Establishing Validity and Cross-Context Equivalence of Measures and Indicators

J Acad Nutr Diet. 2019 Nov;119(11):1817-1830. doi: 10.1016/j.jand.2018.09.005. Epub 2018 Nov 22.


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.

Keywords: Accuracy; Equivalence; Measures; Reliability; Validity.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Data Interpretation, Statistical*
  • Equivalence Trials as Topic
  • Humans
  • Reproducibility of Results*
  • Validation Studies as Topic