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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.
In psychometrics, the Kuder–Richardson formulas, first published in 1937, are a measure of internal consistency reliability for measures with dichotomous choices. They were developed by Kuder and Richardson.
The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
In statistics and research, internal consistency is typically a measure based on the correlations between different items on the same test (or the same subscale on a larger test). It measures whether several items that propose to measure the same general construct produce similar scores. For example, if a respondent expressed agreement with the ...
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
In qualitative research, a member check, also known as informant feedback or respondent validation, is a technique used by researchers to help improve the accuracy, credibility, validity, and transferability (also known as applicability, internal validity, [1] or fittingness) of a study. [2]
Natural experiments leverage events outside the researchers' and subjects' control to address several threats to internal validity, minimising the chance of confounding elements, while sacrificing a few of the features of field data, such as more natural ranges of treatment effects and the presence of organically formed context. [16]
Construct validity concerns how well a set of indicators represent or reflect a concept that is not directly measurable. [1] [2] [3] Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects.