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of conditions. In these situations, there is often a predetermined "critical difference", and for differences in monitored values that are smaller than this critical difference, the possibility of variability as a sole cause of the difference may be considered in addition to, for example, changes in
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Because the same test is administered twice and every test is parallel with itself, differences between scores on the test and scores on the retest should be due solely to measurement error. This sort of argument is quite probably true for many physical measurements. However, this argument is often
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The attribute that is being measured may change between the first test and the retest. For example, a reading test that is administered in
September to a third grade class may yield different results when retaken in June. One would expect some change in children's reading ability over that span of
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on accuracy. It allows the analyst to examine the responses from multiple reviewers as they look at several scenarios multiple times. It produces statistics that evaluate the ability of the appraisers to agree with themselves (repeatability), with each other
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The experience of taking the test itself can change a person's true score. For example, completing an anxiety inventory could serve to increase a person's level of anxiety.
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inappropriate for psychological measurement, because it is often impossible to consider the second administration of a test a parallel measure to the first.
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The second administration of a psychological test might yield systematically different scores than the first administration due to the following reasons:
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George, D., & Mallery, P. (2003). SPSS for
Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston: Allyn & Bacon.
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Practical explanation of repeatability and reproducibility in terms of how data can be different between samples. - oil industry example
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on the same item, under the same conditions, and in a short period of time. A less-than-perfect test–retest reliability causes
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An attribute agreement analysis is designed to simultaneously evaluate the impact of repeatability and
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JCGM 100:2008. Evaluation of measurement data – Guide to the expression of uncertainty in measurement
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The repeatability coefficient is a precision measure which represents the value below which the
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NIST Guidelines for
Evaluating and Expressing the Uncertainty of NIST Measurement Results Cover
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time, a low test–retest correlation might reflect real changes in the attribute itself.
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The following conditions need to be fulfilled in the establishment of repeatability:
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between two repeated test results may be expected to lie with a probability of 95%.
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between separate administrations of the test is high (e.g. 0.7 or higher as in
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when this variation is smaller than a predetermined acceptance criterion.
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383:"Attribute Agreement Analysis for Defect Databases | iSixSigma"
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is the closeness of the agreement between the results of successive
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The
Research Methods Knowledge Base. Last Revised: 20 October 2006
415:(6th ed.). Upper Saddle River, N.J.: Pearson/Prentice Hall.
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Repeatability methods were developed by Bland and Altman (1986).
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the same measuring instrument, used under the same conditions
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Test–retest variability is practically used, for example, in
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Psychological testing : principles and applications
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Closeness of agreement between successive measurements
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184:Accuracy and precision
189:Monitoring (medicine)
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218:Types of Reliability
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205:References
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481:Metrology
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100:If the
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