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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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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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A grid-based sample design framework for household surveys.

Gianluca Boo1, Edith Darin1, Dana R Thomson2,3

  • 1WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.

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|March 27, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel grid-based sample design for household surveys, enhancing representativeness in dynamic populations. The framework uses gridded population data and spatial sampling for more accurate survey frames.

Keywords:
Democratic Republic of the CongoDemographyGridded PopulationHousehold SurveysSample DesignSpatial Sampling

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Area of Science:

  • Survey methodology
  • Spatial statistics
  • Demography

Background:

  • Traditional household survey sampling relies on census data, which quickly becomes outdated in rapidly changing demographics.
  • This inaccuracy compromises the representativeness of sampling frames, impacting survey reliability.

Purpose of the Study:

  • To propose and evaluate an original grid-based sample design framework for household surveys.
  • To address the limitations of traditional sampling frames in dynamic population settings.

Main Methods:

  • Developed a grid-based sampling frame using gridded population estimates and spatial random field concepts.
  • Implemented contextual stratification via Principal Component Analysis (PCA) and k-means clustering on geospatial covariates.
  • Employed proportional to population size sampling and a nonparametric estimator for sample size determination.

Main Results:

  • A case study in the Democratic Republic of the Congo demonstrated the framework's application.
  • The method successfully defined a sampling frame using settled cells and population estimates.
  • Evaluation using Kolmogorov-Smirnov distance identified adequate sample sizes for survey representativeness.

Conclusions:

  • The proposed grid-based sample design framework offers a viable alternative for creating representative sampling frames.
  • The study highlights the potential of spatial sampling concepts in improving household surveys.
  • Further research is encouraged to explore the broader application of this framework.