HTMT2 - An improved criterion for assessing discriminant validity in structural equation modeling

Ellen Roemer*, Florian Schuberth, Jörg Henseler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

132 Citations (Scopus)
294 Downloads (Pure)


Purpose: One popular method to assess discriminant validity in structural equation modeling is the heterotrait-monotrait ratio of correlations (HTMT). However, the HTMT assumes tau-equivalent measurement models, which are unlikely to hold for most empirical studies. To relax this assumption, the authors modify the original HTMT and introduce a new consistent measure for congeneric measurement models: the HTMT2. Design/methodology/approach: The HTMT2 is designed in analogy to the HTMT but relies on the geometric mean instead of the arithmetic mean. A Monte Carlo simulation compares the performance of the HTMT and the HTMT2. In the simulation, several design factors are varied such as loading patterns, sample sizes and inter-construct correlations in order to compare the estimation bias of the two criteria. Findings: The HTMT2 provides less biased estimations of the correlations among the latent variables compared to the HTMT, in particular if indicators loading patterns are heterogeneous. Consequently, the HTMT2 should be preferred over the HTMT to assess discriminant validity in case of congeneric measurement models. Research limitations/implications: However, the HTMT2 can only be determined if all correlations between involved observable variables are positive. Originality/value: This paper introduces the HTMT2 as an improved version of the traditional HTMT. Compared to other approaches assessing discriminant validity, the HTMT2 provides two advantages: (1) the ease of its computation, since HTMT2 is only based on the indicator correlations, and (2) the relaxed assumption of tau-equivalence. The authors highly recommend the HTMT2 criterion over the traditional HTMT for assessing discriminant validity in empirical studies.

Original languageEnglish
Pages (from-to)2637-2650
Number of pages14
JournalIndustrial management & data systems
Issue number12
Early online date3 Sept 2021
Publication statusPublished - 10 Nov 2021


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