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Related Experiment Video

Updated: Feb 19, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Quantity Versus Quality: A Survey Experiment to Improve the Network Scale-up Method.

Dennis M Feehan, Aline Umubyeyi, Mary Mahy

    American Journal of Epidemiology
    |March 27, 2016
    PubMed
    Summary
    This summary is machine-generated.

    The network scale-up method can estimate hidden populations. Researchers found that asking for less network information surprisingly yields more accurate size estimates for populations at risk of HIV.

    Keywords:
    HIVacquired immunodeficiency syndromeepidemiologic methodsnetwork samplingpopulation size estimationsocial networkssurvey research

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    Area of Science:

    • Epidemiology
    • Social Network Analysis
    • Population Size Estimation

    Background:

    • The network scale-up method estimates hidden populations using social network data.
    • Previous research focused on acquaintance networks, limiting flexibility and accuracy.

    Purpose of the Study:

    • To investigate how the type of personal network surveyed impacts the accuracy of the network scale-up method.
    • To estimate the size of human immunodeficiency virus (HIV)-affected populations in Rwanda.
    • To develop a novel sensitivity analysis framework for network scale-up estimates.

    Main Methods:

    • Conducted a nationally representative survey experiment in Rwanda in 2011.
    • Randomized respondents to report on one of two distinct personal networks.
    • Applied the network scale-up method and a new sensitivity analysis framework.

    Main Results:

    • Asking respondents for less network information improved the accuracy of population size estimates.
    • Estimated sizes for four key populations at risk for HIV in Rwanda.
    • New estimates were higher than previous Rwandan data but lower than international benchmarks.

    Conclusions:

    • The type of personal network is a critical, adjustable parameter in the network scale-up method.
    • The generalized method offers increased flexibility and potential accuracy.
    • The study provides a framework for improving network scale-up applications in diverse settings.