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A Comparison of Imputation Techniques for Handling Missing Data

Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio

Camille B. Warner

Frances Payne Bolton School of Nursing, and Department of Sociology, Case Western Reserve University

Piyanee Klainin Yobas

Department of Mental Health and Psychiatric Nursing, Mahidol University, Thailand

Susan L. Jones

College of Nursing, Kent State University, Ohio

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.

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