Skip to main content
ARS Home » Plains Area » Houston, Texas » Children's Nutrition Research Center » Research » Publications at this Location » Publication #359618
Title: Establishing validity and cross-context equivalence of measures and indicators

Author
item FRONGILLO, EDWARD - University Of South Carolina
item BARANOWSKI, TOM - Children'S Nutrition Research Center (CNRC)
item SUBAR, AMY - National Cancer Institute (NCI, NIH)
item TOOZE, JANET - Wake Forest School Of Medicine
item KIRKPATRICK, SHARON - University Of Waterloo

Submitted to: Journal of the Academy of Nutrition and Dietetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2018
Publication Date: 11/22/2018
Citation: Frongillo, E.A., Baranowski, T., Subar, A.F., Tooze, J.A., Kirkpatrick, S.I. 2018. Establishing validity and cross-context equivalence of measures and indicators. Journal of the Academy of Nutrition and Dietetics. https://doi.org/10.1016/j.jand.2018.09.005.
DOI: https://doi.org/10.1016/j.jand.2018.09.005

Interpretive Summary: Many dietary intake assessment articles have been published stating that the findings "validate" the measure used. Others then use the measure in populations and under circumstances quite different from the "validating" research, and say the used a validated measure. Validity in this context is not clearly specified. This article defines validity (accuracy), includes reliability (or precision), and makes the case that validity can only be inferred for a specific measure with the specific population under the explicit circumstances included in the original research. Hopefully this article will lead to a more disciplined use of the term "validation".

Technical Abstract: Quantitative research depends on using measures to collect data that are valid (ie, reflect well the phenomena of interest) and perform equivalently across contexts. Demonstrating validity and cross-context equivalence requires specifically designed studies, but many such studies have problems that have limited their usefulness. This article explains validity and cross-context equivalence of measures (and important related concepts) and clarifies how to establish them. Validation is the process of determining whether a measure or indicator is suitable for providing useful analytical measurement for a given purpose and context. Cross-context equivalence means that a measure performs comparably across contexts. Four types of equivalence are construct, item, measurement, and scalar. Establishing validity and cross-context equivalence requires representing mathematically the errors (ie, imprecision, undependability, and inaccuracy) of a measure and using appropriate statistical methods to quantify these errors. Studies aiming to provide evidence about the validity of a measure need to clarify the purpose and context for use of that measure; choose one of the two conceptual systems for validation; obtain data to establish the extent to which the measure is well constructed, reliable, and accurate; and use analytic methods beyond simple correlations to provide a basis for making reasoned judgment about whether the measure provides useful analytic measurement for the particular purpose(s) and context. Establishing accuracy of a measure requires having available other measures known to be accurate as comparators; in the case that no other measure understood to be more accurate is available, then the study will be able to establish agreement rather than validity.