Statistical agencies alter values of identifiers to protect respondents’ confidentiality. When these identifiers are survey design variables, leaving the original survey weights on the file can be a disclosure risk. Additionally, the original weights may not correspond to the altered values, which impacts the quality of design-based (weighted) inferences. In this paper, we discuss some strategies for altering survey weights when altering design variables. We do so in the context of simulating identifiers from probability distributions, i.e. partially synthetic data. Using simulation studies, we illustrate aspects of the quality of inferences based on the different strategies.
CITATION STYLE
Mitra, R., & Reiter, J. P. (2006). Adjusting survey weights when altering identifying design variables via synthetic data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4302, pp. 177–188). Springer Verlag. https://doi.org/10.1007/11930242_16
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