A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling

• 成果类型: Article; Early Access
• 署名作者: Wang, Yibo; Lee, Sunghee; Elliott, Michael R.
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2516185
• 发表日期: 2025
• 关键词: network size; RECRUITMENT; errors

摘要

Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its nonrandom sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the accuracy of the reported degree is questionable due to reporting errors, evidenced through an unusual frequency of multiples of five and improbably large values. To address this measurement error, we leverage a byproduct of the RDS process (e.g., respondents' recruitment patterns) and develop a novel degree estimator based on a latent variable model of the true degree that accounts for response errors via a reporting mechanism and incorporates recruitment information and external demographic profiles. The effectiveness of the proposed method is demonstrated through a case study and a simulation study, which shows accurate and reliable degree estimates leading to significant improvements in population parameter estimation. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.