Data Visualization for Kansei Analysis

Abstract

Kansei data are multi-dimensional data. It is difficult for an analyzer to interpret data whose dimensionality is higher than three because his/her vision is used only to one -- three dimensions. Visualization by reducing the dimensionality of Kansei data to less than or equal to three dimensions could help the analyzer to understand the data. For an effective visualization, definition of distances between data is important. For the definition, axes that form a space of Kansei data should be determined first. The choice could be questions or objects. A questionnaire uses several objects and many questions. Respondents are asked to answer each question one by one by marking on a rating scale. Questions are usually used as axes for multivariate analysis. Objects are another choice for the axes. By changing our viewpoint from different axes, new relationships between questions/objects could be found.