Abstract
A multidimensional scaling model to deal with sorting data is developed. The model assumes (a) that stimuli are embedded in a multidimensional space, and (b) that a particular sorting is generated if and only if all intra-cluster distances between stimuli in the space are smaller than all inter-cluster distances. A new parameter estimation method is proposed which yields a configuration that satisfies the above requirement (b) as much as possible for each sorting given as data. This model is extended to the individual differences scaling model. The models are applied to a set of artificial sorting data and two sets of real data
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