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Re: st: Comparing coefficients across sub-samples


From   "Fitzgerald, James" <J.Fitzgerald2@ucc.ie>
To   "<statalist@hsphsun2.harvard.edu>" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Comparing coefficients across sub-samples
Date   Wed, 1 Aug 2012 10:02:31 +0000

Hi Adam

Thanks for the suggestion, but unfortunately xtreg does not allow the beta option. 

Regards

James

On 1 Aug 2012, at 09:48, "Adam Cheung" <adam_kalok@yahoo.com.hk> wrote:

> Dear James, 
> 
> You can put the option "beta" after the "regress" command to obtain the standardized beta coefficients:
> 
> regress y x , beta
> 
> Best,
> Adam
> 
> --- 2012年8月1日 星期三,Fitzgerald, James <J.Fitzgerald2@ucc.ie> 寫道﹕
> 
>> 寄件人: Fitzgerald, James <J.Fitzgerald2@ucc.ie>
>> 主題: RE: st: Comparing coefficients across sub-samples
>> 收件人: "Lisa Marie Yarnell" <lisayarnell@yahoo.com>, "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
>> 日期: 2012年8月1日,星期三,下午3:04
>> Hi Lisa
>> 
>> Thank you very much for your response!
>> 
>> I am looking for both the methodology and the command, if it
>> exists.
>> 
>> Does Stata have a command for generating "standardised"
>> betas, or do I just transform my variables by hand and
>> re-run my regressions?
>> 
>> Thanks again
>> 
>> James
>> 
>> ________________________________________
>> From: Lisa Marie Yarnell [lisayarnell@yahoo.com]
>> Sent: 01 August 2012 04:29
>> To: statalist@hsphsun2.harvard.edu;
>> Fitzgerald, James
>> Subject: Re: st: Comparing coefficients across sub-samples
>> 
>> Hi James,
>> 
>> Typically the effect of a predictor in two different groups
>> can be compared with the unstandardized beta. You can do a
>> statistical test of the difference in the betas using the
>> z-score formula below.  I usually just calculate the
>> difference between unstandardized betas from two different
>> models by hand, though Stata might have a command to do this
>> for you.  Is that what you are looking for: the Stata
>> command?
>> 
>>             (b1 – b2) 
>>                
>>      b1 and b2 are the unstandardized
>> regression weights that you want
>> z = --------------------         
>>                
>>           to test the difference
>> between
>>       √(seb12 + seb22)     
>>              seb1
>> and seb2are the standard errors of these unstandardized
>>       ↑         
>>                
>>                
>>           regression weights, found
>> next to the weights themselves
>> This is a square root sign!       
>>               in your
>> SPSS output.  Remember to square them.
>> Take the square root of the
>> entire value in parentheses.
>> 
>> In terms of comparing the *magnitude* of the effect in the
>> two different subsamples, it is more correct to do this
>> qualitatively by comparing the *standardized* beta for the
>> variable of interest against effect size rules of thumb for
>> small/medium/large (which sometimes differ by discipline,
>> such as social sciences/education/engineering).  Just
>> report the standardized beta as the effect size in each
>> group; it would be a qualitative statement about the effect
>> in each group.
>> 
>> Here are rules that I have:
>> Standardized regression coefficients:
>> * Keith’s (2006) rules for effects on school learning: .05
>> = too small to be considered meaningful, .above .05 = small
>> but meaningful effect, .10 = moderate effect, .25 = large
>> effect.
>> * Cohen’s (1988) rules of thumb: .10 = small, .30 =
>> medium, >  (or equal to) .50 = large
>> 
>> Lisa
>> 
>> 
>> 
>> 
>> ----- Original Message -----
>> From: "Fitzgerald, James" <J.Fitzgerald2@ucc.ie>
>> To: "statalist@hsphsun2.harvard.edu"
>> <statalist@hsphsun2.harvard.edu>
>> Cc:
>> Sent: Tuesday, July 31, 2012 4:14 PM
>> Subject: st: Comparing coefficients across sub-samples
>> 
>> Hi Statalisters
>> 
>> I am running the same model on two sub-samples as follows:
>> 
>> xtreg ltdbv lnta tang itang prof mtb if nolowlntalowtang==1,
>> fe cluster(firm)
>> 
>> xtreg ltdbv lnta tang itang prof mtb if nolowlntalowtang==0,
>> fe cluster(firm)
>> 
>> I want to compare the explanatory power of lnta across the
>> two sub-samples i.e. in which sub-sample does lnta explain
>> significantly more of the variation in ltdbv?
>> 
>> Can anyone give me some advice on how to achieve this?
>> 
>> Thanks in advance
>> 
>> James
>> *
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>> 
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>> 
> 
> *
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