Ergebnisse

Structural Equation Models

Models Info

Estimation Method	DWLS	.
Optimization Method	NLMINB
Number of observations	298
Free parameters	69
Standard errors	Standard
Scaled test	None
Converged	TRUE
Iterations	49

Model	Provokation=~GruppeA_B
	Glaubwuerdigkeit=~Exp_1+Exp_2+Exp_3+Vert_1+Vert_2+Vert_3
	Kaufabsicht=~Kauf_1+Kauf_2+Kauf_3
	EinstellungWerbung=~EW_1+EW_2+EW_3+EW_4+EW_5
	EinstellungMarke=~EM_1+EM_2+EM_3+EM_4+EM_5
	Glaubwuerdigkeit ~ GruppeA_B
	Kaufabsicht ~ Glaubwuerdigkeit + GruppeA_B
	EinstellungWerbung ~ Glaubwuerdigkeit + GruppeA_B
	EinstellungMarke ~ Glaubwuerdigkeit + GruppeA_B


Anmerkung. Variable (GruppeA_B) has been coerced to ordered type.
Anmerkung. lavaan WARNING: Could not compute standard errors! The information matrix could not be inverted. This may be a symptom that the model is not identified.
Anmerkung. lavaan WARNING: some estimated lv variances are negative

[3] [4]

Overall Tests

Model tests
Label	X²	df	p
User Model	77.5	160	1.000
Baseline Model	7290.2	190	< .001

Fit indices
		95% Confidence Intervals
SRMR	RMSEA	Lower	Upper	RMSEA p
0.037	0.000	0.000	0.000	1.000

User model versus baseline model
		Model
Comparative Fit Index (CFI)		1.000
Tucker-Lewis Index (TLI)		1.014
Bentler-Bonett Non-normed Fit Index (NNFI)		1.014
Relative Noncentrality Index (RNI)		1.012
Bentler-Bonett Normed Fit Index (NFI)		0.989
Bollen's Relative Fit Index (RFI)		0.987
Bollen's Incremental Fit Index (IFI)		1.012
Parsimony Normed Fit Index (PNFI)		0.833

Additional fit indices
		Model
Hoelter Critical N (CN), α=0.05		730.944
Hoelter Critical N (CN), α=0.01		784.636
Goodness of Fit Index (GFI)		0.999
Adjusted Goodness of Fit Index (AGFI)		0.999
Parsimony Goodness of Fit Index (PGFI)		0.698
McDonald Fit Index (MFI)		1.149
Expected Cross-Validation Index (ECVI)		.
Loglikelihood user model (H0)		.
Loglikelihood unrestricted model (H1)		.
Akaike (AIC)		.
Bayesian (BIC)		.
Sample-size adjusted Bayesian (SABIC)		.

Estimates

Parameters estimates
				95% Confidence Intervals			β 95% Confidence Intervals
Dep	Pred	Estimate	SE	Lower	Upper	β	Lower	Upper	z	p
Glaubwuerdigkeit	GruppeA_B	-0.08709				-0.18315
Kaufabsicht	Glaubwuerdigkeit	0.98115				0.54236
Kaufabsicht	GruppeA_B	0.09558				0.11110
EinstellungWerbung	Glaubwuerdigkeit	0.82657				0.40749
EinstellungWerbung	GruppeA_B	-0.06497				-0.06735
EinstellungMarke	Glaubwuerdigkeit	0.90851				0.49983
EinstellungMarke	GruppeA_B	-0.00162				-0.00187
Anmerkung. lavaan WARNING: Could not compute standard errors! The information matrix could not be inverted. This may be a symptom that the model is not identified.

Measurement model
				95% Confidence Intervals			β 95% Confidence Intervals
Latent	Observed	Estimate	SE	Lower	Upper	β	Lower	Upper	z	p
Provokation	GruppeA_B	1.000	0.00	1.00	1.00
Glaubwuerdigkeit	Exp_1	1.000	0.00	1.00	1.00	0.532
	Exp_2	1.199				0.629
	Exp_3	1.198				0.642
	Vert_1	1.410				0.710
	Vert_2	1.243				0.678
	Vert_3	1.360				0.758
Kaufabsicht	Kauf_1	1.000	0.00	1.00	1.00	0.862
	Kauf_2	1.237				0.942
	Kauf_3	1.170				0.879
EinstellungWerbung	EW_1	1.000	0.00	1.00	1.00	0.859
	EW_2	0.867				0.819
	EW_3	0.863				0.800
	EW_4	0.814				0.772
	EW_5	0.883				0.788
EinstellungMarke	EM_1	1.000	0.00	1.00	1.00	0.841
	EM_2	0.923				0.828
	EM_3	0.920				0.820
	EM_4	0.880				0.808
	EM_5	0.905				0.835

