| Date: | Sun, 9 Nov 2008 20:23:11 -0500 |
| Reply-To: | David Greenberg <dg4@nyu.edu> |
| Sender: | "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU> |
| From: | David Greenberg <dg4@nyu.edu> |
| Subject: | Re: evaluating differences in count data |
|
| In-Reply-To: | <491568B7.1060808@gmail.com> |
| Content-Type: | text/plain; charset=iso-8859-1 |
I think this test is not appropriate. It is the test that would be appropriate if the data were being generated by a Poisson distribution. That was my own initial assumption when I responded to the initial inquiry. However, for each individual the response is binary. The assumption of independence of events that is characteristic of a Poisson distribution does not hold here. David Greenberg, Sociology Department, New York University
----- Original Message -----
From: Marta García-Granero <mgarciagranero@gmail.com>
Date: Saturday, November 8, 2008 5:25 am
Subject: Re: evaluating differences in count data
To: SPSSX-L@LISTSERV.UGA.EDU
> Pirritano, Matthew wrote:
> > Sorry, I did leave a little info out. The intervention was intended
> to
> > increase the use of preventive medical procedures. The increased use
> was
> > encouraged by rewarding their use with monetary incentives. The
> > intervention also provided money to increase the population size
> > overall, which contains a diabetic population. Lipid assays are a
> > preventive measure for diabetics. So, we'd expect the number of lipid
> > assays to go up if doctors are taking advantage of the monetary
> > incentives. Hence the two hypothetical counts I gave below. The number
> > of lipid assays went up, but is increase greater than would be expected
> > if the intervention had no effect ( = the null hypothesis).
> >
>
> See chapter 6 of Statistics at Square One (section "Standard error of
> a
> total")
> http://www.bmj.com/collections/statsbk/6.dtl
>
> They describe a simple method to compare two counts. Using your data:
>
> * Standard error of a total (& difference between two totals) *.
> DATA LIST LIST/n1 n2 (2 F8.0).
> BEGIN DATA
> 280 365
> END DATA.
> COMPUTE zvalue = (n1-n2)/SQRT(n1+n2).
> COMPUTE pvalue = 2*(1-CDF.NORMAL(ABS(zvalue),0,1)).
> LIST.
>
> HTH,
> Marta García-Granero
>
> >
> > Thanks
> > matt
> >
> > Matthew Pirritano, Ph.D.
> > Research Analyst IV
> > Medical Services Initiative (MSI)
> > Orange County Health Care Agency
> > (714) 834-3566
> > -----Original Message-----
> > From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On
> Behalf Of
> > Gene Maguin
> > Sent: Thursday, November 06, 2008 12:19 PM
> > To: SPSSX-L@LISTSERV.UGA.EDU
> > Subject: Re: evaluating differences in count data
> >
> > Matthew,
> >
> >
> >>> I'm trying to find out if there is a way to do inferential tests on
> >>>
> > count
> > data. I am comparing the number of individuals who have had one lipid
> > assay
> > in the year prior to an intervention with the number of individuals
> who
> > have
> > had one lipid assay and in the year after the intervention. So I have
> > two
> > numbers which are counts. Let's say 280 individuals with one lipid assay
> > prior to the intervention and 365 individuals with an assay after the
> > intervention.
> >
> >
> > I'm confused by your description. Are you saying that you had a pool
> of
> > people (say, N=500) who were 'assigned' to a pre-intervention lipid
> > assay,
> > an intervention and a post-intervention lipid assay? 280 of 500 did
> the
> > pre-intervention assay and 365 of the 500 did the post-intervention
> > assay?
> > And you want to compare the proportion who completed the pretest to
> the
> > proportion who completed the posttest?
> >
> > Or, do I misunderstand and you are interested in something else?
> >
> > Gene Maguin
> >
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>
> --
> For miscellaneous statistical stuff, visit:
> http://gjyp.nl/marta/
>
> =====================
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