Month: August 2019

If my sample is just barely large enough to detect my main effect, the smallest interaction that I can reasonably expect is a knockout effect.

For an interaction effect size bXM, the difference between the simple-slopes of the top and bottom quartiles Q will be approximately: bXM * (ln(Q) +1)

In this series I try to convey a couple of insights about power for interactions in linear regressions. First, how to do a power analysis for a interaction in a linear regression (this post), then interpreting the effect size of a interaction (part 2), and finally thinking about how large (or small) an effect size it is reasonable to plan for (part 3).