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More on EM for ML Factor Analysis

Published online by Cambridge University Press:  01 January 2025

Donald B. Rubin  and
Dorothy T. Thayer
Donald B. Rubin*
Affiliation:
University of Chicago
Dorothy T. Thayer
Affiliation:
Educational Testing Service
*
Reprint requests should be addressed to Donald B. Rubin, Department of Statistics, University of Chicago, 5734 University Avenue, Chicago, IL. 60637.
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Abstract

We address several issues that are raised by Bentler and Tanaka's [1983] discussion of Rubin and Thayer [1982]. Our conclusions are: standard methods do not completely monitor the possible existence of multiple local maxima; summarizing inferential precision by the standard output based on second derivatives of the log likelihood at a maximum can be inappropriate, even if there exists a unique local maximum; EM and LISREL can be viewed as complementary, albeit not entirely adequate, tools for factor analysis.

Keywords

EMLISRELmaximum likelihoodfactor analysisalgorithmsprecision of estimation

Information

Type
Original Paper
Information
Psychometrika , Volume 48 , Issue 2 , June 1983 , pp. 253 - 257
Copyright
Copyright © 1983 The Psychometric Society

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Footnotes

This work was partially supported by the Program Statistics Research Project at Educational Testing Service.

References

Bentler, P. M. & Tanaka, J. S. Problems with EM for ML factor analysis. Psychometrika, 1983, in press.CrossRefGoogle Scholar
Jöreskog, K. G. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 1969, 34, 183–202.CrossRefGoogle Scholar
Rubin, D. B., and Thayer, D. T. EM Algorithms for ML factor analysis. Psychometrika, 1982, 47, 69–76.CrossRefGoogle Scholar
Wu, C. F. On the convergence of properties of the EM algorithms. Annals of Statistics, 1983, 95–103.Google Scholar