Variances and Covariances
				95% Confidence Intervals			β 95% Confidence Intervals
Variable 1	Variable 2	Estimate	SE	Lower	Upper	β	Lower	Upper	z	p
GruppeA_B	GruppeA_B	1.0532	0.00	1.05	1.05	1.053
Exp_1	Exp_1	0.5726				0.717
Exp_2	Exp_2	0.4961				0.604
Exp_3	Exp_3	0.4638				0.588
Vert_1	Vert_1	0.4431				0.496
Vert_2	Vert_2	0.4111				0.540
Vert_3	Vert_3	0.3088				0.425
Kauf_1	Kauf_1	0.2563				0.257
Kauf_2	Kauf_2	0.1435				0.113
Kauf_3	Kauf_3	0.2975				0.227
EW_1	EW_1	0.3308				0.262
EW_2	EW_2	0.3442				0.330
EW_3	EW_3	0.3903				0.360
EW_4	EW_4	0.4185				0.404
EW_5	EW_5	0.4426				0.379
EM_1	EM_1	0.3084				0.292
EM_2	EM_2	0.2930				0.315
EM_3	EM_3	0.3092				0.328
EM_4	EM_4	0.3079				0.347
EM_5	EM_5	0.2655				0.303
Provokation	Provokation	-0.0532
Glaubwuerdigkeit	Glaubwuerdigkeit	0.2185				0.966
Kaufabsicht	Kaufabsicht	0.5295				0.716
EinstellungWerbung	EinstellungWerbung	0.7623				0.819
EinstellungMarke	EinstellungMarke	0.5602				0.750
Kaufabsicht	EinstellungWerbung	0.3053				0.481
Kaufabsicht	EinstellungMarke	0.2565				0.471
EinstellungWerbung	EinstellungMarke	0.5660				0.866

Intercepts
			95% Confidence Intervals
Variable	Intercept	SE	Lower	Upper	z	p
GruppeA_B	0.000	0.000	0.000	0.000
Exp_1	3.376
Exp_2	3.507
Exp_3	3.339
Vert_1	3.349
Vert_2	3.493
Vert_3	3.520
Kauf_1	3.084
Kauf_2	2.993
Kauf_3	3.037
EW_1	3.443
EW_2	3.745
EW_3	3.503
EW_4	3.530
EW_5	3.648
EM_1	3.483
EM_2	3.574
EM_3	3.510
EM_4	3.470
EM_5	3.711
Provokation	0.000	0.000	0.000	0.000
Glaubwuerdigkeit	0.000	0.000	0.000	0.000
Kaufabsicht	0.000	0.000	0.000	0.000
EinstellungWerbung	0.000	0.000	0.000	0.000
EinstellungMarke	0.000	0.000	0.000	0.000

Thresholds
				95% Confidence Intervals
Variable	Step	Thresholds	SE	Lower	Upper	z	p
GruppeA_B	t1	-0.008

Additional outputs

Mardia's coefficients
	Coefficient	z	χ²	df	p
Skewness	72.8		3613	1330	< .001
Kurtosis	515.3	35.5			< .001

[5]

