LeDang2022

A data frame of 107 variables and 773 participants. Labelled
factors were converted from numerical to character representation through
<code><a href="https://forcats.tidyverse.org/reference/as_factor.html">haven::as_factor()</a></code>. The provided data frame is a fusion of the converted
labelled factors for the demographic data and the unconverted Likert scale
data for the psychometric data.

datasets

Estimate, assess, test, and study linear, nonlinear, hierarchical
and multigroup structural equation models using composite-based approaches
and procedures, including estimation techniques such as partial least squares
path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc),
generalized structured component analysis (GSCA), generalized structured
component analysis with uniqueness terms (GSCAm), generalized canonical
correlation analysis (GCCA), principal component analysis (PCA),
factor score regression (FSR) using sum score, regression or
Bartlett scores (including bias correction using Croon’s approach),
as well as several tests and typical postestimation procedures
(e.g., verify admissibility of the estimates, assess the model fit,
test the model fit etc.).

Florian Schuberth

cSEM

Composite-Based Structural Equation Modeling

LeDang2022 function

An object of class <code>data.frame</code> with 773 rows and 107 columns.

Format

A data frame of 107 variables and 773 participants. Labelled
factors were converted from numerical to character representation through
<code><a href='https://forcats.tidyverse.org/reference/as_factor.html'>haven::as_factor()</a></code>. The provided data frame is a fusion of the converted
labelled factors for the demographic data and the unconverted Likert scale
data for the psychometric data.

LeDang2022: Data: LeDang2022

Description

Usage

Arguments

Format

Details

References