This primer is divided into 6 sections:
1. Two-way interaction effects in MLR
2. Regions of significance
3. Plotting and probing higher order interactions
4. Centering variables
5. Cautions regarding interactions in standardized regression
6. References
Two-Way Interaction Effects in MLR
An interaction occurs when the magnitude of the effect of one independent variable (X) on a dependent variable (Y) varies as a function of a second independent variable (Z). This is also known as a moderation effect, although some have more strict criteria for moderation effects than for interactions. Interactions occur potentially in situations involving univariate analysis of variance and covariance (ANOVA and ANCOVA), multivariate analysis of variance and covariance (MANOVA and MANCOVA), multiple linear regression (MLR), logistic regression, path analysis, and covariance structure modeling. This primer is concerned with interactions as they occur in MLR. ANOVA and ANCOVA models are special cases of MLR in which one or more predictors are nominal or ordinal "factors." It is straightforward to estimate such models in the MLR framework, but the accompanying web pages were designed for use with interactions among two or three continuous and/or dichotomous predictor variables only.
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