The author, having worked at a university for many years and provided extensive career support, has often encountered gaps between university students' perceptions of professions and those held by career support staff. Communicating these gaps effectively to students often proved challenging, prompting the applicant to consider developing tools that could intuitively illustrate such discrepancies. Similarly, the applicant observed many students with mismatched expectations about the universities they enrolled in, stemming from image gaps. Conveying information not discernible from materials such as university brochures through intuitive visual representations could aid understanding and reduce mismatches.

In psychology, image measurement often utilizes methods like semantic differential (SD) scales and factor analysis. However, interpreting the results typically requires understanding complex outputs, such as factor loading matrices, making them inaccessible to those without foundational knowledge of factor analysis. Consequently, even when images are quantitatively measured, the results are not generalized or clearly expressed. Therefore, this study proposes a method to visually (graphically) represent the results of factor analysis in an intuitive and understandable manner, allowing individuals to grasp how images are perceived and identify differences in perception. This method has broad applications, such as decision-making support, intuitive presentation of evaluation results, design generation, and image creation.

As one approach to graphically representing the results of factor analysis, the applicant proposes using Chernoff face graphs (1973). In Chernoff face graphs, variables are assigned to facial features, such as the width or height of the face or the length of the nose. However, there are no standardized assignment methods, leading to variations in the impressions generated by the graphs depending on the creator’s choices. For example, a Chernoff face graph can represent data for different prefectures, where “population” is assigned to face width, “population density” to face height, and “average annual temperature” to nose length. If, however, the assignments were changed (e.g., “average annual temperature” to face width, “population” to the curvature of the upper half of the face, and “population density” to the curvature of the lower half), the impression conveyed by the graph would differ.

To address these issues, this study proposes a method that automatically assigns the results of factor analysis to facial features based on factor elements. By applying this method, the research aims to develop tools for supporting career decision-making and improving the presentation of evaluation results.

Keywords: Decision making, mental image, face graph, factor analysis.