Where would you begin analysing this? I would really appreciate your help. Despite enjoying stats, this issue has been very much bugging me and I can't get my head around it. Everytime I make a breakthrough I end up back at square one.
I have an analysis to complete. I'm looking at whether attractiveness ratings (7 point Likert scale) differ on the basis of waist-to-hip ratio (WHR) and BMI. I also measured participants sociosexual orientation (SIO).
Both WHR (0.6, 0.7, 0.8, 0.9, 1.0) and BMI (Emaciated, Under, Normal, Over, Obese) are categorical and SIO is continuous (0-100).
I guess what I'm interested in is whether WHR and BMI influence attractiveness ratings, which is a simple within-subject ANOVA (attractrating ~ WHR*BMI, data). But then when adding in the SIO scores it becomes confusing. I'm looking at whether a person's SIO influences their attractiveness ratings. Do they prefer lower BMI/WHR? I assume this serves as a moderator term? So it would make sense to then model this as an ANCOVA and include an interaction between WHR/SIO and BMI/SIO. But, then I have read that using an ANCOVA with within-subject predictors presents issues - and that SIO would need to be mean centered, which may reduce it's interpretability.
Any help would be appreciated as this is stressing me.

From the description, it seems that you have repeated measures within participants, and presumably within whatever it is that the participants are rating. So a model with random intercepts for these would make sense.
If you are going to interact SIO with WHR*BMI then indeed it will make sense to centre it, since the other estimates will be conditional on SIO being 0 - so if you mean-centre it, then it will be conditional on it being at it's mean. On the other hand, if it makes sense to have an SIO score of 0, then there is no need to centre it. The same is also true of WHR and BMI - when these are included in an interaction then it will make sense to centre these too - otherwie the other estimates will be conditional on WHR = 0 and BMI = 0
