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From |
Ari Dothan <ari.dothan@gmail.com> |

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

Subject |
Fwd: st: Re: Factor analysis using ICE |

Date |
Sun, 24 Jul 2011 18:28:51 +0300 |

Thanks Professor Buis, and I apologize for writing to you privately, I had some technical issues Respectfully Ari ---------- Forwarded message ---------- From: Maarten Buis <maartenlbuis@gmail.com> Date: Sun, Jul 24, 2011 at 6:11 PM Subject: st: Re: Factor analysis using ICE To: statalist@hsphsun2.harvard.edu On Sun, Jul 24, 2011 at 1:04 PM, Ari Dothan wrote privately: > In an advice you gave in the > Statalist, http://www.stata.com/statalist/archive/2007-12/msg00507.html, you > suggest as follows: "Notice that it is not necesary to let -ice- > make multiple imputed datasets if you are only interested in the point > estimates for the factor scores. The multiple imputations are only used for > adjusting the standard errors" > I made 10 imputations using my dataset. They yield values which are a lot > different from each other. > Questions: > 1. Don't I just select one random value of the imputed var. if I make just > one imputation? Isn't it meaningless? > 2. If the imputed value is negative in cases in which this is realistically > impossible (such as negative assets) do I replace negative values with > (missing)? Please do not ask questions privately, but use the Statalist for that. See: <http://www.stata.com/support/faqs/res/statalist.html#private> for the reasons why. In general you will get the right point estimates when using only one imputation, but the argument is obviously asymptotic. So there can be large variation form imputation to imputation. In that case you'll probably be better of presenting the average over imputations. However, the fact that you reported large differences between imputations made me worried. There is some inherrit unidentifiebility in factor analysis. If the identification depends on imputed values (e.g. setting the variance of the latent factor at 1), than I doubt whether you can meaningfully combine the estimates from different imputations. At this point I would advise against combining multiple imputation and factor analysis, until you solved this problem of identification. As to your second question, see <http://www.stata.com/statalist/archive/2009-10/msg00861.html> Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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/ -- Ari Dothan * * 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**:**st: Re: Factor analysis using ICE***From:*Maarten Buis <maartenlbuis@gmail.com>

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