RE: st: Comparing coefficients across sub-samples

[Adam Cheung <adam_kalok@yahoo.com.hk>]

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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