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index 2f46d918b5e48bbb401653344bd3ad813ba9dc0a..46e67a72044695e84390ee9d99a9e03002319962 100644
--- a/.gitattributes
+++ b/.gitattributes
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 *.sas7bdat filter=lfs diff=lfs merge=lfs -text
 *.tsv filter=lfs diff=lfs merge=lfs -text
 *.tree filter=lfs diff=lfs merge=lfs -text
+*.docx filter=lfs diff=lfs merge=lfs -text
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diff --git a/102/replication_package/step2/Step2_Analyses b/102/replication_package/step2/Step2_Analyses
new file mode 100644
index 0000000000000000000000000000000000000000..57e0618dc76fbdc5516e03fb177409ac28f9b652
--- /dev/null
+++ b/102/replication_package/step2/Step2_Analyses
@@ -0,0 +1,342 @@
+library(psych)
+
+#Opening data
+
+dat <- read.csv(""Step_Results_CLEAN_EnglishVersion.csv",header=TRUE)
+attach(dat)
+
+#Constructing utilitarian scores for each category
+
+UD <- (U.D.01+U.D.02+U.D.03+U.D.04+U.D.05+U.D.06+U.D.07+U.D.08+U.D.09+U.D.10)/10
+PD <- (P.D.01+P.D.02+P.D.03+P.D.04+P.D.05+P.D.06+P.D.07+P.D.08+P.D.09+P.D.10)/10
+HC <- (H.C.01+H.C.02+H.C.03+H.C.04+H.C.05+H.C.06+H.C.07+H.C.08+H.C.09+H.C.10)/10
+AO <- (A.O.01+A.O.02+A.O.03+A.O.04+A.O.05+A.O.06+A.O.07+A.O.08+A.O.09+A.O.10)/10
+DE <- (D.E.01+D.E.02+D.E.03+D.E.04+D.E.05+D.E.06+D.E.07+D.E.08+D.E.09+D.E.10)/10
+P <- (P.01+P.02+P.03+P.04+P.05+P.06+P.07+P.08+P.09+P.10)/10
+
+#Reverse-coding utilitarian scores when necessary
+
+UD_ut <- 8-UD
+PD_ut <- 8-PD
+HC_ut <- 8-HC
+AO_ut <- 8-AO
+DE_ut <- DE
+P_ut <- 8-P
+
+#Calculating Cronbach's Alpha for utilitarian scores in each category
+
+UDframe <- data.frame(U.D.01,U.D.02,U.D.03,U.D.04,U.D.05,U.D.06,U.D.07,U.D.08,U.D.09,U.D.10)
+alpha(UDframe)
+
+PDframe <- data.frame(P.D.01,P.D.02,P.D.03,P.D.04,P.D.05,P.D.06,P.D.07,P.D.08,P.D.09,P.D.10)
+alpha(PDframe)
+
+HCframe <- data.frame(H.C.01,H.C.02,H.C.03,H.C.04,H.C.05,H.C.06,H.C.07,H.C.08,H.C.09,H.C.10)
+alpha(HCframe)
+
+AOframe <- data.frame(A.O.01,A.O.02,A.O.03,A.O.04,A.O.05,A.O.06,A.O.07,A.O.08,A.O.09,A.O.10)
+alpha(AOframe)
+
+DEframe <- data.frame(D.E.01,D.E.02,D.E.03,D.E.04,D.E.05,D.E.06,D.E.07,D.E.08,D.E.09,D.E.10)
+alpha(DEframe)
+
+Pframe <- data.frame(P.01,P.02,P.03,P.04,P.05,P.06,P.07,P.08,P.09,P.10)
+alpha(Pframe)
+
+#Constructing individual traits measures
+
+CRT <- CRT1_RIGHT+CRT2_RIGHT+CRT3_RIGHT
+
