Linear mixed effect models in Jamovi | 2 | REML & Random Intercepts

In this video, I will demonstrate how to fit a linear mixed effect model.
I will discuss:
What is a mixed effect model?
Fixed effects
Random effects: grouping or clustering factor
The intercept
The slope
Organizing data
Model fitting and model comparison: AIC, BIC, LL
Checking the assumptions
Variance components: variance and mean
Intra-class correlation (ICC)

[@claireluo9173]

Thanks for sharing this video. It's helpful.

[@vicentemirallesliborio3573]

thank you a lot for your video series about mixed models.
Looking forward for the next one!

[@larasimonian2964]

Thanks for the video. Do you also give lessons about twostep clusteranalyse with zoom of teams?

[@maytebarnuevozayas7606]

Thank your sharing the video. I'm doing all the steps you're saying but when the table has to run, it says that the function "reject" couldn't be found. Do you know how to solve that problem?

[@vicenteml7226]

Hi Sir! again thank you for the video. What does it mean when the interaction between 2 fixed factors is statically significant?. I think it means that the effect of factor 1 on the response variable depends on factor 2.
I don't know if this is the case, but it doesn't make sense to me since I have tried to see the interaction in both directions, taking the same data as your would be time*group and group*time and p-value is exactly the same for both. So you can't extract which factor depends on the other. Would you be so kind to explain how to interpret the interaction?.
Thank you in advance!!

[@caiyu538]

Compared with deep learning, statistics gave me impression that it always make it looks very complicated.