Scott's PiScott's pi (named after William A Scott) is a statistic for measuring inter-rater reliability for nominal data in communication studies. Textual entities are annotated with categories by different annotators, and various measures are used to assess the extent of agreement between the annotators, one of which is Scott's pi. Since automatically annotating text is a popular problem in natural language processing, and the goal is to get the computer program that is being developed to agree with the humans in the annotations it creates, assessing the extent to which humans agree with each other is important for establishing a reasonable upper limit on computer performance.
Intraclass correlationIn statistics, the intraclass correlation, or the intraclass correlation coefficient (ICC), is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other. While it is viewed as a type of correlation, unlike most other correlation measures, it operates on data structured as groups rather than data structured as paired observations.
Concordance inter-jugesIn statistics, inter-rater reliability (also called by various similar names, such as inter-rater agreement, inter-rater concordance, inter-observer reliability, inter-coder reliability, and so on) is the degree of agreement among independent observers who rate, code, or assess the same phenomenon. Assessment tools that rely on ratings must exhibit good inter-rater reliability, otherwise they are not valid tests. There are a number of statistics that can be used to determine inter-rater reliability.
Kappa de FleissKappa de Fleiss (nommé d'après Joseph L. Fleiss) est une mesure statistique qui évalue la concordance lors de l'assignation qualitative d'objets au sein de catégories pour un certain nombre d'observateurs. Cela contraste avec d'autres kappas tel que le Kappa de Cohen, qui ne fonctionne que pour évaluer la concordance entre deux observateurs. La mesure calcule le degré de concordance de la classification par rapport à ce qui pourrait être attendu si elle était faite au hasard.
Kappa de CohenEn statistique, la méthode du κ (kappa) mesure l’accord entre observateurs lors d'un codage qualitatif en catégories. L'article introduisant le κ a pour auteur Jacob Cohen – d'où sa désignation de κ de Cohen – et est paru dans le journal Educational and Psychological Measurement en 1960. Le κ est une mesure d'accord entre deux codeurs seulement. Pour une mesure de l'accord entre plus de deux codeurs, on utilise le κ de Fleiss (1981). Le calcul du κ se fait de la manière suivante : où Pr(a) est la proportion de l'accord entre codeurs et Pr(e) la probabilité d'un accord aléatoire.
