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Re: st: compare effect size between dummys and metrics variables in logistic regression


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: compare effect size between dummys and metrics variables in logistic regression
Date   Mon, 27 Sep 2010 16:18:26 +0000 (GMT)

--- On Mon, 27/9/10, Alan Acock wrote:
> In Maarten's response below, one of
> his approaches, as I understand it, is to standardize the
> predictors and Maarten says "All of these standardization
> can in principle be computed for dummy variables." Does he
> mean to standardize a dummy independent variable? If your
> dummy variable represents a clear discrete dichotomy (e.g.
> gender) where there is no underlying continuum then I don't
> understand a one standard deviation change in the dummy
> variable. Going up or down one standard deviation on such a
> variable is not often an interesting substantive topic.
> 
> I have this concern because I often see people report
> standardized beta weights when they have a dummy predictor
> and compare those beta weights to the beta weights for
> continuous predictors. They might say a one standard
> deviation change in race produces a .2 standard deviation
> change in Y whereas a one standard deviation change in
> education produces a .3 standard deviation change in Y. 

We are in complete agreement. The "can in principle be 
computed" should be interpreted in purly mechanical terms:
you can ask Stata to do it, and it will do it. That does 
not mean that the result will make substantive sense. The
rest of my earlier post hopefuly made clear that it 
does not make sense. 

-- Maarten
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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