We studied four methods for handling incomplete categorical data in statistical modeling: (1) maximum likelihood estimation of the statistical model with incomplete data, (2) multiple imputation using a loglinear model, (3) multiple imputation using a latent class model, (4) and multivariate imputation by chained equations. Each method has advantages and disadvantages, and it is unknown which method should be recommended to practitioners. We reviewed the merits of each method and investigated their effect on the bias and stability of parameter estimates and bias of the standard errors. We found that multiple imputation using a latent class model with many latent classes was the most promising method for handling incomplete categorical data, especially when the number of variables used in the imputation model is large.
van der Palm, D. W., van der Ark, L. A., & Vermunt, J. K. (2016). A comparison of incomplete data methods for categorical data. Statistical Methods in Medical Research, 25(2), 754-774. https://doi.org/10.1177/0962280212465502