Ordinal response models are commonly used to describe how opinions, judgments, evaluations,
preferences, and so forth, depend on subjects’ covariates. Since the support of the response variable
is discrete and finite, it is often assumed that classical estimation and testing techniques are not
affected by deviations from the stochastic assumptions. Nevertheless, outlying covariates as well as
anomalous responses can strongly affect the reliability of likelihood based inferential procedures.
Consequently alternative robust estimators and tests are proposed, and their properties
investigated. Furthermore intrinsic robustness features of the link functions are also considered.