Results

ANOVA

ANOVA - Reactionspeed
	Sum of Squares	df	Mean Square	F	p	η²p
contrast1	756860.167	1	756860.167	2.155	0.145	0.022
Residuals	33013121.167	94	351203.417
[3]

ANOVA

ANOVA - Reactionspeed
	Sum of Squares	df	Mean Square	F	p	η²p
contrast1	756860.167	1	756860.167	2.155	0.145	0.022
Residuals	33013121.167	94	351203.417
[3]

Estimated Marginal Means

contrast1

[4]

ANOVA

ANOVA - Reactionspeed
	Sum of Squares	df	Mean Square	F	p
Condition	50953720.222	2	25476860.111	50.847	<.001
Residuals	70647781.083	141	501048.093
[3]

Post Hoc Tests

Post Hoc Comparisons - Condition
Comparison
Condition		Condition	Mean Difference	SE	df	t	p	p_tukey	p_bonferroni
Happy-neutral	-	Angry-happy	-177.583	144.489	141.000	-1.229	0.221	0.438	0.663
Happy-neutral	-	Neutral-happy (control)	-1341.250	144.489	141.000	-9.283	<.001	<.001	<.001
Angry-happy	-	Neutral-happy (control)	-1163.667	144.489	141.000	-8.054	<.001	<.001	<.001
Note. Comparisons are based on estimated marginal means

[4]

ANOVA

ANOVA - t2
	Sum of Squares	df	Mean Square	F	p	η²p
Overall model	1040.681	2	520.340	1.398	0.250
Condition	1040.681	2	520.340	1.398	0.250	0.019
Residuals	52470.625	141	372.132
[3]

Post Hoc Tests

Post Hoc Comparisons - Condition
Comparison
Condition		Condition	Mean Difference	SE	df	t	p_tukey
Happy-neutral	-	Angry-happy	-5.833	3.938	141.000	-1.481	0.303
Happy-neutral	-	Neutral-happy (control)	-0.271	3.938	141.000	-0.069	0.997
Angry-happy	-	Neutral-happy (control)	5.563	3.938	141.000	1.413	0.337
Note. Comparisons are based on estimated marginal means

[4]

ANOVA

ANOVA - DASSdistress
	Sum of Squares	df	Mean Square	F	p	η²p
Overall model	133.010	1	133.010	0.344	0.559
contrast1	133.010	1	133.010	0.344	0.559	0.004
Residuals	36393.229	94	387.162
[3]

Descriptives

Descriptives
	Condition	Reactionspeed
N	Happy-neutral	48
	Angry-happy	48
	Neutral-happy (control)	48
Missing	Happy-neutral	0
	Angry-happy	0
	Neutral-happy (control)	0
Mean	Happy-neutral	1487.542
	Angry-happy	1665.125
	Neutral-happy (control)	2828.792
Median	Happy-neutral	1383.000
	Angry-happy	1714.500
	Neutral-happy (control)	2784.500
Standard deviation	Happy-neutral	552.576
	Angry-happy	630.132
	Neutral-happy (control)	894.839
Minimum	Happy-neutral	530.000
	Angry-happy	634.000
	Neutral-happy (control)	705.000
Maximum	Happy-neutral	2891.000
	Angry-happy	3091.000
	Neutral-happy (control)	5564.000
Skewness	Happy-neutral	0.345
	Angry-happy	0.174
	Neutral-happy (control)	0.392
Std. error skewness	Happy-neutral	0.343
	Angry-happy	0.343
	Neutral-happy (control)	0.343
Kurtosis	Happy-neutral	-0.472
	Angry-happy	-0.897
	Neutral-happy (control)	0.837
Std. error kurtosis	Happy-neutral	0.674
	Angry-happy	0.674
	Neutral-happy (control)	0.674
Shapiro-Wilk W	Happy-neutral	0.976
	Angry-happy	0.962
	Neutral-happy (control)	0.980
Shapiro-Wilk p	Happy-neutral	0.424
	Angry-happy	0.125
	Neutral-happy (control)	0.564

Plots

Reactionspeed

ANOVA

ANOVA - Reactionspeed
	Sum of Squares	df	Mean Square	F	p
Condition	50953720.222	2	25476860.111	50.847	<.001
Residuals	70647781.083	141	501048.093
[3]

Assumption Checks

Homogeneity of Variances Test (Levene's)
F	df1	df2	p
3.612	2	141	0.030
[3]

