Network Sampling Methods for Estimating Social Networks, Population Percentages and Totals of People Experiencing Homelessness

In this article, we propose using network-based sampling strategies to estimate the number of unsheltered people experiencing homelessness within a given administrative service unit, known as a Continuum of Care. Further, we specifically advocate for the network sampling method known as Respondent Driven Sampling (RDS), which has been shown to provide unbiased or low-biased estimates of totals and proportions for hard-to-reach populations in contexts where a sampling frame (e.g., housing addresses) not available. To make the RDS estimator work for estimating the total number of unsheltered people, we introduce a new method that leverages administrative data from the HUD-mandated Homeless Management Information System (HMIS). The HMIS provides high-quality counts and demographics for people experiencing homelessness who sleep in emergency shelters. We then demonstrate this method using network data collected in Nashville, TN, combined with simulation methods to illustrate the efficacy of this approach. Finally, we end with discussing how this could be used in practice.