Missing-Data Adjustments in Large Surveys |
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Authors: | Roderick J A Little |
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Institution: | Department of Biomathematics , School of Medicine, University of California , Los Angeles , CA , 90024 |
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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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Keywords: | Imputation Incomplete data Matching Multiple imputation Regression models Weighting |
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