ANOVA

ANOVA - SQRTSpeed
	Sum of Squares	df	Mean Square	F	p	η²
Overall model	5968.533	2	2984.266	47.072	<.001
Condition	5968.533	2	2984.266	47.072	<.001	0.400
Residuals	8939.064	141	63.398
[3]

Assumption Checks

Homogeneity of Variances Test (Levene's)
F	df1	df2	p
0.256	2	141	0.775
[3]

Estimated Marginal Means

Condition

Estimated Marginal Means - Condition
			95% Confidence Interval
Condition	Mean	SE	Lower	Upper
Happy-neutral	37.892	1.149	35.620	40.164
Angry-happy	40.044	1.149	37.772	42.316
Neutral-happy (control)	52.497	1.149	50.225	54.769

[4]

ANOVA

ANOVA - …
	Sum of Squares	df	Mean Square	F	p
...	.	.	.	.	.
Residuals	.	.	.	.	.
[3]

One-Way ANOVA

One-Way ANOVA (Welch's)
	F	df1	df2	p
Reactionspeed	40.182	2	91.111	<.001

ANOVA

ANOVA - LOG10dassdistress
	Sum of Squares	df	Mean Square	F	p	η²	η²p
Condition	0.044	2	0.022	0.187	0.829	0.003	0.003
Residuals	16.487	141	0.117
[3]

ANOVA

ANOVA - t3
	Sum of Squares	df	Mean Square	F	p
Condition	25040.681	2	12520.340	125.506	<.001
Residuals	14065.958	141	99.759
[3]

Assumption Checks

Homogeneity of Variances Test (Levene's)
F	df1	df2	p
16.476	2	141	<.001
[3]

General Linear Model

Model Info
Info
Estimate	Linear model fit by OLS
Call	SQRTSpeed ~ 1 + distress + Condition + distress:Condition
R-squared	0.522
Adj. R-squared	0.505
[5]

Model Results

ANOVA Omnibus tests
	SS	df	F	p	η²p
Model	7785.656	5	30.172	<.001	0.522
distress	1122.762	1	21.755	<.001	0.136
Condition	5993.055	2	58.063	<.001	0.457
distress ✻ Condition	689.718	2	6.682	0.002	0.088
Residuals	7121.941	138
Total	14907.597	143

Fixed Effects Parameter Estimates
				95% Confidence Interval
Names	Effect	Estimate	SE	Lower	Upper	β	df	t	p
(Intercept)	(Intercept)	43.403	0.600	42.217	44.588	0.000	138	72.395	<.001
distress1	High - Low	5.593	1.199	3.222	7.964	0.548	138	4.664	<.001
Condition1	Angry-happy - Happy-neutral	1.946	1.467	-0.954	4.847	0.191	138	1.327	0.187
Condition2	Neutral-happy (control) - Happy-neutral	14.587	1.469	11.682	17.492	1.429	138	9.930	<.001
distress1 ✻ Condition1	High - Low ✻ Angry-happy - Happy-neutral	2.512	2.934	-3.289	8.314	0.246	138	0.856	0.393
distress1 ✻ Condition2	High - Low ✻ Neutral-happy (control) - Happy-neutral	-7.794	2.938	-13.603	-1.985	-0.763	138	-2.653	0.009

Simple Effects

Simple effects of distress : Omnibus Tests
Moderator levels
Condition	F	Num df	Den df	p	η²p
.	.	.	.	.	.
	.	.	.	.	.
	.	.	.	.	.
Note. Simple effects cannot be estimated. Refine the model or the covariates conditioning (if any)

Simple effects of distress : Parameter estimates
Moderator levels				95% Confidence Interval
Condition	contrast	Estimate	SE	Lower	Upper	β	df	t	p
.	High - Low	.	.	.	.	.	.	.	.
	High - Low	.	.	.	.	.	.	.	.
	High - Low	.	.	.	.	.	.	.	.

