Results

Structural Equation Modelling

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
	FG ~~ 0*FF1
	FG ~~ 0*FF2
	FF1 ~~ 0*FF2
	FF1 ~~ 0*FF2
[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	0.111	0.693	1.128	0.458	8.216	<.001
	y2	0.764	0.050	0.666	0.862	0.442	15.286	<.001
	y3	0.823	0.090	0.646	0.999	0.452	9.152	<.001
	y4	0.971	0.171	0.636	1.306	0.363	5.682	<.001
	y5	0.658	0.075	0.512	0.804	0.380	8.821	<.001
	y6	0.534	0.072	0.393	0.675	0.295	7.412	<.001
	y7	0.556	0.078	0.402	0.709	0.287	7.089	<.001
	y8	0.626	0.092	0.447	0.806	0.306	6.842	<.001
	y9	0.579	0.099	0.385	0.773	0.289	5.842	<.001
	y10	0.560	0.094	0.375	0.746	0.261	5.932	<.001
FF1	y1	0.754	0.156	0.448	1.059	0.379	4.831	<.001
	y2	0.237	0.081	0.078	0.396	0.137	2.922	0.003
	y3	0.496	0.123	0.255	0.737	0.273	4.032	<.001
	y4	2.282	0.213	1.864	2.699	0.854	10.715	<.001
	y5	-0.061	0.042	-0.143	0.022	-0.035	-1.439	0.150
	y6	-0.005	0.021	-0.046	0.037	-0.003	-0.219	0.827
	y7	0.017	0.029	-0.041	0.074	0.009	0.565	0.572
	y8	0.005	0.019	-0.032	0.042	0.002	0.247	0.805
	y9	-0.068	0.035	-0.137	0.001	-0.034	-1.921	0.055
	y10	0.021	0.031	-0.041	0.082	0.010	0.652	0.515
FF2	y1	-0.033	0.046	-0.124	0.058	-0.016	-0.704	0.482
	y2	0.022	0.057	-0.089	0.133	0.013	0.387	0.699
	y3	0.003	0.006	-0.009	0.015	0.002	0.468	0.640
	y4	0.010	0.008	-0.007	0.027	0.004	1.178	0.239
	y5	0.391	0.071	0.252	0.530	0.226	5.506	<.001
	y6	0.793	0.051	0.692	0.894	0.438	15.395	<.001
	y7	0.996	0.050	0.897	1.095	0.514	19.756	<.001
	y8	1.190	0.055	1.082	1.298	0.581	21.519	<.001
	y9	1.133	0.058	1.019	1.247	0.566	19.518	<.001
	y10	1.320	0.050	1.222	1.418	0.616	26.478	<.001

Variances and Covariances
				95% Confidence Intervals
Variable 1	Variable 2	Estimate	SE	Lower	Upper	β	z	p
FG	FF1	0.000	0.000	0.000	0.000	0.000
FG	FF2	0.000	0.000	0.000	0.000	0.000
FF1	FF2	0.000	0.000	0.000	0.000	0.000
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

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.