Please use this identifier to cite or link to this item:
|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
Kent State University
|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.|
|Appears in Collections:||Scopus 2001-2005|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.