Dear Daniel:
The problem with unobserved heterogeneity is that it is well... unobserved. Apparently you have
many predictors of mortality available, so an obvious solution is to add some of these predictors.
In an earlier post you suggested that your variables are collinear, so you probably don't want to
add them all. That is no problem since the fact that they are collinear with the variables left
out means that most of the variance is captured by the variables in the model (it does make the
causal interpretation of these control variables more difficult, but the roll of control variables
is to control, and that is what they do).
I see the results of my models more as a rough indication than anything else. So I tend to worry
less about technicalities like these. In my own research I deal with survey data, and in my
department they tape trained and experienced interviewers from reputable agencies while they are
interviewing and code the interactions between interviewer and interviewed. The results make me
very skeptical about the precision of my data. (See aside below) The paper was written more to
satisfy my nerdish tendencies than that I thought that the impact of this phenomenon would be
large enough to be noticeable above the random noise coming from data collection. (I may be wrong
though; the simulations by Glenn Hoetker seem to point in that direction, though I have not yet
read it as carefully as I should). I pointed you to this phenomenon because in such a sensitivity
analysis this phenomenon might be worthy of a footnote, and my working paper might be helpful in
understanding the literature to which it refers (and also the literature to which Richard Williams
referred).
So, my not entirely satisfactory answer is: dealing with "observed heterogeneity" is much easier
than unobserved heterogeneity. If you use additional modeling on top of that and you get different
results make sure you understand why that is the case and convince yourself that that is
plausible.
HTH,
Maarten
Aside
Taping interviews does result in some funny interactions though: Interviewer: How many times do
you eat grain products for breakfast? Respondent: Well.... never .... eh.... well no, that's not
right, beer is a grain product too, isn't it?
More often the interactions aren't that funny. For instance, the "experienced" interviewer looks
around the room and decides for the respondent in which income and educational category he/she
falls, or asks very suggestive questions, makes mistakes while entering the data, etc. etc. etc.
--- daniel waxman <[email protected]> wrote:
> Maartin Buis directed me to a short paper of his: "Unobserved heterogeneity
> in logistic regression":
>
> http://home.fsw.vu.nl/m.buis/
>
> The concept makes sense--the question is what to do about it.
<snip>
> There are of course many unobserved causes for in-hospital mortality, but
> insofar as this particular model seems to work, do I need to deal with this?
> If one does try to deal with it in a situation such as mine, is it a matter
> of using a method other than simple logistic regression to fit the model, or
> is it more a matter of assessment of goodness if fit?
>
-----------------------------------------
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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