Results

Structural Equation Models

Models Info

Estimation Method	ML	.
Number of observations	3811
Free parameters	64
Converged	TRUE

Loglikelihood user model	-49680.515
Loglikelihood unrestricted model	-49211.655

Model	positive =~ item1 + item2 + item4 + item8
	negative =~ item3 + item5 + item6 + item7 + item9 + item10
	positive ~~ negative

[3]

Overall Tests

Model tests
Label	X²	df	p
User Model	937.7	84	0.00
Baseline Model	20856.4	90	0.00
Constraints Score Test	62.6	18	7.66e-7

Constraints score tests
Type	Par 1		Par 2	X²	df	p
Univariate	p2	==	p37	0.5938	1	0.44094
	p3	==	p38	0.3226	1	0.57005
	p4	==	p39	0.3272	1	0.56734
	p6	==	p41	1.9087	1	0.16711
	p7	==	p42	1.2189	1	0.26959
	p8	==	p43	2.9859	1	0.08399
	p9	==	p44	2.1295	1	0.14449
	p10	==	p45	0.0110	1	0.91650
	p24	==	p59	15.2960	1	9.19e-5
	p25	==	p60	0.2151	1	0.64276
	p26	==	p61	16.9627	1	3.81e-5
	p27	==	p62	0.2569	1	0.61229
	p28	==	p63	9.1453	1	0.00249
	p29	==	p64	0.4449	1	0.50477
	p30	==	p65	0.1010	1	0.75062
	p31	==	p66	21.4721	1	3.59e-6
	p32	==	p67	1.4328	1	0.23132
	p33	==	p68	4.3482	1	0.03705

Fit indices
				RMSEA 95% CI
CFI	TLI	SRMR	RMSEA	Lower	Upper	RMSEA p
0.959	0.956	0.034	0.073	0.069	0.077	0.00

Estimates

Parameters estimates
					95% Confidence Intervals
Group	Dep	Pred	Estimate	SE	Lower	Upper	β	z	p

[4]

Measurement model
					95% Confidence Intervals
Group	Latent	Observed	Estimate	SE	Lower	Upper	β	z	p
1	positive	item1	1.000	0.0000	1.000	1.000	0.833
		item2	0.858	0.0155	0.828	0.888	0.799	55.5	0.00
		item4	0.819	0.0145	0.790	0.847	0.814	56.6	0.00
		item8	0.971	0.0176	0.936	1.005	0.787	55.2	0.00
	negative	item3	1.000	0.0000	1.000	1.000	0.765
		item5	0.906	0.0226	0.861	0.950	0.666	40.1	0.00
		item6	0.849	0.0205	0.809	0.889	0.687	41.4	0.00
		item7	1.068	0.0213	1.026	1.110	0.811	50.2	0.00
		item9	0.904	0.0206	0.864	0.945	0.728	43.9	0.00
		item10	0.895	0.0204	0.855	0.934	0.719	43.9	0.00
2	positive	item1	1.000	0.0000	1.000	1.000	0.824
		item2	0.858	0.0155	0.828	0.888	0.783	55.5	0.00
		item4	0.819	0.0145	0.790	0.847	0.790	56.6	0.00
		item8	0.971	0.0176	0.936	1.005	0.793	55.2	0.00
	negative	item3	1.000	0.0000	1.000	1.000	0.749
		item5	0.906	0.0226	0.861	0.950	0.644	40.1	0.00
		item6	0.849	0.0205	0.809	0.889	0.662	41.4	0.00
		item7	1.068	0.0213	1.026	1.110	0.801	50.2	0.00
		item9	0.904	0.0206	0.864	0.945	0.693	43.9	0.00
		item10	0.895	0.0204	0.855	0.934	0.707	43.9	0.00