Covariances and correlations

Observed covariances (lower triangle) and correlations (upper triangle)
	GruppeA_B	Exp_1	Exp_2	Exp_3	Vert_1	Vert_2	Vert_3	Kauf_1	Kauf_2	Kauf_3	EW_1	EW_2	EW_3	EW_4	EW_5	EM_1	EM_2	EM_3	EM_4	EM_5
GruppeA_B	1.00000	-0.117	-0.0841	-0.0554	-0.164	-0.146	-0.119	0.0119	0.00750	0.0107	-0.138	-0.129	-0.0122	-0.112	-0.149	-0.135	-0.0881	0.00425	-0.0578	-0.0875
Exp_1	-0.10456	0.798	0.4530	0.4656	0.314	0.391	0.382	0.2318	0.20871	0.2194	0.202	0.127	0.1755	0.180	0.144	0.245	0.2366	0.18146	0.1890	0.2099
Exp_2	-0.07618	0.367	0.8204	0.5127	0.433	0.402	0.432	0.2722	0.28548	0.3444	0.284	0.194	0.2350	0.149	0.220	0.263	0.2089	0.20614	0.1812	0.3070
Exp_3	-0.04913	0.369	0.4122	0.7878	0.427	0.434	0.459	0.2747	0.31364	0.2750	0.193	0.192	0.2150	0.188	0.198	0.233	0.2590	0.23960	0.2993	0.2591
Vert_1	-0.15484	0.265	0.3701	0.3582	0.892	0.374	0.592	0.3713	0.36727	0.3575	0.332	0.211	0.2311	0.213	0.193	0.310	0.3404	0.33684	0.3062	0.3417
Vert_2	-0.12737	0.305	0.3172	0.3362	0.308	0.760	0.513	0.2918	0.34083	0.3247	0.325	0.303	0.2405	0.216	0.306	0.288	0.3340	0.22227	0.2246	0.3224
Vert_3	-0.10153	0.291	0.3338	0.3472	0.476	0.381	0.726	0.3511	0.37679	0.3484	0.296	0.245	0.2801	0.174	0.250	0.330	0.3393	0.30420	0.2893	0.3985
Kauf_1	0.01191	0.207	0.2461	0.2434	0.350	0.254	0.299	0.9963	0.79244	0.7755	0.506	0.403	0.4053	0.383	0.360	0.442	0.3859	0.45482	0.4436	0.4137
Kauf_2	0.00847	0.211	0.2920	0.3144	0.392	0.336	0.363	0.8932	1.27512	0.8334	0.532	0.461	0.4199	0.430	0.378	0.512	0.4413	0.48098	0.4984	0.5092
Kauf_3	0.01231	0.224	0.3571	0.2794	0.386	0.324	0.340	0.8862	1.07743	1.3107	0.504	0.444	0.3986	0.372	0.341	0.481	0.3852	0.43020	0.4073	0.4514
EW_1	-0.15493	0.203	0.2890	0.1922	0.352	0.318	0.283	0.5669	0.67411	0.6481	1.260	0.696	0.6165	0.647	0.657	0.679	0.5869	0.57211	0.5650	0.5846
EW_2	-0.13193	0.116	0.1796	0.1737	0.203	0.270	0.213	0.4107	0.53185	0.5195	0.798	1.042	0.6011	0.676	0.718	0.575	0.6193	0.61243	0.6028	0.5830
EW_3	-0.01265	0.163	0.2215	0.1985	0.227	0.218	0.248	0.4209	0.49331	0.4747	0.720	0.638	1.0822	0.626	0.662	0.608	0.5988	0.61011	0.6082	0.5971
EW_4	-0.11365	0.163	0.1374	0.1693	0.204	0.191	0.150	0.3884	0.49349	0.4334	0.738	0.702	0.6627	1.034	0.634	0.590	0.5694	0.64420	0.6089	0.5167
EW_5	-0.16084	0.139	0.2155	0.1899	0.197	0.288	0.230	0.3886	0.46072	0.4224	0.797	0.793	0.7445	0.697	1.168	0.580	0.6032	0.57153	0.5980	0.5761
EM_1	-0.13876	0.224	0.2451	0.2121	0.301	0.258	0.289	0.4527	0.59385	0.5661	0.783	0.603	0.6494	0.616	0.643	1.055	0.6859	0.65851	0.6465	0.7138
EM_2	-0.08496	0.204	0.1824	0.2216	0.310	0.281	0.279	0.3713	0.48036	0.4251	0.635	0.609	0.6004	0.558	0.628	0.679	0.9291	0.71691	0.6827	0.6967
EM_3	0.00412	0.157	0.1811	0.2063	0.309	0.188	0.251	0.4404	0.52691	0.4778	0.623	0.607	0.6157	0.636	0.599	0.656	0.6704	0.94117	0.7148	0.6683
EM_4	-0.05447	0.159	0.1546	0.2502	0.272	0.184	0.232	0.4170	0.53000	0.4390	0.597	0.580	0.5957	0.583	0.608	0.625	0.6197	0.65299	0.8867	0.6449
EM_5	-0.08189	0.176	0.2603	0.2153	0.302	0.263	0.318	0.3866	0.53833	0.4838	0.614	0.557	0.5815	0.492	0.583	0.686	0.6287	0.60693	0.5685	0.8764

