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In science and statistics, validity is the extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is considered to be the degree to which the tool measures what it claims to measure.
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").
In clinical fields, the assessment of validity of a diagnosis and various diagnostic tests are extremely important. As diagnosis augments treatments, medications, and the patient's life, it is extremely important to know that when running diagnostic tests that clinicians are truly testing what they intend to test.
It is generally accepted that the concept of scientific validity addresses the nature of reality and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating to the truth of inferences made from premises.
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 construct in question.
Validity 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 consistent. Within validity, the measurement does not always have to be similar, as it does in reliability. When a measure is both valid and reliable, the results will appear as in the image to the right. Though, just because a measure is reliable, it is not necessarily valid (and vice-versa). Validity is also dependent on the measurement measuring what it was designed to measure, and not something else instead. Validity (similar to reliability) is based on matters of degrees; validity is not an all or nothing idea. There are many different types of validity.
An early definition of test validity identified it with the degree of correlation between the test and a criterion. Under this definition, one can show that reliability of the test and the criterion places an upper limit on the possible correlation between them (the so-called validity coefficient). Intuitively, this reflects the fact that reliability involves freedom from random error and random errors do not correlate with one another. Thus, the less random error in the variables, the higher the possible correlation between them. Under these definitions, a test cannot have high validity unless it also has high reliability. However, the concept of validity has expanded substantially beyond this early definition and the classical relationship between reliability and validity need not hold for alternative conceptions of reliability and validity.
Within 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.
Construct validity refers to the extent to which operationalizations of a construct (i.e., practical tests developed from a theory) do actually measure what the theory says they do. For example, to what extent is an IQ questionnaire actually measuring "intelligence"?
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 construct validity evidence.
Convergent validity refers to the degree to which a measure is correlated with other measures that it is theoretically predicted to correlate with.
Discriminant validity tests whether concepts or measurements that are supposed to be unrelated are, in fact, unrelated.
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 subject matter experts (SME's) evaluating test items against the test specifications.
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) 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 behaviour domain.
Representation validity, also known as translation validity, is about the extent to which an abstract theoretical construct can be turned into a specific practical test
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 women as "witches."
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 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 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. This type of validity is important from a public view standpoint; is this going to look acceptable to the public or not?
The validity of the design of experimental research studies is a fundamental part of the scientific method, and a concern of research ethics. Without a valid design, valid scientific conclusions cannot be drawn.
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 qualitative data.
Statistical conclusion validity involves ensuring the use of adequate sampling procedures, appropriate statistical tests, and reliable measurement procedures. As this type of validity is concerned solely with the relationship that is found among variables, the relationship may be solely a correlation.
Internal validity is an inductive estimate of the degree to which conclusions about causal relationships can be made (e.g. cause and effect), based on the measures used, the research setting, and the whole research design. Good experimental techniques, in which the effect of an independent variable on a dependent variable is studied under highly controlled conditions, usually allow for higher degrees of internal validity than, for example, single-case designs.
Eight kinds of confounding variable can interfere with internal validity (i.e. with the attempt to isolate causal relationships):
External validity concerns the extent to which the (internally valid) results of a study can be held to be true for other cases, for example to different people, places or times. In other words, it is about whether findings can be validly generalized. If the same research study was conducted in those other cases, would it get the same results?
A major factor in this is whether the study sample (e.g. the research participants) are representative of the general population along relevant dimensions. Other factors jeopardizing external validity are:
Ecological validity is the extent to which research results can be applied to real life situations outside of research settings. This issue is closely related to external validity but covers the question of to what degree experimental findings mirror what can be observed in the real world (ecology = the science of interaction between organism and its environment). To be ecologically valid, the methods, materials and setting of a study must approximate the real-life situation that is under investigation.
Ecological validity is partly related to the issue of experiment versus observation. Typically in science, there are two domains of research: observational (passive) and experimental (active). The purpose of experimental designs is to test causality, so that you can infer A causes B or B causes A. But sometimes, ethical and/or methological restrictions prevent you from conducting an experiment (e.g. how does isolation influence a child's cognitive functioning?). Then you can still do research, but it is not causal, it is correlational. You can only conclude that A occurs together with B. Both techniques have their strengths and weaknesses.
On first glance, internal and external validity seem to contradict each other – to get an experimental design you have to control for all interfering variables. That is why you often conduct your experiment in a laboratory setting. While gaining internal validity (excluding interfering variables by keeping them constant) you lose ecological or external validity because you establish an artificial laboratory setting. On the other hand with observational research you can not control for interfering variables (low internal validity) but you can measure in the natural (ecological) environment, at the place where behavior normally occurs. However, in doing so, you sacrifice internal validity.
The apparent contradiction of internal validity and external validity is, however, only superficial. The question of whether results from a particular study generalize to other people, places or times arises only when one follows an inductivist research strategy. If the goal of a study is to deductively test a theory, one is only concerned with factors which might undermine the rigor of the study, i.e. threats to internal validity.
In regard to tests, the validity issues may be examined in the same way as for psychometric tests as outlined above, but there are often particular applications and priorities. In laboratory work, the medical validity of a scientific finding has been defined as the 'degree of achieving the objective' - namely of answering the question which the physician asks. An important requirement in clinical diagnosis and testing is sensitivity and specificity - a test needs to be sensitive enough to detect the relevant problem if it is present (and therefore avoid too many false negative results), but specific enough not to respond to other things (and therefore avoid too many false positive results).
Robins and Guze proposed in 1970 what were to become influential formal criteria for establishing the validity of psychiatric diagnoses. They listed five criteria:
Kendler in 1980 distinguished between:
Nancy Andreasen (1995) listed several additional validators – molecular genetics and molecular biology, neurochemistry, neuroanatomy, neurophysiology, and cognitive neuroscience – that are all potentially capable of linking symptoms and diagnoses to their neural substrates.
Kendell and Jablinsky (2003) emphasized the importance of distinguishing between validity and utility, and argued that diagnostic categories defined by their syndromes should be regarded as valid only if they have been shown to be discrete entities with natural boundaries that separate them from other disorders.
Kendler (2006) emphasized that to be useful, a validating criterion must be sensitive enough to validate most syndromes that are true disorders, while also being specific enough to invalidate most syndromes that are not true disorders. On this basis, he argues that a Robins and Guze criterion of "runs in the family" is inadequately specific because most human psychological and physical traits would qualify - for example, an arbitrary syndrome comprising a mixture of "height over 6 ft, red hair, and a large nose" will be found to "run in families" and be "hereditary", but this should not be considered evidence that it is a disorder. Kendler has further suggested that "essentialist" gene models of psychiatric disorders, and the hope that we will be able to validate categorical psychiatric diagnoses by "carving nature at its joints" solely as a result of gene discovery, are implausible.
In the United States Federal Court System validity and reliability of evidence is evaluated using the Daubert Standard: see Daubert v. Merrell Dow Pharmaceuticals. Perri and Lichtenwald (2010) provide a starting point for a discussion about a wide range of reliability and validity topics in their analysis of a wrongful murder conviction.
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