This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably the plausibility of the underlying assumption of data missing at random. The auxiliary variables can also be considered for inclusion alternatively in imputation models for following multiple imputation analyses. The approach can be particularly helpful in empirical settings where violations of missing at random are suspected, and is illustrated with data from an aging research study.