--- Meryle Weinstein <[email protected]> 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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