one-way repeated measures anova

#1
Hi everyone!
I am currently working on my master thesis. We asked participants to rate initially neutral faces on 7 personality traits after a conditioning procedure in which each initially neutral picture was paired with a picture that differed in attractiveness. This way I have now per participant for each personality trait 3 scores (The scores given to faces paired with high, medium, and low attractive faces). I want to check whether the kind of personality trait influences the differences given in the three groups. When I want to do a one-way repeated measure anova I struggle to differentiate between the IV and the DV because I only have the three scores, for example (high_intelligence, medium_intelligence and low_intelligence). These are the scores the participants gave the initially neutral faces after being paired with an high, medium or low attractive face). I hope my research question is clear. I am really struggling with this hypothesis
 

Karabiner

TS Contributor
#2
I do not quite understand what you mean by saying "I struggle to differentiate between the IV and the DV because I only have the three scores". Seemingly, your dependent variable is a trait rating, for example the intelligence rating, and the independent variable is "condition" (three levels - high, medium, low attractiveness). So you could do 7 Friedman tests as global tests for the effect of "condition".

With kind regards

Karabiner
 
#3
I see how it works, but I can't seem to manage it with my own data. I have three scores per trait variable (one for each level of attractiveness they were paired to) I just want to know whether there is a difference per trait in how much they are influenced by the attractiveness. I can see how "condition" is the IV and the trait rating is the DV, but in the data I'm working with, I only have for example (high_intelligence, medium_intelligence, and low_intelligence. I have no idea how to separate the DV and IV during analysis. It's probably much easier than I make it sound, but I'm very confused and struggling with it.