Multiple imputation of incomplete categorical data using latent class analysis

We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete cate-gorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large im-putation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood We would like to thank Paul Allison and Jay Magidson, as well as the editor and the three anonymous reviewers, for their comments, which very much helped to improve this article. We would also like to thank Greg Richards for providing th