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Missing-Data Adjustments in Large Surveys
Abstract
Useful properties of a general-purpose imputation method for numerical data are suggested and discussed in the context of several large government surveys. Imputation based on predictive mean matching is proposed as a useful extension of methods in existing practice, and versions of the method are presented for unit nonresponse and item nonresponse with a general pattern of missingness. Extensions of the method to provide multiple imputations are also considered. Pros and cons of weighting adjustments are discussed, and weighting-based analogs to predictive mean matching are outlined.
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“Imputation was performed based on predictive mean matching [88] and with the number of iterations set to n = 15.”
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“Missing values were imputed by means of multiple imputation using predictive mean matching (PMM; Little, 1988) in the package multiple imputation by chained equations (MICE; Van Buuren & Groothuis-Oudshoorn, 2011) in R version 2022.07.1.”
Influential
Metadata
- Authors
- Little, R. J. A.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1988-07
- Volume
- 6
- Issue
- 3
- Pages
- 287-296
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W1972851018
- Cited by
- 1,101
- References
- 2
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- closed
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