MULTIDIMENSIONAL SCALING TO EXPLORE COMPLEX ASPECTS IN DISSIMILARITY JUDGMENT

This paper proposes a model to analyze the structure and context effects involved in dissimilarity judgment. With some restrictions incorporated, the model is also interpreted as a kind of the distance-density model and further interpreted as a mixed distance and content model. Algorithms for multidimensional scaling based on the proposed model are implemented using nonlinear optimization methods. The algorithms are evaluated through a Monte Carlo study, and applications are demonstrated with real data. Problems regarding the model, the algorithms and possible applications are discussed.

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