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.

中文翻译：

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建立措施和指标的有效性和跨上下文等效性

定量研究依赖于使用措施来收集有效的数据（即，很好地反映感兴趣的现象）并在不同背景下表现相同。证明有效性和跨上下文等效性需要专门设计的研究，但许多此类研究存在限制其实用性的问题。本文解释了度量（以及重要的相关概念）的有效性和跨上下文等效性，并阐明了如何建立它们。验证是确定度量或指标是否适合为给定目的和上下文提供有用的分析度量的过程。跨上下文等价意味着度量在跨上下文中的表现相当。等价的四种类型是构造、项目、测量和标量。建立有效性和跨上下文等效性需要用数学方法表示测量的误差（即不精确、不可靠和不准确），并使用适当的统计方法来量化这些误差。旨在提供有关措施有效性证据的研究需要澄清使用该措施的目的和背景。选择两个概念系统之一进行验证；获取数据以确定该措施在何种程度上构建良好、可靠和准确；并使用超越简单相关性的分析方法，为合理判断该措施是否为特定目的和背景提供有用的分析测量提供了基础。确定一项措施的准确性需要有其他已知准确的可用措施作为比较器；