Multiple Imputation: An Alternative Solution for Filling Data |
Author(s): |
Umamaheswari.D , NGM College; Vijay anand.R, NGM College |
Keywords: |
Item-non response, imputation, simple imputation, multiple Imputations |
Abstract |
Missing values, common in epidemiologic studies, are a major issue in obtaining valid estimates. Missing data are often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. Simulation studies have suggested that multiple imputation is an attractive method for imputing missing values, but it is relatively complex and requires specialized software. This article reviews several imputation methods used in the social sciences and discusses advantages and disadvantages of these methods in practice. Simpler imputation methods as well as more advanced methods, such as fractional and multiple imputation, are discussed. The paper introduces the implementation in either multiple or fractional imputation approaches. Software packages for using imputation methods in practice are reviewed highlighting newer developments. The paper discusses an example from the social sciences in detail, applying several imputation methods to a missing earnings variable. The objective is to illustrate how to choose between methods in a real data example. A simulation study evaluates various imputation methods, including predictive mean matching, fractional and multiple imputation. This article reviews various imputation methods used within the social sciences to compensate for item-nonresponse bias, and provides the best result in using Multiple Imputation. |
Other Details |
Paper ID: IJSRDV2I11103 Published in: Volume : 2, Issue : 11 Publication Date: 01/02/2015 Page(s): 137-139 |
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