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A Note on Listwise Deletion versus Multiple Imputation

  • Thomas B. Pepinsky (a1)
Abstract

This letter compares the performance of multiple imputation and listwise deletion using a simulation approach. The focus is on data that are “missing not at random” (MNAR), in which case both multiple imputation and listwise deletion are known to be biased. In these simulations, multiple imputation yields results that are frequently more biased, less efficient, and with worse coverage than listwise deletion when data are MNAR. This is the case even with very strong correlations between fully observed variables and variables with missing values, such that the data are very nearly “missing at random.” These results recommend caution when comparing the results from multiple imputation and listwise deletion, when the true data generating process is unknown.

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Contributing Editor: Justin Grimmer

Author’s note: Thanks to Vincent Arel-Bundock, Bryce Corrigan, Florian Hollenbach, and Krzysztof Pelc for discussions and feedback on earlier drafts. I am responsible for all errors. Replication data may be found at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NDTR8K.

Footnotes
References
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Allison, Paul. 2002. Missing data . SAGE Publications.
Allison, Paul. 2014. Listwise deletion is not evil. http://statisticalhorizons.com/listwise-deletion-its-not-evil.
Arel-Bundock, Vincent, and Pelc, Krzysztof J.. 2018. When can multiple imputation improve regression estimates? Political Analysis 26(2):240245.
Dettman, Sebastian, Pepinsky, Thomas B., and Pierskalla, Jan H.. 2017. Incumbency advantage and candidate characteristics in open-list proportional representation systems: Evidence from Indonesia. Electoral Studies 48:111120.
Graham, John W. 2009. Missing data analysis: Making it work in the real world. Annual Review of Psychology 60:549576.
Graham, John W., Olchowski, Allison E., and Gilreath, Tamika D.. 2007. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science 8:206213.
Honaker, James, King, Gary, and Blackwell, Matthew. 2011. Amelia Ii: A program for missing data. Journal of Statistical Software 45(7):147.
King, Gary, Honaker, James, Joseph, Anne, and Scheve, Kenneth. 2001. Analyzing incomplete political science data: An alternative algorithm for multiple imputation. American Political Science Review 95(1):4969.
Lall, Ranjit. 2016. How multiple imputation makes a difference. Political Analysis 24(4):414433.
Little, Roderick J. A. 1992. Regression with missing X’s: A review. J. Am. Stat. Assoc. 87(420):12271237.
Little, Roderick J. A., and Rubin, Donald. 2002. Statistical analysis with missing data . 2nd edn New York: Wiley.
Pepinsky, Thomas. 2018. A note on listwise deletion versus multiple imputation. https://doi.org/10.7910/ DVN/NDTR8K, Harvard Dataverse, V1.
R Core Team. 2007. Ls: Least squares regression for continuous dependent variables. In Zelig: Everyone’s statistical software , ed. Choirat, Christine, Honaker, James, Imai, Kosuke, King, Gary, and Lau, Olivia. http://zeligproject.org/.
Rubin, Donald. 1987. Multiple imputation for nonresponse in surveys . New York: Wiley.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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