RE: st: Comparing two response variables

["Wooldridge, Jeffrey" <wooldri1@msu.edu>]

This doesn't look promising to me. You might find a transformation, such
as taking the natural log, that would lead to a "better" fit. I think
this is similar to the old conundrum about how one cannot compare
R-squareds to choose among dependent variables.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Richard
Williams
Sent: Monday, February 28, 2011 8:05 PM
To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
Subject: Re: st: Comparing two response variables

I too am not sure what "better" is, but my first impulse is to say 
that better = more reliable. Therefore, assuming you have the 
original 10 items, you might use the -alpha- command to see whether 
the 2 additional variables increase or decrease the overall 
reliability of the scale.

At 06:20 PM 2/28/2011, Debs Majumdar wrote:
>Hello,
>
>    I was asked this question today "Is there any way one can say 
> one dependent
>variable is better than the other for the following situation?"
>
>   Suppose, you have two response variables Y1 and Y2 on the same 
> metric, one a
>composite of 8 items and the other with 10 items (same 8 items + 2 
>other). You
>have two predictors (X1 and X2, say) and you run the following
regressions:
>
>  Y1 = a_0 + a_1*X1 + a_2*X2 + e1
>
>  Y2 = b_0 + b_1*X1 + b_2*X2 + e2
>
>Is there anyway to prove Y1 is a better measure for the trait we are 
>measuring
>when compared to Y2?
>
>I don't have a clear cut answer for this. Is using `-sureg' 
>appropriate for this
>case? Any help is appreciated.
>
>Thank you,
>
>Debs
>
>
>
>
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Richard Williams, Notre Dame Dept of Sociology
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