Re: st: RE: Poisson regression with score/scale as DV

[Clinton Thompson <clintonjthompson@gmail.com>]

Many thanks for the replies, Jan & Nick.  As for the suggestion to
create a sum index based on the dichotomization of the ordinal
variables, I must admit that I'm unsure of how/why this would be
superior to the current index.  In my situation, the score follows
from the summing of nine composite questions about the frequency with
which a person engages in an activity where each composite question
has four responses ("Never", "Rarely", "Sometimes", "Often").  The
corresponding values for the responses are [0,3].  Maybe I don't yet
understand the intricacies of the Poisson distribution but re-scaling
the component questions from [0,3] to [0,1] will just re-scale the
score variable from [0,27] to [0,9], which still leaves me w/ a
bounded DV with a pile-up of responses at zero.  Either way (and if I
understand both of you), it sounds like Poisson is a reasonable way to
model this variable/response?

Nick -- I hadn't considered -glm, f(binomial)- but I'll look further
into it.  (And thanks for correcting my reference to Austin Nichols'
presentation.  My spelling implied his last name is Nichol -- not
Nichols.  Embarrassing mistake.)

Thanks again,
Clint


On Tue, Apr 3, 2012 at 10:43 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> Lots of social scientists agree with you, while lots of other social
> and other scientists spend most of the time doing precisely that.
>
> On Tue, Apr 3, 2012 at 9:07 AM, Reinhardt Jan Dietrich
> <jan.reinhardt@paranet.ch> wrote:
>
> ... Ordinal items should definitely not be summed up ...
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