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


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   AW: AW: st: AW: beta coefficients for interaction terms
Date   Tue, 21 Apr 2009 15:49:32 +0200

<> 

You either standardize yourself, and use the normal -regress- command, or let Stata do the work with the -beta- option, but not both at the same time...

*************
	sysuse auto, clear
	reg pr we tr tu, beta
	
	foreach var of varlist pr we tr tu{
		egen std`var'=std(`var')
	}

	reg stdpr stdwe stdtr stdtu
*************



HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von lschoele@rumms.uni-mannheim.de
Gesendet: Dienstag, 21. April 2009 15:38
An: statalist@hsphsun2.harvard.edu
Betreff: Re: AW: st: AW: beta coefficients for interaction terms

Hi,
I have another question regarding beta coefficients:
After coding:
. sysuse auto, clear
. egen shead = std(headroom)
. egen slength = std(length)
. egen smpg = std(mpg)
. gen ia2 = shead*slength
. egen sia2 = std(ia2)
. reg smpg shead slength sia2
Do I have to do .regress, beta?
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.

Thank you Lisa


Zitat von Ulrich Kohler <kohler@wzb.eu>:

> I think Lisa refer the section on standardized regression cofficients of
> that book, particularly to the second item on pg. 201 (English edition).
> That item states that one should not use b*s_x/s_y to create
> standardized regression coefficients in the presence of interaction
> terms. Based on Aiken/West 1991 (28-48) it is recommended that one
> should standardize all variables that are part of the interaction in
> advance. Hence, instead of coding
>
> . sysuse auto, clear
> . gen ia = head*length
> . reg mpg head length ia, beta
>
> you should code
>
> . sysuse auto, clear
> . egen shead = std(headroom)
> . egen slength = std(length)
> . egen smpg = std(mpg)
> . gen ia2 = shead*slength
> . egen sia2 = std(ia2)
> . reg smpg shead slength sia2
>
> The estimated coefficients of the constituent effects then show how much
> standard deviations the dependend variable change when the independent
> variable changes by one standard deviation and the other variable of the
> interaction term is at its mean.
>
> Standardized regression coefficients are often used to find out which of
> the independent variables have the "largest" effect. I must admit that I
> often fail to understand why students want to know that. But leaving
> that aside, if an effect is not constant over the range of another
> variable (i.e. in the presence of an interaction term) the question of
> which independent variable have the largest effect seems pointless.
>
>
> Many regards
> Uli
>
>
> Am Donnerstag, den 16.04.2009, 14:47 +0200 schrieb Martin Weiss:
>> <>
>>
>> Your -gen- statement computes the interaction, but Stata would treat this
>> new variable as a covariate in its own right, w/o any connection to other
>> covariates. A similar issue arises with quadratic terms of a covariate
>> (http://www.stata.com/statalist/archive/2008-08/msg00307.html).
>>
>>
>> The book you mentioned has a subsection on the topic on pages 222-226, and
>> the English version seems to be a straightforward translation of it, AFAIK
>> (http://www.stata-press.com/books/daus2.html, page 222). I cannot find the
>> stuff on the beta coefficient there, though. They do say that you should
>> check for missings with -rowmiss- and that you should subtract the mean from
>> the variables before standardization. The latter is easily accomplished via
>>
>> *****
>> sysuse auto, clear
>>
>> *enter your vars to be standardized here
>> local stdvars "price weight trunk turn"
>>
>> foreach var of local stdvars{
>> summ `var', mean
>> gen std`var'=`var'-r(mean)
>> }
>> *****
>>
>> -egen, std()- would divide by the standard deviation in addition to my
>> code...
>>
>> HTH
>> Martin
>>
>>
>> -----Ursprüngliche Nachricht-----
>> Von: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von
>> lschoele@rumms.uni-mannheim.de
>> Gesendet: Donnerstag, 16. April 2009 14:07
>> An: statalist@hsphsun2.harvard.edu
>> Betreff: Re: st: AW: beta coefficients for interaction terms
>>
>> How do I tell stata, that it is an inetraction term?
>> Here is what I did:
>> gen appearance_attention=apperance*attention
>>
>> Is that telling stata, that the new variable is an interaction term?
>>
>> I am referring to the book "Datenanalyse mit Stata" by Kohler, Kreuter
>>
>> "Note that you can effect the standardization yourself via - egen,
>> std()-" What standardization do you mean? The z-standardization or the
>> "normal" standardization for the beta coefficients, that I need for
>> the interpretation?
>>
>> Best Lisa
>>
>>
>> Zitat von Martin Weiss <martin.weiss1@gmx.de>:
>>
>> > <>
>> >
>> > Well, did you tell Stata in any way that a specific variable is an
>> > "interaction term"? If not, Stata probably treats it as just another
>> > covariate in your regression.
>> >
>> > BTW, which book are you referring to?
>> >
>> > Note that you can effect the standardization yourself via - egen, std()-
>> >
>> >
>> > HTH
>> > Martin
>> >
>> > -----Ursprüngliche Nachricht-----
>> > Von: owner-statalist@hsphsun2.harvard.edu
>> > [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von
>> > lschoele@rumms.uni-mannheim.de
>> > Gesendet: Donnerstag, 16. April 2009 12:35
>> > An: statalist@hsphsun2.harvard.edu
>> > Betreff: st: beta coefficients for interaction terms
>> >
>> > Hi Statalist,
>> >
>> > I am working on a regression model with interactions between some
>> > variables. I read in a book, that I can't use the "normal"
>> > standardized beta coefficients for the interaction terms. They said
>> > that the interpretation of the beta coefficients is not possible until
>> > you z-standardise the interaction variables before you do the
>> > regression.
>> >
>> > Does anyone know, if stata does the z-standardization for the
>> > interaction variables automatically, so I can use the normal
>> > standardized beta coefficients (shown in the stata output) for the
>> > interpretation?
>> > I am using the 9.1 version of stata.
>> >
>> > I hope someone can help me.
>> >
>> > Best Lisa
>> >
>> > *
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>> >
>> >
>> > *
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>> > *   http://www.ats.ucla.edu/stat/stata/
>> >
>> >
>>
>>
>>
>> *
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> --
> kohler@wzb.eu
> 030 25491-361
>
>
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>
>



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