A dataset containing 500 standardized observations on 19 indicator generated from a
population model with 6 concepts, three of which (`c1-c3`

) are composites
forming a second order common factor (`c4`

). The remaining two (`eta1`

, `eta2`

)
are concepts modeled as common factors .

`dgp_2ndorder_cf_of_c`

A matrix with 500 rows and 19 variables:

- y11-y12
Indicators attached to

`c1`

. Population weights are: 0.8; 0.4. Population loadings are: 0.925; 0.65- y21-y24
Indicators attached to

`c2`

. Population weights are: 0.5; 0.3; 0.2; 0.4. Population loadings are: 0.804; 0.68; 0.554; 0.708- y31-y38
Indicators attached to

`c3`

. 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- y41-y43
Indicators attached to

`eta1`

. Population loadings are: 0.8; 0.7; 0.7- y51-y53
Indicators attached to

`eta1`

. Population loadings are: 0.8; 0.8; 0.7

The model is: $$`c4` = gamma1 * `eta1` + zeta1$$ $$`eta2` = gamma2 * `eta1` + beta * `c4` + zeta2$$

with population values `gamma1`

= 0.6, `gamma2`

= 0.4 and `beta`

= 0.35.
The second order common factor is
$$`c4` = lambdac1 * `c1` + lambdac2 * `c2` + lambdac3 * `c3` + epsilon$$