This paper investigates methods that avoid using multiple groups to represent the missing data patterns in covariance structure modeling, attempting instead to do a single-group analysis where the only action the analyst has to take is to indicate that data is missing. A new covariance structure approach developed by B. Muthen and G. Arminger is used. The approach draws on Bayesian theory and is a full-information estimator as is maximum-likelihood estimation. The proposed methodology is described briefly, and tests of its performance on simulated data in a Monte Carlo study under various forms of missing data are reviewed. This easy-to-use approach results in good properties for the parameter estimates. The technique is not, however, yet available in covariance structure software. Two tables are included. (Contains 6 references.) (SLD)