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

作者：Jörg Henseler, Christian M. Ringle, Marko Sarstedt · 发表于：Journal of the Academy of Marketing Science · 年份：2014 · DOI：10.1007/s11747-014-0403-8 · 被引用次数：37157 · 研究领域：Technology Adoption and User Behaviour、Psychometric Methodologies and Testing、Technology and Data Analysis

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.