Hello I have been struggling for many days with this problem. The situation and data set are following. The data set comes from an experiment that asked subjects (ID) to compare several CVs on different experimental conditions. Each CV had two factors, picture(no picture, modified picture, normal picture) and gender (level: male/female). In order to rate CVs, participants were asked to complete likert scale questionnaire with minimum score of 7 to 49. Higher score indicated they "liked" the CV more. Furthermore the participants were sampled in 4 different settings, so the setting as well had a Factor (level: A,B,C,D).
Meaning that setting was between-factor, while picture and gender within-factor.
data.frame looks like this:
ID Setting Male_Normal Male_Modified Male_No_Picture Female_Normal....and so on.. 1 A 7 11 16 49 2 B 10 16 30 30 3 C 11 30 20 20 4 D 30 11 2 10 5 A 20 15 13 15 6 B 10 11 11 10 ..and ..so ..on .. .. ..
Main hypothesis: H1:Scores differ based on Setting H2:Different scores for Gender H3:Different scores for Pictures
Question is how to analyze it correctly in SPSS or/and R and how to do post-hoc afterwards, should univariate test be used or discriminant analyses?
I was trying to run repeated measures MANOVA in SPSS and in I tried approach using the car package R.
In R I did not get past formatting data set so it makes sense to R, or in other words I was not able to create factors for Gender and Pictures properly...
In SPSS I did large rep.measures MANOVA, found significance for everything literally and then I am not sure how to break the effects down. For example if GenderPictureGroup is significant, what would you do?
I know the question is probably not an easy one, though any leads are highly appreciated.
Thank you very much!