This is in continuation of my previous post (Testing the effect of moderators in metafor package) Thanks@Wolfgang for the clarification. Now I used model: A<-rma.mv(yi, vi, mods = ~ factor(soil)+factor(crop)+ ppt, random=~1|id/studyyear, method = "REML", data = a) [I also added precipitation (ppt) now; as a continuous variable]
My questions are:
- How do we extract overall effect of treatment from this model ? (I extracted the overall effect by deleting all the moderators, but wondering if I could get overall effect from this model including moderators?)
- Also the intercept includes both 1 level of soil and 1 level of crop as a reference, I am confused that how do I compare other levels of soil to the intercept since intercept is also acting as reference for crop factor at same time. Is there any way to separate these two? When I delete crop from model, intercept value changes and becomes significant (in this case, does it mean that that 1 soil level is significantly different from zero?). I want to look avg values for each of factor levels and whether they significantly differ from zero. How could I do that?
- I have named individual study ids for each study in the dataset, but a couple of studies are actually conducted on the same field sites. So Should the "id" in my model be the study or the site (and thus do I have to rename ids in the dataset according to sites rather than according to studies)?
- The total number of studies in my dataset are 30, which seems like at the borderline. So if the effect of any factor level comes out as non-significant, does it mean that it is truly non-significant or it is due to the lack of enough data. In other words, How can we calculate the power in drawing conclusions? (Non-significance = underpowered vs. truly non-significant)? I am also testing another response variable for which the no. of studies are only 8. Same question goes for it or should I even conduct the analysis for that at all?
I would really appreciate if you could guide me on this. Thanks.