AN OPTIMAL SCORING METHOD FOR DETECTING CLUSTERS AND INTERRELATIONS FROM MULTI-WAY QUALITATIVE DATA

The author proposed in 1971 one of the optimal scoring methods for detecting clusters and interrelations from multi-way qualitative data, adopting the maximization of squared multiple correlation coefficient as a criterion for scoring. In the present paper, we make clear the meaning of adopting this criterion from the viewpoint of linear regression analysis. Then the method is formalized into more convenient form for computing program, with some related properties. The method has been successfully applied to several sets of artificial three-way binary data whose structures are known beforehand, as well as actual three-way categorical data of anxiety-ridden neurotic patients.