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• 1. Disclosure risk and variance estimation Archived

  Articles and reports: 11-522-X200600110434

  Description:

  Protecting respondents from disclosure of their identity in publicly released survey data is of practical concern to many government agencies. Methods for doing so include suppression of cluster and stratum identifiers and altering or swapping record values between respondents. Unfortunately, stratum and cluster identifiers are usually needed for variance estimation using linearization and for replication methods as resampling is typically done on first-stage sampling units within strata. One might feel that releasing a set of replicate weights that also have stratum and cluster identifiers suppressed might circumvent this problem to some extent, especially using some random resampling such as the bootstrap. In this article, we first demonstrate that by viewing the replicate weights as observations in a high dimensional space one can easily use clustering algorithms to reconstruct the cluster identifiers irrespective of the resampling method even if the resampling weights are randomly altered. We then propose a fast algorithm for swapping cluster and strata identifiers of ultimate units before creating replicate weights without significantly impacting resulting variance estimates of characteristics of interest. The methods are illustrated by application to publicly released data from the National Health and Nutrition Examination Surveys, where such disclosure issues are extremely important..

  Release date: 2008-03-17

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• 2. Multi-way stratification by linear programming made practical Archived

  Articles and reports: 12-001-X20020026433

  Description:

  Sitter and Skinner (1994) present a method which applies linear programming to designing surveys with multi-way stratification, primarily in situations where the desired sample size is less than or only slightly larger than the total number of stratification cells. The idea in their approach is simple, easily understood and easy to apply. However, the main practical constraint of their approach is that it rapidly becomes expensive in terms of magnitude of computation as the number of cells in the multi-way stratification increases, to the extent that it cannot be used in most realistic situations. In this article, we extend this linear programming approach and develop methods to reduce the amount of computation so that very large problems become feasible.

  Release date: 2003-01-29

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Articles and reports (2) ((2 results))

• 1. Disclosure risk and variance estimation Archived

  Articles and reports: 11-522-X200600110434

  Description:

  Protecting respondents from disclosure of their identity in publicly released survey data is of practical concern to many government agencies. Methods for doing so include suppression of cluster and stratum identifiers and altering or swapping record values between respondents. Unfortunately, stratum and cluster identifiers are usually needed for variance estimation using linearization and for replication methods as resampling is typically done on first-stage sampling units within strata. One might feel that releasing a set of replicate weights that also have stratum and cluster identifiers suppressed might circumvent this problem to some extent, especially using some random resampling such as the bootstrap. In this article, we first demonstrate that by viewing the replicate weights as observations in a high dimensional space one can easily use clustering algorithms to reconstruct the cluster identifiers irrespective of the resampling method even if the resampling weights are randomly altered. We then propose a fast algorithm for swapping cluster and strata identifiers of ultimate units before creating replicate weights without significantly impacting resulting variance estimates of characteristics of interest. The methods are illustrated by application to publicly released data from the National Health and Nutrition Examination Surveys, where such disclosure issues are extremely important..

  Release date: 2008-03-17

  ---

• 2. Multi-way stratification by linear programming made practical Archived

  Articles and reports: 12-001-X20020026433

  Description:

  Sitter and Skinner (1994) present a method which applies linear programming to designing surveys with multi-way stratification, primarily in situations where the desired sample size is less than or only slightly larger than the total number of stratification cells. The idea in their approach is simple, easily understood and easy to apply. However, the main practical constraint of their approach is that it rapidly becomes expensive in terms of magnitude of computation as the number of cells in the multi-way stratification increases, to the extent that it cannot be used in most realistic situations. In this article, we extend this linear programming approach and develop methods to reduce the amount of computation so that very large problems become feasible.

  Release date: 2003-01-29

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