I am wanting to determine whether components of Friendship Quality (measured by a scale) are related to a reduction in fear (multiple DVs). By components I mean that the Friendship scale I'm using is made up of 6 subscales. I have already conducted a 2 x 2 multivariate analysis of covariance as I wanted to see whether hand-holding caused a reduction in fear (friendship quality overall was the covariate). However, my supervisor agreed that it would be worthwhile to follow up with linear regression because it tells me exactly which friendship subscale relates to exactly which DV, which the MANCOVA doesn't do. I have a small sample size (38), so if I include the individual 6 subscales as covariates I would have 8 in total which is too many.
I understand that for multiple regression there should be no multicollinearity between the IVs, however that is inherent in the fact that the subscales all relate to friendship quality! Indeed, the bivariate correlations are all very high. VIFs are also over 10 for two of the subscales.
My question is - is it wrong to do several linear regressions - one for each DV and IV together. I feel like the answer is no ... and in that case, what model can I use? Preferably in SPSS! I had originally planned to do several multiple regressions because I can only use one DV at a time, but the data violate all the other assumptions too, apart from approximate normality, linearity and homoscedasticity of residuals - so likely that I'll need a non-parametric test too.
Please be gentle, I have a very limited understanding of regression models as they weren't really covered that thoroughly in the stats units I've done :( Any help would be greatly appreciated!