rn2407stanford
commented
1 month ago library(readxl)
y <- read_excel("QPart1Data_objects.xlsx")
x=y[, 19:190 ]
colnames(x)<-x[1,]
x<-x[-1,]
x=x[,-grep("Text", colnames(x)) ]
x=x[, -grep("quality", colnames(x))]
n=dim(x)[2]/8
for (i in 1:n) L[[i]]<-x[,((i-1)*8+1):(i*8)]
df=data.frame()
for (i in 1:n){
l=L[[i]][,-8]
id=L[[i]][1,8]
m=list()
for (j in 1:ncol(l)) m[[j]]=data.frame(id=id, item=colnames(l)[j], resp=x[,j])
for (i in 1:ncol(l)) colnames(m[[i]])<-c("id", "item", "resp")
d<-data.frame(do.call("rbind",m))
df=rbind(df, d)
}
save(df,file='Part1.Rdata')
ben-domingue
https://link.springer.com/article/10.3758/s13428-021-01540-6#additional-information
The experiment was preregistered on OSF, including the methods, sample size, and analysis plans. The preregistration can be found at: https://osf.io/htnqd/.
NOTE: in this study, the images are the 'id', the items are the below qustions, and the humans are raters (who hopefully have IDs).
the items: The questions were as follows: (a) “How weird is the image?” (b) “How likely is it to see such a scene in the real world?” (c) “How visually complicated is the image?” (d) “How hard is it to identify the object?” (here, the word “object” was replaced with the name of the specific object that appeared in the presented scene; for example, “How hard is it to identify the bottle?”). (e) “How disturbing is the image?” (f) “How much do you like the image?” (g) “How arousing do you find the image?” (h) “Which object is MOST likely to appear in the scene?” Questions (a)–(g) were answered using a seven-point Likert scale of 1 (not at all) to 7 (very much), and question (h) was a multiple-choice question, with the following options: the congruent object (e.g., bottle); the incongruent object (e.g., flashlight); another object that is semantically related to the congruent object (e.g., glass); another object that is semantically related to the incongruent object (e.g., candle); and “other” (with an option to fill in the answer).