I am trying to measure the effect of atmospheric factors as smell or light (IV) on purchase behavior (DV). In total I have xx likert scales that contain 5 likert items and responses are coded from 1 to 5.
I am wondering which approach would be the best to show that my IVs have some relevance. Could someone check if my approach makes sense?
- Clean out the data (delete monotone, take values & outliers into account, check normality)
- Conduct a reliability test with Cronbach's alpha
- Construct validity (convergent and discriminant)
- Harman single factor test
- Factor analysis
- Check if the 5 assumptions about MR are met (linearity, normality etc.)
Do you think that this approach is sufficient in order to show that my model has some value?