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A Robust Factor Analysis Model for Dichotomous Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yang Yixin & Lü Xin & Ma Jian & Qiao Han, 2014. "A Robust Factor Analysis Model for Dichotomous Data," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 437-450, October.
    • Handle: RePEc:bpj:jossai:v:2:y:2014:i:5:p:437-450:n:5
    DOI: 10.1515/JSSI-2014-0437
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    References listed on IDEAS

    as
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    6. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    7. Anders Christoffersson, 1975. "Factor analysis of dichotomized variables," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 5-32, March.
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