# Significant interaction, inconsistent with plots/raw data

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.

• The plot certainly looks like it has negative interaction to me (I wonder if you're thinking 'curves look parallel, therefore no interaction' -- is that the case? If not, please indicate what is leading you to conclude there's no interaction). You might find it easier to see on the scale of the linear predictor (i.e. log scale) – Glen_b Jul 14 '19 at 1:03
• I am not sure the Poisson model is the correct one... try a linear model as well (possibly after transformation)... in addition, make sure you account for within-subject clustering... – Joe_74 Jul 16 '19 at 8:30