Theory and method for constrained estimation in structural equation models with incomplete data

Abstract

The statistical properties and a practical procedure for constrained maximum likelihood estimation in covariance structure analysis with incomplete data are studied. We show that the constrained maximum likelihood estimator possesses nice statistical properties, such as, consistency and normality, and we provide tests of the model structure and of a subset of restrictions. Computationally, a so-called restricted EM algorithm is proposed to obtain the constrained ML estimates. A simulated incomplete data set is used as an illustrative example. The degrees of freedom of the model change substantially with increases in the number of missing data response patterns. Possible effects of missing data on power, and of violation of distributional assumptions, are discussed.