The Intuitive Judgment of Statistical Properties for Verbal Evaluations

• Main Authors: Wen-Feng Hsiao, 蕭文峰
• Other Authors: Hsin-Hui Lin
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/29398180994180290923

博士 === 國立中山大學 === 資訊管理學系研究所 === 89 === Verbal information plays a pivot role in human daily communication. Recent research has pointed out that the performance of human cognition in processing verbal information has no significant difference from that in processing numerical information. However, no proper model is available to describe human cognition in processing of verbal information. Therefore, this dissertation explores the difference between human cognition and normative models in processing verbal terms, and further analyzes the decision rules employed by decision-makers to illustrate the proper form of a descriptive model. The explored verbal operations include the following statistics: representation, mean, and variance. In the study of verbal representation, the differences among numerical representation, fuzzy representation, and cognitive representation of Likert verbal evaluations are revealed. This cognitive representation is obtained by the proposed interval estimation method. The proposed method can simultaneously construct the verbal categories in a Likert scale. The result shows that the cognitive representation is inconsistent with the assumption of equal interval in numerical representation, and those of symmetry and equal space in fuzzy representation. In the study of verbal mean operation, the research first investigated the differences among numerical, fuzzy, and cognitive methods in aggregating verbal terms by conducting three experiments. The results reveal that the numerical operation deviates much from actually decision making. The performances of fuzzy aggregations are also poor. This fact shows that fuzzy aggregations are still not qualified as descriptive operators. However, using cognitive representation to conduct fuzzy number operations can obtain a higher match-rate with the human decision (from 0.62 to 0.77). To understand the decision rules underlying human cognition, the research conduct a Multi-Dimensional Scaling (MDS) analysis. The results show that, other than numerical mean, subjects use two intuitive rules to aggregate opinions, namely, extreme-value and polarity. In the study of verbal variance operation, the research obtained the subjective judgments by a paired-comparison procedure. Furthermore, a factorial experiment is conducted to investigate the factors that might influence subjects’ verbal consensus judgment. The results show that subjects’ verbal consensus judgment is related to numerical variance, entropy, polarity, the interaction between numerical variance and polarity, the interaction between entropy and polarity, and the interaction among numerical variance, entropy, and polarity. Above all, entropy is a more significant descriptive operator than numerical variance. The results of the dissertation could complement the current numerical methods in processing qualitative data. Possible applications of the research findings are also discussed. Keywords: verbal information, cognitive operation, verbal representation, aggregation of verbal opinions, and consensus judgment of verbal opinions.