Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing Data

Abstract

Missing data are a ubiquitous problem in quantitative communication research, yet the missing data handling practices found in most published work in communication leave much room for improvement. In this article, problems with current practices are discussed and suggestions for improvement are offered. Finally, hot deck imputation is suggested as a practical solution to many missing data problems. A computational tool for SPSS (Statistical Package for the Social Sciences) is presented that will enable communication researchers to easily implement hot deck imputation in their own analyses.

Notes

¹Reilly (1992, p. 308) notes that the precision gain using hot deck imputation is maximized when the “auxiliarly covariate(s) (“deck” variables) are highly informative about the missing X … for noninformative Z (“deck” variables), there is no gain in precision, but neither is there any penalty” (see also Andridge & Little, 2010, p. 43)

²As defined by effectiveness in estimating the true (known) t-value from a data set with randomly generated missing values (see Hawthorne & Elliott, 2005, p. 588).