<p>A data frame containing 23 variables with 411 observations. The original
indicators were measured on a 6-point scale. In this version of the dataset,
the indicators are scaled to be between 0 and 100.</p>

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

Manuel E. Rademaker

Tamara Schamberger

Michael Klesel

Theo K. Dijkstra

Jörg Henseler

SQ function

<p>An object of class <code>data.frame</code> with 411 rows and 23 columns.</p>

Format

Data: SQ — SQ

Data: SQ

SQ: Data: SQ

Description

Usage

Arguments

Format

Details

References