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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/20600
Title: A comparison of imputation techniques for handling Missing data
Authors: Carol M. Musil
Camille B. Warner
Piyanee Klainin Yobas
Susan L. Jones
Case Western Reserve University
Mahidol University
Kent State University
Keywords: Nursing
Issue Date: 1-Jan-2002
Citation: Western Journal of Nursing Research. Vol.24, No.7 (2002), 815-829
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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0036834375&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/20600
ISSN: 01939459
Appears in Collections:Scopus 2001-2005

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