Validity (statistics)

Validity is the extent to which a concept,[1] conclusion or measurement is well-founded and likely corresponds accurately to the real world based on probability. The word "valid" is derived from the Latin validus, meaning strong. This should not be confused with notions of certainty nor necessity. The validity of a measurement tool (for example, a test in education) is considered to be the degree of probability to which the tool measures what it claims to measure; in this case, the validity is an equivalent to a percent of how accurately the claim corresponds to reality.

In psychometrics, validity has a particular application known as test validity: "the degree to which evidence and theory support the interpretations of test scores" ("as entailed by proposed uses of tests").[2]

It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical probability and as such is an epistemological and philosophical issue as well as a question of measurement of the possibility that a scientific claim is true. The use of the term in logic is narrower, relating to the truth of inferences made from premises. In logic, and therefore as the term is applied to any epistemological claim, validity refers to the consistency of an argument flowing from the premises to the conclusion; as such, the truth of the claim in logic is not only reliant on validity. Rather, an argumentative claim is true if and only if it is both valid and sound. This means the argument flows without contradiction from the premises or the conclusion, and all of the premises and the conclusion correspond to known facts. As such, "scientific or statistical validity" is not a deductive claim that is necessarily truth preserving, but is an inductive claim that remains true or false in an undecided manner. This is why "scientific or statistical validity" is a claim that is qualified as being either strong or weak in its nature, it is never necessary nor certainly true. This has the effect of making claims of "scientific or statistical validity" open to interpretation as to what, in fact, the facts of the matter mean.

Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or constructs in question.

Test validity

Validity (accuracy)

Validity[3] of an assessment is the degree to which it measures what it is supposed to measure. This is not the same as reliability, which is the extent to which a measurement gives results that are very consistent. Within validity, the measurement does not always have to be similar, as it does in reliability. However, just because a measure is reliable, it is not necessarily valid. E.g. a scale that is 5 pounds off is reliable but not valid. A test cannot be valid unless it is reliable. Validity is also dependent on the measurement measuring what it was designed to measure, and not something else instead.[4] Validity (similar to reliability) is a relative concept; validity is not an all-or-nothing idea. There are many different types of validity.

Construct validity

Construct validity refers to the extent to which operationalizations of a construct (e.g., practical tests developed from a theory) measure a construct as defined by a theory. It subsumes all other types of validity. For example, the extent to which a test measures intelligence is a question of construct validity. A measure of intelligence presumes, among other things, that the measure is associated with things it should be associated with (convergent validity), not associated with things it should not be associated with (discriminant validity).[5]

Construct validity evidence involves the empirical and theoretical support for the interpretation of the construct. Such lines of evidence include statistical analyses of the internal structure of the test including the relationships between responses to different test items. They also include relationships between the test and measures of other constructs. As currently understood, construct validity is not distinct from the support for the substantive theory of the construct that the test is designed to measure. As such, experiments designed to reveal aspects of the causal role of the construct also contribute to constructing validity evidence.[5]

Content validity

Content validity is a non-statistical type of validity that involves "the systematic examination of the test content to determine whether it covers a representative sample of the behavior domain to be measured" (Anastasi & Urbina, 1997 p. 114). For example, does an IQ questionnaire have items covering all areas of intelligence discussed in the scientific literature?

Content validity evidence involves the degree to which the content of the test matches a content domain associated with the construct. For example, a test of the ability to add two numbers should include a range of combinations of digits. A test with only one-digit numbers, or only even numbers, would not have good coverage of the content domain. Content related evidence typically involves a subject matter expert (SME) evaluating test items against the test specifications. Before going to the final administration of questionnaires, the researcher should consult the validity of items against each of the constructs or variables and accordingly modify measurement instruments on the basis of SME's opinion.

A test has content validity built into it by careful selection of which items to include (Anastasi & Urbina, 1997). Items are chosen so that they comply with the test specification which is drawn up through a thorough examination of the subject domain. Foxcroft, Paterson, le Roux & Herbst (2004, p. 49)[6] note that by using a panel of experts to review the test specifications and the selection of items the content validity of a test can be improved. The experts will be able to review the items and comment on whether the items cover a representative sample of the behavior domain.

Face validity

Face validity is an estimate of whether a test appears to measure a certain criterion; it does not guarantee that the test actually measures phenomena in that domain. Measures may have high validity, but when the test does not appear to be measuring what it is, it has low face validity. Indeed, when a test is subject to faking (malingering), low face validity might make the test more valid. Considering one may get more honest answers with lower face validity, it is sometimes important to make it appear as though there is low face validity whilst administering the measures.

Face validity is very closely related to content validity. While content validity depends on a theoretical basis for assuming if a test is assessing all domains of a certain criterion (e.g. does assessing addition skills yield in a good measure for mathematical skills? To answer this you have to know, what different kinds of arithmetic skills mathematical skills include) face validity relates to whether a test appears to be a good measure or not. This judgment is made on the "face" of the test, thus it can also be judged by the amateur.

Face validity is a starting point, but should never be assumed to be probably valid for any given purpose, as the "experts" have been wrong before—the Malleus Malificarum (Hammer of Witches) had no support for its conclusions other than the self-imagined competence of two "experts" in "witchcraft detection," yet it was used as a "test" to condemn and burn at the stake tens of thousands men and women as "witches."[7]

Criterion validity

Criterion validity evidence involves the correlation between the test and a criterion variable (or variables) taken as representative of the construct. In other words, it compares the test with other measures or outcomes (the criteria) already held to be valid. For example, employee selection tests are often validated against measures of job performance (the criterion), and IQ tests are often validated against measures of academic performance (the criterion).

If the test data and criterion data are collected at the same time, this is referred to as concurrent validity evidence. If the test data are collected first in order to predict criterion data collected at a later point in time, then this is referred to as predictive validity evidence.

Concurrent validity

Concurrent validity refers to the degree to which the operationalization correlates with other measures of the same construct that are measured at the same time. When the measure is compared to another measure of the same type, they will be related (or correlated). Returning to the selection test example, this would mean that the tests are administered to current employees and then correlated with their scores on performance reviews.

Predictive validity

Predictive validity refers to the degree to which the operationalization can predict (or correlate with) other measures of the same construct that are measured at some time in the future. Again, with the selection test example, this would mean that the tests are administered to applicants, all applicants are hired, their performance is reviewed at a later time, and then their scores on the two measures are correlated.

This is also when measurement predicts a relationship between what is measured and something else; predicting whether or not the other thing will happen in the future. High correlation between ex-ante predicted and ex-post actual outcomes is the strongest proof of validity.

Other Languages
Deutsch: Validität
eesti: Valiidsus
עברית: תוקף
қазақша: Валидтілік
Nederlands: Validiteit
norsk: Validitet
polski: Trafność
svenska: Validitet
中文: 效度