This function assesses discriminant validity through the
heterotrait-monotrait ratio (HTMT) of the correlations (Henseler, Ringlet &
Sarstedt, 2015). Specifically, it assesses the geometric-mean correlation
among indicators across constructs (i.e. heterotrait-heteromethod
correlations) relative to the geometric-mean correlation among indicators
within the same construct (i.e. monotrait-heteromethod correlations).
The resulting HTMT values are interpreted as estimates of inter-construct
correlations. Absolute values of the correlations are recommended to
calculate the HTMT matrix. Correlations are estimated using the
`lavCor`

function in the lavaan package.

```
htmt(model, data = NULL, sample.cov = NULL, missing = "listwise",
ordered = NULL, absolute = TRUE)
```

model

lavaan model.syntax of a confirmatory factor analysis model where at least two factors are required for indicators measuring the same construct.

data

A `data.frame`

or data `matrix`

sample.cov

A covariance or correlation matrix can be used, instead of
`data`

, to estimate the HTMT values.

missing

If "listwise", cases with missing values are removed listwise from the data frame. If "direct" or "ml" or "fiml" and the estimator is maximum likelihood, an EM algorithm is used to estimate the unrestricted covariance matrix (and mean vector). If "pairwise", pairwise deletion is used. If "default", the value is set depending on the estimator and the mimic option (see details in lavCor).

ordered

Character vector. Only used if object is a `data.frame`

.
Treat these variables as ordered (ordinal) variables. Importantly, all
other variables will be treated as numeric (unless `is.ordered`

in
`data`

). (see also lavCor)

absolute

logical. Whether HTMT values should be estimated based on
absolute correlations (recommended and default is `TRUE`

)

A matrix showing HTMT values (i.e., discriminant validity) between each pair of factors

Henseler, J., Ringle, C. M., & 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

Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016).
Discriminant validity testing in marketing: an analysis, causes for
concern, and proposed remedies.
*Journal of the Academy of Marketing Science, 44*(1), 119--134.
doi:10.1007/s11747-015-0455-4

# NOT RUN { HS.model <- ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' dat <- HolzingerSwineford1939[, paste0("x", 1:9)] htmt(HS.model, dat) # }