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A new criterion for assessing discriminant validity in variance-based structural equation modeling

机译：基于方差的结构方程建模中判别有效性评估的新准则

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摘要

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.
机译：判别有效性评估已成为分析潜在变量之间关系的公认先决条件。对于基于方差的结构方程建模（例如偏最小二乘），Fornell-Larcker准则和交叉载荷检查是评估判别有效性的主要方法。通过仿真研究，我们表明这些方法不能可靠地检测出在常见研究情况下缺乏判别效度。因此，我们提出了一种基于多性状-多方法矩阵的替代方法来评估判别效度：相关性的异质性-单性比率。我们通过蒙特卡洛模拟研究证明了其优越的性能，在该研究中，我们将新方法与Fornell-Larcker准则进行了比较，并对（部分）交叉载荷进行了评估。最后，我们提供了有关在基于方差的结构方程模型中如何处理判别有效性问题的指南。

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