Risultati

ANOVA

ANOVA
	Sum of Squares	df	Mean Square	F	p	η²
SLC_ClassiRischio	1.80	2	0.899	4.90	0.013	0.205
Residuals	6.98	38	0.184

[3]

ANOVA

ANOVA
	Sum of Squares	df	Mean Square	F	p	η²
SLC_ClassiRischio	1.80	2	0.899	4.90	0.013	0.205
Residuals	6.98	38	0.184

[3]

Assumption Checks

Test for Homogeneity of Variances (Levene's)
F	df1	df2	p
0.962	2	38	0.391

[3]

Post Hoc Tests

Post Hoc Comparisons - SLC_ClassiRischio
Comparison
SLC_ClassiRischio		SLC_ClassiRischio	Mean Difference	SE	df	t	p_tukey	p_bonferroni
0	-	1	-0.327	0.180	38.0	-1.81	0.179	0.234
	-	2	-0.585	0.208	38.0	-2.82	0.020	0.023
1	-	2	-0.258	0.251	38.0	-1.03	0.563	0.930

[4]

Estimated Marginal Means

SLC_ClassiRischio

Estimated Marginal Means - SLC_ClassiRischio
			95% Confidence Interval
SLC_ClassiRischio	Mean	SE	Lower	Upper
0	2.16	0.0796	2.00	2.32
1	2.49	0.1620	2.16	2.81
2	2.74	0.1917	2.36	3.13

[4]

Linear Regression

argument is of length zero

Model Fit Measures
Model	R	R²
1	0.00	0.00

Model Coefficients - SAS_Ansia
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.
sessoM0F1	.	.	.	.

Linear Regression

there are aliased coefficients in the model

Model Fit Measures
			Overall Model Test
Model	R	R²	F	df1	df2	p
1	.	.	.	.	.	.
2	.	.	.	.	.	.

Model Comparisons
Comparison
Model		Model	ΔR²	F	df1	df2	p
1	-	2	.	.	.	.	.

Model Specific ResultsModel 1Model 2

Model Coefficients - WART
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.
SAS_Ansia	.	.	.	.
sessoM0F1	.	.	.	.

Model Coefficients - WART
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.
SAS_Ansia	.	.	.	.
sessoM0F1	.	.	.	.
sexBYansia	.	.	.	.

ANOVA

ANOVA
		Sum of Squares		df		Mean Square		F		p

Linear Regression

Model Fit Measures
Model	R	R²
1	.	.

Model Coefficients - …
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.

ANOVA

ANOVA
	Sum of Squares	df	Mean Square	F	p
SLC_ClassiRischio	1.80	2	0.899	4.90	0.013
Residuals	6.98	38	0.184

[3]

Linear Regression

Model Fit Measures
Model	R	R²
1	.	.

Model Coefficients - …
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.

ANOVA

ANOVA
		Sum of Squares		df		Mean Square		F		p

One-Way ANOVA

One-Way ANOVA (Welch's)
		F		df1		df2		p

Linear Regression

Model Fit Measures
Model	R	R²
1	.	.

Model Coefficients - …
Predictor	Estimate	SE	t	p
Intercept	.	.	.	.

Correlation Matrix

Correlation Matrix
		WART	SAS_Ansia
WART	Pearson's r	—
	p-value	—
	95% CI Upper	—
	95% CI Lower	—
SAS_Ansia	Pearson's r	0.609	—
	p-value	< .001	—
	95% CI Upper	0.772	—
	95% CI Lower	0.370	—

Moderation

Moderation Estimates
	Estimate	SE	Z	p
SLC_centrato	0.2273	0.04466	5.09	< .001
SD3_NARC_centrato	0.0250	0.00881	2.84	0.005
SLC_centrato ✻ SD3_NARC_centrato	-0.0106	0.00899	-1.18	0.236

Simple Slope Analysis

Simple Slope Estimates
	Estimate	SE	Z	p
Average	0.227	0.0450	5.05	< .001
Low (-1SD)	0.276	0.0633	4.36	< .001
High (+1SD)	0.179	0.0585	3.05	0.002
Nota. shows the effect of the predictor (SLC_centrato) on the dependent variable (WART) at different levels of the moderator (SD3_NARC_centrato)

Simple Slope Plot

One-Way ANOVA

One-Way ANOVA
		F	df1	df2	p
WART	Welch's	9.49	2	16.2	0.002
	Fisher's	6.87	2	76	0.002

Group Descriptives
	SLC_ClassiRischio	N	Mean	SD	SE
WART	0	57	2.19	0.411	0.0544
	1	15	2.46	0.364	0.0939
	2	7	2.70	0.287	0.1086

Post Hoc Tests

Tukey Post-Hoc Test – WART
		0	1	2
0	Mean difference	—	-0.265	-0.506
	t-value	—	-2.32	-3.21
	df	—	76.0	76.0
	p-value	—	0.059	0.005
1	Mean difference		—	-0.241
	t-value		—	-1.34
	df		—	76.0
	p-value		—	0.379
2	Mean difference			—
	t-value			—
	df			—
	p-value			—

