Ordinary time-series analysis operates on time series data measured on interval scale. In the study of psychology, social science and management, we often encounter time-series data measured on nominal scale. In such cases, investigating the trend of response or selection is an important research activity. One of the ways to clarify the trend is visual expression of the trend. In this study, “time-series analysis of qualitative data”is developed to achieve this purpose. The basic idea behind the method is to assign numerical values or scores to categories so as to maximize correlation between the category scores and the function of time variable. This method includes five models: irreversible model, reversible model, circulative model, increase model, and decrease model. The method proposed in this study can be widely used as a tool for analyzing not only time-series data but also a set of data in which there are some external variables and a category data. An illustrative application to real data is presented to examine the availability of the method. Some technical problems and relation to other methods are also discussed.
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