Respondent Driven Sampling: 

Introduction and Applications 

Sunghee Lee 

University of Michigan 

Federal Committee on Statistical Methodology Research and Policy Conference 

March 7, 2018 

FCSM Conference 

S. Lee 

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Outline 

Introduction 

Application 

Health and Life Study of Koreans (HLSK) 

Summary 

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Introduction 

Respondent Driven Sampling (RDS) 

Network Sampling vs. RDS 

RDS Inferences 

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Respondent Driven Sampling – 1 

• Growing interest in studying hard-to-reach, rare, 

elusive, hidden populations 

– HIV at-risk population: Sex workers, IDUs, MSMs 

– LGBT populations 

– Recent immigrants 

• No clear and practical solution with probability 

sampling 

– High screening costs 

– Hesitant to be identified 

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Respondent Driven Sampling – 2 

• Proposed by Heckathorn (1997, 2002) 

• Popular usage in public health (~$100 million 

research funds by NIH as of 2011) 

• Exploits social networks among rare population 

members for sampling purposes 

– Sampled members also play a role of a recruiter 

– Incentivized recruitment from own network through 

coupons and this continues in waves/chains 

– Recruitment assumed to be random within each 

iミdi┗idual’s ミet┘ork aミd to follo┘ memory-less 

Markov chain and reach equilibrium 

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Respondent Driven Sampling – 3 

WAVE 1 WAVE 2 WAVE 3 WAVE W

Seed 1 Recruit 3 

Recruitment 

Coupon Recruit 1 

Recruit 2 

Recruit 1 

Recruit 2 

Recruit 3 

Seed 2 

Seed 3 

Seed S 

.........

........

......

.........

Recruit R -2 

Recruit R -1 

Recruit R ........

.........

.........

Recruit 3 

Recruit 1 

Recruit 2 

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Respondent Driven Sampling – 4 

WAVE 1 WAVE 2 WAVE 3 WAVE W

Seed 1 Recruit 3 

Recruitment 

Coupon Recruit 1 

Recruit 2 

Recruit 1 

Recruit 2 

Recruit 3 

Seed 2 

Seed 3 

Seed S 

.........

........

......

.........

Recruit R -2 

Recruit R -1 

Recruit R ........

.........

.........

Recruit 3 

Recruit 1 

Recruit 2 

Seed 1 

Recruitment Chain 

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Network/Multiplicity Sampling 

• Sirken (1972, 1975) 

• “aマple froマ a saマple’s ミet┘ork

– Conduct an interview with a sample 

– Roster eligible kinship members with contact 

information 

– Sample from the roster 

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Network Sampling vs. RDS 

Similar: 

• Rely on social networks 

Different: 

• Network specification 

– NS: biological siblings, immediate family members 

– RDS: jazz musicians 

• Who selects the sample 

– NS: researchers 

– RDS: study participants with coupon 

• Selection probability 

– NS: Known 

– RDS: (Mostly) Unknown 

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RDS Inferences 

Issues 

1. Nonprobability 

• Within network selection probability may be computed (e.g., 

# recruits/network size), but 

• Uミclear co┗erage of さミet┘orkざ

• Measureマeミt error iミ さミet┘ork sizeざ

• With or without replacement? 

• Seed selection probability unknown 

2. Dependence 

• Recruiters and recruits are similar 

3. None beyond univariate statistics 

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RDS Inferences: Sampling Variance – 3 

• Bootstrap based on recruitment chains 

1. Randomly sample a seed and preserve its entire 

recruitment chain 

2. Continue until the bootstrap sample size equals to n 

- Can be used for all statistics across all variables 

- Do not assumes first-order Markov process 

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Application: 

Health and Life Study of Koreans (HLSK) 

Funded by the National Science Foundation (GRANT NUMBER SES-1461470) 

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HLSK 

• Targets foreign-born Korean American adults in 

– Los Angeles County 

– State of Michigan 

• Web-RDS survey 

http://sites.lsa.umich.edu/korean-healthlife-study/ 

– Unique number required for participation 

– Incentive payment through checks 

• Target n=800 (currently ~600) 

• Benchmarks from American Community Survey 

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HLSK Formative Research 

• 3 rounds of focus group discussions 

– ~30 participants; 2 rounds in Korean and 1 in English 

– Discussion focused on 

• Web surveys 

 URL, Web site contents, etc. 