Fitted covariances (lower triangle) and correlations (upper triangle)
	GruppeA_B	Exp_1	Exp_2	Exp_3	Vert_1	Vert_2	Vert_3	Kauf_1	Kauf_2	Kauf_3	EW_1	EW_2	EW_3	EW_4	EW_5	EM_1	EM_2	EM_3	EM_4	EM_5
GruppeA_B	1.0000	-0.0974	-0.115	-0.118	-0.130	-0.124	-0.139	0.0101	0.0111	0.0103	-0.122	-0.116	-0.114	-0.110	-0.112	-0.0786	-0.0773	-0.0766	-0.0755	-0.0780
Exp_1	-0.0871	0.7987	0.335	0.341	0.378	0.361	0.404	0.2394	0.2617	0.2442	0.192	0.183	0.179	0.172	0.176	0.2239	0.2202	0.2181	0.2150	0.2222
Exp_2	-0.1044	0.2710	0.821	0.404	0.446	0.426	0.477	0.2830	0.3093	0.2887	0.227	0.216	0.211	0.204	0.208	0.2647	0.2604	0.2578	0.2542	0.2627
Exp_3	-0.1043	0.2709	0.325	0.788	0.455	0.435	0.487	0.2886	0.3155	0.2945	0.231	0.220	0.215	0.208	0.212	0.2700	0.2656	0.2630	0.2593	0.2680
Vert_1	-0.1228	0.3188	0.382	0.382	0.892	0.481	0.538	0.3192	0.3489	0.3257	0.256	0.244	0.238	0.230	0.235	0.2986	0.2937	0.2909	0.2868	0.2963
Vert_2	-0.1083	0.2812	0.337	0.337	0.396	0.761	0.514	0.3050	0.3334	0.3111	0.244	0.233	0.228	0.220	0.224	0.2853	0.2806	0.2779	0.2740	0.2831
Vert_3	-0.1184	0.3075	0.369	0.368	0.433	0.382	0.727	0.3412	0.3730	0.3481	0.273	0.261	0.255	0.246	0.251	0.3192	0.3139	0.3109	0.3065	0.3167
Kauf_1	0.0101	0.2135	0.256	0.256	0.301	0.266	0.290	0.9963	0.8119	0.7577	0.429	0.409	0.400	0.386	0.394	0.4393	0.4321	0.4279	0.4219	0.4360
Kauf_2	0.0125	0.2641	0.317	0.316	0.372	0.328	0.359	0.9151	1.2751	0.8283	0.469	0.447	0.437	0.422	0.431	0.4802	0.4723	0.4677	0.4612	0.4766
Kauf_3	0.0118	0.2499	0.299	0.299	0.352	0.311	0.340	0.8659	1.0708	1.3107	0.438	0.417	0.408	0.394	0.402	0.4482	0.4408	0.4365	0.4304	0.4448
EW_1	-0.1370	0.1926	0.231	0.231	0.271	0.239	0.262	0.4812	0.5950	0.5630	1.261	0.703	0.687	0.663	0.677	0.6424	0.6319	0.6258	0.6170	0.6376
EW_2	-0.1187	0.1669	0.200	0.200	0.235	0.208	0.227	0.4170	0.5157	0.4880	0.806	1.043	0.655	0.632	0.645	0.6122	0.6022	0.5964	0.5880	0.6076
EW_3	-0.1182	0.1662	0.199	0.199	0.234	0.207	0.226	0.4152	0.5134	0.4858	0.803	0.696	1.083	0.617	0.630	0.5982	0.5884	0.5827	0.5745	0.5937
EW_4	-0.1115	0.1567	0.188	0.188	0.221	0.195	0.213	0.3917	0.4844	0.4583	0.757	0.656	0.653	1.035	0.608	0.5773	0.5678	0.5623	0.5544	0.5729
EW_5	-0.1210	0.1701	0.204	0.204	0.240	0.211	0.231	0.4250	0.5256	0.4973	0.822	0.712	0.709	0.669	1.169	0.5895	0.5799	0.5742	0.5662	0.5851
EM_1	-0.0807	0.2056	0.246	0.246	0.290	0.256	0.280	0.4505	0.5571	0.5271	0.741	0.642	0.640	0.603	0.655	1.0554	0.6962	0.6895	0.6798	0.7025
EM_2	-0.0745	0.1897	0.227	0.227	0.267	0.236	0.258	0.4158	0.5142	0.4865	0.684	0.593	0.590	0.557	0.604	0.6895	0.9294	0.6782	0.6687	0.6909
EM_3	-0.0743	0.1891	0.227	0.227	0.267	0.235	0.257	0.4144	0.5125	0.4849	0.682	0.591	0.588	0.555	0.602	0.6873	0.6344	0.9415	0.6622	0.6843
EM_4	-0.0711	0.1810	0.217	0.217	0.255	0.225	0.246	0.3966	0.4905	0.4641	0.653	0.566	0.563	0.531	0.576	0.6577	0.6071	0.6051	0.8869	0.6747
EM_5	-0.0730	0.1859	0.223	0.223	0.262	0.231	0.253	0.4075	0.5039	0.4768	0.670	0.581	0.578	0.546	0.592	0.6757	0.6237	0.6217	0.5949	0.8767

Path Model

Path diagrams

[6]

Referenzen

[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.

[5] Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., Rosseel, Y., Miller, P., Quick, C., Garnier-Villarreal, M., Selig, J., Boulton, A., Preacher, K., Coffman, D., Rhemtulla, M., Robitzsch, A., Enders, C., Arslan, R., Clinton, B., Panko, P., Merkle, E., Chesnut, S., Byrnes, J., Rights, J. D., Longo, Y., Mansolf, M., Ben-Shachar, M. S., Rönkkö, M. (2019). semTools: Useful Tools for Structural Equation Modeling. [R Package]. Retrieved from https://CRAN.R-project.org/package=semTools.

[6] 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.