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Estimation for Structural Equation Models with Missing Data

Published online by Cambridge University Press:  01 January 2025

Sik-Yum Lee
Sik-Yum Lee*
Affiliation:
The Chinese University of Hong Kong
*
Requests for reprints should be sent to Sik-Yum lee, Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., HONG KONG.
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Abstract

A direct method in handling incomplete data in general covariance structural models is investigated. Asymptotic statistical properties of the generalized least squares method are developed. It is shown that this approach has very close relationships with the maximum likelihood approach. Iterative procedures for obtaining the generalized least squares estimates, the maximum likelihood estimates, as well as their standard error estimates are derived. Computer programs for the confirmatory factor analysis model are implemented. A longitudinal type data set is used as an example to illustrate the results.

Keywords

generalized least squaresGauss-Newton algorithmmaximum likelihoodscoring algorithmasymptotic distributionslongitudinal data
Type
Original Paper
Information
Psychometrika , Volume 51 , Issue 1 , March 1986 , pp. 93 - 99
Copyright
Copyright © 1986 The Psychometric Society

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Footnotes

This research was supported in part by Research Grant DAD1070 from the U.S. Public Health Service. The author is indebted to anonymous reviewers for some very valuable suggestions. Computer funding is provided by the Computer Services Centre, The Chinese University of Hong Kong.

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