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Estimation for the multiple factor model when data are missing

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Abstract

A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.

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Reference notes

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This paper is based on the author's doctoral dissertation at the Department of Psychology, University of Illinois at Urbana-Champaign. The author gratefully acknowledges the aid of Drs. Robert Bohrer, Charles Lewis, Robert Linn, Maurice Tatsuoka, and Ledyard Tucker.

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Finkbeiner, C. Estimation for the multiple factor model when data are missing. Psychometrika 44, 409–420 (1979). https://doi.org/10.1007/BF02296204

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  • DOI: https://doi.org/10.1007/BF02296204

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