Harnessing social networks along with consumer-driven electronic communication technologies to identify and engage members of 'hard-to-reach' populations: a methodological case report.
BACKGROUND
Sampling in the absence of accurate or comprehensive information routinely poses logistical, ethical, and resource allocation challenges in social science, clinical, epidemiological, health service and population health research. These challenges are compounded if few members of a target population know each other or regularly interact. This paper reports on the sampling methods adopted in ethnographic case study research with a 'hard-to-reach' population.


METHODS
To identify and engage a small yet diverse sample of people who met an unusual set of criteria (i.e., pet owners who had been treating cats or dogs for diabetes), four sampling strategies were used. First, copies of a recruitment letter were posted in pet-friendly places. Second, information about the study was diffused throughout the study period via word of mouth. Third, the lead investigator personally sent the recruitment letter via email to a pet owner, who then circulated the information to others, and so on. Fourth, veterinarians were enlisted to refer people who had diabetic pets. The second, third and fourth strategies rely on social networks and represent forms of chain referral sampling.


RESULTS
Chain referral sampling via email proved to be the most efficient and effective, yielding a small yet diverse group of respondents within one month, and at negligible cost.


CONCLUSIONS
The widespread popularity of electronic communication technologies offers new methodological opportunities for researchers seeking to recruit from hard-to-reach populations.
Rock
Melanie J
MJ
Population Health Intervention Research Centre, University of Calgary, Calgary, Canada. mrock@ucalgary.ca
D000818:Animals; D000884:Anthropology, Cultural; D002415:Cats; D003920:Diabetes Mellitus / Q000662:veterinary*; D004285:Dogs; D034742:Electronic Mail; D006801:Humans; D009862:Online Systems; D018579:Patient Selection; D012494:Sampling Studies