Evidence map›Paper›PMID 29375167›Full record

ArticleSociological methodology2016

Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations.

Dennis M Feehan, Matthew J Salganik

Open access · greenAbstract read
In one paragraph

Article in Sociological methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
4.3field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 72 citations in OpenAlex.

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  16. Hepatitis C Care Cascades for 3 Populations at High Risk: Low-income Trans Women, Young People Who Inject Drugs, and Men Who Have Sex With Men and Inject Drugs.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  17. Article
  18. Thirty Years of The Network Scale-up Method.Journal of the American Statistical Association · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Dennis M FeehanDepartment of Demography, University of California, Berkeley, CA, USA.
Matthew J SalganikOffice of Population Research, Princeton University, Princeton, NJ, USA.
Princeton University · USUniversity of California, Berkeley · US

Funding

Infrastructure for Population Research at PrincetonR24HD047879 · NICHD · PRINCETON UNIVERSITY · PI MASSEY, DOUGLAS S · 2004 to 2013
$7.3M
Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI MASSEY, DOUGLAS S · 2014 to 2018
$2.0M
Improvements to the network scale-up method for studying hard-to-reach populationR01HD075666 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2012 to 2015
$523k
Improvements to Respondent-Driven Sampling for the Study of Hidden PopulationsR01HD062366 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2009 to 2011
$439k
NICHD NIH HHS P2C HD047879NICHD NIH HHS R01 HD062366NICHD NIH HHS R01 HD075666NICHD NIH HHS R24 HD047879
6 · The paper itself

Abstract

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies.

Identifiers

PMID29375167
PMCPMC5783650
OpenAlexW1502668321

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.