estimating hard-to-reach populations with network sampling...vincent, k. (2018). recent advances on...

Estimating Hard-to-Reach Populations withNetwork Sampling

Kyle Vincent

kyle.shane.vincent@gmail.com

Network Sampling NAS Presentation

Outline

Overview of Contemporary Network-Based Strategies

Novel Strategy

Advantages and Disadvantages

Practice

Final Thoughts

Network Sampling NAS Presentation

What is network sampling?

A referral-based strategy for sampling individuals

Also know as link-tracing, peer-referral

Trace social links to enlarge sample to sufficient size

Strategy can overcome limitations presented withconventional approaches

Network Sampling NAS Presentation

What is network sampling?

Simulated population: N = 147

Network Sampling NAS Presentation

What is network sampling?

Initial sample: n0 = 10

Network Sampling NAS Presentation

What is network sampling?

First wave: n1 = 10

Network Sampling NAS Presentation

What is network sampling?

Second wave: n2 = 10

Network Sampling NAS Presentation

Contemporary Network-Based Strategies

Snowball Sampling

Respondent Driven Sampling (RDS)

Network Scale Up Method (NSUM)

Network Sampling NAS Presentation

Novel Strategy

Motivation is based on:

1) Need for estimate for population size

2) Conduct sophisticated network analyses

Network Sampling NAS Presentation

Novel Strategy

Exploit available resources to select a representative initialsample (need help from partnering agencies, NGOs,service provider, etc.)

Recent work for stratified setup can help with this (Vincent,2018) - relies on mark-recapture theory

Network Sampling NAS Presentation

Novel Strategy

Base preliminary estimates on information corresponding toinitial sample selections (Vincent, 2018).

Consider count of links from initial sample to outside initialsample

Consider count of links within initial sample

Relative measure of density of links within sample

Network Sampling NAS Presentation

Novel Strategy

Base improved estimates on strategy presented in Vincent andThompson (2016), Vincent (2018).

Computationally intensive procedure

Enumerate all sample reorderings, (A,B,C), (B,A,C),(C,A,B), ...

Network Sampling NAS Presentation

Novel Strategy

Colorado Springs Study; Klovdahl et al. (1994). N = 595.

Network Sampling NAS Presentation

Novel Strategy

n0 = 53, N̂ = 758.

Network Sampling NAS Presentation

Novel Strategy

n0 = 85. N̂ = 568. Substantial improvements with link-tracing(Vincent and Thompson, (2016) and Vincent (2018)).

Network Sampling NAS Presentation

Advantages

No need to sample multiple waves

Sampling multiple waves does not add any bias toestimators

Can give estimators based on each wave

Network Sampling NAS Presentation

Advantages

Strategy can be applied in a spatial setting.

Network Sampling NAS Presentation

Advantages

Strategy can be applied in a spatial setting.

Network Sampling NAS Presentation

Advantages

Strategy can be applied in a spatial setting.

Network Sampling NAS Presentation

Advantages

Strategy can be applied in a spatial setting.

Network Sampling NAS Presentation

Advantages

Can augment most network samples with other samples toget some valid MSE/mark-recapture estimates

Gives rise to a rich data set, good for social networkanalyses

Gain some understanding of spatial relations, networktopology

Network Sampling NAS Presentation

Advantages

Ability to adapt/steer link-tracing to units of higher-interest

Also know as adaptive sampling

Gives a higher yield of interesting units in the final sample

Network Sampling NAS Presentation

Adaptive Sampling

Simulated population: N = 147

Network Sampling NAS Presentation

Adaptive Sampling

Initial sample: n0 = 10

Network Sampling NAS Presentation

Adaptive Sampling

First wave:

Network Sampling NAS Presentation

Adaptive Sampling

Second wave:

Network Sampling NAS Presentation

Disadvantages

Requires a good start, i.e. initial sample, need help frompartnering agencies, NGOs, service provider, etc.

Need to know nominations within final sample, link withinsample (and across samples, like in MSE/mark-recaptureapproaches)

Network Sampling NAS Presentation

Practice

International Labour Organization (ILO)

ILO sought a global estimate on modern slavery

Data sources include media reports, governmentdocuments, academic reports, ILO reports, etc., mostlyaccessible online

Idea: conceptualize data sources as network population

Network Sampling NAS Presentation

Practice

Sources are nodes, directed links between nodes if onereferences the other

Apply novel strategy to this population

See Zhang and Vincent (2017) for paper

Network Sampling NAS Presentation

Practice

International Justice Mission (IJM)

Study on trafficked sex workers attached to public andprivate establishments in hotspots of Kolkata and Mumbai,India

Used a link-tracing sampling design

Estimates of population size presented in Parks et al.(2017)

Network Sampling NAS Presentation

Practice

Enlarged nodes represent contacts, green for commercialsex-workers, red for pimps/madams, yellow for other.

Network Sampling NAS Presentation

Final Thoughts

The new strategy:

Can provide efficient estimates of population size, rich dataset with practical design

Facilitate intervention strategies, since these are typicallyachieved through a link-tracing design (Thompson, 2017)

Network Sampling NAS Presentation

References

Klovdahl, A., Potterat, J., Woodhouse, D., Muth, J., Muth,S., and Darrow, W. (1994). Social networks and infectiousdisease: The Colorado Springs Study. Social Science &Medicine 38, 79-88.

Parks, A., Vincent, K., Nie, Z., Russel, A. (2017). A studyof public and private establishment-based commercialsexual exploitation in Kolkata and Mumbai, India. Inpublishing phase.

Thompson, S. (2017). Adaptive and network sampling forinference and interventions in changing populations.Journal of Survey Statistics and Methodology 5, 1-21.

Network Sampling NAS Presentation

References

Vincent, K. and Thompson, S. (2016). Estimatingpopulation size with link-tracing sampling. Journal of theAmerican Statistical Association. Accepted for publication.

Vincent, K. (2018). Recent advances on estimatingpopulation size with link-tracing sampling. Submitted.

Zhang, S. and Vincent, K. (2016). Strategies to estimateglobal prevalence of trafficking for sexual exploitation.CHANCE. Accepted for publication.

Network Sampling NAS Presentation