Linear mixed effects models - the basics

See all my videos at:

1. Simple linear regression vs LMM (01:17)
2. Interpret a random intercept (04:19)
3. Multiple linear regression vs LMM (06:24)
4. Repeated-measures ANOVA vs LMM (08:45)
5. Paired t-test vs LMM (10:38)

[ndCatch]

Watched four videos on this, and this was the one that made it click. Thanks for your relatable breakdown!

[lors]

Thank you very much. This is very helpful for a person who has no prior knowledge to statistic. This will definitely help my research project.

[fabioramilli]

Excellent explanation! I wish I had seen this video years ago, I would have saved myself a lot of time to get in to the topic...

[Grbec]

Great explanation and the visuals take it to the next level. Thank you very much!

[EcologyInsights]

This is the best video I’ve seen on this topic.

[MrRangerXdxY]

I have yet to see such a good video explaining LMM. Thanks from Zürich!

[lodubup]

Spectacularly well explained. Thanks for that.

[kendesmarais]

Excellent job explaining this in an understandable way! Thank you so much!

[nataliastefanikova]

I finally understand the topic! Thank you so much,

[basilio]

Hello Andreas. First, congratulations on your magnificent videos. They are crystal clear and a very good resource. I have made some calculations and it seems that the linear regression model matches the one you showed at the beginning of the video, although the intercept I calculated is 93.0. The rest is the same as you. I don't know if I am missing something. Thank you!

[gangwang]

Excellent explanation! If we have a linear model lm2=weights ~ weeks + personId, then Sum of Squared Error or Residual Standard Error will be 11.8 which is close to LMM model with random intercepts. And Even more if we use a interaction terms "weeks*PersonID" then SSE is 4.5. So, how do we explain the benefits of LMM for these models?

[shipship]

I really enjoyed your video and I have a few questions. Could you please explain when a linear mixed model can be used in situations where there are missing values, such as when only two time points are measured and some subjects are not measured at one of the time points? Also, I'm curious if the random intercept model(two measurements) still has the same p-value as ANOVA with paired-t when dealing with missing values. Thank you!

[kendesmarais]

Can you recommend a text (in english) that addresses the broader subject of mixed effects models in just as an understandable way as your video?

[willlsn]

can I find somewhere examples of random coefficient models where the variable of the random coefficient is not continuous but categorical? ideally written with STATA or SPSS?

[vicvic]

Thank you so much for your excellent explanations. Can you please create a video that explains in simple terms that when we should consider a variable "random" and when "fixed"?

As some feedback, is it possible to pronounce "d" in the word "moDel"? You pronounce it "moWel". This and other odd pronounciations distract the listener.

[fazlfazl]

Hi. Great video. Are there any slide or notes for these lectures that are available????

[OMARRAFIQUE-oztd]

At 11:26, -6.0, -18.0 and -21.0 are not intercepts. They are slopes of Subjects 2, 3 and 4 with the slope of Subject 1 as the reference. Please correct me if I am wrong.

[yvet]

interesting example! But I still have a question: in this example, reason for causing failure is that individuals have different weights at begin, if we use traditional liner model and just adjust this factor as a covariate, is that ok? and what's the different between the two models?

[yolandayeung]

Great video! What if I have independent samples across 3 times measurement time?

[OMARRAFIQUE-oztd]

At 8.21, you say that "multiple leaner regression model does not give an overall intercept". This is confusing as it is actually the expected value of the response variable when all predictors equal zero. Please clarify.