  Ordered categorical data frequently arise in the analysis of biomedical,
agricultural, and social sciences data. The logistic regression model is
attractive in analyzing ordered categorical data because of its use in
interpretation of a parameter estimate. The ordered responses may be clustered
and the subjects within the clusters may be positively correlated. To
accommodate this correlation we add a random component to the linear predictor
of each clustered response. This article presents and compares random-effects
logistic regression models for analyzing ordered categorical data. The proposed
models are applied to an agricultural experimental data. In order to assess the
performance of the random-effects and homogeneous models we perform a
simulation study. Our analysis, application to real data and simulation, show
that the probability of the individual categories are estimated poorly in
homogeneous models. The random-effects models fit the data statistically
significant and estimate the probability of the individual categories more
precisely.
