The Graphical Structure of Respondent-driven Sampling
Bibliographic Data
| ID | 6169965 |
|---|---|
| Authors | Forrest W Crawford (0000-0002-0046-0547, Yale School of Public Health, New Haven, CT, USA, corresponding author) |
| Year | 2016 |
| Volume | 46 |
| Issue | 1 |
| Pages | 187-211 |
| Publication date | 2016-04-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methodology (JOURNAL) |
| Journal identifiers | ISSN: 0081-1750 • E-ISSN: 1467-9531 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/0081175016641713 |
| PMID | 31607761 |
| OpenAlex | W1889289589 |
| Language | EN |
| Citations received | 4 |
| References cited | 47 |
Respondent-driven sampling (RDS) is a chain-referral method for sampling members of hidden or hard-to-reach populations, such as sex workers, homeless people, or drug users, via their social networks. Most methodological work on RDS has focused on inference of population means under the assumption that subjects’ network degree determines their probability of being sampled. Criticism of existing estimators is usually focused on missing data: the underlying network is only partially observed, so it is difficult to determine correct sampling probabilities. In this article, the author shows that data collected in ordinary RDS studies contain information about the structure of the respondents’ social network. The author constructs a continuous-time model of RDS recruitment that incorporates the time series of recruitment events, the pattern of coupon use, and the network degrees of sampled subjects. Together, the observed data and the recruitment model place a well-defined probability distribution on the recruitment-induced subgraph of respondents. The author shows that this distribution can be interpreted as an exponential random graph model and develops a computationally efficient method for estimating the hidden graph. The author validates the method using simulated data and applies the technique to an RDS study of injection drug users in St. Petersburg, Russia
Complex network · Data mining · Degree distribution · Econometrics · Estimator · Exponential random graph models · Graph · Graphical model · Homophily · Inference · Machine learning · Missing data · Population · Random graph · Respondent · Sampling (signal processing · Snowball sampling · Social network (sociolinguistics · Statistics · Artificial Intelligence · Computer Science · Crime, Illicit Activities, and Governance · Demography · HIV, Drug Use, Sexual Risk · Mathematics · Opioid Use Disorder Treatment · Psychology · Social Psychology · Theoretical Computer Science
Statistical adjustment of network degree in respondent-driven sampling estimators
HIV Prevalence Among People Who Inject Drugs in Greater Kuala Lumpur Recruited Using Respondent-Driven Sampling
Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree
New Survey Questions and Estimators for Network Clustering with Respondent-driven Sampling Data
Statistical analysis with missing data
Imputation of Missing Network Data
Modeling social networks from sampled data
Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation
Link prediction in complex networks
Markov Graphs
Evaluation of Respondent-driven Sampling
Assessing respondent-driven sampling
Logit Models and Logistic Regressions for Social Networks
Snowball Sampling
Formative research to optimize respondent-driven sampling surveys among hard-to-reach populations in HIV behavioral and biological surveillance
Bayesian analysis for partially observed network data, missing ties, attributes and actors
Drug Network Characteristics and HIV Risk Among Injection Drug Users in Russia
High HIV Prevalence, Suboptimal HIV Testing, and Low Knowledge of HIV-Positive Serostatus Among Injection Drug Users in St. Petersburg, Russia
Predicting Patterns of Exchange in Economic Exchange Networks
Respondent-Driven Sampling
An Empirical Test of Respondent-Driven Sampling
Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
Macrostructure from Microstructure
Generalizing the Network Scale-up Method
Sampling networks from their posterior predictive distribution
Stickiness of respondent-driven sampling recruitment chains
Respondent-Driven Sampling II
Respondent-Driven Sampling
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,36 |
| Citation span | 2015 - 2023 (9) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |