Diagnostics for Multiple Imputations

Abstract

Multiple imputation technique is becoming a popular method for analyzing data with missing values. Several methods have been proposed for creating multiple imputations and most of these methods assume that the data are missing at random (MAR). However, limited diagnostic tools are available to check whether the imputations created by these methods are reasonable. This article develops a set of diagnostic tools based on certain conditional distributions of the observed and imputed values. These conditional distributions should be similar if the assumed model for creating multiple imputations is a good fit. The tools are formulated in terms of numerical summaries and graphical displays and could be easily implemented using the standard complete data software packages. For implementing these methods the exact nature of the model used by the imputer is not needed. The method is illustrated using a data set with large number of variables of different types with varying amount of missing values.

Keywords: Congeniality, Diagnostics, Missing at Random, Propensity score matching

JEL Classification: C1, C42