Abstract: Whether Respondent-Driven Sampling(RDS) with hypothesis violations can provide an unbiased estimate of the population is a question that needs to be examined. This article focuses on the validity of RDS estimates when random recruitment assumption violations occur.Since the serious consequence of violating random recruitment assumptions is high homophily levels and underrepresentation of overall RDS samples, RDS estimates based on these samples are deemed to be invalid. For this reason, this study offers a comprehensive diagnosis on RDS sample data that violate random recruitment assumptions by employing various approaches of RDS estimators, convergence plots and bottleneck plots, latitude and longitude information, and etc. The diagnostic results show that moderate violations of random recruitment assumptions do not lead to severe RDS bias and the RDS estimates remain valid. In addition, a practical description of the three diagnostic methods is presented. It identifies an applicable criterion for moderate hypothesis violation from a methodological perspective:(1) when the sample homophily level remains below 0.7, the proportion of sample characteristics can complete equilibrium convergence within six recruitment batches; or/and (2) both convergence plots and bottleneck plots of the sample can show that the sample eventually converges and stabilizes at the convergence value; or/and (3) if the effective geographic coverage area of the sample is substantially close to the entire survey area, the random recruitment assumption violation is considered as moderate at this point. A brief discussion of the advantages and limitations of these three diagnostics is also presented to forge a better understanding of RDS methods among Chinese researchers.

Key words: Respondent-Driven Sampling, RDS estimation, random recruitment ass-umption, diagnosis