S-Space College of Social Sciences (사회과학대학) Dept. of Psychology (심리학과) Theses (Master's Degree_심리학과)
On the Robustness of Factor Analysis Models
요인분석 모형의 강건성에 대하여
- 사회과학대학 심리학과
- Issue Date
- 서울대학교 대학원
- factor analysis; robustness; Bayesian statistics; copula; mixed model; outlier; kurtosis; high-correlation matrix
- 학위논문 (석사)-- 서울대학교 대학원 : 심리학과, 2013. 2. 김청택.
- 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.