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The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
[1] [2] [3] Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects. [1] [4] [5] [6] Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence [7] [8] such as content validity and criterion validity. [9] [10]
[1] [2] Criterion validity is often divided into concurrent and predictive validity based on the timing of measurement for the "predictor" and outcome. [2]: page 282 Concurrent validity refers to a comparison between the measure in question and an outcome assessed at the same time.
Test validity is the extent to which a test (such as a chemical, physical, or scholastic test) accurately measures what it is supposed to measure.In the fields of psychological testing and educational testing, "validity refers to the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of tests". [1]
(This is true of measures of all types—yardsticks might measure houses well yet have poor reliability when used to measure the lengths of insects.) Reliability may be improved by clarity of expression (for written assessments), lengthening the measure, [9] and other informal means. However, formal psychometric analysis, called item analysis ...
Test validity, validity in educational and psychological testing; Face validity, the property of a test intended to measure something; Construct validity, refers to whether a scale measures or correlates with the theorized psychological construct it measures; Content validity, the extent to which a measure represents all facets of a given construct
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Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...