Diagnostics for respondent-driven sampling

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for human immunodeficiency virus or acquired immune deficiency syndrome. Data are collected through a peer referral process over social networks. RDS has...

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Veröffentlicht in:Journal of the Royal Statistical Society. Series A, Statistics in society Jg. 178; H. 1; S. 241 - 269
Hauptverfasser: Gile, Krista J., Johnston, Lisa G., Salganik, Matthew J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Blackwell Publishing Ltd 01.01.2015
John Wiley & Sons Ltd
Oxford University Press
Schlagworte:
Acquired immune deficiency syndrome
AIDS
Coupons
Data collection
Diagnostic software
Diagnostics
Diseases
Estimation bias
Estimators
Exploratory data analysis
Hard-to-reach populations
Health care industry
Health risk assessment
High risk
HIV
Human immunodeficiency virus
Inference
Link tracing sampling
Non-ignorable design
Organizations
Population
Population estimates
Populations
Public health
Respiratory distress syndrome
Respondent-driven sampling
Respondents
Risk
Sampling
Sampling bias
Sampling techniques
Social networks
Statistical analysis
Statistics
Survey sampling
HIV/AIDS
diagnostics
hard-to-reach populations
respondent-driven sampling
exploratory data analysis
non-ignorable design
survey sampling
social networks
link-tracing sampling
ISSN:0964-1998, 1467-985X
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Beschreibung
Zusammenfassung:Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for human immunodeficiency virus or acquired immune deficiency syndrome. Data are collected through a peer referral process over social networks. RDS has proven practical for data collection in many difficult settings and has been adopted by leading public health organizations around the world. Unfortunately, inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher and not fully observable. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to understand their RDS data better and encourage future statistical research on RDS sampling and inference.
Bibliographie:istex:AD3773709533D4CDCCDC5BDBA83901141160560C
National Science Foundation - No. CNS-0905086; No. SES-1230081
ark:/67375/WNG-XTBD22D7-8
'Supporting information: Diagnostics for respondent-driven sampling'.
National Institutes of Health-National Institute of Child Health and Development - No. R01-HD062366; No. R24-HD047879
National Institutes of Health - No. R21-A604273
ArticleID:RSSA12059
National Agricultural Statistics Service
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ISSN:0964-1998
1467-985X
DOI:10.1111/rssa.12059