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Re: st: Regression Across Two Groups
From
Muhammad Anees <[email protected]>
To
[email protected]
Subject
Re: st: Regression Across Two Groups
Date
Wed, 14 Dec 2011 14:48:50 +0500
Thanks Nick for your suggestion.
Sure, the data looks like which contains two different sample but
related. Foreign qualified and no foreign qualification are two
different dataset earch with the following sample data, only a sample,
actually the data consist two different samples in two different
files, which I can deal how to combine in stata for my purpose.
ear exper gender subject area language
0-5000 20 m IT rural Urdu
5000-10000 22 m ENGINEER urban English
10001-15000 15 f ECONOMICS rural Urdu
5000-10000 10 m HR urban Urdu
5000-10000 5 f STRT MGT urban English
10001-15000 8 f MARK urbna English
0-5000 9 m SOCIOLOGY rural Urdu
0-5000 17 m IT urban Urdu
On Wed, Dec 14, 2011 at 2:39 PM, Nick Cox <[email protected]> wrote:
> It's categorical/dichotomous, yet the example is [pro]portion of
> earnings from outside main job. Sounds like a fractional response from
> the latter. Muhammad: Give us an example of what observations look
> like before this gets any more obscure, please!
>
> On Wed, Dec 14, 2011 at 9:18 AM, Maarten Buis <[email protected]> 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.
>
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--
Regards
---------------------------
Muhammad Anees
Assistant Professor
COMSATS Institute of Information Technology
Attock 43600, Pakistan
www.aneconomist.com
*
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