ArticlePloS one2015
Network Structure and Biased Variance Estimation in Respondent Driven Sampling.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 37 citations in OpenAlex.
- A factorial experiment grounded in the multiphase optimization strategy to promote viral suppression among people who inject drugs on the Texas-Mexico border: a study protocol.BMC public health · 2023Article
- Respondent Driven Sampling for Immigrant Populations: A Health Survey of Foreign-Born Korean Americans.Journal of immigrant and minority health · 2021Article
- Article
- HIV Prevalence and Associated Risks in a Respondent-Driven Sample of Illicit Stimulant Users in a Southern United States City.AIDS and behavior · 2020Article
- Assessing Bias in Population Size Estimates Among Hidden Populations When Using the Service Multiplier Method Combined With Respondent-Driven Sampling Surveys: Survey Study.JMIR public health and surveillance · 2020Article
- Estimating Hidden Population Sizes with Venue-based Sampling: Extensions of the Generalized Network Scale-up Estimator.Epidemiology (Cambridge, Mass.) · 2019Article
- Maximizing respondent-driven sampling field procedures in the recruitment of sexual minorities for health research.SAGE open medicine · 2019Article
- A SIMULATION-BASED FRAMEWORK FOR ASSESSING THE FEASIBILITY OF RESPONDENT-DRIVEN SAMPLING FOR ESTIMATING CHARACTERISTICS IN POPULATIONS OF LESBIAN, GAY AND BISEXUAL OLDER ADULTS.The annals of applied statistics · 2018Article
- Overlooked Threats to Respondent Driven Sampling Estimators: Peer Recruitment Reality, Degree Measures, and Random Selection Assumption.AIDS and behavior · 2018Article
- One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity.PloS one · 2018Article
- Identification of Homophily and Preferential Recruitment in Respondent-Driven Sampling.American journal of epidemiology · 2018Article
- Social Network Clustering and the Spread of HIV/AIDS Among Persons Who Inject Drugs in 2 Cities in the Philippines.Journal of acquired immune deficiency syndromes (1999) · 2017Article
- NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA.Sociological methodology · 2017Article
- Evaluating Variance Estimators for Respondent-Driven Sampling.Journal of survey statistics and methodology · 2017Article
- Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations.Sociological methodology · 2016Article
- Sampling Migrants from their Social Networks: The Demography and Social Organization of Chinese Migrants in Dar es Salaam, Tanzania.Migration studies · 2016Article
- Correction: Network Structure and Biased Variance Estimation in Respondent Driven Sampling.PloS one · 2016Article
- Brief Report: Respondent-driven Sampling Estimators Under Real and Theoretical Recruitment Conditions of Female Sex Workers in China.Epidemiology (Cambridge, Mass.) · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
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 variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.