“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. The following results are provided: 1. the Fornell-Larcker criterion, 2. cross-loadings, and 3. the HTMT criterion results.We recommend using the HTMT criterion to assess discriminant validity.If the HTMT value is below 0.90, discriminant validity has been established between two reflective constructs. HTMT<1. Heterotrait-monotrait (HTMT) ratio of correlations; Access to Document. We use cookies to help provide and enhance our service and tailor content. The internal consistency of the scales was evaluated using Cronbach's alpha coefficient (α) and composite reliability. a new criterion for assessing discriminant validity in variance-based structural equation modeling Model selection criteria. In the figure below, we again see four measures (each is an item on a scale). HTMT criterion. Mendeley helps you to discover research relevant for your work. Topics: 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), Business and International Management, Economics and Econometrics, Marketing 10.1007/s11747-014-0403-8 Licence: CC BY. However, a new method has emerged for establishing the discriminant validity assessment through heterotrait-monotrait (HTMT) ratio of correlations method. ... loadings with other constructs. Specifically, it assesses the geometric-mean correlation among indicators across constructs (i.e. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. For concepts modeled as composites the HTMT may be computed by setting .only_common_factors = FALSE, however, it is unclear how to interpret values in this case. In some cases, the original correlation estimate may already be greater than the cutoff, making it redundant to fit a "restricted" model. Therefore, this article presents the results of discriminant validity assessment using these methods. In quantitative research, you have to consider the reliability and validity of your methods and measurements.. Validity tells you how accurately a method measures something. conclude that HTMT is a reliable tool for assessing discriminant validity, whereas the Fornell–Larcker criterion has limitations that do not justify its reputation for rigor and its widespread use in empirical research. Mendeley users who have this article in their library. We use cookies to help provide and enhance our service and tailor content. 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. The four types of validity. To establish discriminant validity, you need to show that measures that should not be related are in reality not related. If the HTMT is clearly smaller than one, discriminant validity can be regarded as established. Fronell-Larcker Criterion First, using the HTMT as a criterion scores. To assess convergent validity, the instrument was correlated with two comparators. When running the PLS and PLSc algorithm in SmartPLS, the results report includes discriminant validity assessment outcomes, in the section “Quality Criteria”. The HTMT index is calculated from the data covariance matrix and does not require a model to be estimated. Henseler J, Ringle CM, Sarstedt M (2015). 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. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. 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