Optimal adjustments for inconsistency in imputed data
Optimal adjustments for inconsistency in imputed data
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
127-144
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Pannekoek, Jeroen
5225a4ab-7074-4ef8-82cd-b0797688df9c
29 June 2015
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Pannekoek, Jeroen
5225a4ab-7074-4ef8-82cd-b0797688df9c
Zhang, Li-Chun and Pannekoek, Jeroen
(2015)
Optimal adjustments for inconsistency in imputed data.
Survey Methodology, 41 (1), .
Abstract
Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.
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Published date: 29 June 2015
Organisations:
Social Statistics & Demography
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Local EPrints ID: 391013
URI: http://eprints.soton.ac.uk/id/eprint/391013
ISSN: 0714-0045
PURE UUID: 564a2178-90e1-4680-9c94-0df8f6837827
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Date deposited: 06 Apr 2016 14:15
Last modified: 15 Mar 2024 03:45
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Author:
Jeroen Pannekoek
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