Multiple Imputation describes a strategy for analyzing incomplete data that accounts for uncertainty in the missing data by replacing (imputing) each missing value by several ‘candidates’. The actual implementation of any Multiple Imputation method is typically computationally expensive which is why the concept has only really caught on around the verge of the new millennium, when the first algorithms for Multiple Imputation had become accessible.

In this article, we are going to give a rough overview of the shortcomings of methods for handling missing data prior to Rubin’s work in the late 1970s, and we explore the conceptual innovations that might have lead to Multiple Imputation based on an example, where mean imputation is the steppingstone for more advanced methods. The general concept of Multiple Imputation is explained using a simulated trivariate data set, and the imputation model is based on the standard Bayesian linear model, in order to explain the method as illustrative as possible.