Open AccessA review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists
Author(s) -
Lisa Avery,
Alison Macpherson,
Sarah Flicker,
Michael Rotondi
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0249074
Subject(s) - respondent , outlier , sampling (signal processing) , statistics , degree (music) , estimator , sample (material) , sampling frame , degree distribution , demography , psychology , econometrics , computer science , mathematics , population , complex network , sociology , world wide web , physics , chemistry , filter (signal processing) , chromatography , political science , acoustics , law , computer vision
Objective Respondent driven sampling (RDS) is an important tool for measuring disease prevalence in populations with no sampling frame. We aim to describe key properties of these samples to guide those using this method and to inform methodological research. Methods In 2019, authors who published respondent driven sampling studies were contacted with a request to share reported degree and network information. Of 59 author groups identified, 15 (25%) agreed to share data, representing 53 distinct study samples containing 36,547 participants across 12 countries and several target populations including migrants, sex workers and men who have sex with men. Distribution of reported network degree was described for each sample and characteristics of recruitment chains, and their relationship to coupons, were reported. Results Reported network degree is severely skewed and is best represented by a log normal distribution. For participants connected to more than 15 other people, reported degree is imprecise and frequently rounded to the nearest five or ten. Our results indicate that many samples contain highly connected individuals, who may be connected to at least 1000 other people. Conclusion Because very large reported degrees are common; we caution against treating these reports as outliers. The imprecise and skewed distribution of the reported degree should be incorporated into future RDS methodological studies to better capture real-world performance. Previous results indicating poor performance of regression estimators using RDS weights may be widely generalizable. Fewer recruitment coupons may be associated with longer recruitment chains.