Results

Correlation Matrix

Correlation Matrix
		DASS_DEP_AGGR	DASS_STRESS_AGGR	DASS_ANSIA_AGGR	UCLA_AGGR	RSE_AGGR
DASS_DEP_AGGR	Pearson's r	—
	df	—
	p-value	—
DASS_STRESS_AGGR	Pearson's r	0.784	—
	df	28	—
	p-value	< .001	—
DASS_ANSIA_AGGR	Pearson's r	0.692	0.813	—
	df	28	28	—
	p-value	< .001	< .001	—
UCLA_AGGR	Pearson's r	0.568	0.509	0.322	—
	df	28	28	28	—
	p-value	0.001	0.004	0.083	—
RSE_AGGR	Pearson's r	-0.433	-0.283	-0.290	-0.312	—
	df	28	28	28	28	—
	p-value	0.017	0.130	0.120	0.094	—

Linear Regression

Model Fit Measures
Model	R	R²	Adjusted R²
1	0.836	0.699	0.650

Model Coefficients - DASS_DEP_AGGR
Predictor	Estimate	SE	t	p	Stand. Estimate
Intercept	-0.120	0.632	-0.189	0.851
DASS_STRESS_AGGR	0.468	0.208	2.251	0.033	0.475
DASS_ANSIA_AGGR	0.255	0.266	0.959	0.347	0.186
UCLA_AGGR	0.332	0.210	1.579	0.127	0.210
RSE_AGGR	-0.282	0.186	-1.514	0.143	-0.179

Assumption Checks

Durbin–Watson Test for Autocorrelation
Autocorrelation	DW Statistic	p
0.233	1.44	0.148

[3]

Collinearity Statistics
	VIF	Tolerance
DASS_STRESS_AGGR	3.70	0.271
DASS_ANSIA_AGGR	3.13	0.320
UCLA_AGGR	1.47	0.679
RSE_AGGR	1.16	0.862

[3]

Moderation

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

GLM Mediation Analysis

Models Info

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

[4]

Path Model

Conceptual Diagram

[5]


Diagram notes
Red paths indicate required coefficients

Mediation

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

[6]

Moderation

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

GLM Mediation Analysis

Models Info

Mediators Models
	m1	DASS_DepressX3Qy ~ RSE + DASS_Depress + DASS_Stress
	m2	DASS_Stress_t2 ~ RSE + DASS_Depress + DASS_Stress
Full Model
	m3	DASS_Depress_t3 ~ DASS_DepressX3Qy + DASS_Stress_t2 + RSE + DASS_Depress + DASS_Stress
Indirect Effects
	IE 1	RSE ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3
	IE 2	RSE ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3
	IE 3	DASS_Depress ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3
	IE 4	DASS_Depress ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3
	IE 5	DASS_Stress ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3
	IE 6	DASS_Stress ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3

Sample size	N	30

[4]

Path Model

Conceptual Diagram

[5]


Diagram notes
Covariances among IV are estimated but not shown

Mediation

Indirect and Total Effects
				95% C.I. (a)
Type	Effect	Estimate	SE	Lower	Upper	β	z	p
Indirect	RSE ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3	0.0370	0.1459	-0.2489	0.3229	0.0237	0.253	0.800
	RSE ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3	-0.0595	0.0741	-0.2047	0.0857	-0.0382	-0.803	0.422
	DASS_Depress ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3	0.2235	0.1527	-0.0757	0.5227	0.2570	1.464	0.143
	DASS_Depress ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3	-0.0530	0.0674	-0.1851	0.0790	-0.0610	-0.787	0.431
	DASS_Stress ⇒ DASS_Depress_t2 ⇒ DASS_Depress_t3	0.1614	0.1461	-0.1250	0.4479	0.1744	1.105	0.269
	DASS_Stress ⇒ DASS_Stress_t2 ⇒ DASS_Depress_t3	0.1303	0.1470	-0.1578	0.4183	0.1407	0.886	0.375
Component	RSE ⇒ DASS_Depress_t2	0.0579	0.2282	-0.3893	0.5052	0.0392	0.254	0.800
	DASS_Depress_t2 ⇒ DASS_Depress_t3	0.6379	0.1683	0.3080	0.9678	0.6064	3.789	< .001
	RSE ⇒ DASS_Stress_t2	-0.4026	0.2368	-0.8667	0.0615	-0.2501	-1.700	0.089
	DASS_Stress_t2 ⇒ DASS_Depress_t3	0.1477	0.1622	-0.1702	0.4656	0.1528	0.911	0.363
	DASS_Depress ⇒ DASS_Depress_t2	0.3504	0.2207	-0.0822	0.7830	0.4237	1.587	0.112
	DASS_Depress ⇒ DASS_Stress_t2	-0.3592	0.2291	-0.8081	0.0898	-0.3992	-1.568	0.117
	DASS_Stress ⇒ DASS_Depress_t2	0.2531	0.2191	-0.1764	0.6826	0.2875	1.155	0.248
	DASS_Stress ⇒ DASS_Stress_t2	0.8820	0.2274	0.4363	1.3277	0.9211	3.878	< .001
Direct	RSE ⇒ DASS_Depress_t3	0.1079	0.1964	-0.2770	0.4929	0.0694	0.550	0.583
	DASS_Depress ⇒ DASS_Depress_t3	-0.2221	0.2042	-0.6222	0.1781	-0.2553	-1.088	0.277
	DASS_Stress ⇒ DASS_Depress_t3	0.3587	0.2175	-0.0675	0.7849	0.3874	1.649	0.099
Total	RSE ⇒ DASS_Depress_t3	0.0854	0.2512	-0.4069	0.5778	0.0549	0.340	0.734
	DASS_Depress ⇒ DASS_Depress_t3	-0.0516	0.2430	-0.5279	0.4247	-0.0593	-0.212	0.832
	DASS_Stress ⇒ DASS_Depress_t3	0.6504	0.2413	0.1776	1.1233	0.7025	2.696	0.007
Nota. Confidence intervals computed with method: Standard (Delta method)
Nota. Betas are completely standardized effect sizes

[6]

Linear Regression

Model Fit Measures
Model	R	R²
1	0.766	0.586

Model Coefficients - DASS_Stress_t3
Predictor	Estimate	SE	t	p	Stand. Estimate
Intercept	-2.9019	1.3915	-2.085	0.048
età	0.0745	0.0453	1.646	0.113	0.2880
Genere	0.6156	0.2089	2.947	0.007	0.4537
RSE	0.1006	0.3081	0.326	0.747	0.0580
UCLA_TOT	0.3246	0.2938	1.105	0.281	0.1901
DASS_Depress	-0.7748	0.3057	-2.535	0.019	-0.8001
DASS_Stress	1.1543	0.2778	4.155	< .001	1.1198

ANOVA a Misure Ripetute

Effetti Entro i Sggetti
	Somma dei Quadrati	gdl	Media Quadratica	F	p
MR Fattore 1	0.301	2	0.1507	1.88	0.162
Residuo	4.657	58	0.0803
Nota. Somma dei quadrati Tipo 3

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Effetti Tra Soggetti
	Somma dei Quadrati	gdl	Media Quadratica	F	p
Residuo	12.1	29	0.418
Nota. Somma dei quadrati Tipo 3

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. (2020). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.

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

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

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

[7] Singmann, H. (2018). afex: Analysis of Factorial Experiments. [R package]. Retrieved from https://cran.r-project.org/package=afex.