Combining Link-Tracing Sampling and Cluster Sampling to Estimate the Size of Hidden Populations
Martín H. Félix-Medina and Steven K. Thompson
We present a variant of Link-Tracing Sampling which avoids the ordinary assumption of an initial Bernoulli sample of members of the target population. Instead of that, we assume that a portion of the target population is covered by a sampling frame of accessible sites, such as households, street blocks, or block venues, and that a simple random sample of sites is selected from the frame. As in ordinary Link-Tracing sampling, the people in the initial sample are asked to nominate other members of the population, but in this case we trace only the links between the sampled sites and the nominees. Maximum likelihood estimators of the population size are presented, and estimators of their variances that incorporate the initial sampling design are suggested. The results of a simulation study carried out in this research indicate that our proposed design is effective provided that the nomination probabilities are not too small.
Capture-recapture, design-based approach, finite population, hard-to-access population, maximum likelihood, model-based approach, sampling frame