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Standards for Talking and Thinking About Validity.

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Title: Standards for Talking and Thinking About Validity.
Authors: Newton, Paul B.1 p.newton@ioe.ac.uk, Shaw, Stuart D.2
Source: Psychological Methods. Sep2013, Vol. 18 Issue 3, p301-319. 19p.
Subject Terms: *Test validity, *Predictive validity, *Perception testing, Historical analysis, Scientific community
Abstract: Standards for talking and thinking about validity have been promulgated in North America for decades. In 1954 two foundational standards were announced: (a) Thou shalt not refer to "the validity of the test" and (b) thou shalt use validity modifier labels, such as "content validity" or "predictive validity." Subsequently, in 1985, the latter became, thou shalt not use validity modifier labels. These standards for talking about validity have repeatedly been disregarded over the years. Possible reasons include intentional misuse, while upholding standards for thinking about validity; lack of awareness or misunderstanding of standards for thinking about validity; and genuine divergence from standards for thinking about validity. A historical analysis of disregard for these standards provides a basis for reappraising the concept of validity.We amassed a new body of evidence with which to challenge the frequently asserted claim that a general consensus exists over the meaning of validity. Indeed, the historical analysis provides reason to believe that prospects for achieving consensus over the meaning of validity are low. We recommend that the concept of validity be abandoned in favor of the more general, all-encompassing concept of quality, to be judged in relation to measurement aims, decision making aims, and broader policy aims, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Methods is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Standards for talking and thinking about validity have been promulgated in North America for decades. In 1954 two foundational standards were announced: (a) Thou shalt not refer to "the validity of the test" and (b) thou shalt use validity modifier labels, such as "content validity" or "predictive validity." Subsequently, in 1985, the latter became, thou shalt not use validity modifier labels. These standards for talking about validity have repeatedly been disregarded over the years. Possible reasons include intentional misuse, while upholding standards for thinking about validity; lack of awareness or misunderstanding of standards for thinking about validity; and genuine divergence from standards for thinking about validity. A historical analysis of disregard for these standards provides a basis for reappraising the concept of validity.We amassed a new body of evidence with which to challenge the frequently asserted claim that a general consensus exists over the meaning of validity. Indeed, the historical analysis provides reason to believe that prospects for achieving consensus over the meaning of validity are low. We recommend that the concept of validity be abandoned in favor of the more general, all-encompassing concept of quality, to be judged in relation to measurement aims, decision making aims, and broader policy aims, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Methods is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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