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st: Comparing model fit of logistic regressions with robust standard errors


From   "Dirk Deichmann" <dirk@dirkdeichmann.de>
To   statalist@hsphsun2.harvard.edu
Subject   st: Comparing model fit of logistic regressions with robust standard errors
Date   Mon, 12 Oct 2009 12:28:59 +0200 (MEST)

Hello everyone,
 
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. 

Can you calculate the change in Wald chi square from a restricted to a full model and then look up whether this value is significant? Would you just subtract one Wald chi square value from the other to get to the change in Wald chi square value? Or is it more meaningful to say that if you assess model 1 with say x1, x2, and x3  (Wald chi2(3) = 196.63) to model 2 with x1, x2, x3, and x4 (Wald chi2(4) = 198.9) and both models show Prob > chi2 = 0 that the latter model shows a better fit since it is still significant even though a new variable has been added? Or how should I think about this?
 
Many thanks for your kind support,
 
Dirk


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