Hello,
if you have two moderators in your design [1 IV and 1 DV], one interaction was significant and the other one was not. Is it necessary to interpret why this variable did not play a moderating role? despite the fact that the main effects were significant?
Thank you
Variables or moderators in research designs are meant to certain roles, depending on the nature of the design and its objectives. If a variable fails to play the role intended for it, then something is wrong somewhere. In such a situation, I think the first thing to do is to find out why, before going into interpretation. Was appropriate procedure followed? What about timing and duration? Was there any defect in the quantity/quality of the variable? Answers to these questions will reveal why the expected role was not played. Knowing the why will now provide the ground for interpretation. So, I believe it is necessary to do the interpretation so as to right the wrongs that have been observed, and obtain an accurate result in future.
It depends on your hypotheses. If you had predicted an interaction, then by all means you should discuss/interpret the findings. In either case, you should at least mention it.
There must be a reason you included more than a moderator in your model. Therefore, interpreting why one of the two moderators had no moderating role should be rendered. A good example is provided by Hayes (2022) in the subsection “9.4 More Than One Moderator” of his book, referenced below.
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press. https://www.guilford.com/books/Introduction-to-Mediation-Moderation-and-Conditional-Process-Analysis/Andrew-Hayes/9781462549030
Good luck,
But keep in mind that absence of evidence is not evidence for absence! You may not interpret the non-significant result as evidence that there is no effect. This may not be concluded from significance testing. If you expected an effect, you should have an a priori idea how large the smalles effect of practical relevance should be. You can compare the empirical results, the effect size and confidence intervals, with your a priori idea. If they are the same (or even larger) then you have a power problem, although the effect was in the anticipated range, it did not turn our significant. If it is much smaller and if the empirical effect size is not compatible with your implied effect (by inspecting the confidence intervals) it may be just the case that the true effect is smaller than expected.
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