Multiple imputation in practice-a case study using a complex German establishment survey

Multiple imputation is widely accepted as the method of choice to address item nonresponse in surveys. Nowadays most statistical software packages include features to multiply impute missing values in a dataset. Nevertheless, the application to real data imposes many implementation problems. To define useful imputation models for a dataset that consists of categorical and possibly skewed continuous variables, contains skip patterns and all sorts of logical constraints is a challenging task. Besides, in most applications little attention is paid to the evaluation of the underlying assumptions behind the imputation models. In this paper, we present a case study from a complex imputation project at the German Institute for Employment Research (IAB): the imputation of missing values in one wave of the JAB Establishment Panel. We discuss possible ways to handle the problems mentioned above and provide an overview which of these problems can be tackled by which imputation software. The detailed review of our imputation project that also includes a discussion on how we monitored the quality of the imputation models will be a useful guide for other agencies willing to implement the approach for their own surveys.