Optimizing the Network Sampling With Memory Algorithm

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2022

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Abstract

Network Sampling with Memory (NSM), a novel sampling method that extends existing Respondent Driven Sampling (RDS) methods, is becoming increasingly attractive to sociologists, demographers, and others to sample hidden populations while attempting to explore the full network of the target population, highlighting the importance of improving and testing this method under various conditions. Since its elaboration, three large-scale data collection efforts have employed NSM to efficiently sample from Chinese immigrant populations.Given increased interest in using these methods in the field, this thesis contributes to the literature on the Network Sampling with Memory algorithm in three key ways: (1) Creates a user-friendly version of the sampler for future researchers by creating R functions that can be run easily in R Studio. (2) Tests the NSM sampler under different parameter configurations with 2 different target outcome variables, to help guide the practical researcher to select the optimal parameter configuration depending on the goals of the project. (3) Tests a directed NSM sampling algorithm which preferentially selects nodes that have certain characteristics. We show that different parameter configurations can improve estimation and lower design effects, depending on the outcome variable of interest. We also show that a directed sampler is feasible to implement, and that it can have low design effects at smaller sample sizes.

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Le Barbenchon, Claire (2022). Optimizing the Network Sampling With Memory Algorithm. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/25856.

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