Thus, reliability is expected to be more rigorously adhered to when tests are used to make high-stakes decisions about individuals, such as employment or certification decisions and decisions about clinical placement.
Reliability is considered to be increasingly important when the consequences of test use are more high stakes.
The importance of reliability varies depending on the uses made of the assessment. For example, classical test theory emphasizes reliability coefficients and standard errors of measurement item response theory emphasizes information functions generalizability theory emphasizes generalizability coefficients, dependability indices, and relative and absolute standard errors and classification consistency emphasizes proportion agreement unadjusted or adjusted for chance agreement. The indices are defined differently with different test theories. There are also multiple indices for reporting reliability, including reliability coefficients, generalizability coefficients, standard errors of measurement, and information functions, to name a few. Thus, inconsistency in scores is potentially due to multiple sources of random error, and this definition can be applied to multiple types of replications depending on the generalization that is to be made (e.g., items, forms, raters, or occasions).
The source of the measurement error will determine the type of reliability and ultimately the generalizations about the measurement. In education, the sources of measurement error and the basis for replications include items, forms, raters, or occasions. Reliability is defined as the consistency of scores across replications.
Thus, reliability is a primary concern for all assessments. There are three foundations identified by professional standards for examining the psychometric quality of assessments: validity, reliability, and fairness.
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