1 Content

This file containts the statistical analysis presented in to our paper Studying the Role of Haptic Feedback on Virtual Embodiment in a Drawing Task published in Frontiers in Virtual Reality and Human Behaviours. More information available at https://ns.inria.fr/loki/embodiment/

Loading data for analysis

data = read.table("Subjective_Questionnaires_Data.csv", header=TRUE, sep=";")

data_dessin = read.table("Objective_Performance_Data.csv", header=TRUE, sep=";")

2 BoxPlot of all questionnaire items, groupment and overall embodiment (scaled to 5)

HALF_WIDTH = 85
FULL_WIDTH = 178
GOLDEN_RATIO = 1.61803
data_all_long2 <- data_all_long %>% mutate(variable = recode(variable,"Score_Incarnation" = "Embodiment"))
data_meds = ddply(data_all_long2, .(variable, Condition), summarise, med = median(value))
ggplot(data = data_all_long2, aes(x=variable, y=value)) + geom_boxplot((aes(fill=Condition))) +
  labs(x = "Subjective question", y = "Rating")+
  scale_fill_manual(values=c( "#ffffbf", "#DF5E28", "#F6C584")) +
  #coord_flip() +
   theme(
        axis.text.x=element_text(angle=25, hjust=1, size=6),
        legend.box.background = element_rect(size=1, color="black"),
        legend.background = element_rect(size = 0.3, linetype = "solid", colour = "black"),
        panel.background = element_blank(),
        panel.border = element_blank(),
        axis.line = element_line(),
        axis.title = element_text( face="bold", colour="black", size="12"),
        axis.text = element_text( colour="black", size="12"),
        panel.grid.major.y = element_line( size = .1, color = "grey")) +
  ##geom_text(data = data_meds, aes(x = variable, y = med, label = med), size = 3, vjust = -1.5) +
  ggsave("All_Plots_items_groups.pdf", width = FULL_WIDTH - 15, height = (FULL_WIDTH / 5) * GOLDEN_RATIO, units="mm")

3 Embodiment questionnaire

3.1 Friedman and Wilcoxon tests for Overall Embodiment

resFriedEmbo = friedman.test(data.matrix(data_incarnation_fixed))
pander(resFriedEmbo)

Friedman rank sum test: `data.matrix(data_incarnation_fixed)`
Test statistic	df	P value
52.05	3	2.916e-11 * * *

resWilEmbo = pairwise.wilcox.test(data_incarnation_fixedwil$value, data_incarnation_fixedwil$Condition , paired = TRUE, p.adj = "bonf")
kable(resWilEmbo$p.value)

	CTRL	FFB
FFB	0.0041205	NA
VBT	1.0000000	0.0033722

3.2 Friedman and Wilcoxon tests for overall Ownership and particular ownership items

resOfried = friedman.test(data.matrix(dataO))
pander(resOfried)

Friedman rank sum test: `data.matrix(dataO)`
Test statistic	df	P value
11.92	2	0.002581 * *

resOwil= pairwise.wilcox.test(dataOwil$value, dataOwil$condition , paired = TRUE, p.adj = "bonf")
kable(resOwil$p.value)

	CTRL.Ownership	FFB.Ownership
FFB.Ownership	0.0080828	NA
VBT.Ownership	1.0000000	0.0812702

resO1fried = friedman.test(data.matrix(dataO1))
pander(resO1fried)

Friedman rank sum test: `data.matrix(dataO1)`
Test statistic	df	P value
9.745	2	0.007652 * *

res01wil = pairwise.wilcox.test(dataO1wil$value, dataO1wil$condition , paired = TRUE, p.adj = "bonf")
kable(res01wil$p.value)

	CTRL.O1	FFB.O1
FFB.O1	0.0478279	NA
VBT.O1	0.5492902	0.2544213

resO2fried = friedman.test(data.matrix(dataO2))
pander(resO2fried)

Friedman rank sum test: `data.matrix(dataO2)`
Test statistic	df	P value
6.197	2	0.04512 *

res02wil = pairwise.wilcox.test(dataO2wil$value, dataO2wil$condition , paired = TRUE, p.adj = "bonf")
kable(res02wil$p.value)

