A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling

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.

Keywords: Structural equationmodeling (SEM), Partial least squares (PLS), Results evaluation, Measurementmodel assessment, Discriminantvalidity, Fornell-Larckercriterion, Cross-loadings, Multitrait-multimethod (MTMM)matrix, Heterotrait-monotrait (HTMT) ratio of correlations