For my master's thesis I'm exploring the relationship between attitude towards the advertismenent (Aad), brand types (boutiques and high street) and willingness to recommend (willing or not). Therefore, I need to run two regression analyses:
A bivariate regression (Aad = B0 + B1Brandtype + e)
A multiple regression (Willingness to recommend = B0 + B1Aad + B2BrandType + B3Aad*BrandType + e
Note that the Aad data are sentiment scores derived from Instagram comments (ranging from -0,9 to 0,9, where the negative values indicate a negative attitude towards the ad). The variables willingness and brand type are dummy coded.
However, when I run the two regressions, none of the assumptions are met... (normality, linearity, homoscedasticity). So my question is: would it make sense to log-transform the variable Aad for the assumptions to be met? However, one problem is that I'm dealing with negative values and so I need to add +100 for instance and then log-transform; however, if I transform those I wouldn't be able to recognize the negative attitude anymore? So then I can only say whether it has a relationship or not, but not which brand types significantly receives more positive attitudes? Right?