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Until the development of tau-equivalent reliability, split-half reliability using the Spearman-Brown formula was the only way to obtain inter-item reliability. [4] [5] After splitting the whole item into arbitrary halves, the correlation between the split-halves can be converted into reliability by applying the Spearman-Brown formula.
Often discussed in tandem with KR-20, is Kuder–Richardson Formula 21 (KR-21). [4] KR-21 is a simplified version of KR-20, which can be used when the difficulty of all items on the test are known to be equal.
Cronbach's alpha (Cronbach's ), also known as tau-equivalent reliability or coefficient alpha (coefficient ), is a reliability coefficient and a measure of the internal consistency of tests and measures.
Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories. The definition of is =, where p o is the relative observed agreement among raters, and p e is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly selecting each category.
A quantity similar (but not mathematically equivalent) to congeneric reliability first appears in the appendix to McDonald's 1970 paper on factor analysis, labeled . [2] In McDonald's work, the new quantity is primarily a mathematical convenience: a well-behaved intermediate that separates two values.
In statistics, inter-rater reliability (also called by various similar names, such as inter-rater agreement, inter-rater concordance, inter-observer reliability, inter-coder reliability, and so on) is the degree of agreement among independent observers who rate, code, or assess the same phenomenon.
Fleiss' kappa is a generalisation of Scott's pi statistic, [2] a statistical measure of inter-rater reliability. [3] It is also related to Cohen's kappa statistic and Youden's J statistic which may be more appropriate in certain instances. [4]
Difference between ANOVA and Kruskal–Wallis test with ranks. The Kruskal–Wallis test by ranks, Kruskal–Wallis test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution.