[R-meta] Testing interaction in rma.mv()

[Stefanou Revesz]

Many thanks, Wolfgang.

Stefanou

On Fri, Mar 18, 2022 at 4:33 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> Dear Stefanou,
>
> Just include training_hr and time as 'main effects', no interaction. The intercept then referes to pre-testing occasion (with 0 training hours), the coefficient for time to the average difference between the post-testing occasion versus pre-testing occasion (when 0 training hours are provided at post-testing; you could rescale the variable to make the intercept correspond to a different number of training hours), and the coefficient for training_hr indicates how this average difference changes for a one-unit increase in training_hr.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Stefanou Revesz
> >Sent: Thursday, 17 March, 2022 5:39
> >To: R meta
> >Subject: [R-meta] Testing interaction in rma.mv()
> >
> >Dear Meta Community,
> >
> >My studies measure the effect of a training program to improve
> >teachers' self-efficacy.
> >
> >We have coded for the 'length of the training sessions' (training_hr)
> >up until each testing occasion (time).
> >
> >At the pre-testing occasion, since no training is provided, we coded 0
> >hrs, and for subsequent testing occasions, we coded whatever hours
> >reported in the studies.
> >
> >The problem is that now we CAN'T use 'training_hr * time' in our
> >`model`. Because 'training_hr' at time0 is just '0' for all studies
> >(see below).
> >
> >Q: Is our current coding of 'training_hr' wrong? Sh/Could we code
> >'training_hr' differently for such an interactive model?
> >
> >Any help would be highly appreciated,
> >Stefanou
> >
> ># Data and code
> >d <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/f.csv")
> >
> >subset(d, time == "Baseline")$training_hr
> >#[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> >
> >model <- rma.mv(gi~training_hr*time, v_gi, random = ~1|study/obs, data = d)