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


From   John Antonakis <john.antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: AW: st: AW: beta coefficients for interaction terms
Date   Sun, 21 Jun 2009 13:31:09 +0200

Hi:

As far as I know, there is not reason to standardize the dependent variable (when standardizing the independent variable to obtain their standardized effects).

Given the range of the DV will change substantively when standardizing it is evident that the intercept now is qualitatively different from before and might not be different from zero.

I'd stick to having the DV in raw units.

HTH,
J.

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On 21.06.2009 12:54, lschoele@rumms.uni-mannheim.de wrote:
Hi

but it is important for my paper to show the value of the intercept, and I get different values if I use either the standardized or the not standardized dependent variable. If I have the null hypothesis that is is equal to zero, do I have to use the standardized or not standardized dependent variable? And coming back to my original question: Why do I have to standardize the dependent variable?

Best,
Lisa


Zitat von Alan Neustadtl <alan.neustadtl@gmail.com>:

If it is not meaningful for your paper how is that complete.  I am
sure yo ucould think of other things to include for "completeness" but
you left out.  In general the null hypothesis for the intercept is
that it is equal to zero.  If that is unimportant it is not needed.

Best,
Alan


On Sat, Jun 20, 2009 at 10:26 AM, <lschoele@rumms.uni-mannheim.de> wrote:
Hi Alan,

I do not have a hypothesis for the intercept, but I want to show the
significance in my paper due to completeness.

Best,
Lisa


Zitat von Alan Neustadtl <alan.neustadtl@gmail.com>:

In your case what is your null hypothesis regarding the intercept?
Why do you want to test for significant difference of this estimate
and some other value?

In many cases the intercept is 1) beyond the range of the data (and
therefore a poor estimate), and 2) theoretically uninteresting.

Best,
Alan


On Sat, Jun 20, 2009 at 8:51 AM, <lschoele@rumms.uni-mannheim.de> wrote:

Hi Statalist,

I have one more question regarding this theme.
Why do I have to standardize the dependent variable as well? If I
standardize it, the constant won't be significant anymore. Without
standardizing the constant is highly significant in my case.

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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