Many populations of interest present special challenges for traditional survey methodology when it is difficult or impossible to obtain a traditional sampling frame. In the case of such “hidden” populations at risk of HIV/AIDS, many researchers have resorted to chain-referral sampling. Recent progress on the theory of chain-referral sampling has led to Respondent Driven Sampling (RDS), a rigorous chain-referral methodwhich allows unbiased estimation of the target population. In this article we present new probability-theoretic methods for making estimates fromRDS data. The new estimators offer improved simplicity, analytical tractability, and allow the estimation of continuous variables. An analytical variance estimator is proposed in the case of estimating categorical variables. The properties of the estimator and the associated variance estimator are explored in a simulation study, and compared to alternative RDS estimators using data from a study of New York City jazz musicians. The new estimator gives results consistent with alternative RDS estimators in the study of jazz musicians, and demonstrates greater precision than alternative estimators in the simulation study.

Probability based estimation theory for respondent driven sampling

Survey researchers routinely conduct studies that use different methods of data collection and inference. But for at least the past 60 years, the probabilitysampling framework has been used in most surveys. More recently, concerns about coverage and nonresponse coupled with rising costs have led some to wonder whether non-probability sampling methods might be an acceptable alternative, at least under some conditions (Groves 2006; Savage and Burrows 2007). A wide range of non-probability designs exist and are being used in various settings, including case control studies, clinical trials, evaluation research

Summary Report of the AAPOR Task Force on Non-probability Sampling

In many real-world sampling situations, researchers would like to be able to adaptively increase sampling effort in the vicinity of observed values that are high or otherwise interesting. This article describes sampling designs in which, whenever an observed value of a selected unit satisfies a condition of interest, additional units are added to the sample from the neighborhood of that unit. If any of these additional units satisfies the condition, still more units may be added. Sampling designs such as these, in which the selection procedure is allowed to depend on observed values of the variable of interest, are in contrast to conventional designs, in which the entire selection of units to be included in the sample may be determined prior to making any observations. Because the adaptive selection procedure introduces biases into conventional estimators, several estimators are given that are design unbiased for the population mean with the adaptive cluster designs of this article; that is, the ...

Adaptive Cluster Sampling

SUMMARY The design of an efficient procedure for sampling a rare population can present a challenging task. This paper reviews a variety of methods for sampling rare populations, including screening methods, the use of disproportionate sampling, multiplicity sampling, multiple frames and snowballing. The practical feasibility of the methods is discussed, and examples of applications are given.

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Sampling rare populations

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Z W Birnbaum

Papers

Design of sample surveys to estimate the prevalence of rare diseases : three unbiased estimates