I would like to model the number of options chosen by participants (e.g. number of tasks) to see the effects of a prime (high vs low) and several possible moderators (including an interaction).
My question is which analytic strategy would be the best for the data (e.g. ordinal, poisson regression). My problem is that the DV is non-normally distributed, not continous, not ordinal and not categorical, and bound (0 lowest value, 4 highest).
As far as I understand, multiple linear regression assumes a continuous response variable. So that would mean that my dv (whole integers in a small range 0-4) would violate this assumption. Thus, I was thinking, it would be better to use a poisson regression. However, since my data is bound by 0 and 4, I'm also not sure whether this would be a good option. As my last option, I was considering ordinal regression. But since my dv is not really ordinal, this would be also not perfect.
- DV= number of tasks (0-4)
- IV1= prime (high vs. low)
- IV2= continuous scale
- interaction= IV1*IV2