On the Robustness of Factor Analysis Models

factor analysis;robustness;Bayesian statistics;copula;mixed model;outlier;kurtosis;high-correlation matrix;150

사회과학대학 심리학과 ;

University:서울대학교 대학원

关键词: factor analysis;    robustness;    Bayesian statistics;    copula;    mixed model;    outlier;    kurtosis;    high-correlation matrix;    150;   

Others  :  http://s-space.snu.ac.kr/bitstream/10371/134340/1/000000008509.pdf

美国|英语

来源: Seoul National University Open Repository

【 摘 要 】

In this study, model robustness was examined for mainly two factor analysis models, TFA(Traditional Factor Analysis) and BCFA(Bayesian Copula Factor Analysis). There were three abnormal data scenarios, which were outlier, kurtosis, and high correlation matrix cases. Both models were applied to each of the scenario data. It was revealed that BCFA model outperforms TFA model across the scenarios: the former was superior in terms of robustness when compared to the latter. In fact, BCFA could resolve the ;;big loading’ problems which arise when TFA is applied to the dataset, revealing the factor structure clearly. Additionally, some related issues are discussed in this article.