方法对比

	评分者间信度（Cohen's κ 和 ICC）	克朗巴赫α系数（信度分析）	Fleiss Kappa多评分者一致性系数
领域	心理测量学	统计学	统计学
方法族	Latent structure	Latent structure	Hypothesis test
起源年份	1960 (kappa); 1979 (ICC)	1951	1971
提出者	Cohen (kappa, 1960); Shrout & Fleiss (ICC, 1979)	Lee J. Cronbach	Joseph L. Fleiss
类型	Reliability / agreement analysis	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	inter-rater reliability, interrater agreement, rater agreement, Değerlendiriciler Arası Güvenilirlik (Cohen's κ, ICC)	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	multi-rater kappa, Fleiss kappa, Fleiss' Kappa (Çoklu Değerlendirici Uyumu)
相关	6	4	2
摘要	Interrater reliability quantifies the degree to which two or more independent raters produce consistent scores when evaluating the same individuals or products. The family encompasses Cohen's kappa, introduced in 1960 for categorical judgments, and the Intraclass Correlation Coefficient (ICC) for continuous ratings, together spanning most measurement scenarios encountered in behavioral, health, and educational research.	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.