Thank you all for your help!
Just for clarification, if I do what Maarten suggested I know whether a model improved in fit when the previously added variable turns out to be significant in the Wald test that I conduct after estimating the logistic regression. But I do not know how much the model fit improved, right?
Cheers,
Dirk
----- original message --------
Subject: Re: st: Comparing model fit of logistic regressions with robust standard errors
Sent: Mon, 12 Oct 2009
From: Maarten buis<[email protected]>
> --- On Mon, 12/10/09, Dirk Deichmann wrote:
> > I am applying a logistic regression model with robust
> > standard errors adjusted for clustering.
> >
> > I know there have been some posts about this but to me it
> > still is not clear whether and if so how I can assess the
> > improvement in model fit using the Wald chi square values.
>
> What you should do is estimate the full model, and use
> -test- to test the constraints implicit in the nested model.
> So if you want to leave a variable x1 out of your model,
> you type -logit y x1 other_vars, other_options- followed
> by -test x1-.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
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