Random Intercept Multi-Level Models
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- Опубликовано: 9 сен 2024
- If you want to look at a research question where the data is in nested levels, you can use the simplest version of a multilevel model, which uses a random intercept. We explain the intuition and show you how to use the xtmixed command in STATA to try it for yourself.
If you want to learn more about Group Mean Centering, check out this guide: web.pdx.edu/~ne...
See this video in context and much more on social science research methods and concepts at the Mod-U site: modu.ssri.duke...
This video really helped, thanks for the good work! Just wanted the gist of hierarchical modelling, and I couldn't find a clear video out there shorter than 40 minutes!
Thanks, and good luck!
This is amazing! Thank you! :D
Thanks--good luck!
Thanks for the video, very helpful
Thanks! But (a) What you are discussing is NOT the simplest MLM model because the simplest is the one in which there is NO predictor (i.e., level 1: yij = B0j + eij and level 2: B0j = G00 + U0j) also (b) MLE is not the "standard" method of parameter estimation in MLM, often it is RMLE except when you want to use a likelihood ratio test to compare two separate MLM models.
outstanding , thank you so much!
Is there a difference to the ANCOVA? Always thought this is what the ANCOVA is doing ...
excellent!
super helpful! thanks for this!
Great explanation! I'm just wondering: Why is this different than an effect modifier? [I.E. Isn't this just the same as saying "at different levels of X (I.E, different schools), funding is different, so the effect of X on Y will be different based on the third variable, "Z" funding?]
Is it possible to apply Multilevel models when data set consists of 3 IVs of categorical nature and 1 or more continuous DVs ?..........
My data set consists of 3 categorical IVs (1st IV consists of 2 categories, Second IV also consists of 2 Categories and 3rd IV consists of 3 categories).