Estimation for Structural Equation Models with Missing Data
- 1 March 1986
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 51 (1) , 93-99
- https://doi.org/10.1007/bf02294002
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
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