I don't follow the example here from your posting,
but I have no quarrel with the late great James
S. Coleman and his wonderful book (from 1964,
in my memory) and I am sure he understood the
Poisson better than I do.
I am attacking the application of count models
to non-count data, which I understood Timothy Mak to be
defending, and I don't see my dimensional arguments being
addressed here.
At some point this thread may have got
detached from the original question....
Nick
[email protected]
David Bell
> On Oct 10, 2006, at 1:08 PM, n j cox wrote:
>
> > I think it's pretty much wired in that Poisson,
> > negative binomial, etc., really are for counts.
> >
> Actually, I seem to recall that Poisson processes are based on
> probabilities of changes of state. If you are counting the
> sequential changes, then you have count data and it is
> straightforward to label each state with the number of persons (or
> other entities) the state represents. If, on the other hand, the
> changes are psychological (such as the change from being strongly
> opposed to some action to being "only opposed") then the labels for
> the states are not counts. As I recall, James Coleman, in
> Introduction to Mathematical Sociology (1965) used Poisson models of
> responses to ordinal attitudinal scales.
>
> The approach never became popular in sociology, but it gives a
> justification for using Poisson and related processes on non-count
> data, as Matthew seems to want to do.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