Estimated Marginal Means

distress
				95% Confidence Interval
distress	Mean	SE	df	Lower	Upper
Low	40.607	0.842	138.000	38.942	42.271
High	46.199	0.854	138.000	44.511	47.887

Condition
				95% Confidence Interval
Condition	Mean	SE	df	Lower	Upper
Happy-neutral	37.892	1.037	138.000	35.842	39.942
Angry-happy	39.838	1.038	138.000	37.786	41.890
Neutral-happy (control)	52.479	1.041	138.000	50.421	54.536

Condition:distress
					95% Confidence Interval
Condition	distress	Mean	SE	df	Lower	Upper
Happy-neutral	Low	34.215	1.466	138.000	31.316	37.115
Angry-happy	Low	34.905	1.498	138.000	31.943	37.867
Neutral-happy (control)	Low	52.699	1.409	138.000	49.913	55.485
Happy-neutral	High	41.569	1.466	138.000	38.669	44.468
Angry-happy	High	44.771	1.437	138.000	41.930	47.612
Neutral-happy (control)	High	52.258	1.532	138.000	49.230	55.287

Plots

Assumption Checks

Test for Homogeneity of Residual Variances (Levene's)
F	df1	df2	p
0.798	5	138	0.553

General Linear Model

Model Info
Info
Model Type	Linear Model	OLS Model for continuous y
Model	lm	SQRTSpeed ~ 1 + distress + Condition + distress:Condition
Distribution	Gaussian	Normal distribution of residuals
Omnibus Tests	F
Sample size	144
Converged	yes
Y transform	none
C.I. method	Wald
[5]

Model Results

Model Fit
R²	Adj. R²	df	df (res)	F	p
0.522	0.505	5	138	30.172	<.001
[6]

ANOVA Omnibus tests
	SS	df	F	p	η²p
Model	7785.656	5	30.172	<.001	0.522
distress	1122.762	1	21.755	<.001	0.136
Condition	5993.055	2	58.063	<.001	0.457
distress ✻ Condition	689.718	2	6.682	0.002	0.088
Residuals	7121.941	138
Total	14907.597	143

Parameter Estimates (Coefficients)
				95% Confidence Intervals
Names	Effect	Estimate	SE	Lower	Upper	β	df	t	p
(Intercept)	(Intercept)	43.403	0.600	42.217	44.588	-0.007	138	72.395	<.001
distress1	High - Low	5.593	1.199	3.222	7.964	0.548	138	4.664	<.001
Condition1	Angry-happy - Happy-neutral	1.946	1.467	-0.954	4.847	0.191	138	1.327	0.187
Condition2	Neutral-happy (control) - Happy-neutral	14.587	1.469	11.682	17.492	1.429	138	9.930	<.001
distress1 ✻ Condition1	(High - Low) ✻ (Angry-happy - Happy-neutral)	2.512	2.934	-3.289	8.314	0.246	138	0.856	0.393
distress1 ✻ Condition2	(High - Low) ✻ (Neutral-happy (control) - Happy-neutral)	-7.794	2.938	-13.603	-1.985	-0.763	138	-2.653	0.009
[7]

Simple Effects

ANOVA for Simple Effects of distress
Moderator
Condition	F	Num df	Den df	p	η²p
Happy-neutral	12.573	1	138	<.001	0.084
Angry-happy	22.592	1	138	<.001	0.141
Neutral-happy (control)	0.045	1	138	0.833	0.000

Parameter Estimates for simple effects of distress
Moderator				95% Confidence Intervals
Condition	Effect	Estimate	SE	Lower	Upper	β	df	t	p
Happy-neutral	High - Low	7.353	2.074	3.253	11.454	0.720	138	3.546	<.001
Angry-happy	High - Low	9.866	2.076	5.761	13.970	0.966	138	4.753	<.001
Neutral-happy (control)	High - Low	-0.441	2.081	-4.556	3.674	-0.043	138	-0.212	0.833

Results Plots

Assumption Checks

Test for Homogeneity of Residual Variance
Test	Statistics	df1	df2	p
Breusch-Pagan Test	6.309	5		0.277
Levene's Test	0.798	5	138	0.553
Note. Levene's test is done only for factors.

References

[1] The jamovi project (2024). jamovi. (Version 2.6) [Computer Software]. Retrieved from https://www.jamovi.org.

[2] R Core Team (2024). R: A Language and environment for statistical computing. (Version 4.4) [Computer software]. Retrieved from https://cran.r-project.org. (R packages retrieved from CRAN snapshot 2024-08-07).

[3] Fox, J., & Weisberg, S. (2023). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.

[4] Lenth, R. (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. [R package]. Retrieved from https://cran.r-project.org/package=emmeans.

[5] Gallucci, M. (2019). GAMLj: General analyses for linear models. [jamovi module]. Retrieved from https://gamlj.github.io/.

[6] Gallucci, M. (2020). Model goodness of fit in GAMLj. . link.

[7] Lüdecke, Ben-Shachar, Patil & Makowski (2020). Extracting, Computing and Exploring the Parameters of Statistical Models using R. CRAN. link.