+NFCsum <- (6-NFC.01)+(6-NFC.02)+NFC.03+(6-NFC.04)+(6-NFC.05)+(6-NFC.06)+(6-NFC.07)+NFC.08+(6-NFC.09)+(6-NFC.10)+(6-NFC.11)+NFC.12+(6-NFC.13)+NFC.14+(6-NFC.15)+(6-NFC.16)+NFC.17+(6-NFC.18)+(6-NFC.19)
+NFC <- NFCsum/19
+
+mean(NFC)
+sd(NFC)
+
+FIsum <- FI.01+FI.02+FI.03+FI.04+FI.05+FI.06+FI.07+FI.08+FI.09+FI.10+FI.11+FI.12
+FI <- FIsum/12
+
+IRIsum <- IRI_01+(6-IRI_02)+IRI_03+(6-IRI_04)+(6-IRI_05)+IRI_06+IRI_07
+IRI <- IRIsum/7
+
+SRPsum <- SRP_01+SRP_02+SRP_03+SRP_04+SRP_05+SRP_06+SRP_07+SRP_08+SRP_09+SRP_10+SRP_11+SRP_12+SRP_13+SRP_14+SRP_15+SRP_16+SRP_17+SRP_18+SRP_19+SRP_20+SRP_21+SRP_22+(8-SRP_23)+(8-SRP_24)+(8-SRP_25)+(8-SRP_26)+SRP_27+(8-SRP_28)+SRP_29+SRP_30
+SRP <- SRPsum/30
+
+TASsum <- TAS_01+TAS_02+TAS_03+TAS_04+TAS_05+TAS_06+TAS_07+TAS_08+(6-TAS_09)+TAS_10+TAS_11+TAS_12+(6-TAS_13)+TAS_14+(6-TAS_15)+TAS_16+TAS_17+(6-TAS_18)+(6-TAS_19)+TAS_20
+TAS <- TASsum/20
+
+#Calculating average utilitarian scores for each category
+
+mean(UD_ut)
+sd(UD_ut)
+
+mean(PD_ut)
+sd(PD_ut)
+
+mean(HC_ut)
+sd(HC_ut)
+
+mean(AO_ut)
+sd(AO_ut)
+
+mean(DE_ut)
+sd(DE_ut)
+
+mean(P_ut)
+sd(P_ut)
+
+#Calculating correlations utilitarian scores between each category
+
+cor.test(UD_ut,PD_ut)
+cor.test(UD_ut,HC_ut)
+cor.test(UD_ut,AO_ut)
+cor.test(UD_ut,DE_ut)
+cor.test(UD_ut,P_ut)
+cor.test(PD_ut,HC_ut)
+cor.test(PD_ut,AO_ut)
+cor.test(PD_ut,DE_ut)
+cor.test(PD_ut,P_ut)
+cor.test(HC_ut,AO_ut)
+cor.test(HC_ut,DE_ut)
+cor.test(HC_ut,P_ut)
+cor.test(AO_ut,DE_ut)
+cor.test(AO_ut,P_ut)
+cor.test(DE_ut,P_ut)
+
+#Calculating coherence between utilitarian scores using Cronbach's Alpha
+
+alpha(data.frame(UD_ut,PD_ut,HC_ut,AO_ut,DE_ut,P_ut))
+
+#Calculating Cronbach's Alpha for individual traits measures
+
+NFCframe <- data.frame((6-NFC.01),(6-NFC.02),NFC.03,(6-NFC.04),(6-NFC.05),(6-NFC.06),(6-NFC.07),NFC.08,(6-NFC.09),(6-NFC.10),(6-NFC.11),NFC.12,(6-NFC.13),NFC.14,(6-NFC.15),(6-NFC.16),NFC.17,(6-NFC.18),(6-NFC.19))
+alpha(NFCframe)
+
+FIframe <- data.frame(FI.01,FI.02,FI.03,FI.04,FI.05,FI.06,FI.07,FI.08,FI.09,FI.10,FI.11,FI.12)
+alpha(FIframe)
+
+IRIframe <- data.frame(IRI_01,(6-IRI_02),IRI_03,(6-IRI_04),(6-IRI_05),IRI_06,IRI_07)
+alpha(IRIframe)
+
+SRPframe <- data.frame(SRP_01,SRP_02,SRP_03,SRP_04,SRP_05,SRP_06,SRP_07,SRP_08,SRP_09,SRP_10,SRP_11,SRP_12,SRP_13,SRP_14,SRP_15,SRP_16,SRP_17,SRP_18,SRP_19,SRP_20,SRP_21,SRP_22,(8-SRP_23),(8-SRP_24),(8-SRP_25),(8-SRP_26),SRP_27,(8-SRP_28),SRP_29,SRP_30)
