Clarification of Relationship between Projective Tests and Questionnaires Although questionnairebased quantitative surveys can be implemented quickly and efficiently for groups, they are also known to have challenges, such as limited contents and skewed results due to intentional responses of the survey targets. In this regard, the projection method can be used to deeply understand the internal aspects of a client, including hisher unconscious side. If the relationship between the questionnaire and the projection methods can be shown and the classification of the psychological aspects that each method measures can be clarified, then the significance of the assessment that combines multiple methods will become more evident. In this study, we conducted a survey by using the combination of the Baum Test and the Group TAT, which was developed for group use based on TAT, one of the projection methods, together with SRS18, which is a scale to measure stress reaction. We clarified the relationship between the tests and evaluated the psychological aspects covered in each test by analyzing the correlation among the measured aspects. This study was conducted with the cooperation of Kenta Koshimura, Ren Yamamura, and Takuya Oizumi, who are graduates of the Faculty of Information Sciences and Arts Toyo University.

Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITONMB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLSGAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.