Large-Sample Variance of Fleiss Generalized Kappa.

Cohen's kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss' generalized kappa. Fleiss' generalized kappa and its large-sample variance are still widely used by researchers and were implemente...

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Publicado en:Educational & Psychological Measurement Vol. 81; no. 4; pp. 781 - 791
Autor principal: Gwet, Kilem L.
Formato: Artículo
Publicado: Sage Publications Inc. Aug2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Aug2021
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        atl: Large-Sample Variance of Fleiss Generalized Kappa.
      aug:
        au: Gwet, Kilem L.
        affil: AgreeStat Analytics, Gaithersburg, MD, USA
      su:
        Statistics
        Sample size (Statistics)
        Confidence intervals
        Inter-observer reliability
        Sampling errors
        Research bias
        Data analysis software
        Statistical models
      sug:
        subj:
          Statistics
          Sample size (Statistics)
          Confidence intervals
          Inter-observer reliability
          Sampling errors
          Research bias
          Data analysis software
          Statistical models
      keyword:
        Cohen kappa
        Fleiss kappa
        Gwet AC1
        interrater reliability
        Cohen kappa
        Fleiss kappa
        Gwet AC1
        interrater reliability
      ab: Cohen's kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss' generalized kappa. Fleiss' generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package "rel." The purpose of this article is to show that the large-sample variance of Fleiss' generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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