Title: Ordinal response and ordinal predictors: A regression model and monotonicity direction classification of effects
Authors: Javier Espinosa - University College London (United Kingdom) [presenting]
Christian Hennig - UCL (United Kingdom)
Abstract: A regression model is proposed for the analysis of an ordinal response variable depending on a set of multiple covariates containing ordinal and potentially other variables. The proportional odds cumulative logit model is used for the ordinal response, and constrained maximum likelihood estimation is used to account for the ordinality of covariates. Ordinal predictors are coded by dummy variables. The parameters associated with the categories of the ordinal predictor(s) are constrained, enforcing them to be monotonic (isotonic or antitonic). A decision rule is introduced for classifying the ordinal predictors' monotonicity directions, also providing information whether observations are compatible with both or no monotonicity direction. In addition, a monotonicity test for the parameters of any ordinal predictor is proposed. The monotonicity constrained model is proposed together with five estimation methods and compared to the unconstrained one based on simulations. The model is applied to real data explaining a 10-Points Likert scale quality of life self-assessment variable by ordinal and other predictors.