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From |
Jack Willis <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Factor analysis: Stata vs. SPSS - Different results |

Date |
Tue, 27 Sep 2005 17:01:42 -0500 |

In Stata can you run -pca- and do a rotate command, as "verimax"? Or is "rotate" just available in factor and this you have to use pcf?

Herv� Stolowy <[email protected]> asks:When I run a factor analysis with Stata factor var1 var2 ... varN, pcf mineigen(1) rotate, varimax and with SPSS (Analyze>Data reduction>Extraction: Principal components>Rotation: varimax), in the Rotated Factor Loadings, I find that some factors have the same figures in Stata and SPSS, but with opposite signs. This does not happen for all factors but only some of them. The others are similar in both results. Being a beginner, I would expect to find the same matrix with both software. There is probably a logical explanation but I miss it.I would have guessed (given the words I am seeing for your SPSS command selection) that you were running in SPSS the equivalent of Stata's -pca- followed by -rotate-. But, I could easily be wrong about that. Maybe someone who knows more about SPSS can comment on that? I just want to make sure that you are clear on the fact that -factor, pcf- is not the same as -pca-. Rencher (2002) pages 415-416 says concerning the "principal component method" of "factor analysis" that "... This name is perhaps unfortunate in that it adds to the confusion between factor analysis and principal component analysis. ..." And he goes on to explain more about it. Assuming you are asking for the same thing in both SPSS and Stata, I would still not be surprised by a change of sign for some columns of the reported factor loadings. Starting on page 414, Rencher (2002) discusses the nonuniqueness of factor loadings. The loadings can be multiplied by an orthogonal matrix and still reproduce the same covariance matrix. Sign flips are a common event. The interpretation of the underlying factors remains fundamentally the same. For example, if one column of the factor loading matrix was factor1 var1 -0.8 var2 0.1 var3 -0.7 var4 0.9 var5 0.7 researchers would say that this factor is comparing var1 and var3 against var4 and var5 (with var2 close to zero and not important for this factor). If the signs were flipped for this column, you would still end up with the same interpretation of it comparing 1 and 3 against 4 and 5. By the way, the arbitrariness of the sign happens in many other multivariate techniques. Heuristically think of it like this -- if one of these multivariate techniques were trying to draw a picture of your house, they might draw your house or the mirror image of your house (a sign flip). Either way, it still is a visual description of your house. Reference: Rencher, A.C. (2002) Methods of Multivariate Analysis, 2nd Ed., Wiley: New York. Ken Higbee [email protected] StataCorp 1-800-STATAPC * * 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/

--

Jack Willis

At Brook Besor

"Those who stayed with the stores are to have the same share

as those who went into battle. All must share and share alike." (1 Sam 30:24)

*

* 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/

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