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Related Concept Videos

Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling Soils in a Heterogeneous Research Plot
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A nonparametric method to generate synthetic populations to adjust for complex sampling design features.

Qi Dong1, Michael R Elliott2, Trivellore E Raghunathan2

  • 1Netflix, Inc. 100 Winchester Cir, Los Gatos, CA 95032.

Survey Methodology
|December 5, 2017
PubMed
Summary

This study introduces a novel method to generate synthetic populations from complex survey data. The approach adjusts for intricate sampling designs, enabling analysis as simple random samples for improved statistical inference.

Keywords:
Bayesian bootstrapInverse samplingPosterior predictive distributionSynthetic populations

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Area of Science:

  • Statistics
  • Survey Methodology
  • Computational Statistics

Background:

  • Standard statistical methods often assume simple random sampling (SRS), yielding independent and identically distributed (IID) data.
  • Applying IID-based methods to complex survey data without accounting for design features can result in incorrect inferences.
  • Accurate analysis of complex survey data is crucial for applications like synthetic population generation in missing data or disclosure risk analyses.

Purpose of the Study:

  • To propose a method for generating synthetic populations that inverts complex sampling design features.
  • To enable the analysis of complex survey data as simple random samples by adjusting for design complexities.
  • To facilitate the use of finite population Bayesian inference for complex survey data.

Main Methods:

  • Extension of the finite population Bayesian bootstrap literature.
  • Development of a nonparametric method to generate synthetic populations from a posterior predictive distribution.
  • The method adjusts complex survey data to simulate simple random samples from a superpopulation perspective.

Main Results:

  • A simulation study demonstrated the method's effectiveness with a stratified, clustered, unequal-probability of selection sample design.
  • The proposed method was successfully applied to generate synthetic populations for the 2006 National Health Interview Survey (NHIS) and the Medical Expenditure Panel Survey (MEPS).
  • Generated synthetic populations allow for analysis as simple random samples, mitigating issues arising from complex survey designs.

Conclusions:

  • The proposed method provides a robust approach to generating synthetic populations from complex survey data.
  • This technique enhances the applicability of standard statistical methods to data from complex sample surveys.
  • The approach is valuable for settings requiring synthetic data, such as missing data imputation and disclosure risk assessment.