Significance of moderation


New Member
Hi there,

I have 3 moderators. Following regression analysis only one moderator is significant. However, the R square change is just 0.02. I have decided not to include it in my model because of the following reason:

"The significance of the model and the increase in R square was expected due to the large sample size. Furthermore, none of the moderators entered into the equation in Model 2 were significant apart from ONE at the 0.05 level. However, the decision was taken not to include ONE into the model due to the small amount of variance (B = 0.08) it contributed to the model and because there was no change in the adjusted R square result following the addition of the moderating variables (including ONE) in Step 2."

My supervisor is not convinced if this is completely true. Does anyone know any reference to back up my decision not to include this moderator? Or is there a better way of rephrasing it? As far as I know, Aguinis and Gottfredson (2010, p. 784) do not particularly make any particularly useful conclusion in this regard:

"Reporting interaction effect size estimates such as f square and R square change does not necessarily provide information on the practical importance of a given effect. In many contexts, small effect sizes are very meaningful for practice (Aguinis et al., 2009). Conversely, in other contexts a large effect size may not be very meaningful and impactful. Thus, they proposed a ‘‘customer-centric’’ approach to reporting research results, which involves conducting a qualitative study that describes the importance of the results for specific stakeholder groups in specific

Thank you.