1990 年 17 巻 28 号 p. 49-80
This paper proposes a mutidimensional scaling method for asymmetric proximity data whose diagonal entries are not all equal. For a suggested model, necessary and sufficient conditions and algebraic solutions are presented. The method consisting of two algorithms is developed, which gives multidimensional configuration, unidimensional scale of any psychological quantities and the degree of asymmetry of the data. The quantities should be interpreted through data analysis or examined with any hypothesis. A simulation study using artificial data demonstrates that the method works successfully, showing sufficient robustness and capability to recover the original structure. Illustrative applications are presented. Extensions of the model are argued, and properties of the model and the algorithms are discussed with another simulation study.
