Diagnostics for Multiple Imputations

14 Pages Posted: 21 Nov 2007

See all articles by Trivellore Raghunathan

Trivellore Raghunathan

University of Michigan at Ann Arbor - Survey Research Center, Survey Methodology Program; University of Michigan at Ann Arbor - School of Public Health, Department of Biostatistics

Irina Bondarenko

University of Michigan at Ann Arbor - School of Public Health

Date Written: November 21, 2007

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

Suggested Citation

Raghunathan, Trivellore and Bondarenko, Irina, Diagnostics for Multiple Imputations (November 21, 2007). Available at SSRN: https://ssrn.com/abstract=1031750 or http://dx.doi.org/10.2139/ssrn.1031750

Trivellore Raghunathan (Contact Author)

University of Michigan at Ann Arbor - Survey Research Center, Survey Methodology Program ( email )

426 Thompson Street
Institute for Social Research
Ann Arbor, MI 48106-1248
United States
(734) 647-4619 (Phone)

University of Michigan at Ann Arbor - School of Public Health, Department of Biostatistics ( email )

1415 Washington Heights
Ann Arbor, MI 48109-2029
United States

Irina Bondarenko

University of Michigan at Ann Arbor - School of Public Health ( email )

1415 Washington Heights
Ann Arbor, MI 48109-2029
United States

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