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On Structural Equation Modeling with Data that are not Missing Completely at Random

Published online by Cambridge University Press:  01 January 2025

Bengt Muthén ,
David Kaplan  and
Michael Hollis
Bengt Muthén*
Affiliation:
Graduate School of Education
David Kaplan
Affiliation:
Graduate School of Education
Michael Hollis
Affiliation:
Graduate School of Architecture and Urban Planning, University of California, Los Angeles
*
Requests for reprints should be sent to Bengt Muthén, Graduate School of Education, University of California, Los Angeles, CA 90024.
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Abstract

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.

Keywords

maximum likelihoodignorabilityselectivityfactor analysisfactorial invarianceLISREL
Type
Original Paper
Information
Psychometrika , Volume 52 , Issue 3 , September 1987 , pp. 431 - 462
Copyright
Copyright © 1987 The Psychometric Society

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Footnotes

The research of the first author was supported by grant No. SES-8312583 from the National Science Foundation and by a Spencer Foundation grant. We wish to thank Chuen-Rong Chan for drawing the path diagram.

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