Linear mixed effect models in Jamovi | 3 | Factor coding, scaling, & residual normality

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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)
  1. Vahid Aryadoust, PhD
  2. linear mixed effect model
  3. random effects
  4. fixed effects
  5. random slope
  6. random intercept
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Комментарии
Автор

These 3 related videos explaining LMM jamovi outputs are fantastic...thanks for taking the time to explain to us mere mortals.

christophermawhinney
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superhelpful! thank you so much. looking forward to watch any video you'll upload.

vicentemirallesliborio
Автор

Hello again! I was wondering if you could help me with two questions related to this topic:

- Choosing a different scale for the dependent variable in the 'covariates scaling' menu, is it the same as choosing a link function in a generalized mixed model?
- And this same concept, but moving to a more practical level: choosing a generalized mixed model with a Gaussian distribution and a 'log' link function, would it be the same as a linear mixed model with a 'log' scale in your dependent variable? As far as I am concerned, I do not think so, since I tried both and I obtained different p-values for fixed factors.


Thank you a lot in advance,
Regards,

vicentemirallesliborio