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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   Tue, 13 Dec 2011 23:13:31 +0500

Dear Maarten,

Thank you for your comment. Does it mean, the comparison does not
induce comparison of the error structures across the two groups? And
as the model specification has implication for the residuals structure
and predictions, the main point of concern, the comparison should be
taken care for such issues. I would be glade to see if such issues
would be of relevance in my case where I only intend to check the
across group differences and their relevance.

Please let me take some time to work out such issues in more details,
as I am new to this topic.

On Tue, Dec 13, 2011 at 11:04 PM, Maarten Buis <[email protected]> wrote:
> On Tue, Dec 13, 2011 at 6:44 PM, Cameron McIntosh wrote:
>> I will note that one of the easiest ways to do this is via the Mplus package (, which through a special THETA parameterization allows the difference in residual variance to be directly estimated for the multi-group case in both logit and probit models. Thus, cross-group differences in residual variation will not be absorbed by the model coefficients, and not confound the comparison.
> I find that rather suspect: The residuals we are talking here about
> are the differences between the latent (and thus unobserved) variable
> and the predicted probability. The only information in the data
> concerning any patterns in the variance of these residuals is in the
> form the fit of a model with a more complex functional form for the
> relationship between the explanatory variables on the probability of
> success. So I find it hard to see how one could separate the
> estimation of the parameters from the estimation of patterns in the
> residual variance. As a consequence, these models tend to be very
> (i.e. way too) sensitive to model specification. Moreover, the
> difference between the complex functional form and the "regular"
> functional form are really subtle, which means that there is very
> little information from the data that these models can use. In
> essence, the problem is real and it cannot be solved.
> -- Maarten
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> --------------------------
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Muhammad Anees
Assistant Professor
COMSATS Institute of Information Technology
Attock 43600, Pakistan

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