Replication Data for: Listwise Deletion In High Dimensions
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
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Title |
Replication Data for: Listwise Deletion In High Dimensions
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Identifier |
https://doi.org/10.7910/DVN/T8BG2K
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Creator |
Wang, J. Sophia
Peter M. Aronow |
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Publisher |
Harvard Dataverse
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Description |
We consider the properties of listwise deletion when both n and the number of variables grow large. We show that when (i) all data has some idiosyncratic missingness and (ii) the number of variables grows superlogarithmically in n, then, for large n, listwise deletion will drop all rows with probability 1. Using two canonical datasets from the study of comparative politics and international relations, we provide numerical illustration that these problems may emerge in real world settings. These results suggest, in practice, using listwise deletion may mean using few of the variables available to the researcher.
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Subject |
Social Sciences
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Contributor |
Wang, J. Sophia
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