+alpha(SRPframe)
+
+TASframe <- data.frame(TAS_01,TAS_02,TAS_03,TAS_04,TAS_05,TAS_06,TAS_07,TAS_08,(6-TAS_09),TAS_10,TAS_11,TAS_12,(6-TAS_13),TAS_14,(6-TAS_15),TAS_16,TAS_17,(6-TAS_18),(6-TAS_19),TAS_20)
+alpha(TASframe)
+
+#Correlation between different individual traits measures
+
+cor.test(CRT,NFC)
+cor.test(CRT,FI)
+cor.test(CRT,IRI)
+cor.test(CRT,SRP)
+cor.test(CRT,TAS)
+cor.test(NFC,FI)
+cor.test(NFC,IRI)
+cor.test(NFC,SRP)
+cor.test(NFC,TAS)
+cor.test(FI,IRI)
+cor.test(FI,SRP)
+cor.test(FI,TAS)
+cor.test(IRI,SRP)
+cor.test(IRI,TAS)
+cor.test(SRP,TAS)
+
+#Correlations between individual traits and utilitarian scores
+
+cor.test(CRT,UD_ut)
+cor.test(CRT,PD_ut)
+cor.test(CRT,HC_ut)
+cor.test(CRT,AO_ut)
+cor.test(CRT,DE_ut)
+cor.test(CRT,P_ut)
+cor.test(NFC,UD_ut)
+cor.test(NFC,PD_ut)
+cor.test(NFC,HC_ut)
+cor.test(NFC,AO_ut)
+cor.test(NFC,DE_ut)
+cor.test(NFC,P_ut)
+cor.test(FI,UD_ut)
+cor.test(FI,PD_ut)
+cor.test(FI,HC_ut)
+cor.test(FI,AO_ut)
+cor.test(FI,DE_ut)
+cor.test(FI,P_ut)
+cor.test(IRI,UD_ut)
+cor.test(IRI,PD_ut)
+cor.test(IRI,HC_ut)
+cor.test(IRI,AO_ut)
+cor.test(IRI,DE_ut)
+cor.test(IRI,P_ut)
+cor.test(SRP,PD_ut)
+cor.test(SRP,HC_ut)
+cor.test(SRP,AO_ut)
+cor.test(SRP,DE_ut)
+cor.test(SRP,P_ut)
+cor.test(TAS,UD_ut)
+cor.test(TAS,PD_ut)
+cor.test(TAS,HC_ut)
+cor.test(TAS,AO_ut)
+cor.test(TAS,DE_ut)
+cor.test(TAS,P_ut)
+
+#Calculating a global utilitarian score
+
+util <- (UD_ut+PD_ut+HC_ut+DE_ut+AO_ut+P_ut)/6
+mean(util)
+sd(util)
+
+#Correlations between global utilitarian score and individual traits measures
+
+cor.test(util,CRT)
+cor.test(util,NFC)
+cor.test(util,FI)
+cor.test(util,IRI)
+cor.test(util,SRP)
+cor.test(util,TAS)
+cor.test(util,TAS)
+cor.test(util,CRT)
+cor.test(util,NFC)
+
+#Calculating average utilitarian score (and standard deviation) for each individual scenario
+
+8-mean(U.D.01)
+sd(U.D.01)
+8-mean(U.D.02)
+sd(U.D.02)
+8-mean(U.D.03)
+sd(U.D.03)
+8-mean(U.D.04)
+sd(U.D.04)
+8-mean(U.D.05)
+sd(U.D.05)
+8-mean(U.D.06)
+sd(U.D.06)
+8-mean(U.D.07)
+sd(U.D.07)
+8-mean(U.D.08)
+sd(U.D.08)
+8-mean(U.D.09)
+sd(U.D.09)
+8-mean(U.D.10)
+sd(U.D.10)
+8-mean(P.D.01)
+sd(P.D.01)
+8-mean(P.D.02)
+sd(P.D.02)
+8-mean(P.D.03)
+sd(P.D.03)
+8-mean(P.D.04)
+sd(P.D.04)
+8-mean(P.D.05)
+sd(P.D.05)
+8-mean(P.D.06)
+sd(P.D.06)
+8-mean(P.D.07)
+sd(P.D.07)
+8-mean(P.D.08)
+sd(P.D.08)
+8-mean(P.D.09)
+sd(P.D.09)
+8-mean(P.D.10)
