A Note on Listwise Deletion versus Multiple Imputation
Bibliographic Data
| ID | 7971085 |
|---|---|
| Authors | Thomas B Pepinsky (0000-0002-4000-217X, Cornell University, corresponding author) |
| Year | 2018 |
| Volume | 26 |
| Issue | 4 |
| Pages | 480-488 |
| Publication date | 2018-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/pan.2018.18 |
| OpenAlex | W2886765421 |
| Language | EN |
| Citations received | 73 |
| References cited | 11 |
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
Imputation (statistics · Machine learning · Missing data · Statistics · Bayesian Methods and Mixture Models · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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| Unique citing works | 73 |
|---|---|
| Citations per year | 9,13 |
| Citation span | 2018 - 2026 (9) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 72 |