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Re: st: Count data interaction terms


From   DA Gibson <[email protected]>
To   [email protected]
Subject   Re: st: Count data interaction terms
Date   Tue, 23 Aug 2011 14:54:37 +0100

Hi Maarten

Really sorry to keep bothering you, i just want to completely clarify something.

If i run a poisson analysis for number of doctor appointments with
independent dummy variables (equalling 1) for being obese and smoking
as well as an interaction term between them and get results showing
IRR values of, for example, .3(baseline) 1.4(obese), 2.3(smoker) and
1.4(interaction).

Then the interpretation is that an obese individual who doesnt smoke
is likely to visit the doctor ((1.4-1)*100%) 40% more times than their
non-obese counterpart. A non-obese individual who does smoke is likely
to visit the doctor ((2.3-1)*100%) 130% more times than their
non-smoker counterpart. And the interaction term would suggest that an
individual who both smokes and is obese would visit the doctor
((1.4-1)+(2.3-1)+(1.4-1)*100%) 210% more often than someone who isnt
obese and doesnt smoke?

Or does the interaction term multiply the effect of either obese or
smoking or both?

Many Thanks

Danny

On 23 August 2011 11:20, DA Gibson <[email protected]> wrote:
> HI Maarten
>
> Thanks a lot for that.
>
> Cheers
>
> Danny
>
> On 23 August 2011 11:12, Maarten Buis <[email protected]> wrote:
>> On Tue, Aug 23, 2011 at 11:40 AM, DA Gibson <[email protected]> wrote:
>>> Hi Maarten
>>>
>>> Thanks for that, i do have one further question however. In the
>>> example the IRR on the c.persons#c.child interaction term is positive
>>> at .77 however in your email you say that the interpretation is that
>>> the effect is reduced.
>>
>> These effects are ratios rather than differences, so any number less
>> than 1 is a "negative" effect. If you multiply some number with a
>> number less than one, than the result will be smaller than the
>> original number. You can transform ratios into percentage changes by
>> doing (ratio - 1)*100%, i.e. multiplying the effect of persons by
>> 0.77, is equivalent to saying that the effect of persons changes with
>> -23% when the extra person is a child.
>>
>> Hope this helps,
>> Maarten
>>
>> --------------------------
>> Maarten L. Buis
>> Institut fuer Soziologie
>> Universitaet Tuebingen
>> Wilhelmstrasse 36
>> 72074 Tuebingen
>> Germany
>>
>>
>> http://www.maartenbuis.nl
>> --------------------------
>> *
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>

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