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A Comparison of Imputation Techniques for Handling Missing Data

Researchers are commonly faced with the problem of missing data. This article presents theoretical and empirical information for the selection and application of approaches for handling missing data on a single variable. An actual data set of 492 cases with no missing values was used to create a sim...

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Bibliographic Details
Published in:Western journal of nursing research 2002-11, Vol.24 (7), p.815-829
Main Authors: Musil, Carol M., Warner, Camille B., Yobas, Piyanee Klainin, Jones, Susan L.
Format: Article
Language:English
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Summary:Researchers are commonly faced with the problem of missing data. This article presents theoretical and empirical information for the selection and application of approaches for handling missing data on a single variable. An actual data set of 492 cases with no missing values was used to create a simulated yet realistic data set with missing at random (MAR) data. The authors compare and contrast five approaches (listwise deletion, mean substitution, simple regression, regression with an error term, and the expectation maximization [EM] algorithm) for dealing with missing data, and compare the effects of each method on descriptive statistics and correlation coefficients for the imputed data (n = 96) and the entire sample (n = 492) when imputed data are included. All methods had limitations, although our findings suggest that mean substitution was the least effective and that regression with an error term and the EM algorithm produced estimates closest to those of the original variables.
ISSN:0193-9459
1552-8456
DOI:10.1177/019394502762477004