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Greg

I don't know of anything specifically that addresses your questions, but our recent paper testing the assumptions in the RDS approach answers some of them.  Here is the reference and abstract:

 

Li, J., Valente, T. W., Shin, H. S., Weeks, M., Zelenev, A., Moothi, G., Mosher, H., Heimer, R., Robles, E., Palmer, G., & Obidoa, C. (2018).  Overlooked threats to respondent driven sampling: estimators:  Peer recruitment realities, degree measures, and random selection assumption.  AIDS and Behavior, 22, 2340–2359.

 

Abstract Intensive sociometric network data were collected from a typical respondent driven sample (RDS) of 528 people who inject drugs residing in Hartford, Connecticut in 2012–2013. This rich dataset enabled us to analyze a large number of unobserved network nodes and ties for the purpose of assessing common assumptions underlying RDS estimators. Results show that several assumptions central to RDS estimators, such as random selection, enrollment probability proportional to degree, and recruitment occurring over recruiter’s network ties, were violated. These problems stem from an overly simplistic conceptualization of peer recruitment processes and dynamics. We found nearly half of participants were recruited via coupon redistribution on the street. Non-uniform patterns occurred in multiple recruitment stages related to both recruiter behavior (choosing and reaching alters, passing coupons, etc.) and recruit behavior (accepting/rejecting coupons, failing to enter study, passing coupons to others). Some factors associated with these patterns were also associated with HIV risk.

 

-Tom

 

Thomas W. Valente, PhD

Professor and Interim Chair

Department of Preventive Medicine

Keck School of Medicine

University of Southern California

 

-----Original Message-----
From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Greg Doyle
Sent: Wednesday, May 01, 2019 12:25 AM
To: [log in to unmask]
Subject: Empirical research using snowball sampling to examine power and influence

 

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I am looking for sociology-related papers that describe empirical projects examining influence or power in real-world human social networks constructed through snowball sampling from multiple random seed nodes.

 

I am particularly interested in papers that discuss the implications of missing data, the validity of centrality measures, and boundary specification for extrapolating beyond the sample to the actual social context it represents.

 

I'm trying to understand the problems that arise when making the case that a snowball-sampled network is a reasonable proxy for a real social world - especially around the issue of how large a sample needs to be relative the estimate size of the total social world in question and how many seed nodes are required. For example, if one wanted to find the most central doctor out of all the doctors in a town is it possible to sample less than 100 % of all the doctors and if so how to estimate the minimum % required and the minumum number of start points.

 

Thanks very much

 

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