Large-scale Imputation for Complex Surveys

Much of the recent research into imputation methodology has focused on developing optimal procedures for a single variable or set of variables, where the patterns of missingness and underlying distributions follow standard distributions. In contrast, it is frequently necessary to impute for many variables from a single survey, with an even larger set of potential covariates and complex covariance structures among the variables to be imputed. Further, the imputations need to be completed in a relatively short time frame within a constrained budget. The analyst also is unlikely to be able to anticipate all of the important analyses for which the imputed data are to be used. This often prevents analysts from being able to produce optimal imputations for each variable. Instead, one tries to produce a set of imputed variables that minimize the attenuation of key relationships, hopefully reduces nonresponse bias, and satisfies the time and budgetary constraints.