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Re: st: Regression Across Two Groups


From   Yuval Arbel <yuval.arbel@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regression Across Two Groups
Date   Wed, 14 Dec 2011 11:27:07 +0200

Muhammad,

You can look at my correspondence with David Ashcraft.

For your convenience I copies part of it:

David,

You can simply use Difference in Difference (DD) analysis:

Run a regression on the group of managers who take the first (second)
approach. Then predict what would have happened to the performance of
each manager in the case that he/she takes the other approach and use
the -ttest- to see whether the difference is significant.

Note to define dummy variables in any case that variables are ordinal,
i.e., the numerical values have no quantitative meaning

I use this approach quite often. You can look at the second part of my
following paper published in RSUE:

Arbel, Yuval; Ben Shahar,Danny; Gabriel, Stuart  and Yossef Tobol:
"The Local Cost of Terror: Effects of the Second Palestinian Intifada
on Jerusalem House Prices".Regional Science and Urban Economics (2010)
40:  415-426

On Wed, Dec 14, 2011 at 11:18 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Wed, Dec 14, 2011 at 6:25 AM, Muhammad Anees wrote:
>> Sorry for not clarifying the story about the types of variables, like
>> earnings which I have at hand as a categorical/dichotomous variable.
>> For example if an individual has a portion of earnings from doing
>> consultancies or involved in any R&D organizations beside their normal
>> routine jobs. In this case, I was interested in comparing the
>> regression models (across foreign qualified and not foreign qualified)
>> of earnings on other predictors say experience, research training, job
>> nature, industry, region (rural and urban) using logit/probit in case
>> of categorical variables and similarly using OLS for continuous
>> dependent variable which at least I do not have at this stage.
>
> This is still not clear. The independent/explanatory/right-hand-side/x
> variables aren't relevant here, they can be of any type, it is the
> type of  the dependent/explained/left-hand-side/y variable that
> matters. Earnings is typically collected as either a continuous
> variable (how much do you earn?) or as a choice from a set of
> intervals (did you earn less than x$, between x$ and y$, etc.?). None
> of these are correctly modeled as a logit/probit. In the former case I
> would use a -glm- with the -link(log)- option, in the latter case I
> would start with assigning each category with a reasonable
> representative number and than use -glm- with the -link(log)- option.
> There are other solutions for the latter problem, e.g. -intreg-, but
> if the underlying distribution is non-normal, which is likely to be
> the case with earnings, then it is unclear whether these alternatives
> are any better. The comparison is than just a matter of adding the
> appropriate dummies and/or interactions.
>
> -- Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
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-- 
Dr. Yuval Arbel
School of Business
Carmel Academic Center
4 Shaar Palmer Street, Haifa, Israel
e-mail: yuval.arbel@gmail.com

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