Methods for Inference from Respondent-Driven Sampling Data

  title={Methods for Inference from Respondent-Driven Sampling Data},
  author={Krista Gile and Isabelle S. Beaudry and Mark S. Handcock and Miles Q. Ott},
Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to join the sample. Although the method is often effective at attaining large and varied samples, its reliance on convenience samples, social network contacts, and participant decisions makes it subject to a large number of statistical concerns. This… 

Inferring bivariate association from respondent‐driven sampling data

Respondent‐driven sampling (RDS) is an effective method of collecting data from many hard‐to‐reach populations. Valid statistical inference for these data relies on many strong assumptions. In

General regression methods for respondent-driven sampling data

This work proposes a methodology for general regression techniques using respondent-driven sampling data to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent- driven sampling study in Montreal, Canada.

Sampling from networks: respondent-driven sampling

It is highlighted that it is possible, in some cases, to simulate population networks by mimicking the characteristics of real-world RDS data while retaining accuracy and precision for target network features in the samples.

Neighbourhood Bootstrap for Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique for `hard-to-reach' populations that aims to leverage individuals' social relationships to reach potential

Respondent-driven sampling (RDS) method: Introduction and its potential use for social psychology research

Contrary to other non-probability sampling methods in which researchers actively recruit potential participants, respondent-driven sampling (RDS) relies on connection and trust within social networks

A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists

The authors' results indicate that many samples contain highly connected individuals, who may be connected to at least 1000 other people, and the imprecise and skewed distribution of the reported degree should be incorporated into future RDS methodological studies to better capture real-world performance.

Can respondent driven sampling be used to recruit new mothers? A mixed methods study in metropolitan Washington DC

New mothers are not easily recruited using RDS because they have a limited number of contacts who are also new mothers and those recruited through RDS are more likely to be older, Caucasian and of high socioeconomic status, indicating it is not an effective way to recruit a representative sample of new mothers.

How to Implement Respondent-Driven Sampling in Practice: Insights from Surveying 24-Hour Migrant Home Care Workers

This article draws on the experience from an ongoing research project employing respondent-driven sampling (RDS) to survey (illicit) 24-hour home care workers. We highlight issues around the

Mathematical Modeling and Inference for Degree-capped Egocentric Network Sampling

A mathematical model of this sampling procedure for offline social network sampling is considered and analytical solutions to recover some of the lost information about the underlying network are sought.

Reassessing geographic bottlenecks in a respondent-driven sampling based multicity study in Brazil.

The spatial dynamics of drug users' recruitment chains in the context of a respondent-driven sampling (RDS) study in the city of Recife, Brazil is analyzed to understand the geographic bottlenecks, influenced by social geography, which have been a major challenge for RDS-based studies.



7. Respondent-Driven Sampling: An Assessment of Current Methodology

It is indicated that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness.

Estimating hidden population size using Respondent-Driven Sampling data.

The proposed approach uses a successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes to estimate the size of a target population based on data collected through RDS.

New Survey Questions and Estimators for Network Clustering with Respondent-driven Sampling Data

This work takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.

Unequal edge inclusion probabilities in link-tracing network sampling with implications for Respondent-Driven Sampling

: Respondent-Driven Sampling (RDS) is a widely adopted link- tracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling

The sensitivity of respondent‐driven sampling

Summary.  Researchers in many scientific fields make inferences from individuals to larger groups. For many groups, however, there is no list of members from which to draw a random sample.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method

A tree bootstrap method is introduced for estimating uncertainty in RDS estimates based on resampling recruitment trees that not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects.

Modeling and analyzing respondent‐driven sampling as a counting process

This work proposes to use the timing of recruitment, typically collected and discarded, in order to estimate the population size via a counting process model, and develops large‐sample theory, proving consistency and asymptotic normality.

Respondent-driven sampling : A new approach to the study of hidden populations

A new variant of chain-referral sampling, respondent-driven sampling, is introduced that employs a dual system of structured incentives to overcome some of the deficiencies of such samples and discusses how respondent- driven sampling can improve both network sampling and ethnographic investigation.

Respondent-driven Sampling on Directed Networks

Respondent-driven sampling (RDS) is a widely used method for generating chain-referral samples from hidden populations. It is an extension of the snowball sampling method and can, given that some a

Evaluation of Respondent-driven Sampling

Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required when interpreting findings based on the sampling method.