Simple Explanation of Mixed Models (Hierarchical Linear Models, Multilevel Models)

Learning Objectives:
* The assumption of independence and "duplicating" your dataset
* Consequences of violating independence
* HLM vs mixed models, vs multilevel models
* What mixed models are doing geometrically
* Fixed vs. random effects
* Visual representation of
- random slope/intercept models
- random slopes models
- random intercepts models

[billco]

To me, you are the first person who teaches Stats by telling story, it feels like I am chatting with you, not in the feeling of learning. Thank you so much ... Dustin ? I love your videos, one of the best educators I have watched!

[louizakloppenborg]

oh my god i graduated a year ago and remember finding it hard to understand this concept, and now one year later I randomly stumble on this video and I understand it completely by how simple you explained it. Impressive, thank you

[ghaiszriki]

As Einstein said: "If you can't explain it simply, you don't understand it well enough"
Very clear and simply explained, thanks a million.
💚

[gabewinterful]

I wish this video was out four years ago when I started analyzing my phd data, but glad to see it before the defense so I have some more confidence in explaining the analysis I’ve done in simpler words 😊 thanks a lot!

[Jake-nljm]

One suggestion to make sure is in your lessons on random effects could be to clarify the difference between a random effect and an interaction, although it may be too much to go into a full explanation I feel like a disclaimer/warning could at least be valuable. I feel like you explained random effects very well, but an inexperienced person coming into this may follow the theoretical explanation and then when you show the plots think, "OH! I know how to do that, you run 'lm(y~x1*x2), vs lm(y~x1+x2)!' and then be in for some pain later. I remember learning stats and having that misconception for a brief period.

Thank you for the content you produce, it is valuable and appreciated, your content is excellent for learning and great for back-to-the-basics review.

[WeirdPatagonia]

Hello! Thank you for your video! Greetings from Chile :) That said, I have studied mixed models a bit and I still don't understand why someone would want a fixed intercept or a fixed slope. I know that if you assume the effect is always the same (like calorie consumption and weight gain), you could use a fixed slope. OK. But anyway, if you use random slopes in this situation, these slopes should be really similar, so it wouldn't make such a big difference, right? Why don't we just use random slopes and random intercepts all the time? If they are similar for each group, it will be OK, and if they are different for each group, great, we modeled it. Is there any advantage of a fixed slope over a random one?

[icupsy]

Thanks for your fantastic videos! The simpson's paradox often "solved" by adding an interaction term (X*cluster) in GLM and then conduct separate GLMs in each cluster in some psychological studies. Could you please help me clearify the differences between this method and HLM or MVM? Thanks!

[charlieivarsson]

Could you show how a mixed model is used to evaluate a pharmacological effect over time. Let's say a psychiatric drug at week 0, 3, 9 and 12? How do you tell if the difference is significant?

[luisa]

I have a question: in microbiology we work with strains, which are clones and genetically identical within a strain; same in Cancer research when we work with specific cell lines. If I understood you right, then the results are not independent if we use the same strain or same cell line for our biological assays?

[ast]

But we do fixed intercepts when we have categorial data modeled by dummy variables right? 14:45

[Salvador_Dali]

if you normalize the data to observe the relative change e.g., i guess it makes sense to fix the intercept, right?

[qwerty]

12:58 you cant interpret a linear effect on its own when its square is significant in the model, right? Wouldnt this relate to fixing the intercept while allowing the slope to vary?

[LucaSubitoni]

Is it possible to fit a Linear Mixed Effect model using a binary predictor (e.g. time factor: pre vs post) and then compute the significance of this factor?

I read about the Satterthwaithe method which could be used to estimate the p value of the fixed model coefficients, is this correct?

[sjrigatti]

How is a mixed effects model with random slopes and intercepts different from just fitting 3 different linear models, one for each cluster?