+sd(P.D.10)
+8-mean(H.C.01)
+sd(H.C.01)
+8-mean(H.C.02)
+sd(H.C.02)
+8-mean(H.C.03)
+sd(H.C.03)
+8-mean(H.C.04)
+sd(H.C.04)
+8-mean(H.C.05)
+sd(H.C.05)
+8-mean(H.C.06)
+sd(H.C.06)
+8-mean(H.C.07)
+sd(H.C.07)
+8-mean(H.C.08)
+sd(H.C.08)
+8-mean(H.C.09)
+sd(H.C.09)
+8-mean(H.C.10)
+sd(H.C.10)
+8-mean(A.O.01)
+sd(A.O.01)
+8-mean(A.O.02)
+sd(A.O.02)
+8-mean(A.O.03)
+sd(A.O.03)
+8-mean(A.O.04)
+sd(A.O.04)
+8-mean(A.O.05)
+sd(A.O.05)
+8-mean(A.O.06)
+sd(A.O.06)
+8-mean(A.O.07)
+sd(A.O.07)
+8-mean(A.O.08)
+sd(A.O.08)
+8-mean(A.O.09)
+sd(A.O.09)
+8-mean(A.O.10)
+sd(A.O.10)
+8-mean(P.01)
+sd(P.01)
+8-mean(P.02)
+sd(P.02)
+8-mean(P.03)
+sd(P.03)
+8-mean(P.04)
+sd(P.04)
+8-mean(P.05)
+sd(P.05)
+8-mean(P.06)
+sd(P.06)
+8-mean(P.07)
+sd(P.07)
+8-mean(P.08)
+sd(P.08)
+8-mean(P.09)
+sd(P.09)
+8-mean(P.10)
+sd(P.10)
+mean(D.E.01)
+sd(D.E.01)
+mean(D.E.02)
+sd(D.E.02)
+mean(D.E.03)
+sd(D.E.03)
+mean(D.E.04)
+sd(D.E.04)
+mean(D.E.05)
+sd(D.E.05)
+mean(D.E.06)
+sd(D.E.06)
+mean(D.E.07)
+sd(D.E.07)
+mean(D.E.08)
+sd(D.E.08)
+mean(D.E.09)
+sd(D.E.09)
+mean(D.E.10)
+sd(D.E.10)
+
+#Cluster analysis
+
+library(pvclust)
+
+mydata <- data.frame(UD_ut,PD_ut,HC_ut,AO_ut,DE_ut,P_ut)
+fit <- pvclust(mydata, method.hclust="ward", method.dist="euclidean")
+plot(fit)
+
+pvrect(fit, alpha=.95)
+
+#Correlation plot
+
+> pairs.panels(mydata, 
++              method = "pearson", # correlation method
++              hist.col = "#00AFBB",
++              density = FALSE,  # show density plots
++              ellipses = FALSE, # show correlation ellipses
++              lm = TRUE,
++              stars = TRUE)
\ No newline at end of file
diff --git a/102/replication_package/step2/Step2_Materials.docx b/102/replication_package/step2/Step2_Materials.docx
new file mode 100644
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--- /dev/null
+++ b/102/replication_package/step2/Step2_Materials.docx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47b171007ed468cb77ae1cfa788be1ba8e61902aa9a741ba9bef3f2fc3c18aa1
+size 35248
diff --git a/102/replication_package/step2/Step2_Procedure.pdf b/102/replication_package/step2/Step2_Procedure.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..b75701e7989167593b91b6c3c1cc3064a664d8f7
--- /dev/null
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+oid sha256:8fb31c915219f71bfbff451bb811aefcf2d5061bcb55474ddb332cad3b801b76
+size 107879
