This article presents the development of a new stochastic
multidimensional scaling (MDS) method, which
operates on paired comparisons data and renders a
spatial representation of subjects and stimuli. Subjects
are represented as vectors and stimuli as points in a T-dimensional
space, where the scalar products, or projections
of the stimulus points onto the subject vectors,
provide respective information as to the utility
(or whatever latent construct is under investigation) of
the stimuli to the subjects. The psychometric literature
concerning related MDS methods that also operate on
paired comparisons data is reviewed, and a technical
description of the new method is provided. A small
monte carlo analysis performed on synthetic data with
the new method is also presented. To illustrate the
versatility of the model, an application measuring consumer
satisfaction and investigating the impact of hypothesized
determinants, using one of the optional reparameterized
models, is described. Future areas of
further research are identified.
DeSarbo, Wayne S, Oliver, Richard L & de Soete, Geert. (1986). A probabilistic multidimensional scaling vector model. Applied Psychological Measurement, 10, 79-98. doi:10.1177/014662168601000107
DeSarbo, Wayne S.; Oliver, Richard L.; De Soete, Geert.
A probabilistic multidimensional scaling vector model.
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