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

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.

Subjects

Cross-loadings

Discriminant validity

Fornell-Larcker criterion

Heterotrait-monotrait (HTMT) ratio of correlations

Measurement model assessment

Multitrait-multimethod (MTMM) matrix

Partial least squares (PLS)

Results evaluation

Structural equation modeling (SEM)

DDC Class

000: Allgemeines, Wissenschaft

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