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
>>>>> >
>>>>> > *
>>>>> > * For searches and help try:
>>>>> > * http://www.stata.com/help.cgi?search
>>>>> > * http://www.stata.com/support/statalist/faq
>>>>> > * http://www.ats.ucla.edu/stat/stata/
>>>>> >
>>>>> >
>>>>> > *
>>>>> > * For searches and help try:
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>>>>> > * http://www.stata.com/support/statalist/faq
>>>>> > * http://www.ats.ucla.edu/stat/stata/
>>>>> >
>>>>> >
>>>>>
>>>>>
>>>>>
>>>>> *
>>>>> * For searches and help try:
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>>>>> * http://www.stata.com/support/statalist/faq
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>>>>>
>>>>>
>>>>> *
>>>>> * For searches and help try:
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>>>>> * http://www.ats.ucla.edu/stat/stata/
>>>>
>>>> --
>>>> [email protected]
>>>> 030 25491-361
>>>>
>>>>
>>>> *
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>>>>
>>>>
>>>
>>>
>>>
>>> *
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>>>
>>
>> *
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>>
>>
>
>
>
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