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External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times.
Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity. In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study.
Internal and external reliability and validity explained. Uncertainty models, uncertainty quantification, and uncertainty processing in engineering; The relationships between correlational and internal consistency concepts of test reliability; The problem of negative reliabilities
In other words, the relevance of external and internal validity to a research study depends on the goals of the study. Furthermore, conflating research goals with validity concerns can lead to the mutual-internal-validity problem, where theories are able to explain only phenomena in artificial laboratory settings but not the real world. [13] [14]
The major threats to internal validity are history, maturation, testing, instrumentation, statistical regression, selection, experimental mortality, and selection-history interactions. One way to minimize the influence of artifacts is to use a pretest-posttest control group design.
Internal validity – Extent to which a piece of evidence supports a claim about cause and effect; Model identification – Statistical property which a model must satisfy to allow precise inference; Overfitting – Flaw in mathematical modelling; Perplexity – Concept in information theory
An alternative way of thinking about internal consistency is that it is the extent to which all of the items of a test measure the same latent variable. The advantage of this perspective over the notion of a high average correlation among the items of a test – the perspective underlying Cronbach's alpha – is that the average item ...
This equation is similar to the equation involving (,) in the introduction (this is the matrix version of that equation). When X and e are uncorrelated , under certain regularity conditions the second term has an expected value conditional on X of zero and converges to zero in the limit, so the estimator is unbiased and consistent.