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Re: AW: st: AW: beta coefficients for interaction terms


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: AW: st: AW: beta coefficients for interaction terms
Date   Tue, 21 Apr 2009 17:27:14 +0000 (GMT)

Yes, but on a general note: don't try to interpret interaction 
effects without writing down the equation and doing some 
computations on paper. When doing interactions I always use my 
whiteboard and compute the effects for several values of the 
variables. I have never been able to properly interpret 
interaction terms without writing down the effects.

-- Maarten

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

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


--- On Tue, 21/4/09, lschoele@rumms.uni-mannheim.de <lschoele@rumms.uni-mannheim.de> wrote:

> From: lschoele@rumms.uni-mannheim.de <lschoele@rumms.uni-mannheim.de>
> Subject: Re: AW: st: AW: beta coefficients for interaction terms
> To: statalist@hsphsun2.harvard.edu
> Date: Tuesday, 21 April, 2009, 5:17 PM
> Thank you very much Maarten.
> So for the interpration of the beta coefficicients:
> For example Y=5.09-2.30X+4.50Z+1.20XZ
> The interpretion for my interaction term would be: the
> interaction  
> lowers the negative effect of X and it increases the
> positiv effect of  
> Z. Am I getting it right?
> 
> Lisa
> 
> Zitat von Maarten buis <maartenbuis@yahoo.co.uk>:
> 
> >
> > --- On Tue, 21/4/09, lschoele@rumms.uni-mannheim.de
> wrote:
> >> Without having iteraction terms, as far as I know
> you have
> >> to coed -regress, beta- to get the standardised
> beta
> >> coefficients, so you can  tell which variable has
> the
> >> biggest effect.
> >
> > With interactions you have to very precise about what
> you
> > exactly want. The whole point of an interaction is
> that
> > the effect of a variable is allowed to change when
> > another variable changes. So the question which
> variable
> > has the bigger effect now has multiple answers.
> >
> > The way forward is to go back to your substantive
> problem
> > and try to figure out what it is exactly what you want
> to
> > know, and derive your interaction term and
> standardizations
> > from that.
> >
> > One way that could make sense is to present the
> difference
> > between standardized effects for different values of
> both
> > variables in a graph like in the example below:
> >
> > *------------------------ begin example
> -------------------
> > sysuse auto, clear
> > local vlist "price mpg rep78"
> > foreach var of varlist `vlist' {
> > 	sum `var'
> > 	qui gen double z_`var' = (`var' -
> r(mean))/r(sd)
> > }
> > gen z_mpgXz_rep78 = z_mpg*z_rep78
> >
> >
> > reg z_price z_mpg z_rep78 z_mpgXz_rep78
> >
> > gen effdif0 = _b[z_mpg] + _b[z_mpgXz_rep78]* z_mpg -
> ///
> >               (_b[z_rep78])
> >
> > gen effdif_2 = _b[z_mpg] + _b[z_mpgXz_rep78]* z_mpg -
> ///
> >               (_b[z_rep78] + _b[z_mpgXz_rep78]*-2)
> >
> > gen effdif2 = _b[z_mpg] + _b[z_mpgXz_rep78]* z_mpg -
> ///
> >               (_b[z_rep78] + _b[z_mpgXz_rep78]*2)
> >
> > twoway line effdif_2 effdif0 effdif2 mpg, sort ///
> >    ytitle("difference in standardized
> effects" ///
> >           "of mileage and repair status")   
>   ///
> >    legend(order(1 "z_rep78 = -2"            
>   ///
> >                 2 "z_rep78 = 0"             
>   ///
> >                 3 "z_rep78 = 2"))           
>   ///
> >    yline(0)
> > *------------------- end example
> --------------------------
> >
> > Hope this helps,
> > Maarten
> >
> > -----------------------------------------
> > Maarten L. Buis
> > Institut fuer Soziologie
> > Universitaet Tuebingen
> > Wilhelmstrasse 36
> > 72074 Tuebingen
> > Germany
> >
> > http://home.fsw.vu.nl/m.buis/
> > -----------------------------------------
> >
> >
> >
> >
> >
> >
> > *
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> >
> >
> 
> 
> 
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