Rebutting Existing Misconceptions About Multiple Imputation as a Method for Handling Missing Data

By Joost R. van Ginkel, M. Linting, Ralph C. A. Rippe, Anja van der Voort. Journal of Personality Assessment, 2019

521 citations recorded by the source corpus.

Abstract Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b2683934103305d955f1cd025fc729d9348837ce