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Case-Deletion Diagnostics for Factor Analysis Models With Continuous and Ordinal Categorical Data

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  • Sik-Yum Lee
  • Liang Xu

Abstract

This article proposes a method to detect influential observations in a factor analysis model with continuous and ordinal categorical variables. The key ideas are to treat the latent factors as hypothetical missing data and then develop the diagnostic measures on the basis of the conditional expectation of the complete-data log-likelihood function in the EM algorithm. A one-step approximation is proposed to reduce the computational burden. Building blocks for achieving the diagnostic measures are computed via observations generated by the Gibbs sampler. Results from a simulation study and an illustrative real example are presented.

Suggested Citation

  • Sik-Yum Lee & Liang Xu, 2003. "Case-Deletion Diagnostics for Factor Analysis Models With Continuous and Ordinal Categorical Data," Sociological Methods & Research, , vol. 31(3), pages 389-419, February.
    • Handle: RePEc:sae:somere:v:31:y:2003:i:3:p:389-419
    DOI: 10.1177/0049124102239081
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    References listed on IDEAS

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    1. J.‐Q. Shi & S.‐Y. Lee, 2000. "Latent variable models with mixed continuous and polytomous data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 77-87.
    2. Yutaka Tanaka & Yoshimasa Odaka, 1989. "Influential observations in principal factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 475-485, September.
    3. Beth Reboussin & Kung-Yee Liang, 1998. "An estimating equations approach for the LISCOMP model," Psychometrika, Springer;The Psychometric Society, vol. 63(2), pages 165-182, June.
    4. Sik-Yum Lee & Jian-Qing Shi, 2001. "Maximum Likelihood Estimation of Two-Level Latent Variable Models with Mixed Continuous and Polytomous Data," Biometrics, The International Biometric Society, vol. 57(3), pages 787-794, September.
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