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dc.contributor.authorZapf, Antonia
dc.contributor.authorCastell, Stefanie
dc.contributor.authorMorawietz, Lars
dc.contributor.authorKarch, André
dc.date.accessioned2016-10-07T10:52:37Z
dc.date.available2016-10-07T10:52:37Z
dc.date.issued2016
dc.identifier.citationMeasuring inter-rater reliability for nominal data - which coefficients and confidence intervals are appropriate? 2016, 16:93 BMC Med Res Methodolen
dc.identifier.issn1471-2288
dc.identifier.pmid27495131
dc.identifier.doi10.1186/s12874-016-0200-9
dc.identifier.urihttp://hdl.handle.net/10033/620542
dc.description.abstractReliability of measurements is a prerequisite of medical research. For nominal data, Fleiss' kappa (in the following labelled as Fleiss' K) and Krippendorff's alpha provide the highest flexibility of the available reliability measures with respect to number of raters and categories. Our aim was to investigate which measures and which confidence intervals provide the best statistical properties for the assessment of inter-rater reliability in different situations.
dc.language.isoenen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleMeasuring inter-rater reliability for nominal data - which coefficients and confidence intervals are appropriate?en
dc.typeArticleen
dc.contributor.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.en
dc.identifier.journalBMC medical research methodologyen
refterms.dateFOA2018-06-13T00:17:35Z
html.description.abstractReliability of measurements is a prerequisite of medical research. For nominal data, Fleiss' kappa (in the following labelled as Fleiss' K) and Krippendorff's alpha provide the highest flexibility of the available reliability measures with respect to number of raters and categories. Our aim was to investigate which measures and which confidence intervals provide the best statistical properties for the assessment of inter-rater reliability in different situations.


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