diff --git a/102/replication_package/step2/Step2_Results.docx b/102/replication_package/step2/Step2_Results.docx
new file mode 100644
index 0000000000000000000000000000000000000000..dcf27a38dbd5c1001f8bbe43fd359f1f270ba4d2
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+oid sha256:2fe3c4cbcb41289c9619d1db2006f43721e124e8516c7a8c09a1e90dd7726c73
+size 3251730
diff --git a/102/replication_package/step2/Step2_Results_CLEAN_EnglishVersion.csv b/102/replication_package/step2/Step2_Results_CLEAN_EnglishVersion.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ef7bd8590d748e4106a75e0956931523e9211cc1
--- /dev/null
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+version https://git-lfs.github.com/spec/v1
+oid sha256:374ffcdba02495afec0f553856779200bd449683bf3af0645ce18142e02437dd
+size 53248
diff --git a/102/replication_package/step2/Step2_Results_CLEAN_FrenchVersion.csv b/102/replication_package/step2/Step2_Results_CLEAN_FrenchVersion.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8f8b7dc4b26f9682b507a63f0a16774e22652a00
--- /dev/null
+++ b/102/replication_package/step2/Step2_Results_CLEAN_FrenchVersion.csv
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diff --git a/102/replication_package/step5/Step 5 ATA analysis.R b/102/replication_package/step5/Step 5 ATA analysis.R
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+++ b/102/replication_package/step5/Step 5 ATA analysis.R	
@@ -0,0 +1,145 @@
+# Step 5 Attitudes Towards Animals analysis
+
+library(psych)
+library(dplyr)
+
+dat <- Step5_CleanData
+
+##Excluding participants who failed attention checks
+dat <- dat[dat$AOT.Attentioncheck1. == '5', ]
+dat <- dat[dat$AOT.Attentioncheck2. == '1',]
+attach(dat)
+
+##Demographics
+
+mean(InfoD01)
+sd(InfoD01)
+table(InfoD02)
+
+##Attitudes towards animal scales
+dat$ATA.AE2. <- 8-ATA.AE2R.
+dat$ATA.AE6. <- 8-ATA.AE6R.
+dat$ATA.AE7. <- 8-ATA.AE7R.
+dat$ATA.AE7. <- 8-ATA.AE7R.
+dat$ATA.AE8. <- 8-ATA.AE8R.
+dat$ATA.AE10. <- 8-ATA.AE10R.
+dat$ATA.AE11. <- 8-ATA.AE11R.
+dat$ATA.AE16. <- 8-ATA.AE16R.
+
+ATA1_frame <- data.frame(ATA.AE1., ATA.AE2.)
+alpha(ATA1_frame)
+ATA1 <- rowMeans(ATA1_frame)
+
+ATA2_frame <- data.frame(ATA.AE3., ATA.AE4., ATA.AE5.,ATA.AE6., ATA.AE7., ATA.AE8., ATA.AE9., ATA.AE10., ATA.AE11.)
+alpha(ATA2_frame)
+ATA2 <- rowMeans(ATA2_frame)
+
+ATA3_frame <- data.frame(ATA.AE12., ATA.AE13., ATA.AE14.,ATA.AE15., ATA.AE16., ATA.AE17.)
