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Re: AW: st: AW: beta coefficients for interaction terms
Thank you very much.
I was just a little confused, since in the example I was given the DV
was standardized.
Example
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
Best,
Lisa
Zitat von John Antonakis <[email protected]>:
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, [email protected] 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 <[email protected]>:
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, <[email protected]> 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 <[email protected]>:
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, <[email protected]> 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 <[email protected]>:
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: [email protected]
[mailto:[email protected]] Im Auftrag von
[email protected]
Gesendet: Donnerstag, 16. April 2009 14:07
An: [email protected]
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 <[email protected]>:
<>
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: [email protected]
[mailto:[email protected]] Im Auftrag von
[email protected]
Gesendet: Donnerstag, 16. April 2009 12:35
An: [email protected]
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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