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Design-based estimators for snowball sampling
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Snowball sampling, where existing study subjects recruit further subjects from amongtheir acquaintances, is a popular approach when sampling from hidden populations.Since people with many in-links are more likely to be selected, there will be a selectionbias in the samples obtained. In order to eliminate this bias, the sample data must beweighted. However, the exact selection probabilities are unknown for snowball samplesand need to be approximated in an appropriate way. This paper proposes differentways of approximating the selection probabilities and develops weighting techniquesusing the inverse of the selection probabilities. Some numerical examples for smallgraphs and simulations on larger networks are provided to compare the efficiencyof the weighting techniques. The simulation results indicate that the suggested re-weighted estimators should be preferred to traditional estimators with equal sampleweights for the initial snowball sampling waves.

Place, publisher, year, edition, pages
2010. 23-27 p.
National Category
Social Sciences
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-88948OAI: oai:DiVA.org:su-88948DiVA: diva2:614939
Conference
Workshop on Survey Sampling Theory and Methodology August 23-27, 2010 Vilnius, Lithuania
Available from: 2013-04-08 Created: 2013-04-08 Last updated: 2013-05-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf