Sample allocation balancing overall representativeness and stratum precision.

Purpose: In large-scale surveys, it is often necessary to distribute a preset sample size among a number of strata. Researchers must make a decision between prioritizing overall representativeness or precision of stratum estimates. Hence, I evaluated different sample allocation strategies based on s...

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Publicado en:Annals of Epidemiology Vol. 28; no. 8; pp. 570 - 576
Autor principal: Diaz-Quijano, Fredi Alexander
Formato: research Journal Article
Publicado: Elsevier B.V. Aug2018
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Aug2018
      vid: 28
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      pub: Elsevier B.V.
      place: New York, New York
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        130837906
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        130837906
        10.1016/j.annepidem.2018.04.011
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        atl: Sample allocation balancing overall representativeness and stratum precision.
      aug:
        au: Diaz-Quijano, Fredi Alexander
        affil: Department of Epidemiology, Faculdade de Saúde Pública da Universidade de São Paulo, São Paulo, Brazil
      sug:
        subj:
          Selection Bias
          Sample Size
          Human
          Computer Simulation
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
          Clinical Assessment Tools
      ab: Purpose: In large-scale surveys, it is often necessary to distribute a preset sample size among a number of strata. Researchers must make a decision between prioritizing overall representativeness or precision of stratum estimates. Hence, I evaluated different sample allocation strategies based on stratum size.Methods: The strategies evaluated herein included allocation proportional to stratum population; equal sample for all strata; and proportional to the natural logarithm, cubic root, and square root of the stratum population. This study considered the fact that, from a preset sample size, the dispersion index of stratum sampling fractions is correlated with the population estimator error and the dispersion index of stratum-specific sampling errors would measure the inequality in precision distribution. Identification of a balanced and efficient strategy was based on comparing those both dispersion indices.Results: Balance and efficiency of the strategies changed depending on overall sample size. As the sample to be distributed increased, the most efficient allocation strategies were equal sample for each stratum; proportional to the logarithm, to the cubic root, to square root; and that proportional to the stratum population, respectively.Conclusions: Depending on sample size, each of the strategies evaluated could be considered in optimizing the sample to keep both overall representativeness and stratum-specific precision.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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