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On The Robustness Of Respondent-Driven Sampling Estimators To Measurement Error
Journal of Survey Statistics and Methodology
|April 22, 2022
Summary
Respondent-driven sampling (RDS) surveys may have measurement errors in network degrees. However, RDS estimators remain largely unbiased and consistent, with only a slight increase in variance, making them reliable for hard-to-reach populations.
Area of Science:
- Social Sciences
- Statistics
- Epidemiology
Background:
- Respondent-driven sampling (RDS) is widely used for surveying hidden populations.
- RDS relies on strong assumptions for accurate statistical inference.
- Accurate measurement of network degrees is crucial for RDS validity.
Purpose of the Study:
- To investigate the accuracy of network degree measurements in RDS surveys.
- To assess the impact of measurement error on RDS estimators.
- To provide theoretical support for the use of RDS despite potential inaccuracies.
Main Methods:
- Theoretical analysis of RDS estimators under a measurement error model.
- Mathematical proofs demonstrating estimator consistency and bias.
- Simulation studies to evaluate performance under imperfect measurements.
Main Results:
- Significant measurement error in network degrees is likely in typical RDS studies.
- Most RDS estimators remain consistent even with measurement error.
- The bias introduced by measurement error is minimal.
- Variance of RDS estimators increases with measurement error.
Conclusions:
- RDS estimators are robust to measurement error in network degrees.
- The consistency of RDS estimators is maintained under imperfect measurement.
- While variance increases, the core inferential capabilities of RDS remain largely intact.
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