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dc.contributor.authorClarsen, Benjamin Matthew
dc.contributor.authorSkogen, Jens Christoffer
dc.contributor.authorNilsen, Thomas Sevenius
dc.contributor.authorAarø, Leif Edvard
dc.coverage.spatialNorwayen_US
dc.date.accessioned2021-05-12T09:34:27Z
dc.date.available2021-05-12T09:34:27Z
dc.date.created2021-05-06T13:43:18Z
dc.date.issued2021-04
dc.identifier.citationClarsen, B., Skogen, J.C., Nilsen, T.S., Aarø, L.E. (2021) Revisiting the continuum of resistance model in the digital age: a comparison of early and delayed respondents to the Norwegian counties public health survey. BMC Public Health, 21en_US
dc.identifier.issn1471-2458
dc.identifier.urihttps://hdl.handle.net/11250/2755136
dc.description.abstractBackground The continuum of resistance model’s premise is that delayed respondents to a survey are more similar to non-respondents than early respondents are. For decades, survey researchers have applied this model in attempts to evaluate and adjust for non-response bias. Despite a recent resurgence in the model’s popularity, its value has only been assessed in one large online population health survey. Methods Respondents to the Norwegian Counties Public Health Survey in Hordaland, Norway, were divided into three groups: those who responded within 7 days of the initial email/SMS invitation (wave 1, n = 6950); those who responded after 8 to 14 days and 1 reminder (wave 2, n = 4950); and those who responded after 15 or more days and 2 reminders (wave 3, n = 4045). Logistic regression analyses were used to compare respondents’ age, sex and educational level between waves, as well as the prevalence of poor general health, life dissatisfaction, mental distress, chronic health problems, weekly alcohol consumption, monthly binge drinking, daily smoking, physical activity, low social support and receipt of a disability pension. Results The overall response to the survey was 41.5%. Respondents in wave 1 were more likely to be older, female and more highly educated than those in waves 2 and 3. However, there were no substantial differences between waves for any health outcomes, with a maximal prevalence difference of 2.6% for weekly alcohol consumption (wave 1: 21.3%, wave 3: 18.7%). Conclusions There appeared to be a mild continuum of resistance for demographic variables. However, this was not reflected in health and related outcomes, which were uniformly similar across waves. The continuum of resistance model is unlikely to be useful to adjust for nonresponse bias in large online surveys of population health.en_US
dc.language.isoengen_US
dc.publisherBioMed Centralen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectfolkehelseen_US
dc.titleRevisiting the continuum of resistance model in the digital age: a comparison of early and delayed respondents to the Norwegian counties public health surveyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s). 2021en_US
dc.subject.nsiVDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801en_US
dc.source.volume21en_US
dc.source.journalBMC Public Healthen_US
dc.source.issue1en_US
dc.identifier.doi10.1186/s12889-021-10764-2
dc.identifier.cristin1908533
dc.source.articlenumber730 (2021)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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