{"title":"Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements","authors":"A. Martín Andrés, M. Álvarez Hernández","doi":"10.1007/s11634-024-00581-x","DOIUrl":null,"url":null,"abstract":"<p>To measure the degree of agreement between <i>R</i> observers who independently classify <i>n</i> subjects within <i>K</i> categories, various <i>kappa</i>-type coefficients are often used. When <i>R</i> = 2, it is common to use the Cohen' <i>kappa</i>, Scott's <i>pi</i>, Gwet’s <i>AC1/2</i>, and Krippendorf's <i>alpha</i> coefficients (weighted or not). When <i>R</i> > 2, some pairwise version based on the aforementioned coefficients is normally used; with the same order as above: Hubert's <i>kappa</i>, Fleiss's <i>kappa</i>, Gwet's <i>AC1/2,</i> and Krippendorf's <i>alpha</i>. However, all these statistics are based on biased estimators of the expected index of agreements, since they estimate the product of two population proportions through the product of their sample estimators. The aims of this article are three. First, to provide statistics based on unbiased estimators of the expected index of agreements and determine their variance based on the variance of the original statistic. Second, to make pairwise extensions of some measures. And third, to show that the old and new estimators of the Cohen’s <i>kappa</i> and Hubert’s <i>kappa</i> coefficients match the well-known estimators of concordance and intraclass correlation coefficients, if the former are defined by assuming quadratic weights. The article shows that the new estimators are always greater than or equal the classic ones, except for the case of Gwet where it is the other way around, although these differences are only relevant with small sample sizes (e.g. <i>n</i> ≤ 30).</p>","PeriodicalId":49270,"journal":{"name":"Advances in Data Analysis and Classification","volume":"57 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Data Analysis and Classification","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11634-024-00581-x","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
To measure the degree of agreement between R observers who independently classify n subjects within K categories, various kappa-type coefficients are often used. When R = 2, it is common to use the Cohen' kappa, Scott's pi, Gwet’s AC1/2, and Krippendorf's alpha coefficients (weighted or not). When R > 2, some pairwise version based on the aforementioned coefficients is normally used; with the same order as above: Hubert's kappa, Fleiss's kappa, Gwet's AC1/2, and Krippendorf's alpha. However, all these statistics are based on biased estimators of the expected index of agreements, since they estimate the product of two population proportions through the product of their sample estimators. The aims of this article are three. First, to provide statistics based on unbiased estimators of the expected index of agreements and determine their variance based on the variance of the original statistic. Second, to make pairwise extensions of some measures. And third, to show that the old and new estimators of the Cohen’s kappa and Hubert’s kappa coefficients match the well-known estimators of concordance and intraclass correlation coefficients, if the former are defined by assuming quadratic weights. The article shows that the new estimators are always greater than or equal the classic ones, except for the case of Gwet where it is the other way around, although these differences are only relevant with small sample sizes (e.g. n ≤ 30).
期刊介绍:
The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.