Correspondence analysis is generally a data analysis technique that expresses a two-dimensional or higher contingency table in a low-dimensional space to see the combination of rows and columns. In general, a statistical analysis method applied to contingency table analysis uses a chi-square test, but the chi-square test has a limitation in that it cannot show the coupling pattern for rows and columns. As an alternative, correspondence analysis is used. In general, the row profiles of correspondence analysis sum to 1. For data evaluated on the Likert scale for various attributes of each entity, the sum of rows is different for each row, so general correspondence analysis cannot be performed. Correspondence analysis can be applied to such data by doubling the number of column categories by adding the positive and negative values of each attribute so that the sum is equal. This is called the doubling technique. As a result of exploratory multivariate data analysis that does not rely on normal distribution and statistical models for Likert data, a correspondence analysis study using a doubling technique was proposed (Han, 2019). By quantifying the examiners and subjects, and expressing the result using a graphic technique, it was easy to visually recognize and interpret geometrically clear meaning. However, for the method proposed by Han (2019), the stability evaluation for quantification analysis results has not yet been developed. Therefore, in order to develop a methodology for the stability evaluation of the materialistic method for Likert data analysis, which is often seen in real life such as public opinion surveys or consumer preference surveys, but lacks development of analysis methods, and to show its usefulness through case analysis do.
原文链接
分享
引用
