文章基本信息

标题：Investigating the Repeatability of the Extracted Factors in Relation to the Type of Rotation Used, and the Level of Random Error: A Simulation Study
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作者：Dimitris Panaretos ; George Tzavelas ; Malvina Vamvakari 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2020
卷号：18
期号：2
页码：390-404
DOI：10.6339/JDS.202004_18(2).0010
出版社：Tingmao Publish Company
摘要：Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The purpose of this study was to evaluate whether the selection of rotation type affects the repeatability of the patterns derived from FA, under various scenarios of random error introduced, based on simulated data from the Standard Normal distribution. It was observed that when applying promax non - orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, irrespective of the level of random error introduced in the model.
关键词：factor analysis; multivariate analysis; recognition pattern analysis; rotation; repeatability