<>
"I'm not sure what Martin suggests will work."
I did not want to add to what Maarten had offered, just to provide background reading for Catherine.
HTH
Martin
-------- Original-Nachricht --------
> Datum: Thu, 31 Dec 2009 11:31:18 -0800
> Von: Alan Acock <[email protected]>
> An: [email protected]
> Betreff: Re: st: Standardized Beta and Multiple Imputation in Stata 11
> I'm not sure what Martin suggests will work. Unless I'm missing
> something (fairly likely), what Maarten says is that it is not clear
> what mean and variance you would use since each imputed dataset will
> have a different mean and variance for each variable. One very tedious
> solution would be something like this:
>
> mi estimate, noisily dftable: regress ln_wagem gradem agem ttl_expm ///
> tenurem not_smsa south blackm, beta
>
> where the noisily gives all the intermediate results and will show the
> beta values (and R-square) for each of the m solutions.
>
> Pooling the betas and the R-squares is not something that Rubin Rules
> does. You could simply average them. You could do a log of each before
> averaging and then reverse the transformation to deal with the ceiling
> effect in the distribution of betas and R-square. I'm not sure there
> is a formally justified solution.
>
> --Alan Acock
>
> On Dec 31, 2009, at Thu Dec 52:21 , Martin Weiss wrote:
>
> > <>
> >
> >
> > "The way to get those is to standardize all variables in your model
> > prior to estimating the regression command."
> >
> > Catherine can find technical details in this thread:
> http://www.stata.com/statalist/archive/2009-03/msg00115.html
> >
> >
> > HTH
> > Martin
> > -------- Original-Nachricht --------
> >> Datum: Thu, 31 Dec 2009 10:04:51 +0000 (GMT)
> >> Von: Maarten buis <[email protected]>
> >> An: [email protected]
> >> Betreff: Re: st: Standardized Beta and Multiple Imputation in Stata
> >> 11
> >
> >> --- On Wed, 30/12/09, McDonald, Catherine wrote:
> >>> There is an option in the Linear Regression
> >>> estimation to check off 'standardized beta coefficients' in
> >>> reporting. But when I do this, nothing changes in my
> >>> coefficients. I don't see any beta coefficients reported-the
> >>> coefficients are the same as if I did not check it off. Can
> >>> someone give me advice on this?
> >>
> >> The reason is probably that the -mi estimate- dialog box
> >> "inherrited" that option from the normal -regress- dialog box.
> >> I am not that surprised that it doesn't work, because -mi estimate-
> >> seems to work with the returned coefficients as stored in e(b),
> >> and the -standardize- option does not change that matrix.
> >>
> >> The way to get those is to standardize all variables in your
> >> model prior to estimating the regression command. The question
> >> now becomes which mean and standard devation do I use, and I
> >> am not sure, though I guess it doesn't matter that much.
> >>
> >> Hope this helps,
> >> Maarten
> >>
> >> --------------------------
> >> Maarten L. Buis
> >> Institut fuer Soziologie
> >> Universitaet Tuebingen
> >> Wilhelmstrasse 36
> >> 72074 Tuebingen
> >> Germany
> >>
> >> http://www.maartenbuis.nl
> >> --------------------------
> >>
> >>
> >>
> >>
> >> *
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