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On Use of Multidimensional Rank-Order Data for Multidimensional Scaling

Hei-Ki Dong

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Abstract

The present note suggests an alternative procedure for converting the multidimensional rank-order data for multidimensional scaling. In the past, the multidimensional rank-order data were converted into pair-comparison data or tetrad-comparison data. The proposed alternative converts the multidimensional rank-order data into triad-comparison data, from which the proximities are obtained for multidimensional scaling.

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What this paper is about

The present note suggests an alternative procedure for converting the multidimensional rank-order data for multidimensional scaling. In the past, the multidimensional rank-order data were converted into pair-comparison data or tetrad-comparison data. The proposed alternative converts the multidimensional rank-order data into triad-comparison data, from which the proximities are obtained for multidimensional scaling.

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Available abstract

The present note suggests an alternative procedure for converting the multidimensional rank-order data for multidimensional scaling. In the past, the multidimensional rank-order data were converted into pair-comparison data or tetrad-comparison data. The proposed alternative converts the multidimensional rank-order data into triad-comparison data, from which the proximities are obtained for multidimensional scaling.

Key concepts: Multidimensional scaling, Rank (graph theory), Multidimensional data, Multidimensional analysis, Scaling, Tetrad, Multidimensional systems, Computer science

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