Correcting for differential recruitment in respondent-driven sampling data using ego-network information
نویسندگان
چکیده
منابع مشابه
Assessment of Random Recruitment Assumption in Respondent-Driven Sampling in Egocentric Network Data.
BACKGROUND One of the key assumptions in respondent-driven sampling (RDS) analysis, called "random selection assumption," is that respondents randomly recruit their peers from their personal networks. The objective of this study was to verify this assumption in the empirical data of egocentric networks. METHODS We conducted an egocentric network study among young drug users in China, in which...
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 ...
متن کاملModelling the Effect of Differential Recruitment on the Bias of Estimators for Respondent-Driven Sampling
Respondent Driven Sampling has previously been modelled as a random walk on a network. In this document we show that this model can be used to encompass within-group differential recruitment, and examine the implications for bias of several common estimators.
متن کاملDiagnostics 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 HIV. Data are collected through peer-referral over social networks. RDS has proven practical for data collection in many difficult settings and is widely used. Inference from RDS data requires many strong assumptions because the sampling design is...
متن کاملEstimating hidden population size using Respondent-Driven Sampling data.
Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2020
ISSN: 1935-7524
DOI: 10.1214/20-ejs1718