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

2014/08/21 by Jörg Henseler, Christian M. Ringle, Marko Sarstedt · 35,145 citations
Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Computer science #Discriminant #Discriminant validity #Econometrics #Latent variable #Linear discriminant analysis #Mathematics #Monte Carlo method #Partial least squares regression #Psychometric Methodologies and Testing #Psychometrics #Statistics #Structural equation modeling #Technology Adoption and User Behaviour #Technology and Data Analysis #Variance (accounting)

paper · pdf · doi:10.1007/s11747-014-0403-8

published in Journal of the Academy of Marketing Science 43(1), 115-135 (Springer Science+Business Media)

openalex publication_date 2014/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

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.

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