	CTRL.O2	FFB.O2
FFB.O2	0.2939814	NA
VBT.O2	0.9764060	0.0695887

resO3fried = friedman.test(data.matrix(dataO3))
pander(resO3fried)

Friedman rank sum test: `data.matrix(dataO3)`
Test statistic	df	P value
2.844	2	0.2412

3.3 Friedman and Wilcoxon tests for overall Agency and particular agency items

resAfried = friedman.test(data.matrix(dataA))
pander(resAfried)

Friedman rank sum test: `data.matrix(dataA)`
Test statistic	df	P value
10.89	2	0.00432 * *

resAwil = pairwise.wilcox.test(dataAwil$value, dataAwil$condition , paired = TRUE, p.adj = "bonf")
kable(resAwil$p.value)

	CTRL.Agency	FFB.Agency
FFB.Agency	0.1040757	NA
VBT.Agency	0.5328913	0.2777667

## On a besoin de seulement deux conditions, on peut pas le faire direct avec les trois conditions. Après, on veut faire le TOST sur des conditions particulières, donc pas si débile
##resAbis <- wilcox.test(value ~ condition, data = dataAwil,
##                   exact = FALSE)
##resAbis

resA1fried = friedman.test(data.matrix(dataA1))
pander(resA1fried)

Friedman rank sum test: `data.matrix(dataA1)`
Test statistic	df	P value
11.66	2	0.002936 * *

resA1wil = pairwise.wilcox.test(dataA1wil$value, dataA1wil$condition , paired = TRUE, p.adj = "bonf")
kable(resA1wil$p.value)

	CTRL.A1	FFB.A1
FFB.A1	0.0134145	NA
VBT.A1	0.9543472	0.0511346

resA2fried = friedman.test(data.matrix(dataA2))
pander(resA2fried)

Friedman rank sum test: `data.matrix(dataA2)`
Test statistic	df	P value
2	2	0.3679

resA3fried = friedman.test(data.matrix(dataA3))
pander(resA3fried)

Friedman rank sum test: `data.matrix(dataA3)`
Test statistic	df	P value
0.1404	2	0.9322

resA4fried = friedman.test(data.matrix(dataA4))
pander(resA4fried)

Friedman rank sum test: `data.matrix(dataA4)`
Test statistic	df	P value
0.5625	2	0.7548

3.4 Friedman and Wilcoxon tests for overall Tactile Sensations and particular tactile sensations items

resTfried = friedman.test(data.matrix(dataT))
pander(resTfried)

Friedman rank sum test: `data.matrix(dataT)`
Test statistic	df	P value
18.7	2	8.707e-05 * * *

resTwil = pairwise.wilcox.test(dataTwil$value, dataTwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTwil$p.value)

	CTRL.Tactile_Sensations	FFB.Tactile_Sensations
FFB.Tactile_Sensations	0.0013971	NA
VBT.Tactile_Sensations	0.0294497	0.0134498

resT1fried = friedman.test(data.matrix(dataT1))
pander(resT1fried)

Friedman rank sum test: `data.matrix(dataT1)`
Test statistic	df	P value
0.3939	2	0.8212

resT2fried = friedman.test(data.matrix(dataT2))
pander(resT2fried)

Friedman rank sum test: `data.matrix(dataT2)`
Test statistic	df	P value
14.14	2	0.0008515 * * *

resT2wil = pairwise.wilcox.test(dataT2wil$value, dataT2wil$condition , paired = TRUE, p.adj = "bonf")
kable(resT2wil$p.value)

	CTRL.T2	FFB.T2
FFB.T2	0.0063507	NA
VBT.T2	0.0099834	0.9324137

resT3fried = friedman.test(data.matrix(dataT3))
pander(resT3fried)

Friedman rank sum test: `data.matrix(dataT3)`
Test statistic	df	P value
16.12	2	0.0003157 * * *

resT3wil = pairwise.wilcox.test(dataT3wil$value, dataT3wil$condition , paired = TRUE, p.adj = "bonf")
kable(resT3wil$p.value)

	CTRL.T3	FFB.T3
FFB.T3	0.0025064	NA
VBT.T3	0.1741369	0.022413

resT4fried = friedman.test(data.matrix(dataT4))
pander(resT4fried)

