Comparison of Two Multiple Imputation Procedures in a Cancer Screening Survey

Commonly in survey research, multiple, different analyses are conducted by one or more than one researcher on the same data set. The conclusions from these analyses should be consistent despite the presence of missing data. Multiple imputation is frequently used to ensure consistency of analyses. Two methods for multiple imputation of missing data are a combination of hot deck and regression imputation, and multivariate normal multiple imputation. It is unknown whether these methods will give similar results in practical situations with large numbers of variables. We applied both multiple imputation methods to a cancer screening survey data with 2 continuous, 48 Likert scale items, and 74 binary response items. Correlations and variances of imputated data sets were compared in a first attempt to investigate similarity of the imputation methods. The results of both methods were found to be similar; either of the two methods are endorsed for surveys similar to the data set presented.