Descriptives

Descriptives

N
Missing
Mean
Median
Minimum
Maximum

Linear Regression

Model Fit Measures
Model	R	R²
1	0.391	0.153

Model Coefficients - WART
Predictor	Estimate	SE	t	p
Intercept	2.456	0.102	24.14	< .001
D_MILD_LOW	-0.265	0.114	-2.32	0.023
D_mild_High	0.241	0.180	1.34	0.185

Regressione Lineare

Misure di Adattamento del Modello
Modello	R	R²
1	0.514	0.264

Coefficienti del Modello - WART
Predittore	Stima	SE	t	p
Intercettare	1.091	0.252	4.33	< .001
SAS_Ansia	0.702	0.144	4.88	< .001
sessoM0F1	0.576	0.437	1.32	0.192
sexBYansia	-0.360	0.242	-1.49	0.141

Moderation

Moderation Estimates
	Estimate	SE	Z	p
SAS_Ansia	0.5289	0.1151	4.595	< .001
sessoM0F1	-0.0559	0.0811	-0.689	0.491
SAS_Ansia ✻ sessoM0F1	-0.3600	0.2353	-1.530	0.126

Simple Slope Analysis

Simple Slope Estimates
	Estimate	SE	Z	p
Average	0.529	0.117	4.53	< .001
Low (-1SD)	0.709	0.145	4.90	< .001
High (+1SD)	0.349	0.186	1.88	0.060
Nota. shows the effect of the predictor (SAS_Ansia) on the dependent variable (WART) at different levels of the moderator (sessoM0F1)

GLM Mediation Analysis

Models Info

Get started		Select the dependent variable
		Select at least one mediator
Get Started		Select at least one factor or covariate as independent variable
Overal Model	.	The mediational model is incomplete

[5]

Path Model

Conceptual Diagram

[6]


Diagram notes
Red paths indicate required coefficients

Mediation

Indirect and Total Effects
Type		Effect		Estimate		SE		Lower		Upper		β		z		p

[7]

Moderation

Moderation Estimates
	Estimate	SE	Z	p
SAS_Ansia	0.5289	0.1151	4.595	< .001
sessoM0F1	-0.0559	0.0811	-0.689	0.491
SAS_Ansia ✻ sessoM0F1	-0.3600	0.2353	-1.530	0.126

Simple Slope Analysis

Simple Slope Estimates
	Estimate	SE	Z	p
Average	0.529	0.117	4.53	< .001
Low (-1SD)	0.709	0.145	4.90	< .001
High (+1SD)	0.349	0.186	1.88	0.060
Nota. shows the effect of the predictor (SAS_Ansia) on the dependent variable (WART) at different levels of the moderator (sessoM0F1)

Regressione Lineare

Misure di Adattamento del Modello
Modello	R	R²
1	0.527	0.277

Coefficienti del Modello - WART
Predittore	Stima	SE	t	p
Intercettare	2.2810	0.04136	55.14	< .001
SD3_NARC_centrato	0.0251	0.00947	2.64	0.010
SLC_centrato	0.2274	0.04613	4.93	< .001
SLCbyNARC	-0.0106	0.00961	-1.11	0.272

Moderation

Moderation Estimates
	Estimate	SE	Z	p
SD3_narcisismo	0.0250	0.00916	2.73	0.006
SLC	0.2273	0.04452	5.11	< .001
SD3_narcisismo ✻ SLC	-0.0106	0.00931	-1.14	0.253

Mediation

Mediation Estimates
Effect	Estimate	SE	Z	p
Indirect	0.159	0.0666	2.38	0.017
Direct	0.403	0.1227	3.29	0.001
Total	0.562	0.1135	4.95	< .001

Moderation

Moderation Estimates
	Estimate	SE	Z	p
SAS_Ansia	.	.	.	.
.	.	.	.	.
SAS_Ansia	.	.	.	.

Mediation

Mediation Estimates
Effect	Estimate	SE	Z	p
Indirect	.	.	.	.
Direct	.	.	.	.
Total	.	.	.	.

Moderation

Moderation Estimates
	Estimate	SE	Z	p
.	.	.	.	.
.	.	.	.	.
	.	.	.	.

Riferimenti

[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] Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.

[4] Lenth, R. (2018). emmeans: Estimated Marginal Means, aka Least-Squares Means. [R package]. Retrieved from https://cran.r-project.org/package=emmeans.

[5] Gallucci, M. (2020). jAMM: jamovi Advanced Mediation Models. [jamovi module]. Retrieved from https://jamovi-amm.github.io/.

[6] Soetaert, K. (2019). diagram: Functions for Visualising Simple Graphs (Networks), Plotting Flow Diagrams. [R package]. Retrieved from https://cran.r-project.org/package=diagram.

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