Asymptotic comparison of missing data procedures for estimating factor loadings

C. Hendricks Brown*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Large sample properties of four methods of handling multivariate missing data are compared. The criterion for comparison is how well the loadings from a single factor model can be estimated. It is shown that efficiencies of the methods depend on the pattern or arrangement of missing data, and an evaluation study is used to generate predictive efficiency equations to guide one's choice of an estimating procedure. A simple regression-type estimator is introduced which shows high efficiency relative to the maximum likelihood method over a large range of patterns and covariance matrices.

Original languageEnglish (US)
Pages (from-to)269-291
Number of pages23
Issue number2
StatePublished - Jun 1 1983


  • EM algorithm

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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