cSEM (version 0.3.1)

threecommonfactors: Data: threecommonfactors

Description

A dataset containing 500 standardized observations on 9 indicator generated from a population model with three concepts modeled as common factors.

Usage

threecommonfactors

Format

A matrix with 500 rows and 9 variables:

y11-y13

Indicators attachted to the first common factor (eta1). Population loadings are: 0.7; 0.7; 0.7

y21-y23

Indicators attachted to the second common factor (eta2). Population loadings are: 0.5; 0.7; 0.8

y31-y33

Indicators attachted to the third common factor (eta3). Population loadings are: 0.8; 0.75; 0.7

The model is: $$`eta2` = gamma1 * `eta1` + zeta1$$ $$`eta3` = gamma2 * `eta1` + beta * `eta2` + zeta2$$

with population values gamma1 = 0.6, gamma2 = 0.4 and beta = 0.35.

Examples

# NOT RUN {
#============================================================================
# Correct model (the model used to generate the data)
#============================================================================
model_correct <- "
# Structural model
eta2 ~ eta1
eta3 ~ eta1 + eta2

# Measurement model
eta1 =~ y11 + y12 + y13
eta2 =~ y21 + y22 + y23
eta3 =~ y31 + y32 + y33 
"

a <- csem(threecommonfactors, model_correct)

## The overall model fit is evidently almost perfect:
testOMF(a, .R = 30, .verbose = FALSE) # .R = 30 to speed up the example
# }