Handling missing data in nursing research with multiple imputation

Background: In the data analysis phase of research, missing values present a challenge to nurse investigators. Common approaches for addressing missing data generally include complete-case analysis, available-case analysis, and single-value imputation methods. These methods have been the subject of increasing criticism with respect to their tendency to underestimate standard errors, overstate statistical significance, and introduce bias. Objectives: This article reviews the limitations of standard approaches for handling missing data, and suggests multiple imputation is a useful method for nursing research. Method: Secondary analysis was conducted to examine the effect of a public policy on the health of women using a data set that had a large degree and complex patterns of missing data. Discussion: In the example, accommodation of the incomplete data was critical to making valid inferences; however, complete-case, available-case, or single imputation could not be defended as an adequate method for dealing with the missing data patterns. Alternative methods for dealing with incomplete data were sought, and a multiple imputation approach was selected given the missing data pattern. Nurse researchers confronting similar complex patterns of missing data may find multiple imputation a useful procedure for conducting data analysis and avoiding the bias associated with other methods of handling missing data.