I'm analyzing experimental data and the model shows a significant treatment effect, but the raw data and graph of the effect don't seem to match it. I want to understand why. I've been looking at this for too long so I may be missing something obvious.
The df, "distress," is a count variable without zero-inflation. Participants (N = 60) were randomized to receive a treatment designed to reduce distress or a control. They completed a baseline measure, received the experimental treatment, and then were assessed at posttreatment and a follow-up.
The model I'm running is as follows:
glmer(distress ~ condition*time + (1|id), family="poisson", data = df)
It gives the following output, showing a significant treatment effect in reducing distress:
However, the plotted interaction with model-predicted values makes it look like there's no difference, or a negligible difference (grey shading is 95% CI):
The raw means show a slightly larger decrease for the intervention group from pre to post, but the control group decreases more from pre to follow-up.