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st: Model choice for predicting ordered non-normal categorical variable

From   "Scott Winship" <>
To   <>
Subject   st: Model choice for predicting ordered non-normal categorical variable
Date   Wed, 30 Oct 2002 09:55:30 -0500

I am trying to estimate a model predicting "food security" (a construct
ranging roughly from adequate food levels with no insecurity to severe
hunger).  Food security is a latent variable, where I observe whether or not
a household experienced each of 18 food-related problems.  The problems
generally are of increasing severity (decreasing prevalence).  A sizable
majority of households experience none of the 18 problems.

I have so far estimated OLS and ordered probit models, which I realize are
not ideal.  I have also estimated a probit model predicting whether a
household experiences 0 or more than 0 problems along with an ordered probit
model for those households experiencing at least one problem.  Have been
told to consider a negative binomial or zero-inflated poisson regression
model, but these don't seem quite right (given that I'm not looking at
counts of independent events).  Seems like I'd want a model similar to an
ordered probit but with an assumed latent distribution that was non-normal.

Any suggestions are appreciated.  - Scott

     Scott Winship
     Ph.D. Candidate in
     Sociology & Social Policy
     Harvard University

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