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On the Robustness of Factor Analysis Models
요인분석 모형의 강건성에 대하여

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Authors
박준석
Advisor
김청택
Major
사회과학대학 심리학과
Issue Date
2013-02
Publisher
서울대학교 대학원
Keywords
factor analysisrobustnessBayesian statisticscopulamixed modeloutlierkurtosishigh-correlation matrix
Description
학위논문 (석사)-- 서울대학교 대학원 : 심리학과, 2013. 2. 김청택.
Abstract
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.
Language
English
URI
https://hdl.handle.net/10371/134340
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College of Social Sciences (사회과학대학)Dept. of Psychology (심리학과)Theses (Master's Degree_심리학과)
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