Statistical validity

In statistics a valid measure is one which is measuring what it is supposed to measure. Validity implies reliability (accuracy). A valid measure must be reliable, but a reliable measure need not be valid. Validity refers to getting results that accurately reflect the concept being measured.

Validity can be defined in a number of ways.

criterion validity.A common approach is to correlate measures with a criterion measure known to be valid.


 * concurrent validityWhen the criterion measure is collected at the same time as the measure being validated the goal is to establish


 * predictive validity when the criterion is collected later the goal is to establish

construct validitySeparate from criterion validity is, where an investigator examines whether a measure is related to other variables as required by theory.

Content validity, or face validity, is simply a demonstration that the items of a test are drawn from the domain being measured; it does not guarantee that the test actually measures phenomena in that domain.

According to classical test theory, predictive or concurrent validity cannot exceed the square of the correlation between two versions of the same measure -- that is, validity cannot exceed reliability.