RE: st: Zero-inflated binomial regression

["Steichen, Thomas J." <SteichT@rjrt.com>]

There is also the zero-inflated negative binomial model (ZINB), 
which allows overdispersion (a variance that exceeds the mean)
in the number of academic after-school programs.

Maarten's commentary below would apply to this model also
except that the "number of programs" woulds not need to be
determined through a strict poisson (variance = mean) process.

Tom

-----------------------------------
Thomas J. Steichen
steicht@rjrt.com
-----------------------------------
 
-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis
Sent: Tuesday, October 23, 2007 9:39 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Zero-inflated binomial regression

--- Meryle Weinstein <mgw216@nyu.edu> wrote:
> I have count data and have been doing analyses using negative
> binomial regression. I've been doing reading and think that the
> zero-inflated  binomial regression may be more appropriate given the
> number of zeros in data (243 out of 626). 

Two comments: 
1) I assume you mean zero inflated poisson (-zip- in Stata) instead of
zero-inflated binomial.

2) The negative binomial is also meant to deal with excessive zeros,
although it assumes these came into existence through a different
process. 

> The data is the count of academic after-school programs in an
> elementary school zone.  The zones could have zero because
> they don't have any after-school programs (which is the majority of
> cases) or zero because there are no academic programs.   What 
> I don't understand and haven't been able to find in the readings is
> how to choose the variables for inflate.  

With -zip- you assume that there are two types of districts, a type of
district that will always have 0 programs, and a type of district
whereby the number of programs is determined through a poisson
regression (which may include 0 programs). You haven't observed the
type, but only the count and this is a mixture of the two processes.
The -inflate(varlist)- option tells -zip- which variables predict the
type of district. So you choose those variables you think will
influence the probability of being an "always zero program district".

For more on this I highly recomend "Regression Models for Categorical
Dependent Variables Using Stata" by J. Scott Long and Jeremy Freese.

http://www.stata.com/bookstore/regmodcdvs.html

Hope this helps,
Maarten

-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


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