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From |
Reinhardt Jan Dietrich <jan.reinhardt@paranet.ch> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

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

Date |
Tue, 3 Apr 2012 10:25:11 +0000 |

Although dichomotimisation could become the name of a new method ;-) -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: Dienstag, 3. April 2012 11:49 To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: Poisson regression with score/scale as DV I do know how to spell "dichotomise"... On Tue, Apr 3, 2012 at 10:34 AM, Nick Cox <njcoxstata@gmail.com> wrote: > 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 ... * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Poisson regression with score/scale as DV***From:*Clinton Thompson <clintonjthompson@gmail.com>

**st: RE: Poisson regression with score/scale as DV***From:*Reinhardt Jan Dietrich <jan.reinhardt@paranet.ch>

**Re: st: RE: Poisson regression with score/scale as DV***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: RE: Poisson regression with score/scale as DV***From:*Clinton Thompson <clintonjthompson@gmail.com>

**Re: st: RE: Poisson regression with score/scale as DV***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: RE: Poisson regression with score/scale as DV***From:*Nick Cox <njcoxstata@gmail.com>

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