Survey design

All (2) ((2 results))

• 1. Updating size measures in a probabilities proportional to size without replacement (PPSWOR) design Archived

  Articles and reports: 12-001-X198900214564

  Description:

  It is sometimes required that a probabilities proportional to size without replacement (PPSWOR) sample of first stage units (psu’s) in a multistage population survey design be updated to take account of new size measures that have become available for the whole population of such units. However, because of a considerable investment in within-psu mapping, segmentation, listing, enumerator recruitment, etc., we would like to retain the same sample psu’s if possible, consistent with the requirement that selection probabilities may now be regarded as being proportional to the new size measures. The method described in this article differs from methods already described in the literature in that it is valid for any sample size and does not require enumeration of all possible samples. Further, it does not require that the old and the new sampling methods be the same and hence it provides a convenient way not only of updating size measures but also of switching to a new sampling method.

  Release date: 1989-12-15

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• 2. Sample allocation in multivariate surveys Archived

  Articles and reports: 12-001-X198900114578

  Description:

  The optimum allocation to strata for multipurpose surveys is often solved in practice by establishing linear variance constraints and then using convex programming to minimize the survey cost. Using the Kuhn-Tucker theorem, this paper gives an expression for the resulting optimum allocation in terms of Lagrangian multipliers. Using this representation, the partial derivative of the cost function with respect to the k-th variance constraint is found to be -2 \alpha_{k^*} g (x^*) / v_k, where g (x^*) is the cost of the optimum allocation and where \alpha_{k^*} and v_k are, respectively, the k-th normalized Lagrangian multiplier and the upper bound on the precision of the k-th variable. Finally, a simple computing algorithm is presented and its convergence properties are discussed. The use of these results in sample design is demonstrated with data from a survey of commercial establishments.

  Release date: 1989-06-15

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Analysis (2) ((2 results))

• 1. Updating size measures in a probabilities proportional to size without replacement (PPSWOR) design Archived

  Articles and reports: 12-001-X198900214564

  Description:

  It is sometimes required that a probabilities proportional to size without replacement (PPSWOR) sample of first stage units (psu’s) in a multistage population survey design be updated to take account of new size measures that have become available for the whole population of such units. However, because of a considerable investment in within-psu mapping, segmentation, listing, enumerator recruitment, etc., we would like to retain the same sample psu’s if possible, consistent with the requirement that selection probabilities may now be regarded as being proportional to the new size measures. The method described in this article differs from methods already described in the literature in that it is valid for any sample size and does not require enumeration of all possible samples. Further, it does not require that the old and the new sampling methods be the same and hence it provides a convenient way not only of updating size measures but also of switching to a new sampling method.

  Release date: 1989-12-15

  ---

• 2. Sample allocation in multivariate surveys Archived

  Articles and reports: 12-001-X198900114578

  Description:

  The optimum allocation to strata for multipurpose surveys is often solved in practice by establishing linear variance constraints and then using convex programming to minimize the survey cost. Using the Kuhn-Tucker theorem, this paper gives an expression for the resulting optimum allocation in terms of Lagrangian multipliers. Using this representation, the partial derivative of the cost function with respect to the k-th variance constraint is found to be -2 \alpha_{k^*} g (x^*) / v_k, where g (x^*) is the cost of the optimum allocation and where \alpha_{k^*} and v_k are, respectively, the k-th normalized Lagrangian multiplier and the upper bound on the precision of the k-th variable. Finally, a simple computing algorithm is presented and its convergence properties are discussed. The use of these results in sample design is demonstrated with data from a survey of commercial establishments.

  Release date: 1989-06-15

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