+alpha(ATA3_frame)
+ATA3 <- rowMeans(ATA3_frame)
+
+
+cor.test(ATA1,ATA2)
+cor.test(ATA1,ATA3)
+cor.test(ATA2,ATA3)
+
+
+##Measures of utilitarianism
+#GUI = Geneva Utilitarianism Inventory 
+
+# Sacrificial Dilemmas 
+GUISD_frame <- data.frame (UD1, UD2, UD10, PD3, PD8)
+alpha(GUISD_frame)
+GUISD <-  8- rowMeans(GUISD_frame)
+
+#Harmless Crimes 
+GUIHC_frame <- data.frame(HC1, HC2, HC3, HC4, HC9)
+alpha(GUIHC_frame)
+GUIHC <- 8 - rowMeans(GUIHC_frame)
+
+#Action vs Omission 
+GUIAO_frame <- data.frame(AO1, AO5, AO6, AO8, AO10)
+alpha(GUIAO_frame)
+GUIAO <- 8 - rowMeans(GUIAO_frame)
+
+#Demanding Ethics 
+GUIDE_frame <- data.frame(DE2, DE3, DE4, DE5, DE7)
+alpha(GUIDE_frame)
+GUIDE <- rowMeans(GUIDE_frame)
+
+#Punishment 
+GUIP_frame <- data.frame(P3, P4, P6, P7, P10)
+alpha(GUIP_frame)
+GUIP <- 8-rowMeans(GUIP_frame)
+
+
+##Measures of cognitive style
+
+##CRT
+dat$CRT1B <- ifelse(CRT1 == "2" , 1, 0)
+dat$CRT2B <- ifelse(CRT2 == "225" , 1, 0)
+dat$CRT3B <- ifelse(CRT3 == "5" , 1, 0)
+dat$CRT <- rowSums(dat[,c("CRT1B", "CRT2B", "CRT3B")])
+
+datONE <- dat # new dataset with only those who knew no more than 1 item of the modified CRT 
+datONE <- filter(datONE, datONE$CRTknowledge1 <= 1)
+
+CRTa <- dat[,c("CRT1B", "CRT2B", "CRT3B")]
+CRTaONE <- datONE[,c("CRT1B", "CRT2B", "CRT3B")]
+
+alpha(CRTa, cumulative = FALSE, n.obs = 234)
+alpha(CRTaONE,cumulative = FALSE, n.obs =  188) 
+
+
+# Faith in Intuition for Facts
+FI_frame <- data.frame(FI.FI1.,FI.FI2., FI.FI3.,  FI.FI4.)
+alpha(FI_frame) 
+FI <- rowMeans(FI_frame)
+ 
+
+# Actively Open-minded Thinking
+AOT.AOT4. <- 6-AOT.AOT4R.
+AOT.AOT5. <- 6-AOT.AOT5R.
+AOT.AOT6. <- 6-AOT.AOT6R.
+AOT.AOT7. <- 6-AOT.AOT7R.
+
+AOT_frame <- data.frame(AOT.AOT1.,AOT.AOT2.,AOT.AOT3.,AOT.AOT4.,AOT.AOT5.,AOT.AOT6.,AOT.AOT7.,AOT.AOT8.)
+alpha(AOT_frame)
+AOT <- rowMeans(AOT_frame)
+
+#### Interpersonal Reactivity Index 
+IRI.IRI2. = 6- IRI.IRI2R.
+IRI.IRI4. = 6- IRI.IRI4R.
+IRI.IRI5. = 6- IRI.IRI5R.
+
+IRI_frame <- data.frame(IRI.IRI1. , IRI.IRI2. ,  IRI.IRI3. ,  IRI.IRI4. ,  IRI.IRI5. ,  IRI.IRI6. ,  IRI.IRI7. )
+alpha(IRI_frame)
+IRI <- rowMeans(IRI_frame)
+
+#Self Report Psychopathy Scale 
+SRP.SRP23. = 8- SRP.SRP23R.
+SRP.SRP24. = 8- SRP.SRP24R.
+SRP.SRP25. = 8- SRP.SRP25R.
+SRP.SRP26. = 8- SRP.SRP26R.
+SRP.SRP28. = 8- SRP.SRP28R.
+
+SRP_frame <- data.frame(SRP.SRP01.,SRP.SRP02., SRP.SRP03., SRP.SRP04., SRP.SRP05., 	SRP.SRP06., SRP.SRP07., SRP.SRP08., 	SRP.SRP09., SRP.SRP10., SRP.SRP11.,	SRP.SRP12., SRP.SRP13., SRP.SRP14., 	SRP.SRP15.,	SRP.SRP16., 	SRP.SRP17., 	SRP.SRP18., SRP.SRP19., 	SRP.SRP20., 	SRP.SRP21.,	SRP.SRP22., 	SRP.SRP23.,	SRP.SRP24.,	SRP.SRP25., 	 SRP.SRP26., 	SRP.SRP27., 	SRP.SRP28.	, SRP.SRP29.,	SRP.SRP30. )
+alpha(SRP_frame)
+SRP <- rowMeans(SRP_frame)
+
+## 
+cor.test (GUISD, ATA1)
+
+
+
+
+## Supplementary materials
+
+## Principal component analysis on the Attitudes towards Animals measures
+
+C <- lowerCor(dat[, c("ATA.AE1.", "ATA.AE2.", "ATA.AE3.", "ATA.AE4.", "ATA.AE5.","ATA.AE6.", "ATA.AE7.", "ATA.AE8.", "ATA.AE9.", "ATA.AE10.", "ATA.AE11.","ATA.AE12.", "ATA.AE13.", "ATA.AE14.","ATA.AE15.", "ATA.AE16.", "ATA.AE17.")])
