著者

小島 隆矢 若林 直子 白川 真裕 彭 博 伊丹 弘美

出版者

日本建築学会

雑誌

日本建築学会環境系論文集 (ISSN:13480685)

巻号頁・発行日

vol.86, no.784, pp.578-589, 2021-06-30 (Released:2021-06-30)

参考文献数

21

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1. Background and objectiveThis study examines the methodology of "individual scaling method" in order to value each person's vocabulary and viewpoints. "Individual scales" refers to evaluation items made by subjects' own terms. Thus, these scales are different from person to person. In the previous report, we proposed principal component analysis (PCA) method for evaluation data measured by individual scales. In this PCA method, evaluation object is regarded as observations, and individual scales of all subjects is regarded as variables. And, individual difference in vocabulary and viewpoint is described as difference in distribution of factor loadings vectors in principal component space. Therefore, it is difficult to analyze individual difference of each object. In this paper, methodology for analyzing individual differences of each object in individual scales method was studied and proposed.2. Methodology of analysisFor the above purpose, the application method of "partial score" used in the analysis method called MFA (MultipleFactor Analysis) was discussed. And the following method was proposed.1) Exclude subjects with low correlation between partial score and global score (principal component score).2) Use "standardized partial score" to analyze individual differences for each object.3) Correcting techniques for "ipsative data" may be necessary when analyzing individual or group differences between multiple objects.In addition, using "HCA (Hierarchical Component Analysis)" as the analysis method is expected to improve the performance of partial scores.3. Case studyAs a case study, a survey on positioning analysis for dental clinic was conducted. The background, purpose and method of this survey were described.4. Analysis and discussionPCA, MFA (Block-based PCA), and HCA were applied to the case study data, and the following results were obtained.1) The output principal component scores did not differ between the methods.2) HCA was the best in the performance of statistical tests for partial scores. This shows that reliability of partial score by HCA is higher than that of other methods.3) It was found that the positioning of the dental clinic is different between those who are positive for visiting dental clinic and those who are not.5. Consideration on Methodology of AnalysisBased on the results of analysis as a case study, methodology was further considered. First, as a criterion to exclude subjects with low correlation between partial score and global score , “R-square≧0.4” was proposed. Next, how to use FA and PCA properly was considered, and necessity of correcting techniques for "ipsative data" was discussed.6. Future tasks and prospectsThe future tasks are to apply the proposed method to many cases. And, using partial scores, it seems possible to analyze individual scale method data, including different objects for each person. It is also a future task to consider this method.