The ICALC Toolkit is a companion my book, Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata, published by Sage in October 2018. Part I covers the principles of interpreting interaction effects, with a particular emphasis on the challenges of interpretation for GLM’’s with non-linear link functions. Part II focuses on the application of these principles to a variety of GLMs sing ICALC to produce the necessary calculations, tables and graphics. To purchase the book or to see more details, click here.
What is ICALC?
ICALC stands for Interaction CALCulator. The ICALC Toolkit for Stata is available at no charge (click here). It consists of a set-up tool plus four separate tools for understanding the effect of the focal variable on the outcome as moderated by the predictor(s) with which the focal variable interacts:
INTSPEC Tool: Specify the elements of your interaction model—the main effect variables, the two-way effect terms, and the three-way effect terms (if any). Optionally choose a display name and/or set display values for each of the interacting variables.
GFI (Gather, Factor, and Inspect) Tool: Find the algebraic expression for the effect of the focal variable on the modeled outcome. Determine if the focal variable’s effect changes sign as it varies with the values of its moderators..
SIGREG Tool: Create a table showing the focal variable’s effect and statistical significance as it varies across the values of the moderating predictor(s). Effects may be scaled in the original, as-estimated metric, or when relevant as factor changes and/or marginal changes.
EFFDISP Tool: Create visual counterparts to the significance region tables produced by SIGREG. Plot information about the varying magnitude and optionally the significance of the focal variable’s moderated effect.
OUTDISP Tool: Create tables and/or graphs of the predicted values of the outcome as it varies with the interacting predictors. Includes alternative displays for GLMs’ with non-linear link functions, such as logistic regression, ordinal regression models and count models.