calculateHTMT {cSEM}R Documentation



Computes either the heterotrait-monotrait ratio of correlations (HTMT) based on Henseler et al. (2015) or the HTMT2 proposed by Roemer et al. (2021). While the HTMT is a consistent estimator for the construct correlation in case of tau-equivalent measurement models, the HTMT2 is a consistent estimator for congeneric measurement models. In general, they are used to assess discriminant validity.


 .object               = NULL,
 .type_htmt            = c('htmt','htmt2'),
 .absolute             = TRUE,
 .alpha                = 0.05,
 .ci                   = c("CI_percentile", "CI_standard_z", "CI_standard_t", 
                           "CI_basic", "CI_bc", "CI_bca", "CI_t_interval"),
 .inference            = FALSE,
 .only_common_factors  = TRUE,
 .R                    = 499,
 .seed                 = NULL,



An R object of class cSEMResults resulting from a call to csem().


Character string indicating the type of HTMT that should be calculated, i.e., the original HTMT ("htmt") or the HTMT2 ("htmt2"). Defaults to "htmt"


Logical. Should the absolute HTMT values be returned? Defaults to TRUE .


A numeric value giving the significance level. Defaults to 0.05.


A character strings naming the type of confidence interval to use to compute the 1-alpha% quantile of the bootstrap HTMT values. For possible choices see infer(). Ignored if .inference = FALSE. Defaults to "CI_percentile".


Logical. Should critical values be computed? Defaults to FALSE.


Logical. Should only concepts modeled as common factors be included when calculating one of the following quality criteria: AVE, the Fornell-Larcker criterion, HTMT, and all reliability estimates. Defaults to TRUE.


Integer. The number of bootstrap replications. Defaults to 499.


Integer or NULL. The random seed to use. Defaults to NULL in which case an arbitrary seed is chosen. Note that the scope of the seed is limited to the body of the function it is used in. Hence, the global seed will not be altered!




Computation of the HTMT/HTMT2 assumes that all intra-block and inter-block correlations between indicators are either all-positive or all-negative. A warning is given if this is not the case.

To obtain bootstrap confidence intervals for the HTMT/HTMT2 values, set .inference = TRUE. To choose the type of confidence interval, use .ci. To control the bootstrap process, arguments .R and .seed are available. Note, that .alpha is multiplied by two because typically researchers are interested in one-sided bootstrap confidence intervals for the HTMT/HTMT2.

Since the HTMT and the HTMT2 both assume a reflective measurement model only concepts modeled as common factors are considered by default. 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.


A named list containing:


Henseler J, Ringle CM, Sarstedt M (2015). “A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling.” Journal of the Academy of Marketing Science, 43(1), 115–135. doi:10.1007/s11747-014-0403-8.

Roemer E, Schuberth F, Henseler J (2021). “HTMT2 – an improved criterion for assessing discriminant validity in structural equation modeling.” Industrial Management & Data Systems, 121(12), 2637–2650.

See Also

assess(), csem, cSEMResults

[Package cSEM version 0.5.0 Index]