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How do you report non significant interactions?
When reporting non-significant results, the p-value is generally reported as the a posteriori probability of the test-statistic. For example: t(28) = 1.10, SEM = 28.95, p = . 268.
How do you know if an interaction effect is significant?
To determine whether each main effect and the interaction effect is statistically significant, compare the p-value for each term to your significance level to assess the null hypothesis. Usually, a significance level (denoted as α or alpha) of 0.05 works well.
How do you tell if there is a significant interaction?
How do you report an interaction effect?
The easiest way to communicate an interaction is to discuss it in terms of the simple main effects. Describe one simple main effect, then describe the other in such a way that it is clear how the two are different.
When are the main effects of an interaction not significant?
It’s a question I get pretty often, and it’s a more straightforward answer than most. There is really only one situation possible in which an interaction is significant, but the main effects are not: a cross-over interaction.
When is an interaction term statistically significant?
If the interaction term is statistically significant, then the differences between the slopes for the variables included in the interaction term are statistically significant. With an R-squared that high, be sure that you’re not overfitting your model.
Is the difference between B1 and A2 significant?
In most data sets, this difference would not be significant. But there clearly is an interaction. The difference in the B1 means is clearly different at A1 than it is at A2 (one difference is positive, the other negative). So yes, you would would interpret this interaction and it is giving you meaningful information.
Can you interpret an interaction that makes theoretical sense?
You can definitely interpret it. If the interaction makes theoretical sense then there is no reason not to leave it in, unless concerns for statistical efficiency for some reason override concerns about misspecification and allowing your theory and your model to diverge.