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
Note. 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

References

[1] The jamovi project (2019). jamovi. (Version 1.0) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.

[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.