GAMLj-main effects weird post-hoc results
Posted: Fri Nov 15, 2019 6:00 pm
Hi,
I am running a linear mixed effects analysis using GAMLJ package on jamovi. I have a model like below:
confidence ~ speed + probe duration + speed*probe duration + (1|ID)
speed is coded as ordinal variable. it has three levels, slow, medium and fast. confidence (DV) is also ordinal, 1, 2 and 3.
the main effect of speed is significant. When I look at estimates table for post-hoc results, I see that slow-medium is negative. This is weird though, the mean value for slow speed is actually LARGER than medium (I checked from descriptives. Also, thats what it seems like in the plots too). I tried to change the variable type to nominal, nothing has changed.
I also tried discarding the interaction term from my model. There, things seemed normal (slow-medium difference was positive, which sounds much more rational).
What can be the cause of this discrepancy? Or is it really a discrepancy or is there something that I miss, and everything is actually fine?
a quick PS: I use a filter to divide my data. i.e., I have another variable called "response" with two levels, and I use a filtering like (if "response==1") to play with data that I am particularly interested in.
thanks in advance, tutku
I am running a linear mixed effects analysis using GAMLJ package on jamovi. I have a model like below:
confidence ~ speed + probe duration + speed*probe duration + (1|ID)
speed is coded as ordinal variable. it has three levels, slow, medium and fast. confidence (DV) is also ordinal, 1, 2 and 3.
the main effect of speed is significant. When I look at estimates table for post-hoc results, I see that slow-medium is negative. This is weird though, the mean value for slow speed is actually LARGER than medium (I checked from descriptives. Also, thats what it seems like in the plots too). I tried to change the variable type to nominal, nothing has changed.
I also tried discarding the interaction term from my model. There, things seemed normal (slow-medium difference was positive, which sounds much more rational).
What can be the cause of this discrepancy? Or is it really a discrepancy or is there something that I miss, and everything is actually fine?
a quick PS: I use a filter to divide my data. i.e., I have another variable called "response" with two levels, and I use a filtering like (if "response==1") to play with data that I am particularly interested in.
thanks in advance, tutku