to be 1. In my understanding this is a quality of principal components analysis. Is pcf a method for principal components and not for principal factors? Puzzling to me is that in any analysis I've done pcf finds its own quite heroic assumption to be wrong, i.e. the items do have some unique variance. Thus it seems pcf results contradict pcf assumptions. What am I missing here? Finally, I'd like to use the calculated factor scores in further regression analysis. Telling from the figures, with pcf the latent constructs in question are measured in a 'better' way than with the other methods. One could argue that the pcf solution yields a clearer structure, accumulates more variance on the common factor, and thus helps to measure construct Ksi better. Would you follow? Is it advisable to use pcf if one wants to obtain factor scores? Any help and thoughts appreciated! Best, Kai _____________________________________ Kai Muehleck International Social Justice Project (ISJP) Institut fuer Sozialwissenschaften Humboldt-Universitaet zu Berlin Unter den Linden 6 10099 Berlin Germany +49-30-2093-5382 +49-30-2093-4430 (Fax) [email protected] www.isjp.de * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Crowded axis title and labels***From:*Ramani Gunatilaka <[email protected]>

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