So typically I would have no problems with such interpretations. But my design is a little complicated so would appreciate any insight into this.
I am interested in looking at the effect of transitioning across experimental wealth levels on generosity (how much money they gave to a recipient after transitioning). In my study, I have four different between-subject groups. People who transition from poor to rich via merit, people who transition from poor to rich via luck, people who transition from rich to rich via merit, and people who transition from rich to rich via luck.
The problem here is - there are already baseline differences between groups at Time 1, which makes effects at Time 2 a little hard to interpret. To deal with this, I modelled Block (block 1 is before transitioning and block 2 is after) as a factor in my model as follows:
ResponseModel1 <- lme4::lmer(response ~ f_transition*f_manner*f_block + (1 | id), data = aggrdata, control = lmerControl(optimizer = "bobyqa", optCtrl=list(maxfun=200000000)))
The two-way interaction shown below is significant, but the three-way is not. The two-way seems to suggest that the difference between the poor to rich, and rich to rich groups is only significant in the luck condition. However, is this two-way interaction misleading because there are already baseline differences in groups at Block 1? What would be the best way to interpret this? Should I only look at the three-way?