An Empirical Examination of Respondent Driven Sampling Design Effects Among HIV Risk Groups from Studies Conducted Around the World
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For studies using respondent driven sampling (RDS), the current practice of collecting a sample twice as large as that used in simple random sampling (SRS) (i.e. design effect of 2.00) may not be sufficient. This paper provides empirical evidence of sample-to-sample variability in design effects using data from nine studies in six countries among injecting drug users, female sex workers, men who have sex with men and male-to-female transgender (MTF) persons. We computed the design effect as the variance under RDS divided by the variance under SRS for a broad range of demographic and behavioral variables in each study. We also estimated several measures for each variable in each study that we hypothesized might be related to design effect: the number of waves needed for equilibrium, homophily, and mean network size. Design effects for all studies ranged from 1.20 to 5.90. Mean design effects among all studies ranged from 1.50 to 3.70. A particularly high design effect was found for employment status (design effect of 5.90) of MTF in Peru. This may be explained by a “bottleneck”—defined as the occurrence of a relatively small number of recruitment ties between two groups in the population. A design effect of two for RDS studies may not be sufficient. Since the mean design effect across all studies was 2.33, an effect slightly above 2.00 may be adequate; however, an effect closer to 3.00 or 4.00 might be more appropriate.
KeywordsRespondent driven sampling Design effects HIV/AIDS surveillance Hard-to-reach populations
- 1.Carlson RG, Wang J, Siegal HA, Falck RS, Guo J. An ethnographic approach to targeted sampling: problems and solutions in AIDS prevention research among injection drug and crack-cocaine users. Hum Organ. 1994;53(3):279–86.Google Scholar
- 3.Family Health International. Behavioral surveillance surveys: guidelines for repeated behavioral surveys in populations at risk of HIV. Arlington: Family Health International. 2000. http://www.fhi.org/en/HIVAIDS/pub/guide/bssguidelines.htm. Accessed 15 Nov 2012.
- 13.Johnston LG, Saumtally A, Corceal S, Mahadoo I, Oodally F. High HIV and hepatitis C prevalence amongst injecting drug users in Mauritius: findings from a population size estimation and respondent driven sampling survey. Int J Drug Policy. 2011;22(4):252–8. Epub 2011 Jun 22.PubMedCrossRefGoogle Scholar
- 14.Johnston LG.: Integrated behavioral and biological surveillance survey among injecting drug users in Mauritius, 2009; Mauritius Ministry of Health, Port Louis. 2009. www.gov.mu/portal/sites/sida/idu.pdf. Accessed 15 Nov 2012.
- 17.Volz E, Heckathorn DD. Probability-based estimation theory for respondent-driven sampling. J Off Stat. 2008;24(Suppl 1):79–97.Google Scholar
- 20.Szwarcwald CL, de Souza Júnior PR, Damacena GN, Junior AB, Kendall C. Analysis of data collected by RDS among sex workers in 10 Brazilian cities, 2009: estimation of the prevalence of HIV, variance, and design effect. J Acquir Immune Defic Syndr. 2011;57(Suppl 3):S129–35.PubMedCrossRefGoogle Scholar
- 21.Johnston LG, Khanam R, Reza M, et al. The effectiveness of respondent driven sampling for recruiting males who have sex with males in Dhaka, Bangladesh: a pilot study. AIDS Behav. 2007;2(2):294–304.Google Scholar