Treatments of Missing Data: A Monte Carlo Comparison of RBHDI, Iterative Stochastic Regression Imputation, and Expectation-Maximization

This article describes a Monte Carlo investigation of 4 methods for treating incomplete data. Data sets conforming to a single structured model, but varying in sample size, distributional characteristics, and proportion of data deleted, were randomly produced. Resemblance-based hot-deck imputation, iterated stochastic regression imputation, structured-model expectation-maximization, and saturated-model expectation-maximization were applied to these data sets, and these methods were then compared in terms of their ability to reconstruct the original data, the intact-data variances and covariances, and the population variances and covariances. The results favored the expectation-maximization methods, regardless of sample size, proportion of data missing, and distributional characteristics of the data. The results are discussed with respect to practical considerations in the choice of missing-data treatment, including the possibilities of model misspecification, convergence failure, and the need to make data available to other investigators.