A MARGINALIZATION MODEL FOR THE MULTIDIMENSIONAL UNFOLDING ANALYSIS OF RANKING DATA

A marginalization model for the multidimensional unfolding analysis of ranking data is presented. A subject samples one of a number of rando m points that are multivariate normally distributed. The subject perce ives the distances from the point to ail the stimulus points fixed in the same multidimensional space. The distances are error perturbed in this perception process. He/she produces a ranking dependent on these error-perturbed distances. The marginal probability of a ranking is ob tained according to this ranking model and by integrating out the subj ect (ideal point) parameters, assuming the above distribution. One adv antage of the model is that the individual differences are captured us ing the posterior probabilities of subject points. Three sets of ranki ng data are analyzed by the model.