EXTENSIONS OF RESPONDENT-DRIVEN SAMPLING: ANALYZING CONTINUOUS VARIABLES AND CONTROLLING FOR DIFFERENTIAL RECRUITMENT

Authors


  • This research was made possible by grants from the National Endowment for the Arts, the National Institutes of Health/National Institute on Drug Abuse, and the Centers for Disease Control and Prevention. I thank Richard Campbell, Naihua Duan, Greg Duncan, Matthew Salganik, Michael Spiller III, Erik Volz, Cyprian Wejnert, and Carol Worthman for helpful comments and advice. Direct correspondence to Douglas D. Heckathorn, Cornell University, 344 Uris Hall, Ithaca, NY 14853-7601; e-mail: douglas.heckathorn@cornell.edu.

Abstract

Respondent-driven sampling (RDS) is a network-based method for sampling hidden and hard-to-reach populations that has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. This includes resolving a major concern regarding bias in chain-referral samples—that is, producing a population estimate that is independent of the seeds (initial subjects) with which sampling began. However, RDS estimates are limited to nominal variables, and one of the assumptions required for the proof of lack of bias is the absence of differential recruitment. One aim of this paper is to analyze the role of differential recruitment, quantify the bias it produces, and propose a new estimator that controls for it. The second aim is to extend RDS so that it can be employed to analyze continuous variables in a manner that controls for differential recruitment. The third aim is to describe means for carrying out multivariate analyses using RDS data. The analyses employ data from an RDS sample of 264 jazz musicians in the greater New York metropolitan area, taken in 2002.

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