2000 年 27 巻 1 号 p. 15-32
We propose an optimal scaling method to analyze the data describing the repeated choice responses of individuals among categories. In the method, each individual is represented as a polynomial growth curve, while each category is expressed as a point in a low-dimensional space. The solution which is explicitly obtained with eigenvalue decomposition provides the configuration representing the longitudinal changes of individuals with the growth curves. We further modify the method so that it performs the clustering of individual growth curves simultaneously with optimal scaling. The solution is obtained with the alternating least squares algorithm that iterates the scaling through eigenvalue decomposition and the clustering by a K-means method. The resulting configuration of clustered growth curves allows us to easily find some trends in individual changes.