Title: Interpreting interactions of ordinal or continuous variables in moderated regression using the zero slope comparison: tutorial, new extensions, and cancer symptom applications

Authors: Richard B. Francoeur

Addresses: Adelphi University School of Social Work, Social Work Building, 1 South Avenue, #701, Garden City, NY 11530, USA

Abstract: 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.

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.

DOI: 10.1504/IJSSS.2011.038937

International Journal of Society Systems Science, 2011 Vol.3 No.1/2, pp.137 - 158

Available online: 07 Mar 2011 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article