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Reliability (statistics) ... In its general form, the reliability coefficient is defined as the ratio of true score variance to the total variance of test scores.
A study estimates that approximately 97% of studies use as a reliability coefficient. [3] However, simulation studies comparing the accuracy of several reliability coefficients have led to the common result that is an inaccurate reliability coefficient. [42] [43] [6] [44] [45]
There are a number of statistics that can be used to determine inter-rater reliability. Different statistics are appropriate for different types of measurement. Some options are joint-probability of agreement, such as Cohen's kappa , Scott's pi and Fleiss' kappa ; or inter-rater correlation, concordance correlation coefficient , intra-class ...
Cohen's kappa coefficient (κ, lowercase Greek kappa) is a statistic that is used to measure inter-rater reliability (and also intra-rater reliability) for qualitative (categorical) items. [1] It is generally thought to be a more robust measure than simple percent agreement calculation, as κ takes into account the possibility of the agreement ...
Reliability provides a convenient index of test quality in a single number, reliability. However, it does not provide any information for evaluating single items. Item analysis within the classical approach often relies on two statistics: the P-value (proportion) and the item-total correlation (point-biserial correlation coefficient).
Internal consistency is usually measured with Cronbach's alpha, a statistic calculated from the pairwise correlations between items. Internal consistency ranges between negative infinity and one. Coefficient alpha will be negative whenever there is greater within-subject variability than between-subject variability. [1]
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]
Generalizability theory, or G theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.e., reproducibility) of measurements under specific conditions. It is particularly useful for assessing the reliability of performance assessments.