First off, I echo Peter's sentiments where you should just use the raw data and should not dichotomize continuous data in the way you have. This almost always comes at an information loss that affects the tests you run on it (Royston et al., 2006). As he also noted, one can just use regression here, where your two numeric IVs are simply entered as predictors into the regression (with an interaction term) and the engagement in politics variable is entered as the outcome/DV.
This can all be easily achieved within Jamovi. First you enter your variables as main effects (here I use the
Parenthood data from the
lsj module in Jamovi, though I first convert
dan.grump into continuous form).
To build the interaction, you just need to add both predictors together by hitting Ctrl and selecting them together in the Model Building section, then putting them in the same block as shown. You now see the
* symbol which denotes an interaction:
The point estimates show that the interaction isn't making much of a difference, but as Peter noted it may be useful to visualize this in some way. One way is to show the simple slopes (see Cohen et al., 2003 for more details), which can be generically visualized with the estimated marginal means (EMMs) section (which estimates the EMMs for categorical variables if they are not continuous like they are here).
The simple slopes show the interaction is very weak. If there was an interaction, the lines from
baby.sleep would vary considerably, but here they are almost exactly the same.
- Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed). L. Erlbaum Associates.
- Royston, P., Altman, D. G., & Sauerbrei, W. (2006). Dichotomizing continuous predictors in multiple regression: A bad idea. Statistics in Medicine, 25, 127–141. https://doi.org/10.1002/sim.2331