Friedman rank sum test: `data.matrix(dataT4)`
Test statistic	df	P value
11.25	2	0.003613 * *

resT4wil = pairwise.wilcox.test(dataT4wil$value, dataT4wil$condition , paired = TRUE, p.adj = "bonf")
kable(resT4wil$p.value)

	CTRL.T4	FFB.T4
FFB.T4	0.0055365	NA
VBT.T4	0.3192144	0.010308

3.5 Friedman and Wilcoxon tests for overall Self-Location and particular self-location items

resL = friedman.test(data.matrix(dataL))
pander(resL)

Friedman rank sum test: `data.matrix(dataL)`
Test statistic	df	P value
4.083	2	0.1298

resL1fried = friedman.test(data.matrix(dataL1))
pander(resL1fried)

Friedman rank sum test: `data.matrix(dataL1)`
Test statistic	df	P value
5.045	2	0.08027

resL2fried = friedman.test(data.matrix(dataL2))
pander(resL2fried)

Friedman rank sum test: `data.matrix(dataL2)`
Test statistic	df	P value
2.627	2	0.2688

resL3fried = friedman.test(data.matrix(dataL3))
pander(resL3fried)

Friedman rank sum test: `data.matrix(dataL3)`
Test statistic	df	P value
0.16	2	0.9231

4 Workload questionnaire

4.1 Friedman tests for NASA TLX items

restlx1 = friedman.test(data.matrix(dataTLX_Mental))
pander(restlx1)

Friedman rank sum test: `data.matrix(dataTLX_Mental)`
Test statistic	df	P value
7.284	2	0.02621 *

restlx2 = friedman.test(data.matrix(dataTLX_Physical))
pander(restlx2)

Friedman rank sum test: `data.matrix(dataTLX_Physical)`
Test statistic	df	P value
2.259	2	0.3232

restlx3 = friedman.test(data.matrix(dataTLX_Temporal))
pander(restlx3)

Friedman rank sum test: `data.matrix(dataTLX_Temporal)`
Test statistic	df	P value
9.234	2	0.009882 * *

restlx4 = friedman.test(data.matrix(dataTLX_Performance))
pander(restlx4)

Friedman rank sum test: `data.matrix(dataTLX_Performance)`
Test statistic	df	P value
9.508	2	0.008618 * *

restlx5 = friedman.test(data.matrix(dataTLX_Effort))
pander(restlx5)

Friedman rank sum test: `data.matrix(dataTLX_Effort)`
Test statistic	df	P value
9.524	2	0.008549 * *

restlx6 = friedman.test(data.matrix(dataTLX_Frustration))
pander(restlx6)

Friedman rank sum test: `data.matrix(dataTLX_Frustration)`
Test statistic	df	P value
10.75	2	0.004631 * *

4.2 Wilcoxon test for NASA TLX items

resTLX1 = pairwise.wilcox.test(dataTLX_Mentalwil$value, dataTLX_Mentalwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX1$p.value)

	CTRL.TLX1	FFB.TLX1
FFB.TLX1	0.0490675	NA
VBT.TLX1	1.0000000	0.034517

resTLX2 = pairwise.wilcox.test(dataTLX_Physicalwil$value, dataTLX_Physicalwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX2$p.value)

	CTRL.TLX2	FFB.TLX2
FFB.TLX2	1	NA
VBT.TLX2	1	1

resTLX3 = pairwise.wilcox.test(dataTLX_Temporalwil$value, dataTLX_Temporalwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX3$p.value)

	CTRL.TLX3	FFB.TLX3
FFB.TLX3	1.0000000	NA
VBT.TLX3	0.0276166	0.0496289

resTLX4 = pairwise.wilcox.test(dataTLX_Performancewil$value, dataTLX_Performancewil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX4$p.value)

	CTRL.TLX4	FFB.TLX4
FFB.TLX4	1.0000000	NA
VBT.TLX4	0.1484883	0.0148417

resTLX5 = pairwise.wilcox.test(dataTLX_Effortwil$value, dataTLX_Effortwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX5$p.value)

	CTRL.TLX5	FFB.TLX5
FFB.TLX5	0.0231635	NA
VBT.TLX5	1.0000000	0.0308577

resTLX6 = pairwise.wilcox.test(dataTLX_Frustrationwil$value, dataTLX_Frustrationwil$condition , paired = TRUE, p.adj = "bonf")
kable(resTLX6$p.value)

