Results

Structural Equation Models

Models Info

Estimation Method	ML	.
Optimization Method	NLMINB
Number of observations	5000
Free parameters	47
Standard errors	Standard
Scaled test	None
Converged	TRUE
Iterations	1
Iterations	1
Model	efa("efa1")FG+efa("efa1")FF1+efa("efa1")*FF2=~y1+y2+y3+y4+y5+y6+y7+y8+y9+y10
Model
Note. lavaan->lav_model_vcov(): The variance-covariance matrix of the estimated parameters (vcov) does not appear to be positive definite! The smallest eigenvalue (= 4.284338e-14) is close to zero. This may be a symptom that the model is not identified.
[3] [4]

Overall Tests

Model tests
Label	X²	df	p
User Model	24.364	18	0.143
Baseline Model	8960.687	45	<.001

Fit indices
		95% Confidence Intervals
SRMR	RMSEA	Lower	Upper	RMSEA p
0.006	0.008	0.000	0.016	1.000

User model versus baseline model
	Model
Comparative Fit Index (CFI)	0.999
Tucker-Lewis Index (TLI)	0.998
Bentler-Bonett Non-normed Fit Index (NNFI)	0.998
Relative Noncentrality Index (RNI)	0.999
Bentler-Bonett Normed Fit Index (NFI)	0.997
Bollen's Relative Fit Index (RFI)	0.993
Bollen's Incremental Fit Index (IFI)	0.999
Parsimony Normed Fit Index (PNFI)	0.399

Estimates

Measurement model
				95% Confidence Intervals
Latent	Observed	Estimate	SE	Lower	Upper	β	z	p
FG	y1	0.911	0.066	0.782	1.039	0.458	13.889	<.001
	y2	0.764	0.046	0.674	0.855	0.442	16.547	<.001
	y3	0.823	0.059	0.706	0.939	0.452	13.846	<.001
	y4	0.971	0.023	0.927	1.015	0.363	43.088	<.001
	y5	0.658	0.054	0.551	0.764	0.380	12.133	<.001
	y6	0.534	0.036	0.462	0.605	0.295	14.707	<.001
	y7	0.556	0.035	0.488	0.623	0.287	16.078	<.001
	y8	0.626	0.033	0.561	0.691	0.306	18.801	<.001
	y9	0.579	0.036	0.508	0.650	0.289	16.023	<.001
	y10	0.560	0.033	0.496	0.625	0.261	16.991	<.001
FF1	y1	0.754	0.121	0.517	0.990	0.379	6.249	<.001
	y2	0.237	0.050	0.139	0.335	0.137	4.739	<.001
	y3	0.496	0.088	0.324	0.669	0.273	5.634	<.001
	y4	2.282	0.229	1.832	2.731	0.854	9.943	<.001
	y5	-0.061	0.033	-0.125	0.004	-0.035	-1.850	0.064
	y6	-0.005	0.022	-0.047	0.038	-0.003	-0.213	0.831
	y7	0.017	0.029	-0.041	0.074	0.009	0.568	0.570
	y8	0.005	0.019	-0.032	0.042	0.002	0.246	0.806
	y9	-0.068	0.035	-0.137	0.001	-0.034	-1.920	0.055
	y10	0.021	0.033	-0.044	0.085	0.010	0.621	0.535
FF2	y1	-0.033	0.046	-0.123	0.058	-0.016	-0.706	0.480
	y2	0.022	0.056	-0.088	0.132	0.013	0.389	0.697
	y3	0.003	0.006	-0.009	0.015	0.002	0.472	0.637
	y4	0.010	0.008	-0.006	0.025	0.004	1.262	0.207
	y5	0.391	0.070	0.253	0.529	0.226	5.544	<.001
	y6	0.793	0.051	0.692	0.893	0.438	15.503	<.001
	y7	0.996	0.050	0.898	1.094	0.514	19.889	<.001
	y8	1.190	0.055	1.082	1.298	0.581	21.680	<.001
	y9	1.133	0.058	1.020	1.246	0.566	19.659	<.001
	y10	1.320	0.050	1.223	1.417	0.616	26.640	<.001

Variances and Covariances
				95% Confidence Intervals
Variable 1	Variable 2	Estimate	SE	Lower	Upper	β	z	p
y1	y1	2.552	0.087	2.382	2.723	0.646	29.381	<.001
y2	y2	2.343	0.069	2.207	2.479	0.785	33.760	<.001
y3	y3	2.384	0.066	2.254	2.513	0.721	35.957	<.001
y4	y4	0.985	1.111	-1.193	3.162	0.138	0.886	0.375
y5	y5	2.412	0.069	2.277	2.546	0.804	35.169	<.001
y6	y6	2.366	0.053	2.261	2.470	0.722	44.344	<.001
y7	y7	2.453	0.059	2.338	2.568	0.653	41.885	<.001
y8	y8	2.382	0.063	2.259	2.505	0.569	37.912	<.001
y9	y9	2.378	0.061	2.259	2.498	0.594	39.061	<.001
y10	y10	2.542	0.073	2.399	2.685	0.553	34.854	<.001
FG	FG	1.000	0.000	1.000	1.000	1.000
FF1	FF1	1.000	0.000	1.000	1.000	1.000
FF2	FF2	1.000	0.000	1.000	1.000	1.000
FG	FF1	-0.000	0.028	-0.055	0.055	-0.000	-0.000	1.000
FG	FF2	-0.000	0.064	-0.126	0.126	-0.000	-0.000	1.000
FF1	FF2	-0.000	0.035	-0.069	0.069	-0.000	-0.000	1.000

Intercepts
			95% Confidence Intervals
Variable	Intercept	SE	Lower	Upper	z	p
y1	-0.069	0.028	-0.124	-0.014	-2.463	0.014
y2	-0.033	0.024	-0.081	0.015	-1.341	0.180
y3	-0.055	0.026	-0.105	-0.004	-2.126	0.034
y4	-0.058	0.038	-0.132	0.016	-1.548	0.122
y5	-0.040	0.024	-0.088	0.008	-1.634	0.102
y6	-0.018	0.026	-0.068	0.033	-0.690	0.490
y7	-0.005	0.027	-0.058	0.049	-0.170	0.865
y8	0.004	0.029	-0.053	0.060	0.130	0.897
y9	0.015	0.028	-0.040	0.071	0.542	0.588
y10	0.016	0.030	-0.043	0.076	0.544	0.586
FG	0.000	0.000	0.000	0.000
FF1	0.000	0.000	0.000	0.000
FF2	0.000	0.000	0.000	0.000

Path Model

Path diagrams

[5]

References

[1] The jamovi project (2025). jamovi. (Version 2.7) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2025). R: A Language and environment for statistical computing. (Version 4.5) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN snapshot 2025-05-25).

[3] Gallucci, M., Jentschke, S. (2021). SEMLj: jamovi SEM Analysis. [jamovi module]. For help please visit https://semlj.github.io/.

[4] Rosseel, Y. (2019). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. link.

[5] Epskamp S. , Stuber S., Nak J., Veenman M,, Jorgensen T.D. (2019). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output. [R Package]. Retrieved from https://CRAN.R-project.org/package=semPlot.