+
+principal(C, nfactors =3, n.obs = 234, rotate = "none")
+
+fa.parallel(C, n.obs= 234, fa = "pc", nfactors = 3)
+
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diff --git a/102/replication_package/step6/step6.2/Step6.2_Analyses.Rhistory b/102/replication_package/step6/step6.2/Step6.2_Analyses.Rhistory
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+dat <- read.table("Step6.2_Total.dat",header=TRUE)
+
+##Participants
+
+length(levels(as.factor(dat$SUBJECT)))
+table(dat$GENDER)/48
+mean(dat$AGE)
+sd(dat$AGE)
+
+	##Exclusion of mistranslated scenario
+
+dat <- dat[dat$SCENARIO!="P_08",]
+
+	##Exclusion
+
+COND <- as.factor(dat$CONDITION1_PRESSURE)
+levels(COND) <- c("SLOW","FAST")
+
+tab <- table(dat$SUBJECT[COND=="FAST"],dat$BINARY[COND=="FAST"],dat$CATEGORY[COND=="FAST"])
+(tab[,1,1]+tab[,2,1])<3
+(tab[,1,2]+tab[,2,2])<3
+(tab[,1,3]+tab[,2,3])<3
+(tab[,1,4]+tab[,2,4])<3
+(tab[,1,5]+tab[,2,5])<3
+(tab[,1,6]+tab[,2,6])<3
+
+dat <- dat[dat$SUBJECT!=64,]
+
+##Average time constraint in the Fast condition
+
+COND <- as.factor(dat$CONDITION1_PRESSURE)
+levels(COND) <- c("SLOW","FAST")
+
+mean(dat$BINARY_LIMIT[COND=="FAST"],na.rm=TRUE)
+sd(dat$BINARY_LIMIT[COND=="FAST"],na.rm=TRUE)
+
+##Computation of utilitarian scores
+
+raw_score <- as.numeric(as.factor(dat$BINARY))-1
+dat2 <- aggregate(raw_score,list(SUBJECT=dat$SUBJECT,CATEGORY=dat$CATEGORY,COND=COND),mean,na.rm=TRUE)
+
+score <- dat2$x
+score[dat2$CATEGORY=="UD"] <- 1-score[dat2$CATEGORY=="UD"]
+score[dat2$CATEGORY=="PD"] <- 1-score[dat2$CATEGORY=="PD"]
+score[dat2$CATEGORY=="HC"] <- 1-score[dat2$CATEGORY=="HC"]
+score[dat2$CATEGORY=="AO"] <- 1-score[dat2$CATEGORY=="AO"]
+score[dat2$CATEGORY=="P"] <- 1-score[dat2$CATEGORY=="P"]
+
+##Mean and SD for Utilitarian Scores
+
+mean(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
+mean(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+##Correlations between scores
+
+cor.test(score[dat2$CATEGORY=="UD" & dat2$COND=="FAST"],score[dat2$CATEGORY=="UD" & dat2$COND=="SLOW"])
+cor.test(score[dat2$CATEGORY=="PD" & dat2$COND=="FAST"],score[dat2$CATEGORY=="PD" & dat2$COND=="SLOW"])
+cor.test(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"])
+cor.test(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"])
+cor.test(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"])
+cor.test(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"])
+
+##Comparison between conditions
+
+CATEGORY <- dat2$CATEGORY
+CONDITION <- dat2$COND
+dat3 <- data.frame(score,CATEGORY,CONDITION)
+
+library(lsr)
+
+t.test(score[dat3$CATEGORY=="UD" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="UD" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datUD <- dat3[dat3$CATEGORY=="UD",]
+cohensD(score ~ CONDITION, data = datUD, method = "paired")
+
+t.test(score[dat3$CATEGORY=="PD" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="PD" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datPD <- dat3[dat3$CATEGORY=="PD",]
+cohensD(score ~ CONDITION, data = datPD, method = "paired")
+
+t.test(score[dat3$CATEGORY=="HC" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="HC" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datHC <- dat3[dat3$CATEGORY=="HC",]
+cohensD(score ~ CONDITION, data = datHC, method = "paired")
+
+t.test(score[dat3$CATEGORY=="AO" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="AO" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datAO <- dat3[dat3$CATEGORY=="AO",]
