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.

```
calculateHTMT(
.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,
...
)
```

`.object` |
An R object of class cSEMResults resulting from a call to |

`.type_htmt` |
Character string indicating the type of HTMT that should be
calculated, i.e., the original HTMT (" |

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

`.alpha` |
A numeric value giving the significance level.
Defaults to |

`.ci` |
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 |

`.inference` |
Logical. Should critical values be computed? Defaults to |

`.only_common_factors` |
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 |

`.R` |
Integer. The number of bootstrap replications. Defaults to |

`.seed` |
Integer or |

`...` |
Ignored. |

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:

the values of the HTMT/HTMT2, i.e., a matrix with the HTMT/HTMT2 values at its lower triangular and if

`.inference = TRUE`

the upper triangular contains the upper limit of the 1-2*.alpha% bootstrap confidence interval if the HTMT/HTMT2 is positive and the lower limit if the HTMT/HTMT2 is negative.the lower and upper limits of the 1-2*.alpha% bootstrap confidence interval if

`.inference = TRUE`

; otherwise it is`NULL`

.the number of admissible bootstrap runs, i.e., the number of HTMT/HTMT2 values calculated during bootstrap if

`.inference = TRUE`

; otherwise it is`NULL`

. Note, the HTMT2 is based on the geometric and thus cannot always be calculated.

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.

[Package *cSEM* version 0.5.0 Index]