First time data analyst, working on my thesis.
Below is a portion of the PROCESS output for one of my moderation analyses. I have been trying to make sense of this.
Alcohol use = DV
JVQ = IV
PWB = Moderator
Gender = Covariate 1
SU_7R = Covariate 2
How can it be that my summary is significant and the only significant variable in the model is SU_7R_pu (Likert type variable ranging 1-7)? Is this telling me that the 15.25% variance accounted for in the model is mostly accounted for by the covariate SU_7R_pu? Also, how do I interpret the conditional effect of the covariates impact of X->Y?
For $b_1$, $b_2$, and $b_3$ Hayes' book is quite clear, but when it comes to making sense of the output for the covariates, I'm a little lost. Does it even make sense to say that there was an conditional effect of the covariates on the model, or only to say that it was controlled for and the model was adjusted? Does the model still get adjusted if the covariate is nonsignificant?
Is it possible to have a significant model F statistic and no significant coefficients?
In an other case, with a different DV, the model is nonsignificant; however the same covariate is significant ... what is the difference between these two cases in terms of interpretability?