Publication: A comparison of imputation techniques for handling Missing data
2
Issued Date
2002-01-01
Resource Type
ISSN
01939459
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2-s2.0-0036834375
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Mahidol University
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SCOPUS
Bibliographic Citation
Western Journal of Nursing Research. Vol.24, No.7 (2002), 815-829
Suggested Citation
Carol M. Musil, Camille B. Warner, Piyanee Klainin Yobas, Susan L. Jones A comparison of imputation techniques for handling Missing data. Western Journal of Nursing Research. Vol.24, No.7 (2002), 815-829. doi:10.1177/019394502762477004 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/20600
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Title
A comparison of imputation techniques for handling Missing data
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Abstract
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.