Validity (statistics)

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This article discusses validity in social sciences; for the term in logic see validity.

In statistics a valid measure is one which is measuring what it is supposed to measure. Validity implies reliability (consistency). 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.

In psychology, validity is the ability of a test to measure what it was designed to measure, the degree to which the operational definition of a variable accurately reflects the variable it is designed to measure or manipulate.

Validity can be defined a number of ways, though there are no distinct "types" of validity. Validity is, first and foremost, a logical exercise, rather than a computational endeavor. Establishing validity is, essentially, supporting the claim made that the test measures or predicts the construct it purports to predict. At the heart of any validity discussion must be the idea of construct validity, which will be discussed below. The first area of validity that must be considered is the validity of the criterion upon which either groups are distinguished or predictions made. One of the most popular means of determinging criterion validity, is to correlate measures with a criterion measure known to be valid, such as a measure of job performance that actually assesses performance on a given job or a specific task. When the criterion measure is collected at the same time as the measure being validated the goal is to establish concurrent validity; when the criterion is collected later the goal is to establish predictive validity. Similar to criterion validity is construct validity, 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 (correlation between the predictor and the predicted) cannot exceed the square root of the correlation between two versions of the same measure -- that is, reliability limits validity.

Contents

[edit] Internal Validity

[edit] External Validity

The issue of external validity concerns the extent to which one may safely generalize the conclusion derived from an evaluation.

[edit] Ecological Validity

[edit] Population Validity

[edit] Construct Validity

[edit] Intentional Validity

[edit] Representation Validity or Translation Validity

[edit] Face Validity

[edit] Content Validity

[edit] Observation Validity

[edit] Predictive Validity

[edit] Criterion Validity

[edit] Concurrent Validity

[edit] Convergent Validity

[edit] Discriminant Validity

[edit] Statistical Conclusion

[edit] Factors Jeopardizing Internal and External Validity

Campbell and Stanley (1963) define internal validity as the basic requirements for an experiment to be interpretable - did the experiment make a difference in this instance? External validity addresses the question of generalizability - to whom can we generalize this experiment's findings?


Eight extraneous variables can interfere with internal validity:

1. History, the specific events occurring between the first and second measurements in addition to the experimental variables

2. Maturation, processes within the participants as a function of the passage of time (not specific to particular events), e.g., growing older, hungrier, more tired, and so on.

3. Testing, the effects of taking a test upon the scores of a second testing.

4. Instrumentation, changes in calibration of a measurement tool or changes in the observers or scorers may produce changes in the obtained measurements.

5. Statistical regression, operating where groups have been selected on the basis of their extreme scores.

6. Selection, biases resulting from differential selection of respondents for the comparison groups.

7. Experimental mortality, or differential loss of respondents from the comparison groups.

8. Selection-maturation interaction, etc. e.g., in multiple-group quasi-experimental designs


Four factors jeopardizing external validity or representativeness are:

9. Reactive or interaction effect of testing, a pretest might increase

10. Interaction effects of selection biases and the experimental variable.

11. Reactive effects of experimental arrangements, which would preclude generalization about the effect of the experimental variable upon persons being exposed to it in non-experimental settings

12. Multiple-treatment interference, where effects of earlier treatments are not erasable.

[edit] See also

[edit] External links

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