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Imputation methods in the social sciences: a methodological review

Record type: Monograph (Working Paper)

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. 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 considered. The paper introduces the reader new to the imputation literature to key ideas and methods. For those already familiar with imputation methods the paper highlights some new developments and clarifies some recent misconceptions in the use of imputation methods. The emphasis is on efficient hot deck imputation methods, implemented 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. Certain forms of fractional and multiple hot deck methods are found to perform well with regards to bias and efficiency of a point estimator and robustness against model misspecifications. Standard parametric imputation methods are not found adequate for the application considered.

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Citation

Durrant, Gabriele B. (2005) Imputation methods in the social sciences: a methodological review NCRM (NCRM Working Paper Series, NCRM-002).

More information

Published date: 2005
Keywords: item-nonresponse, imputation, fractional imputation, multiple imputation, estimation of distribution functions.

Identifiers

Local EPrints ID: 34816
URI: http://eprints.soton.ac.uk/id/eprint/34816
PURE UUID: a51b4696-dcf3-4ec7-be3d-4c9c4b9e3a00

Catalogue record

Date deposited: 17 May 2006
Last modified: 17 Jul 2017 15:49

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