• Concept of RDS 

• Coupons 

 Up to 2 coupons 

 さE┝pireざ iミ ヲ ┘eeks

• Level of incentives 

 $20 for main, $5 for follow-up, $0 for recruitment 

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HLSK Data Collection 

• Started with 12 seeds in LA in June 2016 

• MI added in November 2016 

• LA seeds (initially) 

– Recruited through referral 

– Balanced on gender, age, dominant language 

– In-person introduction about the study 

 It became clear the protocols would not work 

– Provide recruitment incentives 

– Add more seeds 

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HLSK Data Collection Progress 

n=336 

123 seeds 

638 coupons 

n=270 

88 seeds 

519 coupons 

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HLSK vs. ACS – 1 

• American Community Survey 2011-2015 data 

• HLSK sample estimates 

– Unweighted (UW) 

– RDS-I 

– Weighted: RDS-II 

– Weighted: Post-stratification (PS) by age, sex, educ 

– Weighted: RDS-II + PS 

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HLSK vs. ACS – 2 

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HLSK vs. ACS – 3 

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HLSK vs. ACS – 4 

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HLSK vs. ACS – 5 

• HLSK sample estimate CI 

– Unweighted (UW), Naïve 

– RDS-I, Naïve 

– RDS-I, Chain-bootstrap (CB) 

– Weighted: RDS-II, Naïve 

– Weighted: RDS-II, CB 

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HLSK vs. ACS – 6 

ACS

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Summary 

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What did we learn? – 1 

• Non-cooperation is an issue for generating long 

chains (memorylessness unlikely) 

• Had to iマpro┗ise to マake RD“ さ┘orkざ

• Sample size (hence, chain length) is a random 

variable affected by many (mostly unknown) 

factors 

• Inferences unclear and limited 

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What did we learn? – 2 

• YET, difficult-to sample groups can be recruited 

– highly-educated young recent immigrants 

– low Korean density areas (e.g., MI UP) 

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Where should we go? 

• Non-cooperation is critical for 

– meeting theoretical assumptions (hence, inferences) 

– study design 

– replications of the same study 

• Yet to be addressed in the literature and 

accounted for in inferences 

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Thank you 

sungheel@umich.edu 

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References 

• Heckathorn, D.D. ヱ997. さRespoミdeミt-Driven Sampling: A New Approach to 

the Study of Hiddeミ Populatioミs.ざ “ociet┞ for the “tud┞ of “ocial Problems, 

44(2): 174–199. 

• Heckathorn, D.D. ヲヰヰヲ. さRespoミdeミt-Driven Sampling II: Deriving Valid 

Population Estimates from Chain-Referral “aマples of Hiddeミ Populatioミs.ざ 

Social Problems, 49(1): 11–34. 

• Lee, “. ヲヰヰ9. さUミderstaミdiミg Respoミdeミt Dri┗eミ “aマpliミg froマ a Total

Survey Error Perspective.ざ “ur┗e┞ Practice, 2(6): 1-6. 

• Lee, S., Suzer-Gurtekin, ).T., Wagミer, J. aミd Valliaミt, R. ふヲヰヱ7ぶ. さTotal “ur┗e┞ 

Error and Respondent Driven Sampling: Focus on Nonresponse and 

Measurement Errors in the Recruitment Process and the Network Size 

Reports aミd Iマplicatioミs for Iミfereミces.ざ Journal of Official Statistics, 33(2): 

335-366. 

• Sirken, M.G. ヱ97ヲ. さ“tratified “aマple “ur┗e┞s ┘ith Multiplicit┞.ざ Jourミal of 

American Statistical Association 67: 224–227. 

• Sirken, M.G. ヱ975. さNet┘ork “ur┗e┞s of Rare aミd “eミsiti┗e Coミditioミs.ざ 

Advances in Health Survey Research Methods, NCHSR Research Proceedings 

31. Hyattsville, MD: National Center Health Statistics. 

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