Estimation and correction of bias in network simulations based on respondent-driven sampling data
نویسندگان
چکیده
منابع مشابه
Network Structure and Biased Variance Estimation in Respondent Driven Sampling
This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of varianc...
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متن کاملNetwork Model-Assisted Inference from Respondent-Driven Sampling Data.
Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights ...
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ondent-driven s were found to progress very fast. Studies with large samples can be expected to proceed up to 20 times faster than with traditional methods; in fact, to prevent bias from temporal filtering, d, because of sation should ential recruits. findings, based on theoretical and empirical evidence, indicate that web-based RDS can be much faster, easier, and cheaper than both regular RDS ...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2020
ISSN: 2045-2322
DOI: 10.1038/s41598-020-63269-0