dgp_2ndorder_cf_of_c

A dataset containing 500 standardized observations on 19 indicator generated from a
population model with 6 concepts, three of which (<code>c1-c3</code>) are composites
forming a second order common factor (<code>c4</code>). The remaining two (<code>eta1</code>, <code>eta2</code>)
are concepts modeled as common factors .

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

dgp_2ndorder_cf_of_c function

A matrix with 500 rows and 19 variables:<dl>
<dt>y11-y12</dt>
<dd>Indicators attached to <code>c1</code>.
Population weights are: 0.8; 0.4.
Population loadings are: 0.925; 0.65</dd><dt>y21-y24</dt>
<dd>Indicators attached to <code>c2</code>.
Population weights are: 0.5; 0.3; 0.2; 0.4.
Population loadings are: 0.804; 0.68; 0.554; 0.708</dd><dt>y31-y38</dt>
<dd>Indicators attached to <code>c3</code>.
Population weights are: 0.3; 0.3; 0.1; 0.1; 0.2; 0.3; 0.4; 0.2.
Population loadings are: 0.496; 0.61; 0.535; 0.391; 0.391; 0.6; 0.5285; 0.53</dd><dt>y41-y43</dt>
<dd>Indicators attached to <code>eta1</code>.
Population loadings are: 0.8; 0.7; 0.7</dd><dt>y51-y53</dt>
<dd>Indicators attached to <code>eta1</code>.
Population loadings are: 0.8; 0.8; 0.7</dd>
</dl>The model is:
$$`c4` = gamma1 * `eta1` + zeta1$$
$$`eta2` = gamma2 * `eta1` + beta * `c4` + zeta2$$
with population values <code>gamma1</code> = 0.6, <code>gamma2</code> = 0.4 and <code>beta</code> = 0.35.
The second order common factor is
$$`c4` = lambdac1 * `c1` + lambdac2 * `c2` + lambdac3 * `c3` + epsilon$$

Format

Data: Second order common factor of composites — dgp_2ndorder_cf_of_c

A matrix with 500 rows and 19 variables:<dl>
<dt>y11-y12</dt>
<dd>Indicators attached to <code>c1</code>.
Population weights are: 0.8; 0.4.
Population loadings are: 0.925; 0.65</dd>

<dt>y21-y24</dt>
<dd>Indicators attached to <code>c2</code>.
Population weights are: 0.5; 0.3; 0.2; 0.4.
Population loadings are: 0.804; 0.68; 0.554; 0.708</dd>

<dt>y31-y38</dt>
<dd>Indicators attached to <code>c3</code>.
Population weights are: 0.3; 0.3; 0.1; 0.1; 0.2; 0.3; 0.4; 0.2.
Population loadings are: 0.496; 0.61; 0.535; 0.391; 0.391; 0.6; 0.5285; 0.53</dd>

<dt>y41-y43</dt>
<dd>Indicators attached to <code>eta1</code>.
Population loadings are: 0.8; 0.7; 0.7</dd>

<dt>y51-y53</dt>
<dd>Indicators attached to <code>eta1</code>.
Population loadings are: 0.8; 0.8; 0.7</dd>


</dl>

The model is:
$$`c4` = gamma1 * `eta1` + zeta1$$
$$`eta2` = gamma2 * `eta1` + beta * `c4` + zeta2$$
with population values <code>gamma1</code> = 0.6, <code>gamma2</code> = 0.4 and <code>beta</code> = 0.35.
The second order common factor is
$$`c4` = lambdac1 * `c1` + lambdac2 * `c2` + lambdac3 * `c3` + epsilon$$

dgp_2ndorder_cf_of_c: Data: Second order common factor of composites

Description

Usage

Arguments

Format