方法对比

	Bland-Altman 方法一致性分析	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	1986	1951	1971
提出者	J. Martin Bland & Douglas G. Altman	Lee J. Cronbach	Joseph L. Fleiss
类型	Graphical and statistical method comparison	Reliability / internal consistency coefficient	Non-parametric agreement measure
开创性文献	Bland, J.M. & Altman, D.G. (1986). Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement. Lancet, 327(8476), 307–310.	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	Bland-Altman plot, limits of agreement analysis, method agreement analysis, Bland-Altman Uyum Analizi	coefficient alpha, alpha reliability, internal consistency reliability, Güvenilirlik Analizi (Cronbach Alpha)	multi-rater kappa, Fleiss kappa, Fleiss' Kappa (Çoklu Değerlendirici Uyumu)
相关	5	4	2
摘要	The Bland-Altman analysis is a graphical and statistical technique for assessing agreement between two measurement methods applied to the same subjects. Introduced by J. Martin Bland and Douglas G. Altman in their landmark 1986 Lancet paper, it plots the difference between the two methods against their mean for each subject, and derives the bias (mean difference) along with limits of agreement (LoA) that capture 95% of differences in the population.	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.