	CTRL.TLX6	FFB.TLX6
FFB.TLX6	0.0490675	NA
VBT.TLX6	1.0000000	0.0103015

5 Performance over the drawings: Metrics analysis

5.1 Normality test over the Drawings metrics

m <- aov(Precision ~ Condition, data=data_dessin_acute)
##pander(normalCheck(m))

m2 <- aov(Completeness ~ Condition, data=data_dessin_compl)
##pander(normalCheck(m2))

5.2 ANOVA on ART for Completeness & Accuracy

mCompl = art(Completeness ~ factor(Condition) + (1|participant), data = data_dessin_compl)
kable(anova(mCompl))

	Term	F	Df	Df.res	Pr(>F)
factor(Condition)	factor(Condition)	2.21607	2	46	0.120548

mAcute = art(Precision ~ factor(Condition) + (1|participant), data = data_dessin_acute)
kable(anova(mAcute))

	Term	F	Df	Df.res	Pr(>F)
factor(Condition)	factor(Condition)	3.983055	2	46	0.0253889

6 ANOVA on ART

6.1 Overall Embodiment

mEmbo = art(Score_Incarnation ~ factor(passation) * factor(Condition) + (1|participant), data = data_art)
kable(anova(mEmbo))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.1191420	5	18	0.1099136
factor(Condition)	factor(Condition)	9.3460131	2	36	0.0005380
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.9099805	10	36	0.5344932

6.2 Groupment of Ownership

mO = art(Ownership ~ factor(passation) * factor(Condition) + (1|participant), data = data_art)
kable(anova(mO))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.3080741	5	18	0.3044646
factor(Condition)	factor(Condition)	5.2446392	2	36	0.0100252
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.8788101	10	36	0.5608743

6.2.1 Item O1

mO1 = art(O1 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mO1))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.055169	5	18	0.1189762
factor(Condition)	factor(Condition)	5.638673	2	36	0.0074078
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.000878	10	36	0.4610882

6.2.2 Item O2

mO2 = art(O2 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mO2))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.8729831	5	18	0.1493384
factor(Condition)	factor(Condition)	3.3085841	2	36	0.0479648
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.4232263	10	36	0.9258307

6.2.3 Item O3

mO3 = art(O3 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mO3))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.6447526	5	18	0.6689026
factor(Condition)	factor(Condition)	0.6298155	2	36	0.5384571
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.9981040	10	36	0.4632403

6.3 Groupment of Agency

mA = art(Agency ~ factor(passation) * factor(Condition) + (1|participant), data = data_art)
kable(anova(mA))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.152553	5	18	0.3697116
factor(Condition)	factor(Condition)	3.433788	2	36	0.0431643
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.078264	10	36	0.4035621

6.3.1 Item A1

mA1 = art(A1 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mA1))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.045195	5	18	0.4218926
factor(Condition)	factor(Condition)	6.068108	2	36	0.0053574
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.125009	10	36	0.3712721

6.3.2 Item A2

mA2 = art(A2 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mA2))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.2908477	5	18	0.9118236
factor(Condition)	factor(Condition)	1.8615571	2	36	0.1700849
factor(passation):factor(Condition)	factor(passation):factor(Condition)	4.4155410	10	36	0.0004448

6.3.3 Item A3

mA3 = art(A3 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mA3))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.8824209	5	18	0.5127435
factor(Condition)	factor(Condition)	0.1010823	2	36	0.9041142
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.0017260	10	36	0.4604317

6.3.4 Item A4

mA4 = art(A4 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mA4))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.0711703	5	18	0.4087097
factor(Condition)	factor(Condition)	0.6527931	2	36	0.5266419
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.7074177	10	36	0.7114534

6.4 Groupment of Self-Location

mSL = art(Self_Location ~ factor(passation) * factor(Condition) + (1|participant), data = data_art)
kable(anova(mSL))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.501649	5	18	0.2385168
factor(Condition)	factor(Condition)	1.607882	2	36	0.2143644
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.279717	10	36	0.2781018

6.4.1 Item L1

mL1 = art(L1 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mL1))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.5224352	5	18	0.0672680
factor(Condition)	factor(Condition)	2.1379655	2	36	0.1326247
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.9843199	10	36	0.4740208

