방법 비교

	Bland-Altman 방법 비교 분석	크론바흐 알파 (신뢰도 분석)	플레이스 카파 (Fleiss' Kappa)
분야	통계학	통계학	통계학
계열	Hypothesis test	Latent structure	Hypothesis test
기원 연도	1986	1951	1971
창시자	J. Martin Bland & Douglas G. Altman	Lee J. Cronbach	Joseph L. Fleiss
유형	Graphical and statistical method comparison	Reliability / internal consistency coefficient	Non-parametric agreement measure
원전	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.
별칭	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)
관련	5	4	2
요약	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.