Spitznagel E L, Helzer J E
Arch Gen Psychiatry. 1985 Jul;42(7):725-8. doi: 10.1001/archpsyc.1985.01790300093012.
Because it corrects for chance agreement, kappa (kappa) is a useful statistic for calculating interrater concordance. However, kappa has been criticized because its computed value is a function not only of sensitivity and specificity, but also the prevalence, or base rate, of the illness of interest in the particular population under study. For example, it has been shown for a hypothetical case in which sensitivity and specificity remain constant at .95 each, that kappa falls from .81 to .14 when the prevalence drops from 50% to 1%. Thus, differing values of kappa may be entirely due to differences in prevalence. Calculation of agreement presents different problems depending on whether one is studying reliability or validity. We discuss quantification of agreement in the pure validity case, the pure reliability case, and those studies that fall somewhere between. As a way of minimizing the base rate problem, we propose a statistic for the quantification of agreement (the Y statistic), which can be related to kappa but which is completely independent of prevalence in the case of validity studies and relatively so in the case of reliability.
由于kappa（κ）校正了机遇一致性，因此它是计算评分者间一致性的一个有用统计量。然而，kappa受到了批评，因为其计算值不仅是灵敏度和特异度的函数，还取决于所研究特定人群中感兴趣疾病的患病率或基础率。例如，对于一个假设的案例，其中灵敏度和特异度均保持在0.95不变，当患病率从50%降至1%时，kappa从0.81降至0.14。因此，kappa的不同值可能完全是由于患病率的差异。一致性的计算根据研究的是信度还是效度而呈现不同的问题。我们讨论了纯效度情况、纯信度情况以及介于两者之间的研究中一致性的量化。作为一种最小化基础率问题的方法，我们提出了一种用于一致性量化的统计量（Y统计量），它可以与kappa相关，但在效度研究中完全独立于患病率，在信度研究中相对独立于患病率。
