The goal: interpret the direction of the interaction term.

Variables: I have two continuous predictors (heart rate [HR] and pupil dilatation [PD]), predicting a dichotomous outcome (diagnosis of conduct disorder - no=0, yes=1)

I am controlling for age and gender.

Results: The interaction between HR and PD is significant, the beta is positive (suggesting a positive interaction - as HR increases so does PD). And my odds ratio is above 1 (3.1). I interpret the odds ratio meaning the increase in both HR and PD elevates the likelihood of being diagnosed with conduct disorder.

Here's the catch; I am not sure if I am looking at this correctly. I have been told that the positive interaction could indicate HR and PD are both negative resulting in a positive interaction also. How do I find out if the positive interaction means it is

*high HR and high PD*or if it is

*low HR and low PD*?

My closing thoughts are that the odds ratio may help explain this but I am not entirely sure if this is accurate. Another method I know may be plotting a simple slopes model but this is tricky because I have covariates.

I am open to suggestions, and would love to have some feedback on how to interpret this interaction term.

....................Estimate Std.

(Intercept):....-2.3594

age:..............0.2037

gender1:.......-1.8419*

HP:................-0.0324

BD:................1.4095*

HP:BD:...........1.1849*

..........................OR

(Intercept)..........0.0944774

age:...................1.2259709

gender:...............0.1585216

hp:.....................0.9681189

BD:....................4.0936999

HP:BD................3.032873

Many thanks in advance