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WeightNameValue
1000 Titel
  • Feasibility of using respondent-driven sampling to recruit participants in superdiverse neighbourhoods for a general health survey
1000 Autor/in
  1. Samkange-Zeeb, Florence |
  2. Foraita, Ronja |
  3. Rach, Stefan |
  4. Brand, Tilman |
1000 Erscheinungsjahr 2019
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2019-01-20
1000 Erschienen in
1000 Quellenangabe
  • 64(3):451-459
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2019
1000 Embargo
  • 2020-01-20
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s00038-018-1191-6 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • OBJECTIVES: Respondent-driven sampling (RDS), a modified chain-referral system, has been proposed as a strategy for reaching ‘hidden’ populations. We applied RDS to assess its feasibility to recruit ‘hard-to-reach’ populations such as migrants and the unemployed in a general health survey and compared it to register-based sampling (RBS). METHODS: RDS was applied parallel to standard population RBS in two superdiverse neighbourhoods in Bremen, Germany. Prevalences of sample characteristics of interest were estimated in RDS Analyst using the successive sampling estimator. These were then compared between the samples. RESULTS: Only 115 persons were recruited via RDS compared to 779 via RBS. The prevalence of (1) migrant background, (2) unemployment and (3) poverty risk was significantly higher in the RDS than in the RBS sample. The respective estimates were (1) 51.6 versus 32.5% (95% CIRDS 40.4–62.7), (2) 18.1 versus 7.5% (95% CIRDS 8.4–27.9) and (3) 55.0 versus 30.4% (95% CIRDS 41.3–68.7). CONCLUSIONS: Although recruitment was difficult and the number of participants was small, RDS proved to be a feasible method for reaching migrants and other disadvantaged persons in our study.
1000 Sacherschließung
lokal Respondent-driven sampling
lokal Feasibility
lokal Superdiverse
lokal Migrants
lokal Hard-to-reach
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-8175-1719|https://orcid.org/0000-0003-2216-6653|https://orcid.org/0000-0001-5241-0253|https://orcid.org/0000-0001-5140-7511
1000 Label
1000 Förderer
  1. Horizon 2020 |
  2. Seventh Framework Programme |
  3. Sixth Framework Programme |
  4. Leibniz Institute for Prevention Research and Epidemiology (BIPS) |
1000 Fördernummer
  1. 462-14-091
  2. 462-14-091
  3. 462-14-091
  4. -
1000 Förderprogramm
  1. research and innovation
  2. research, technological development and demonstration
  3. research and technological development
  4. -
1000 Dateien
  1. Nutzungsvereinbarung
  2. Springer Self-archiving policy
  3. § 38 UrhG - Einzelnorm
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Horizon 2020 |
    1000 Förderprogramm research and innovation
    1000 Fördernummer 462-14-091
  2. 1000 joinedFunding-child
    1000 Förderer Seventh Framework Programme |
    1000 Förderprogramm research, technological development and demonstration
    1000 Fördernummer 462-14-091
  3. 1000 joinedFunding-child
    1000 Förderer Sixth Framework Programme |
    1000 Förderprogramm research and technological development
    1000 Fördernummer 462-14-091
  4. 1000 joinedFunding-child
    1000 Förderer Leibniz Institute for Prevention Research and Epidemiology (BIPS) |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6416487.rdf
1000 Erstellt am 2019-09-25T14:39:50.021+0200
1000 Erstellt von 266
1000 beschreibt frl:6416487
1000 Bearbeitet von 266
1000 Zuletzt bearbeitet Mon Sep 28 15:14:25 CEST 2020
1000 Objekt bearb. Mon Sep 28 15:14:24 CEST 2020
1000 Vgl. frl:6416487
1000 Oai Id
  1. oai:frl.publisso.de:frl:6416487 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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