Object-Oriented Bayesian Networks for Modeling the Respondent Measurement Error

摘要

In this article, Object-Oriented Bayesian Networks (OOBN) are proposed as a tool to model measurement errors in a categorical variable due to respondent. A mixed measurement error model is presented and an OOBN implementing such a model is introduced. The insertion of evidence represented by the observed value and its propagation throughout the network yields for each unit the probability distribution of the true value given the observed. Two methods are used to predict the individual true value and their performance is evaluated via simulation.