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Re: st: Regression Across Two Groups
Nick Cox <firstname.lastname@example.org>
Re: st: Regression Across Two Groups
Wed, 14 Dec 2011 10:03:42 +0000
Thanks for the example.
I see earnings as a coarsely categorised variable, fit for -intreg- or
-ologit-. In what sense is earnings dichotomous (means, has two
categories)? Why did you say you were interested in logit models?
On Wed, Dec 14, 2011 at 9:48 AM, Muhammad Anees <email@example.com> wrote:
> 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 <firstname.lastname@example.org> 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@example.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.
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> Muhammad Anees
> Assistant Professor
> COMSATS Institute of Information Technology
> Attock 43600, Pakistan
> * For searches and help try:
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> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
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