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Respondent-driven sampling and the homophily configuration graph.

Ian E Fellows1

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Summary

We developed a new statistical model and estimator for Respondent-Driven Sampling (RDS) that reduces bias from sampling challenges. This method improves estimates for hard-to-reach populations in public health research.

Keywords:
RDSconfiguration graphhard-to-reach population samplingnetwork samplingsocial networks

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Area of Science:

  • Social Sciences
  • Statistics
  • Public Health

Background:

  • Respondent-Driven Sampling (RDS) is widely used for surveying hidden populations, particularly in public health.
  • Analyzing RDS data is complex due to its intricate sampling design.
  • Existing methods struggle with issues like seed bias and short recruitment chains.

Purpose of the Study:

  • To propose a novel graph-based model for the RDS mechanism.
  • To develop a new RDS estimator that addresses common biases.
  • To provide a statistically robust method for estimating population characteristics from RDS data.

Main Methods:

  • Introduced the Homophily Configuration Graph model for RDS.
  • Developed a new estimator based on this graph model.
  • Validated the estimator through simulation studies on diverse network structures.

Main Results:

  • The new estimator demonstrates robustness against seed bias, differential activity, and recruitment chain length.
  • The proposed estimator converges to the Salganik-Heckathorn estimator under specific conditions (small sample fraction).
  • Simulation studies indicate reduced bias compared to existing RDS estimators.

Conclusions:

  • The Homophily Configuration Graph model offers a new theoretical framework for RDS.
  • The developed estimator provides a more accurate and reliable method for analyzing RDS data.
  • This approach enhances the utility of RDS in public health and other fields for studying hard-to-reach groups.