Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Variance estimation, design effects, and sample size calculations for respondent-driven sampling.

Matthew J Salganik1

  • 1Department of Sociology, 1180 Amsterdam Avenue, New York, NY 10027, USA. mjs2105@columbia.edu

Journal of Urban Health : Bulletin of the New York Academy of Medicine
|August 29, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026
Same author

The origins of unpredictability in life outcome prediction tasks.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Science advances·2024
Same author

Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge.

Socius : sociological research for a dynamic world·2023
Same author

Introduction to the Special Collection on the Fragile Families Challenge.

Socius : sociological research for a dynamic world·2023
Same author

Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge.

Socius : sociological research for a dynamic world·2023
Same journal

The Impact of Streetlight Placement on Trends and Disparities in Pedestrian-Motor Vehicle Crashes.

Journal of urban health : bulletin of the New York Academy of Medicine·2026
Same journal

The Drug Market's Impact on the Overdose Risk Environment for Women Who Use Drugs During the COVID-19 Pandemic.

Journal of urban health : bulletin of the New York Academy of Medicine·2026
Same journal

Media Discourse on Healthy Cities: Topic Modeling of South Korean News Articles (2004-2024).

Journal of urban health : bulletin of the New York Academy of Medicine·2026
Same journal

Navigating the CBD: How Urban Risk Environment Shapes Daily Life for People Who Use Drugs in Edmonton's Central Business District.

Journal of urban health : bulletin of the New York Academy of Medicine·2026
Same journal

Change in Advertising Exposure of Unhealthy Commodities After the Implementation of an Outdoor Advertising Restrictions Policy in Bristol, UK: A Descriptive Study.

Journal of urban health : bulletin of the New York Academy of Medicine·2026
Same journal

A Multidimensional Approach to Measure Structural Racism and Its Association with Fatal and Nonfatal Community Firearm Violence at the Census Tract Level in New York City.

Journal of urban health : bulletin of the New York Academy of Medicine·2026
See all related articles

Respondent-driven sampling (RDS) helps study hidden populations, but its estimate variability is unclear. A new bootstrap method creates reliable confidence intervals for RDS estimates, improving accuracy for public health research.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Hidden populations, including injection drug users and sex workers, are crucial for understanding public health issues.
  • Studying these groups is challenging due to their nature, hindering accurate data collection and disease prevention.
  • Respondent-driven sampling (RDS) is a method to obtain unbiased prevalence estimates in hidden populations.

Purpose of the Study:

  • To address the lack of knowledge regarding the sample-to-sample variability of prevalence estimates derived from RDS.
  • To introduce and validate a bootstrap method for constructing confidence intervals around RDS estimates.
  • To estimate design effects for RDS and provide guidance on sample size calculations.

Main Methods:

  • A novel bootstrap method was developed to generate confidence intervals for RDS estimates.

Related Experiment Videos

  • Simulations were conducted to compare the performance of the bootstrap method against the naive method.
  • Design effects for RDS were estimated using both simulations and real-world data.

    • Main Results:

    • The proposed bootstrap method demonstrated superior performance in constructing confidence intervals compared to the naive method.
    • The study estimated design effects for RDS across various scenarios.
    • Practical recommendations for power calculations and sample size determination for RDS studies were provided.

    Conclusions:

    • The bootstrap method offers a more reliable approach for confidence intervals in RDS studies.
    • A sample size approximately twice that of simple random sampling is generally recommended for RDS studies.
    • These findings enhance the utility of RDS for accurate public health research on hidden populations.