2002 Fiscal Year Final Research Report Summary
Developments of data analysis and its analysis system for evaluating structure of Kansei Quality
| Project/Area Number |
13680519
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
NISHINA Ken Nagoya Institute of Technology, School of Engineering, Associate Professor, 工学部, 助教授 (60115681)
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| Co-Investigator(Kenkyū-buntansha) |
KANDA Koji Nagoya Institute of Technology, School of Engineering, Research Associate, 工学部, 助手 (30288047)
SUMI Katsunori Nagoya Institute of Technology, School of Engineering, Associate Professor, 工学部, 助教授 (70242906)
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| Project Period (FY) |
2001 – 2002
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| Keywords | Semantic Differential (SD) method / Pairwise Comparison method / Multi-dimensional Scaring (MDS) / Capability of Kansei / Individual Difference / EXCEL VBA |
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| Research Abstract |
In this research, the Semantic Differential (SD) methods and the Pairwise Comparison methods are addressed. The methodologies of data analysis, their applications and analysis system are developed, considering the individual differences deeply.
(1) Semantic Differential methods ; an analytical method for identification of individual difference is proposed. There are three variation patterns in the correlation coefficients or the covariations between scores of pair of adjectives. These are the "direction pattern," which is distributed over the positive and negative sides, the "strength pattern," which has a one-sided distribution and the "extreme pattern" which indicates a misevaluated case.
The proposed method is to identify the above patterns by using the principal component analysis and to classify the subjects according to the evaluation structures and the strength of these structures (definiteness). The method was applied to SD data for the high Kansei Quality of some hubcap designs, and was shown to be useful.
(2) Pairwise Comparison methods ; Two cases are addressed. One is that the directed scores are given, the other is the undirected scores, that is, the similarity scores are given. In the former, the usefulness of Nozawa's method is examined through some case studies. In addition, the structure of the Kansei evaluation is analyzed supposing a hierarchy structure. In the analysis of individual differences the SN ratio and the interaction analysis are utilized.
In the latter, an analysis for measuring the similarity between individual evaluations is proposed. It is composed of the individual MDS and the Procrustes Rotation method. The proposed method can be regarded as a pre-analysis of INDSCAL, which is a popular method for the similarity data.
An analysis system for the above data analysis is developed using EXCEL VBA.
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