Carnegie Mellon University
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Multiple Imputation for Missing Data: Making the Most of What you Know

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journal contribution
posted on 2007-03-01, 00:00 authored by Mark FichmanMark Fichman, Jonathon M. Cummings
Missing data are a common problem in organizational research. Missing data can occur due to attrition in a longitudinal study or non-response to questionnaire items in a laboratory or field setting. Improper treatments of missing data (e.g., listwise deletion, mean imputation) can lead to biased statistical inference using complete case analysis statistical techniques. This paper presents a simulation and data analysis case study using a method for dealing with missing data, multiple imputation (MI) (Rubin, 1987; Schafer, 1997), that allows for valid statistical inference with complete case statistical analysis. Software for implementing multiple imputation under a multivariate normal model is freely (Schafer, 1997; King, Honaker, Joseph & Scheve, 2001) and widely available (e.g. SAS, SOLAS). It should be routinely considered for imputing missing data. We illustrate the application of this technique using data from the HomeNet project (Kraut, Patterson, Lundmark, Kiesler, Mukhopadhyay & Scherlis, 1998).