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Evaluation of Redundancy Analysis and development of a Generalized Procedure with Applications

冗余分析的评估和通用程序的开发与应用

基本信息

批准号：
07680317
负责人：
SAITO Takayuki
金额：
$ 1.02万
依托单位：
Tokyo Institute of Technology (1996)Hokkaido University (1995)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680317/
关键词：
redundancy analysis mixed measurement data multivariate analysis educational measurement sensory evaluation wearing comfort offensive odors 連続変数離散変数

项目摘要

In a variety of disciplines, there is great necesssity to analyze relationships between two sets of multivariates data. Redundancy analysis (RDA), among others, has been proposed to meet such requirement. However there have been few applications of RDA to real data and so its usefulness remained unknown. This research aimed to evaluate RDA through applied studies and also to suggest some development for RDA.Regading methodological aspect, we suggested an improved procedure (QRDA) of RDA so as to deal with multivariated data at mixed measurement levels, incorporating treatment to deal with degeneracy. We performed three applied studies. First, RDA was applied to educational data in order explore relationships between high school grade point averages and the national center examination tests. Second, RDA was utilized to examine relationships between fabric physical properties of girdles and the wearing comfort. Third, we analyzed sensory inspection data of offensive odors by using QRDA.We clarified relatioships between a set of chemical properties of the odors and another set of psychological variables in terms of sensory evaluation. Through those applications, it was concluded that RDA and QRDA are useful for data analysis.

在许多学科中，分析两组多变量数据之间的关系是非常必要的。其中，冗余分析（RDA）已经被提出来满足这样的要求。然而，RDA很少应用于真实的数据，因此其有用性仍然未知。本研究的目的是通过应用研究来评估RDA，并提出RDA的一些发展建议。在方法学方面，我们提出了一种改进的RDA程序（QRDA），以处理混合测量水平上的多变量数据，并结合处理简并。我们进行了三项应用研究。首先，RDA应用于教育数据，以探讨高中平均成绩和国家中心考试测试之间的关系。其次，利用RDA分析腰带织物物理性能与穿着舒适性的关系。第三，我们利用QRDA分析了难闻气味的感官检测数据，阐明了气味的一组化学性质与另一组感官评价心理变量之间的关系。通过这些应用，可以得出结论，RDA和QRDA是有用的数据分析。