Covariance Structure Model Fit Testing Under Missing Data: An Application of the Supplemented EM Algorithm

摘要

We apply the Supplemented EM algorithm (Meng Rubin, 1991) to address a chronic problem with the two-stage fitting of covariance structure models in the presence of ignorable missing data: the lack of an asymptotically chi-square distributed goodness-of-fit statistic. We show that the Supplemented EM algorithm provides a convenient computational procedure that leads to such a chi-square statistic, and we provide a SAS macro implementing this method. Our derivations are corroborated with results from a small simulation study. We also apply the proposed method to 2 empirical data sets: (a) confirmatory factor analysis of Mardia, Kent, Bibby's 1979 Open-book Closed-book data and (b) conditional latent curve modeling of adolescent aggressive behavior as discussed by Curran (1997).