Variances and Covariances
					95% Confidence Intervals
Group	Variable 1	Variable 2	Estimate	SE	Lower	Upper	β	z	p
1	positive	negative	-0.752	0.0308	-0.813	-0.692	-0.817	-24.5	0.00
	item1	item1	0.458	0.0187	0.421	0.495	0.306	24.4	0.00
	item2	item2	0.432	0.0165	0.400	0.465	0.361	26.2	0.00
	item4	item4	0.353	0.0138	0.326	0.380	0.337	25.5	0.00
	item8	item8	0.602	0.0225	0.558	0.646	0.381	26.8	0.00
	item3	item3	0.579	0.0212	0.538	0.621	0.415	27.3	0.00
	item5	item5	0.840	0.0284	0.784	0.895	0.556	29.6	0.00
	item6	item6	0.658	0.0225	0.614	0.702	0.528	29.2	0.00
	item7	item7	0.483	0.0190	0.446	0.520	0.342	25.4	0.00
	item9	item9	0.593	0.0209	0.552	0.634	0.470	28.4	0.00
	item10	item10	0.609	0.0213	0.567	0.651	0.482	28.6	0.00
	positive	positive	1.037	0.0418	0.955	1.119	1.000	24.8	0.00
	negative	negative	0.817	0.0362	0.746	0.888	1.000	22.6	0.00
2	positive	negative	-0.753	0.0333	-0.818	-0.687	-0.843	-22.6	0.00
	item1	item1	0.477	0.0215	0.435	0.519	0.321	22.3	0.00
	item2	item2	0.468	0.0195	0.429	0.506	0.386	24.0	0.00
	item4	item4	0.407	0.0171	0.373	0.440	0.375	23.8	0.00
	item8	item8	0.562	0.0238	0.515	0.609	0.371	23.6	0.00
	item3	item3	0.618	0.0249	0.569	0.666	0.439	24.8	0.00
	item5	item5	0.914	0.0342	0.847	0.981	0.585	26.8	0.00
	item6	item6	0.732	0.0276	0.678	0.786	0.562	26.5	0.00
	item7	item7	0.503	0.0219	0.460	0.546	0.359	23.0	0.00
	item9	item9	0.699	0.0269	0.647	0.752	0.520	26.0	0.00
	item10	item10	0.632	0.0245	0.584	0.680	0.500	25.8	0.00
	positive	positive	1.009	0.0445	0.922	1.096	1.000	22.7	0.00
	negative	negative	0.790	0.0379	0.715	0.864	1.000	20.8	0.00

Intercepts
				95% Confidence Intervals
Group	Variable	Intercept	SE	Lower	Upper	z	p
1	item1	2.994	0.025	2.944	3.044	118.044	0.00
	item2	3.173	0.022	3.129	3.216	141.865	0.00
	item4	3.232	0.021	3.190	3.273	153.121	0.00
	item8	3.370	0.025	3.321	3.420	132.397	0.00
	item3	2.638	0.024	2.591	2.685	110.786	0.00
	item5	2.820	0.024	2.774	2.867	118.619	0.00
	item6	3.203	0.022	3.161	3.246	146.798	0.00
	item7	2.432	0.024	2.384	2.480	99.442	0.00
	item9	2.174	0.022	2.130	2.218	97.135	0.00
	item10	2.785	0.022	2.741	2.828	125.572	0.00
	positive	0.000	0.000	0.000	0.000
	negative	0.000	0.000	0.000	0.000
2	item1	2.994	0.025	2.944	3.044	118.044	0.00
	item2	3.173	0.022	3.129	3.216	141.865	0.00
	item4	3.232	0.021	3.190	3.273	153.121	0.00
	item8	3.370	0.025	3.321	3.420	132.397	0.00
	item3	2.638	0.024	2.591	2.685	110.786	0.00
	item5	2.820	0.024	2.774	2.867	118.619	0.00
	item6	3.203	0.022	3.161	3.246	146.798	0.00
	item7	2.432	0.024	2.384	2.480	99.442	0.00
	item9	2.174	0.022	2.130	2.218	97.135	0.00
	item10	2.785	0.022	2.741	2.828	125.572	0.00
	positive	-0.221	0.035	-0.290	-0.152	-6.266	3.70e-10
	negative	0.109	0.031	0.048	0.171	3.497	4.70e-4

Descriptives

Descriptives
		gender
N		3811
Missing		0
Mean		1.45
Median		1
Standard deviation		0.497
Minimum		1
Maximum		2

Frequencies

Frequencies of gender
Levels	Counts	% of Total	Cumulative %
1	2110	55.4 %	55.4 %
2	1701	44.6 %	100.0 %

Structural Equation Models

Models Info

Setup	.	Please define a model to begin

[3]

Overall Tests

Model tests
Label		X²		df		p

Fit indices
CFI		TLI		SRMR		RMSEA		Lower		Upper		RMSEA p

Estimates

Parameters estimates
Dep		Pred		Estimate		SE		Lower		Upper		β		z		p

[4]

Variances and Covariances
Variable 1		Variable 2		Estimate		SE		Lower		Upper		β		z		p

References

[1] The jamovi project (2022). jamovi. (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2021). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).

[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.