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A Note on Listwise Deletion versus Multiple Imputation

2018; Cambridge University Press; Volume: 26; Issue: 4 Linguagem: Inglês

10.1017/pan.2018.18

1476-4989

Thomas B. Pepinsky,

Bayesian Methods and Mixture Models

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.