RE: st: which -cmp- option to use for poisson model with count data?

[Nick Cox <n.j.cox@durham.ac.uk>]

For the record, that's close to what I said. 

On dependence: I don't think it is crucial that you can't ask 3 experts without asking 2 first, and so forth. How you get to what is recorded as an outcome of 3 is a separate issue. Dependence would mean that my chance of asking so many experts would depend on somebody else's chances, somehow. 

Nick 
n.j.cox@durham.ac.uk 

Maarten Buis

On Mon, May 7, 2012 at 4:58 PM, Laura R. wrote:
> the distribution of the variable "number of experts" consulted is not
> "zero-inflated", but rather follows a normal distribution from 0 to 5.

That is very implausibly, if not impossible (assuming that with
"normal distribution" you mean the Gaussian distribution that is often
called "bell shaped" in introductory statistics books).

> As there theoretically can be more than 5 experts, Nick sais, if I
> understand correctly, that this would be a hint to use Poisson model,
> as I would have to label the highest "category" "5 or more" in ordered
> probit.

Neither Poisson nor ordered probit/logit care (and thus ignore) how
you label values, so your summary of Nick's reasoning is not true.
Based on the information you have given us I would lean towards
Poisson, but to make this kind of choice you really need to have
detailed knowledge of the data, the exact question that was asked and
the possible answer categories (don't trust the variable label, look
in the codebook and the original questionnaire), the process that is
being measured, etc. etc.

> However, I have read that the events have to be independent of each
> other in the Poisson model, e.g. emergency room admission (taking
> David Roodman's example). This would be a reason for not using
> Poisson. E.g., deciding on getting a third child probably depends on
> how life is with 2 children --> ordered probit model. COnsulting
> another expert can also depend on what the last one had said.
>
> I think I will try the ordered probit model again, as this can be used
> within -cmp-, while the Poisson model cannot. If the parallel
> regression assumption or other assumptions for ordered probit models
> turn out to be violated, I will try the Poisson model, but then I have
> to come up with an idea similar to -cmp- that can be used with
> Poisson.


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