Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques

Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another.Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed-and random-effects regression.Often, these interactive effects must be further probed to fully explicate the nature of the conditional relation.The most common method for probing interactions is to test simple slopes at specific levels of the predictors.A more general method is the Johnson-Neyman (J-N) technique.This technique is not widely used, however, because it is currently limited to categorical by continuous interactions in fixed-effects regression and has yet to be…