Generalized ordered logit/partial proportional odds models for ordinal dependent variables
Richard Williams
Department of Sociology
University of Notre Dame
Notre Dame, IN
[email protected]
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Abstract. This article describes the gologit2 program for generalized ordered
logit models. gologit2 is inspired by Vincent Fu’s
gologit routine
(Stata Technical
Bulletin Reprints 8: 160–164) and is backward compatible with
it but offers several additional powerful options. A major strength of
gologit2 is that it can fit three special cases of the generalized
model: the proportional odds/parallel-lines model, the partial proportional
odds model, and the logistic regression model. Hence, gologit2 can
fit models that are less restrictive than the parallel-lines models fitted
by ologit (whose assumptions are often violated) but more
parsimonious and interpretable than those fitted by a nonordinal method,
such as multinomial logistic regression (i.e., mlogit). Other key
advantages of gologit2 include support for linear constraints, survey
data estimation, and the computation of estimated probabilities via the
predict command.
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Richard Williams
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gologit2, gologit, logistic regression, ordinal regression, proportional odds, partial proportional odds, generalized ordered logit model, parallel-lines model
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