Imputation methods in the social sciences: a methodological review
Imputation methods in the social sciences: a methodological review
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.
item-nonresponse, imputation, fractional imputation, multiple imputation, estimation of distribution functions.
National Centre for Research Methods, School of Social Sciences, University of Southampton
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
2005
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Durrant, Gabriele B.
(2005)
Imputation methods in the social sciences: a methodological review
(NCRM Working Paper Series, NCRM-002)
National Centre for Research Methods, School of Social Sciences, University of Southampton
Record type:
Monograph
(Working Paper)
Abstract
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.
This record has no associated files available for download.
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: 18 May 2024 01:36
Export record
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics