Bridging Design and Behavioral Research with
Composite-Based Structural Equation Modeling

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Assessing the discriminant validity of latent variables by means of the heterotrait-monotrait ratio of correlations (HTMT)

Behavioral scientists have an interest in the discriminant validity of their latent variables. Discriminant validity means that two latent variables that represent different theoretical concepts are statistically different. A frequently applied approach for assessing discriminant validity is the Fornell-Larcker criterion (Fornell & Larcker, 1981). However, if used in combination with results of varianced-based structural equation modeling such as traditional partial least squares path modeling and generalized structured component analysis, the Fornell-Larcker criterion lacks sensitivity (Rönkkö & Evermann, 2013), and if used in combination with consistent estimates, it lacks specificity (Voorhees, Brady, Calantone & Ramirez, 2016).

A novel approach for assessing discriminant validity was introduced by Henseler, Ringle and Sarstedt (2015): the heterotrait-monotrait ratio of correlations (HTMT). The HTMT is a measure of similarity between latent variables. If the HTMT is clearly smaller than one, discriminant validity can be regarded as established. In many practical situations, a threshold of 0.85 reliably distinguishes between those pairs of latent variables that are discriminant valid and those that are not. Monte Carlo simulations provide evidence for the HTMT's favorable classification performance (Franke & Sarstedt, 2019; Voorhees, Brady, Calantone & Ramirez, 2016). Moreover, the HTMT is relatively easy to compute; it only requires the correlations of the observed variables as input. No exploratory or confirmatory factor analysis is needed. It certainly does not come as a surprise that the paper introducing the HTMT has become one of the most impactful recent marketing papers (see Shugan’s Top 20 Marketing Meta-Journal).

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