6.4.2 Item L2

mL2 = art(L2 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mL2))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.7402051	5	18	0.6032919
factor(Condition)	factor(Condition)	0.2678938	2	36	0.7665005
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.3236833	10	36	0.2553947

6.4.3 Item L3

mL3 = art(L3 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mL3))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.3318429	5	18	0.8870724
factor(Condition)	factor(Condition)	0.2044999	2	36	0.8159951
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.8404964	10	36	0.0882413

6.5 Groupment of Tactile sensations

mT = art(Tactile_Sensations ~ factor(passation) * factor(Condition) + (1|participant), data = data_art)
kable(anova(mT))

## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.3631066	5	18	0.0815170
factor(Condition)	factor(Condition)	14.6441560	2	36	0.0000222
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.7787407	10	36	0.6482996

6.5.1 Item T1

mT1 = art(T1 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mT1))

## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.9240618	5	18	0.1400870
factor(Condition)	factor(Condition)	0.4965391	2	36	0.6127404
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.7280084	10	36	0.6932765

6.5.2 Item T2

mT2 = art(T2 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mT2))

## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular
## boundary (singular) fit: see ?isSingular

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.9204673	5	18	0.1407181
factor(Condition)	factor(Condition)	9.4622245	2	36	0.0004985
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.7192226	10	36	0.7010435

6.5.3 Item T3

mT3 = art(T3 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mT3))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	3.6284144	5	18	0.0191057
factor(Condition)	factor(Condition)	14.1288696	2	36	0.0000296
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.8917027	10	36	0.5498986

6.5.4 Post-hoc pairwise test for T3

attach(data0)
pw = pairwise.t.test(T3, interaction(passation), p.adj = "bonferroni")
detach(data0)
kable(pw$p.value)

	1	2	3	4	5
2	1.0000000	NA	NA	NA	NA
3	0.3835758	1.0000000	NA	NA	NA
4	1.0000000	1.0000000	0.9925147	NA	NA
5	1.0000000	1.0000000	0.1501001	1	NA
6	1.0000000	0.1654441	0.0107491	1	1

6.5.5 Item T4

mT4 = art(T4 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mT4))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	1.3759227	5	18	0.2795490
factor(Condition)	factor(Condition)	6.9665199	2	36	0.0027699
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.4481359	10	36	0.9117118

6.6 ANOVA on ART for NASA TLX

6.6.1 Item Mental Load

mTLXMental = art(TLX1 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mTLXMental))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.0948647	5	18	0.9919041
factor(Condition)	factor(Condition)	4.5433592	2	36	0.0174008
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.8856484	10	36	0.5550420

6.6.2 Item Physical Load

mTLXPhysical = art(TLX2 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mTLXPhysical))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.5801659	5	18	0.7147635
factor(Condition)	factor(Condition)	0.2068773	2	36	0.8140793
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.4490536	10	36	0.1991452

6.6.3 Item Temporal Load

mTLXTemporal = art(TLX3 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mO1))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.055169	5	18	0.1189762
factor(Condition)	factor(Condition)	5.638673	2	36	0.0074078
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.000878	10	36	0.4610882

6.6.4 Item Performance

mTLXPerformance = art(TLX4 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mTLXPerformance))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.4501301	5	18	0.8076809
factor(Condition)	factor(Condition)	5.6579321	2	36	0.0073000
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.4101081	10	36	0.2153278

6.6.5 Item Effort

mTLXEffort = art(TLX5 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mTLXEffort))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	0.6042859	5	18	0.6975429
factor(Condition)	factor(Condition)	6.2034376	2	36	0.0048430
factor(passation):factor(Condition)	factor(passation):factor(Condition)	1.6135557	10	36	0.1422062

6.6.6 Item Frustration

mTLXFrustration = art(TLX6 ~ factor(passation) * factor(Condition) + (1|participant), data = data1)
kable(anova(mTLXFrustration))

	Term	F	Df	Df.res	Pr(>F)
factor(passation)	factor(passation)	2.0113458	5	18	0.1256352
factor(Condition)	factor(Condition)	7.2227285	2	36	0.0023049
factor(passation):factor(Condition)	factor(passation):factor(Condition)	0.9659337	10	36	0.4886218

EmbodimentExp

Grégoire Richard