方法对比

	Cohen's Kappa Coefficient	克朗巴赫α系数（信度分析）	Fleiss Kappa多评分者一致性系数
领域	统计学	统计学	统计学
方法族	Hypothesis test	Latent structure	Hypothesis test
起源年份	1960	1951	1971
提出者	Jacob Cohen	Lee J. Cronbach	Joseph L. Fleiss
类型	Inter-rater reliability coefficient	Reliability / internal consistency coefficient	Non-parametric agreement measure
开创性文献	Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.	Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.	Fleiss, J.L. (1971). Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin, 76(5), 378–382.
别名	kappa coefficient, kappa statistic, Cohen's Kappa (Değerlendiriciler Arası Uyum)	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	multi-rater kappa, Fleiss kappa, Fleiss' Kappa (Çoklu Değerlendirici Uyumu)
相关	3	4	2
摘要	Cohen's kappa (κ) is a statistical measure of inter-rater reliability for categorical classifications, introduced by Jacob Cohen in 1960. Unlike simple percent agreement, kappa corrects for the level of agreement that would be expected purely by chance, making it the standard metric when two raters independently assign observations to the same set of mutually exclusive categories.	Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.	Fleiss' Kappa is a non-parametric statistic for measuring the degree of agreement among three or more raters who classify items into mutually exclusive nominal categories. Introduced by Joseph L. Fleiss in 1971 as a generalization of Cohen's Kappa beyond two raters, it corrects observed agreement for the level of agreement expected by chance alone, making it the standard reliability index in medical diagnosis studies, content analysis, and multi-coder research.