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

[Reinhardt Jan Dietrich <jan.reinhardt@paranet.ch>]

I certainly agree with Nick, at least to a large degree ... If you dichotomize you will of course loose information as well and just adding everything up should not be done after inquiring into the dimensionality of the data (for instance with pca if you don't have any idea or confa if you have a hypothesis on this). For sure summing up ordinal items is meaningless because of different reference standards people will apply. Most reviewers would critisize this as an operation that is mathematically not feasible either ...
What you could also do is apply some ordinal probit or logit model to your problem ...
Jan


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: Dienstag, 3. April 2012 11:35
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: RE: Poisson regression with score/scale as DV

Jan and I are not bound to agree. I don't agree with the argument that
it's clearly OK to count yes-no answers to different questions but
clearly not OK to add graded answers to different questions.

Scores will never satisfy measurement purists, but the job of the data
analyst is to squeeze the juice out of never-ideal data, not to
pontificate about perfect oranges that would yield perfect juice.

In this case, one obvious difficulty is whether (e.g.) my "Often" is
equivalent to anybody else's, let alone everybody else's, but
dichomotimising the scale would not remove that difficulty.

It's unclear whether piling up just refers to skewness or you have
zero inflation too.


On Tue, Apr 3, 2012 at 10:09 AM, Clinton Thompson
<clintonjthompson@gmail.com> wrote:
> 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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