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

Liang Xu,

The Chinese University of Hong Kong

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.

Key Words: influential observations • EM algorithm • Gibbs sampler • one-step approximation

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