+cohensD(score ~ CONDITION, data = datAO, method = "paired")
+
+t.test(score[dat3$CATEGORY=="DE" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="DE" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datDE <- dat3[dat3$CATEGORY=="DE",]
+cohensD(score ~ CONDITION, data = datDE, method = "paired")
+
+t.test(score[dat3$CATEGORY=="P" & dat3$CONDITION=="FAST"],score[dat3$CATEGORY=="P" & dat3$CONDITION=="SLOW"],paired=TRUE)
+datP <- dat3[dat3$CATEGORY=="P",]
+cohensD(score ~ CONDITION, data = datP, method = "paired")
+
+##Figures
+
+cond_class <- ordered(dat3$CONDITION, levels = c("FAST", "SLOW"))
+type_class <- ordered(dat3$CATEGORY, levels = c("UD", "PD", "HC","AO","DE","P"))
+dat4 <- data.frame(score,type_class,cond_class)
+
+library(ggplot2)
+ggplot(dat3, aes(type_class, score, fill=factor(cond_class))) +
+  geom_boxplot()+
+  scale_y_continuous(breaks = seq(0, 1, by = 0.1))+
+  ggtitle("Study 2 (Time constraint) - Utilitarian scores per type of scenarios and condition")+
+  xlab("Type of scenarios")+
+  ylab("Utilitarian scores")+
+  scale_fill_discrete(name = "Condition", labels = c("Fast", "Slow"))+
+  theme(axis.title=element_text(size=16,face="bold"), axis.text=element_text(size=14),legend.title=element_text(size=16,face="bold"),
+  legend.text=element_text(size=14), plot.title=element_text(size=20,face="bold",hjust=0.5))+
+  annotate(geom="text", x=1, y=1.1, label="d=-0.09, p=.33", color="black",size=5)+
+  annotate(geom="text", x=2, y=1.1, label="d=-0.16, p=.08", color="black",size=5)+
+  annotate(geom="text", x=3, y=1.1, label="d=0.60, p<.001***", color="black",size=5)+
+  annotate(geom="text", x=4, y=1.1, label="d=-0.15, p=.10", color="black",size=5)+
+  annotate(geom="text", x=5, y=1.1, label="d=0.26, p=.005**", color="black",size=5)+
+  annotate(geom="text", x=6, y=1.1, label="d=0.03, p=.75", color="black",size=5)
+
+ggsave('Step6.2.tiff', width = 15, height = 10,dpi=600, compression = "lzw")
+
+##Preparation of data for mini-meta
+
+library(effsize)
+
+	#D
+
+mean(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="FAST"],na.rm=TRUE)
+
+mean(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[(dat2$CATEGORY=="UD" | dat2$CATEGORY=="PD") & dat2$COND=="SLOW"],na.rm=TRUE)
+
+cohen.d((datUD$score+datPD$score) ~ datUD$CONDITION)
+
+	#HC
+
+mean(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="HC" & dat2$COND=="FAST"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="HC" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+cohen.d(datHC$score ~ datHC$CONDITION)
+
+	#AO
+
+mean(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="AO" & dat2$COND=="FAST"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="AO" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+cohen.d(datAO$score ~ datAO$CONDITION)
+
+	#DE
+
+mean(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="DE" & dat2$COND=="FAST"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="DE" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+cohen.d(datDE$score ~ datDE$CONDITION)
+
+	#P
+
+mean(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="P" & dat2$COND=="FAST"],na.rm=TRUE)
+
+mean(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
+sd(score[dat2$CATEGORY=="P" & dat2$COND=="SLOW"],na.rm=TRUE)
+
+cohen.d(datP$score ~ datP$CONDITION)
+
+
+
+
+
+
+
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