Hi Lynne,

you might want to check this paper, it's on cound data modeling and different models handling equi- and overdispersed data are discussed. The application field is pharmacometrics but the paper is very well written and the references could be helpful too.

Plan et al. J Pharmacokinet Pharmacodyn (2009) 36:353–366

Performance in population models for count data, part I: maximum likelihood approximations

Regards,
Maciej 

On 31 Aug 2012, at 19:03, Douglas Bates <bates@stat.wisc.edu> wrote:

> On Thu, Aug 30, 2012 at 5:25 PM, Lynne Clay <lynne.clay@xtra.co.nz> wrote:
>> Dear Prof Bates,
>> I'm a doctoral candidate in NZ trying to analyse survey data with random
>> effects with my outcome being a count.  I discovered your lme4 package and
>> have been using this with success, however, I need to check for
>> overdispersion and it is at this point I am having problems.  The formula I
>> have used before has been (1/df)*deviance and if I use this my model is
>> highly overdispersed.  I read on one of the discussion boards that adding an
>> extra random effect (1|id#) addresses the overdispersion problem which I
>> have included but overdispersion continues.
>> 
>> Can overdispersion be calculated in this manner?
> 
> I'm sorry but I know nothing about overdispersion.  To me it is
> completely artificial because there is no probability distribution on
> which to base a statistical model with these properties.
> 
>> Do you have any suggestions of how to deal with this?
> 
> Sorry but I don't.  I have taken the liberty of sending a copy of this
> reply to the R-SIG-Mixed-Models mailing list in the hope that readers
> of that list can help you.
>> 
>> 
>> Lynne
>> 
>> 
>> Lynne Clay
>> PhD Candidate
>> School of Physiotherapy
>> University of Otago
>> PO Box 56
>> Dunedin 9054
>> New Zealand
> 
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