Data are said to be nonignorably missing if the probabilities of missingness depend on unobserved quantities. Traditional election models for nonignorable nonresponse are outcome-based, tying these probabilities to the partially observed values directly e.g., by a logistic regression) and can be quite unstable. With multivariate responses, the number of distinct missingness patterns ecomes large, making outcome-based selection modeling unattractive. In some examples, however, the information in the binary missing-data indicators is well summarized by a simple latent-class structure, suggesting that relationships between these indicators and the partially observed items might be explainable by the latent classes. In this talk, we present a latent-class selection model for multivariate incomplete data and investigate its properties. We then compare multiple imputations drawn from