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Re: st: Standardized Beta and Multiple Imputation in Stata 11


From   Alan Acock <acock@mac.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Standardized Beta and Multiple Imputation in Stata 11
Date   Thu, 31 Dec 2009 11:31:18 -0800

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 <maartenbuis@yahoo.co.uk>
An: statalist@hsphsun2.harvard.edu
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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