%0 Journal Article
%T On Marginal Dependencies of the 2£¿¡Á£¿2 Kappa
%A Matthijs J. Warrens
%J Advances in Statistics
%D 2014
%R 10.1155/2014/759527
%X Cohen¡¯s kappa is a standard tool for the analysis of agreement in a 2£¿¡Á£¿2 reliability study. Researchers are frequently only interested in the kappa-value of a sample. Various authors have observed that if two pairs of raters have the same amount of observed agreement, the pair whose marginal distributions are more similar to each other may have a lower kappa-value than the pair with more divergent marginal distributions. Here we present exact formulations of some of these properties. The results provide a better understanding of the 2£¿¡Á£¿2 kappa for situations where it is used as a sample statistic. 1. Introduction Results from experimental studies and research studies can often be summarized in a table [1]. An example is a reliability study in which two observers rate the same sample of subjects on the presence/absence of a trait or an ability [2, 3]. In this example the four cells of the table are the proportion of times the observers agreed on the presence of the trait, the proportion of times a trait was present according to the first observer but absent according to the second observer, the proportion of times a trait was absent according to the first observer but present according to the second observer, and the proportion of times the observers agreed on the absence of the trait. To assess the quality of the ratings, the agreement between the ratings is taken as an indicator of the quality of the category definitions and the observers¡¯ ability to apply them. A standard tool for estimating agreement in a reliability study is Cohen¡¯s kappa [4¨C8]. Its value is 1 when there is perfect agreement, 0 when agreement is equal to that expected under independence, and negative when agreement is less than expected by chance. Several authors have presented population models for Cohen¡¯s kappa [2, 7]. Under these models kappa can be interpreted as an association coefficient. However, kappa is also frequently used as a sample statistic [4, 8¨C11], for example, when calculating kappa for a sample of subjects is one step in a series of research steps. In this case, researchers are merely interested in the agreement in the sample not that of a population. As a sample statistic, kappa is known to be marginal or prevalence dependent since it takes the marginal totals with which raters use the rating categories into account [12¨C14]. The value of kappa depends on the prevalence of the condition being diagnosed. Values of kappa can be quite low if a condition is quite common or very rare. Various authors have shown that if two pairs of observers have the same amount of
%U http://www.hindawi.com/journals/as/2014/759527/