Journal article
Multiple imputation for combining confidential data owned by two agencies
Journal of the Royal Statistical Society. Series A, Statistics in society, Vol.172(2), pp.511-528
04/2009
DOI: 10.1111/j.1467-985X.2008.00574.x
Abstract
Statistical agencies that own different databases on overlapping subjects can benefit greatly from combining their data. These benefits are passed on to secondary data analysts when the combined data are disseminated to the public. Sometimes combining data across agencies or sharing these data with the public is not possible: one or both of these actions may break promises of confidentiality that have been given to data subjects. We describe an approach that is based on two stages of multiple imputation that facilitates data sharing and dissemination under restrictions of confidentiality. We present new inferential methods that properly account for the uncertainty that is caused by the two stages of imputation. We illustrate the approach by using artificial and genuine data.
Details
- Title: Subtitle
- Multiple imputation for combining confidential data owned by two agencies
- Creators
- Christine N. Kohnen - Macalester CollegeJerome P. Reiter - Duke University
- Resource Type
- Journal article
- Publication Details
- Journal of the Royal Statistical Society. Series A, Statistics in society, Vol.172(2), pp.511-528
- Publisher
- Wiley
- DOI
- 10.1111/j.1467-985X.2008.00574.x
- ISSN
- 0964-1998
- eISSN
- 1467-985X
- Number of pages
- 18
- Grant note
- National Science Foundation; National Science Foundation (NSF) 1042181 / Direct For Social, Behav & Economic Scie; National Science Foundation (NSF); NSF - Directorate for Social, Behavioral & Economic Sciences (SBE)
- Language
- English
- Date published
- 04/2009
- Academic Unit
- Biostatistics
- Record Identifier
- 9984721218102771
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