Multiple imputation for missing data


  • Patricia A. Patrician

    Corresponding author
    1. Walter Reed Army Medical Center, Washington, DC
    • Lieutenant Colonel, Army Nurse Corps, Nursing Research Service, P.O. Box 59645, Walter Reed Station, Washington, DC 20012.
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      Assistant Chief, Nursing Research Service.

  • The author thanks Linda Aiken, Douglas Sloane, and Julie Sochalski for reviewing earlier drafts and Paul D. Allison for his thoughtful critique of this paper.

  • The opinions or assertions contained here are the private views of the author and are not to be construed as official or as reflecting the views of the Department of the Army or the Department of Defense.


Missing data occur frequently in survey and longitudinal research. Incomplete data are problematic, particularly in the presence of substantial absent information or systematic nonresponse patterns. Listwise deletion and mean imputation are the most common techniques to reconcile missing data. However, more recent techniques may improve parameter estimates, standard errors, and test statistics. The purpose of this article is to review the problems associated with missing data, options for handling missing data, and recent multiple imputation methods. It informs researchers' decisions about whether to delete or impute missing responses and the method best suited to doing so. An empirical investigation of AIDS care data outcomes illustrates the process of multiple imputation. © 2002 John Wiley & Sons, Res Nurs Health 25:76–84, 2002.