> > The bivariate logistic models consist of one continuous dependent variable
> > of interest which is included in every model (var1, below) together with a
> > different (mostly dichotomous) variable (var2) for each model, i.e.:
> >
> > .logistic death var1 var2
> >
> > The point is to assess the change in the odds ratio associated with var1
> > with inclusion of var2.
>
>
> It looks like you are doing some sensitivity analysis or extreme bounds analysis like
> (Sala-i-Martin 1997), is that right?
>
> Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review,
> American Economic Association, vol. 87(2), pages 178-83.
>
I have two additional comments on this enterprise:
1. The ommited variable bias in logistic regression works a bit different in logistic regression
than in OLS regression: Omitted variables will effect estimated effects of the variable of
interest even if the omited variable is uncorrelated with the variable of interest. I have a
working paper (Unobserved heterogeneity in logistic regression) on my website
(http://home.fsw.vu.nl/m.buis/) which shows this with some simple examples.
2. Have you taken care of missing values (i.e. have you checked that the sample size is the same
in each model)? If not, than you may make some strange comparisons. You could either choose to
only use data that are completely observed on all variables that are used in at least ones model,
or do some multiple impution (e.g. by using -ice-, see -findit ice-)
HTH,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z214
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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