On structural equation modeling with data that are not missing completely at random
type: Item
instance of: scholarly article
title: On structural equation modeling with data that are not missing completely at random
zbMATH Open document ID: 0627.62066
DOI: 10.1007/BF02294365
author: B. George
published in: Psychometrika
publication date: 1987-01-01T00:00:00Z
review text: 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 modelling software, such as LISREL and LISCOMP. Several sets of artificial data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62P15
zbMATH DE Number: 4020244
zbMATH Keywords: ignorability
zbMATH Keywords: selectivity
zbMATH Keywords: factor analysis
zbMATH Keywords: general latent variable model
zbMATH Keywords: missing data
zbMATH Keywords: maximum likelihood estimation
zbMATH Keywords: selection
zbMATH Keywords: factorial invariance
zbMATH Keywords: structural equation modelling
zbMATH Keywords: LISREL
describes a project that uses: BMDP
describes a project that uses: LISREL
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4020244