A Robust Factor Analysis Model for Dichotomous Data

Yang Yixin (), Lü Xin (), Ma Jian () and Qiao Han ()
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Yang Yixin: School of the Gifted Young, University of Science and Technology of China, Hefei230026, China
Lü Xin: Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing100190, China
Ma Jian: Department of Information Systems, City University of Hong Kong, Hong Kong, China
Qiao Han: Management School, University of Chinese Academy of Sciences, Beijing100180, China

Journal of Systems Science and Information, 2014, vol. 2, issue 5, 437-450

Abstract: Factor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness. This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariate t-distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases, which indicates this model expands the application range and practical value of the factor analysis model.

Keywords: factor analysis; dichotomous data; item response theory; robustness (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:2:y:2014:i:5:p:437-450:n:5

DOI: 10.1515/JSSI-2014-0437

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