Abstract
A probabilistic multidimensional model is described for analyzing preference ratio judgments. This model combines the unfolding model of Coombs with the probabilistic model of Hefner, in which stimuli and individuals are represented by multivariate normal distributions. A simple procedure is described for approximating the maximum likelihood estimates of the location and variance parameters of the model. Two simulations show how well this procedure works, especially when there is considerable variability in the data.