Keywords: cancer, depression, effect modifiers, moderated regression, moderators, sickness behaviour, statistical interactions, symptom clusters, zero slope comparisons, continuous variables, ordinal variables, medical symptoms, multiple regression, multiple interdependencies, follow-up procedures, magnifier effects, aggravating effects, relieving effects, buffering effects, x-y relationships, multiple interactions, complex interactions, curvilinearity, co-moderator variables, linear interactions, curvilinear interactions, novel extensions, co-occurring symptoms, sickness malaise, society, systems science, assessment methods, social systems
Interpreting interactions of ordinal or continuous variables in moderated regression using the zero slope comparison: tutorial, new extensions, and cancer symptom applications
Moderated multiple regression (MMR) can model behaviours as multiple interdependencies within a system. When MMR reveals a statistically significant interaction term composed of ordinal or continuous variables, a follow-up procedure is required to interpret its nature and strength across the primary predictor (x) range. A follow-up procedure should probe when interactions reveal magnifier (or aggravating) effects and/or buffering (or relieving) effects that qualify the x-y relationship, especially when interpreting multiple interactions, or a complex interaction involving curvilinearity or multiple co-moderator variables. After a tutorial on the zero slope comparison (ZSC), a rarely used, quick approach for interpreting linear interactions between two ordinal or continuous variables, I derive novel extensions to interpret curvilinear interactions between two variables and linear interactions among three variables. I apply these extensions to interpret how co-occurring cancer symptoms at different levels influence one another – based on their interaction – to predict feelings of sickness malaise.