Design-Based Estimators for Snowball Sampling

Abstract

Snowball sampling, where existing study subjects recruit further subjects from among their acquaintances, is a popular approach when sampling from hidden populations. Since people with many in-links are more likely to be selected, there will be a selection bias in the samples obtained. In order to eliminate this bias, the sample data must be weighted. However, the exact selection probabilities are unknown for snowball samples and need to be approximated in an appropriate way. This paper proposes different ways of approximating the selection probabilities and develops weighting techniques using the inverse of the selection probabilities. Some simulations on larger networks are provided to compare the efficiency of the weighting techniques. The simulation results indicate that the suggested re-weighted estimators should be preferred to traditional estimators with equal sample weights for the initial snowball sampling waves.

Keywords: hidden population, chain-referral sampling, network analysis, selection bias, weighting, simulation.

JEL Classification: C00, C10, C13, C8, C42