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• 1. Simultaneous use of multiple imputation for missing data and disclosure limitation Archived

  Articles and reports: 12-001-X20040027755

  Description:

  Several statistical agencies use, or are considering the use of, multiple imputation to limit the risk of disclosing respondents' identities or sensitive attributes in public use data files. For example, agencies can release partially synthetic datasets, comprising the units originally surveyed with some collected values, such as sensitive values at high risk of disclosure or values of key identifiers, replaced with multiple imputations. This article presents an approach for generating multiply-imputed, partially synthetic datasets that simultaneously handles disclosure limitation and missing data. The basic idea is to fill in the missing data first to generate m completed datasets, then replace sensitive or identifying values in each completed dataset with r imputed values. This article also develops methods for obtaining valid inferences from such multiply-imputed datasets. New rules for combining the multiple point and variance estimates are needed because the double duty of multiple imputation introduces two sources of variability into point estimates, which existing methods for obtaining inferences from multiply-imputed datasets do not measure accurately. A reference t-distribution appropriate for inferences when m and r are moderate is derived using moment matching and Taylor series approximations.

  Release date: 2005-02-03

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• 1. Simultaneous use of multiple imputation for missing data and disclosure limitation Archived

  Articles and reports: 12-001-X20040027755

  Description:

  Several statistical agencies use, or are considering the use of, multiple imputation to limit the risk of disclosing respondents' identities or sensitive attributes in public use data files. For example, agencies can release partially synthetic datasets, comprising the units originally surveyed with some collected values, such as sensitive values at high risk of disclosure or values of key identifiers, replaced with multiple imputations. This article presents an approach for generating multiply-imputed, partially synthetic datasets that simultaneously handles disclosure limitation and missing data. The basic idea is to fill in the missing data first to generate m completed datasets, then replace sensitive or identifying values in each completed dataset with r imputed values. This article also develops methods for obtaining valid inferences from such multiply-imputed datasets. New rules for combining the multiple point and variance estimates are needed because the double duty of multiple imputation introduces two sources of variability into point estimates, which existing methods for obtaining inferences from multiply-imputed datasets do not measure accurately. A reference t-distribution appropriate for inferences when m and r are moderate is derived using moment matching and Taylor series approximations.

  Release date: 2005-02-03

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