Statistics

Discriminant validity

Accounting

Latent variable

Economics

Computer science

Psychometrics

Structural equation modeling

Business

Variance (accounting)

Psychology

Mathematics

Partial least squares regression

Econometrics

Discriminant

Artificial intelligence

Monte Carlo method

Internal consistency

Linear discriminant analysis

Journal of the Academy of Marketing Science

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.

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