Search for: All records

Abstract One of the goals of open science is to promote the transparency and accessibility of research. Sharing data and materials used in network research is critical to these goals. In this paper, we present recommendations for whether, what, when, and where network data and materials should be shared. We recommend that network data and materials should be shared, but access to or use of shared data and materials may be restricted if necessary to avoid harm or comply with regulations. Researchers should share the network data and materials necessary to reproduce reported results via a publicly accessible repository when an associated manuscript is published. To ensure the adoption of these recommendations, network journals should require sharing, and network associations and academic institutions should reward sharing. 

Abstract To enumerate people experiencing homelessness in the United States, the federal Department of Housing and Urban Development (HUD) mandates its designated local jurisdictions regularly conduct a crude census of this population. This Point-in-Time (PIT) body count, typically conducted on a January night by volunteers with flashlights and clipboards, is often followed by interviews with a separate convenience sample. Here, we propose employing a network-based (peer-referral) respondent-driven sampling (RDS) method to generate a representative sample of unsheltered people, accompanied by a novel method to generate a statistical estimate of the number of unsheltered people in the jurisdiction. First, we develop a power analysis for the sample size of our RDS survey to count unsheltered people experiencing homelessness. Then, we conducted 3 large-scale population-representative samples in King County, WA (Seattle metro) in 2022, 2023, and 2024. We describe the data collection and the application of our new method, comparing the 2020 PIT count (the last visual PIT count performed in King County) to the new method of 2022 and 2024 PIT counts. We conclude with a discussion and future directions. This article is part of a Special Collection on Methods in Social Epidemiology. 

Society often ascribes negative stereotypes to people experiencing homelessness. However, people experiencing homelessness have been found to display highly nuanced social behaviors. We employ a field dictator game to examine prosocial behavior among 173 unhoused individuals in Nashville, TN. We test whether an unhoused population displays ingroup bias, wherein they are more generous toward other people experiencing homelessness (the hypothesized ingroup) than people not experiencing homelessness (the hypothesized out-group). Additionally, we explore relationships between sociodemographic and personal characteristics (social support, perceptions of deservedness/generosity) and dictator game behavior. We did not observe ingroup bias. However, on average, participants allocated 29% of their game endowment to recipients, consistent with cross-cultural dictator game studies. We found that the duration of homelessness, social support, and gender were associated with dictator game allocations. Additionally, people experiencing homelessness were more generous when they perceived other unhoused individuals would be more generous and deserving.