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Adaptive sampling in behavioral surveys

S K Thompson1

  • 1Department of Statistics, Pennsylvania State University, University Park 16802, USA.

NIDA Research Monograph
|January 1, 1997
PubMed
Summary
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Adaptive sampling methods improve the study of hard-to-reach populations, like drug users. These techniques adjust sampling efforts based on observed data, increasing efficiency and reducing uncertainty in population estimates.

Area of Science:

  • Social Sciences
  • Statistics
  • Public Health

Background:

  • Studying hidden populations, such as drug users, presents significant challenges for conventional survey methods.
  • Traditional large-scale surveys often yield few participants from these groups, leading to high uncertainty in population characteristic estimates.
  • Ethnographic methods using link-tracing or snowball sampling can reach more individuals but limit generalizability to the broader hidden population.

Purpose of the Study:

  • To introduce and explain adaptive sampling designs for effectively studying hidden or hard-to-reach populations.
  • To highlight the advantages of adaptive sampling over conventional methods in terms of efficiency and accuracy.
  • To discuss the integration of graph sampling concepts within adaptive sampling frameworks.

Main Methods:

Related Experiment Videos

  • Adaptive sampling procedures dynamically adjust the selection of participants based on variables of interest discovered during the survey.
  • Examples include increasing sampling effort in areas where drug use is reported.
  • Graph sampling involves selecting initial nodes (people) or edges (links) and following connections to expand the sample, with adaptive versions adjusting based on observed data.

Main Results:

  • Adaptive sampling designs, including adaptive cluster sampling and network sampling, allow for more efficient data collection from dispersed or hidden populations.
  • These methods can reduce sampling uncertainty compared to conventional approaches.
  • Adjustment methods for nonsampling errors are applicable to both adaptive and conventional designs.

Conclusions:

  • Adaptive sampling offers a more effective strategy for surveying populations that are rare, hidden, or difficult to access.
  • Integrating graph sampling principles with adaptive techniques enhances the ability to map and understand social networks within these populations.
  • Further research and application of adaptive sampling are crucial for improving the reliability of estimates for hidden populations.