102/replication_package/step5/Step 5 ATA analysis.R

# 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)