calculateRhoC( .object = NULL, .model_implied = TRUE, .only_common_factors = TRUE, .weighted = FALSE )
calculateRhoT( .object = NULL, .alpha = 0.05, .closed_form_ci = FALSE, .only_common_factors = TRUE, .output_type = c("vector", "data.frame"), .weighted = FALSE, ... )
Logical. Should weights be scaled using the model-implied indicator correlation matrix? Defaults to
Logical. Should only concepts modeled as common factors be included when calculating one of the following quality critera: AVE, the Fornell-Larcker criterion, HTMT, and all reliability estimates. Defaults to
Logical. Should estimation be based on a score that uses the weights of the weight approach used to obtain
.object?. Defaults to
An integer or a numeric vector of significance levels. Defaults to
Logical. Should a closed-form confidence interval be computed? Defaults to
Character string. The type of output. One of "vector" or "data.frame". Defaults to "vector".
Since reliability is defined with respect to a classical true score measurement
model only concepts modeled as common factors are considered by default.
For concepts modeled as composites reliability may be estimated by setting
.only_common_factors = FALSE, however, it is unclear how to
interpret reliability in this case.
Reliability is traditionally based on a test score (proxy) based on unit weights.
To compute congeneric and tau-equivalent reliability based on a score that
uses the weights of the weight approach used to obtain
.weighted = TRUE
For the tau-equivalent reliability ("
rho_T" or "
cronbachs_alpha") a closed-form
confidence interval may be computed Trinchera2018cSEM by setting
.closed_form_ci = TRUE (default is
.alpha is a vector
several CI's are returned.
.output_type = "vector")
a named numeric vector containing the reliability estimates.
.output_type = "data.frame"
calculateRhoT() returns a
data.frame with as many rows as there are
constructs modeled as common factors in the model (unless
.only_common_factors = FALSE in which case the number of rows equals the
total number of constructs in the model). The first column contains the name of the construct.
The second column the reliability estimate.
.closed_form_ci = TRUE the remaining columns contain lower and upper bounds
for the (1 -
.alpha) confidence interval(s).
calculateRhoC: Calculate the congeneric reliability
calculateRhoT: Calculate the tau-equivalent reliability