The HTMT Criterion: Henseler, Ringle and Sarstedt (2015) show by means of a simulation study that the classical approaches (i.e., the Fornell-Larcker criterion and cpross-loadings) do not reliably detect a lack of discriminant validity in common research situations. These authors therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations (HTMT). Henseler, Ringle and Sarstedt (2015) demonstrate this approach’s superior performance by means of a Monte Carlo simulation study, in which they compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, they provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

Take a look at the following video to learn more about the HTMT criterion:

HTMT in SmartPLS: When running the PLS and PLSc algorithm in SmartPLS, the results report includes discriminant validity assessment outcomes, in the section “Quality Criteria”. The following results are provided: (a) the Fornell-Larcker criterion, (b) cross-loadings, and (c) the HTMT criterion results. Use the HTMT criterion to assess discriminant validity! If the HTMT value is below 0.90, discriminant validity has been established between two reflective constructs. If you like to obtain the HTMT_Inference results, you need to run the bootstrapping routine. When starting the bootstrapping routine, it is important that you select the option “Complete Bootstrapping”. Then, in the bootstrapping results report, you find the bootstrapped HTMT criterion results in the section “Quality Criteria”.

Take a look at the following video to see how to use SmartPLS to get HTMT results:


Literature: For detailed explanations of the HTMT criterion for discriminant validity assessment in variance-based structural equations modeling, see Henseler, J., Ringle, C. M., and Sarstedt, M. 2015. A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1): 115-135.