Data: threecommonfactors
A dataset containing 500 standardized observations on 9 indicator generated from a population model with three concepts modeled as common factors.
threecommonfactors
A matrix with 500 rows and 9 variables:
Indicators attachted to the first common factor (eta1).
Population loadings are: 0.7; 0.7; 0.7
Indicators attachted to the second common factor (eta2).
Population loadings are: 0.5; 0.7; 0.8
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.
#============================================================================ # 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
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