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. 43 [1]: 115-135 January 2015) is the hottest paper in the field of Economics & Business for the period ending October 31, 2016. Our latest report of New Hot Papers in Essential Science Indicators shows that the paper âA new criterion for assessing discriminant validity in variance-based structural equation modeling,â (J. Acad. Discriminant Validity Assessment Abstract. Sci. 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. Revised on June 19, 2020. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. Published on September 6, 2019 by Fiona Middleton. However, a new method has emerged for establishing the discriminant validity assessment through heterotrait-monotrait (HTMT) ratio of correlations method. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). If research reveals that a testâs validity coef- Assessment of discriminant validity is a must in any research that involves latent variables for the prevention of multicollinearity issues. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. Those correlations, sometimes called . The discriminant validity assessment has the goal to ensure that a reflective construct has the strongest relationships with its own indicators (e.g., in comparison with than any other construct) in the PLS path model (Hair et al., 2017). 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. Abstract. However, two conclusions that are new to discriminant validity literature can be drawn: First, the lack of cross-loadings in the population (i.e., factorial validity) is not a strict prerequisite for discriminant validity assessment as long as the cross-loadings are modeled appropriately. (2015) 43:115â135 DOI 10.1007/s11747-014-0403-8 METHODOLOGICAL PAPER A new criterion ⦠A new criterion for assessing discriminant validity in variance-based structural equation modeling A new criterion for assessing discriminant validity in variance-based structural equation modeling Henseler, Jörg; Ringle, Christian; Sarstedt, Marko 2014-08-22 00:00:00 J. of the Acad. Estimating and Evaluating Convergent and Discriminant Validity Evidence 257 correlated with those crucial variables, test developers and test users gain increased confidence in the test. Henseler, J., Ringle, C. M., & 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. Sci. validity coefficients, are fundamental for establishing validity. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Mark. 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. Abstract. References. The four types of validity. Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. Fornell and Larcker criterion is the most widely used method for this purpose. Mark.