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Multiple imputation for missing data † ‡
Author(s) -
Patrician Patricia A.
Publication year - 2002
Publication title -
research in nursing and health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 85
eISSN - 1098-240X
pISSN - 0160-6891
DOI - 10.1002/nur.10015
Subject(s) - missing data , imputation (statistics) , computer science , data mining , statistics , longitudinal data , econometrics , mathematics , machine learning
Abstract 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.