On Use of Multidimensional Rank-Order Data for Multidimensional Scaling
Hei-Ki Dong · Perceptual and Motor Skills · 1982
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.