When to leave in significant two way interactions?
And as always, leave in any lower order terms, significant or not, for any higher order terms in the model. That means you have to leave in all insignificant two-way interactions for any significant 3-ways.
When to include lower order interactions in regression?
Always include the lower order interactions, otherwise it is mis-attributing the variance to higher order interactions explained by lower order interactions (i.e. single factors and 2-way interactions). In your case, it sounds like the model is under-powered and results become insignificant with more DF used.
When to drop an interaction in a model?
The interaction uses up df and changes the meaning of the lower order coefficients and complicates the model. So if you were just checking for it, drop it. But if you actually hypothesized an interaction that wasn’t significant, leave it in the model. The insignificant interaction means something in this case–it helps you evaluate your hypothesis.
What is the moral side of the interaction order?
In his work Goffman repeatedly dealt with the moral sides of the interaction order. For Goffman the self is a moral fact which implies rights and duties when showing respect and which can be observed in ritual practices and codes of everyday interaction.
What happens when you take out the interaction?
When you take out the interaction, you are essentially setting it equal to zero. In other words, you are forcing the male and female slopes to be equal. Both running the groups separately and including an interaction allow each slope to be estimated uniquely.
When do you ignore interaction effects in statistics?
When you have statistically significant interactions, you cannot interpret the main effect without considering the interaction effects. Given the intentionally intuitive nature of our silly example, the consequence of disregarding the interaction effect is evident at a passing glance.