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dc.contributor.authorFinbråten, Hanne Søberg
dc.contributor.authorLarsson, Bodil Wilde
dc.contributor.authorNordström, Gun
dc.contributor.authorPettersen, Kjell Sverre
dc.contributor.authorTrollvik, Anne
dc.contributor.authorGuttersrud, Øystein
dc.date.accessioned2019-01-23T13:16:12Z
dc.date.accessioned2019-02-22T14:23:41Z
dc.date.available2019-01-23T13:16:12Z
dc.date.available2019-02-22T14:23:41Z
dc.date.issued2018-06-28
dc.identifier.citationFinbråten HS, Larsson BW, Nordström G, Pettersen KS, Trollvik AT, Guttersrud Ø. Establishing the HLS-Q12 short version of the European Health Literacy Survey Questionnaire: latent trait analyses applying Rasch modelling and confirmatory factor analysis. BMC Health Services Research. 2018;18:506:1-17en
dc.identifier.issn1472-6963
dc.identifier.issn1472-6963
dc.identifier.urihttps://hdl.handle.net/10642/6663
dc.description.abstractBackground: The European Health Literacy Survey Questionnaire (HLS-EU-Q47) is widely used in assessing health literacy (HL). There has been some controversy whether the comprehensive HLS-EU-Q47 data, reflecting a conceptual model of four cognitive domains across three health domains (i.e. 12 subscales), fit unidimensional Rasch models. Still, the HLS-EU-Q47 raw score is commonly interpreted as a sufficient statistic. Combining Rasch modelling and confirmatory factor analysis, we reduced the 47 item scale to a parsimonious 12 item scale that meets the assumptions and requirements of objective measurement while offering a clinically feasible HL screening tool. This paper aims at (1) evaluating the psychometric properties of the HLS-EU-Q47 and associated short versions in a large Norwegian sample, and (2) establishing a short version (HLS-Q12) with sufficient psychometric properties. Methods: Using computer-assisted telephone interviews during November 2014, data were collected from 900 randomly sampled individuals aged 16 and over. The data were analysed using the partial credit parameterization of the unidimensional polytomous Rasch model (PRM) and the ‘between-item’ multidimensional PRM, and by using one-factorial and multi-factorial confirmatory factor analysis (CFA) with categorical variables. Results: Using likelihood-ratio tests to compare data-model fit for nested models, we found that the observed HLSEU-Q47 data were more likely under a 12-dimensional Rasch model than under a three- or a one-dimensional Rasch model. Several of the 12 theoretically defined subscales suffered from low reliability owing to few items. Excluding poorly discriminating items, items displaying differential item functioning and redundant items violating the assumption of local independency, a parsimonious 12-item HLS-Q12 scale is suggested. The HLS-Q12 displayed acceptable fit to the unidimensional Rasch model and achieved acceptable goodness-of-fit indexes using CFAen
dc.description.sponsorshipThe data collection was funded by the Norwegian Nurses’ Organisation, Inland Norway University of Applied Sciences and the Public Health Nutrition research group at Oslo Metropolitan University.en
dc.language.isoenen
dc.publisherBMCen
dc.relation.ispartofseriesBMC Health Services Research;(2018) 18:506
dc.rights© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectConfirmatory factor analysesen
dc.subjectCategorical dataen
dc.subjectHealth literacyen
dc.subjectRasch modellingsen
dc.subjectShort versionsen
dc.subjectValidationsen
dc.titleEstablishing the HLS-Q12 short version of the European Health Literacy Survey Questionnaire: latent trait analyses applying Rasch modelling and confirmatory factor analysisen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2019-01-23T13:16:12Z
dc.description.versionpublishedVersionen
dc.identifier.doihttp://dx.doi.org/10.1186/s12913-018-3275-7
dc.identifier.cristin1594379
dc.source.journalBMC Health Services Research


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© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.