How to decide whether an effect is fixed or random in mixed models

If you need help going from wide to long format, see these videos:

To see other mixed model videos, see this link:

[eubutuoy]

At 3:15 and 3:25 you contradict yourself. Fix it: "if a variable doesn't change within the cluster, then it cannot be fit as a random effect". Other than that, it was a very clear explanation.

[noa-pszu]

insightful, straight to the point, and full of personality. your passion shines through your videos :) keep up the amazing work!

[tatjanajak]

How can there be a lecture which makes you wonder, laugh and learn in the same time? But here it is. Awesome as always.

[taranaferdous]

OMG!!! I am watching it over and over, especially the part where you moved your face video! HaHaHa. Hilarious. =))
Thank you so much for making this so easy to understand!!! And fun! :))

[MrNeytrall]

As for the AIC comparison, you are misinterpreting it. When you compare AIC, you should look at the value of deltaAIC or even Akaike's weight. The difference of 1 is too small to matter, which means your models don't differ much, so we should apply the parsimony principle and choose a simpler model. (Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach by Kenneth P. Burnham, David R. Anderson, page 71.) Isn't that what Bayes factor and p-value also pointed to?

[TheBjjninja]

Dr. Fife, can you recommend a good book for mixed effect modeling? Thanks

[djgresearch]

Loved the intro & great explanation! However, my dependent variable is often binary, say 5 year mortality. Is it easy to adapt the slope and model comparisons to logistic regression?

[jpss]

Hello, I want to know that when you have the exact same values or don't vary like in MEANSES you cannot fit as fixed and random effect?

[natsumi]

at 3:25 i think you ment when there is no variation it cannot be set as a random effect? in the emphasis part you say fixed.
thanks for your videos btw! they are helping me a lot! cheers from brazil

[Hanna-Nyasa]

I WISH I COULD CLONE YOU AND GET YOU TO TEACH ME 24/7.
Crazy how much a teacher can change your approach to a subject lol

[overcup]

You seem to give two contradictory statements - at about 3:10 you say that mean SES cannot be fit as a random effect, then immediately reiterate that it cannot be fit as a fixed effect. Am I misunderstanding, or is one of those two sentences in error?

[hamidnikbakht]

Easy peasy, lemon squeezy, don't let statistics make you feel queasy! (You're welcome!)

[stellar]

God damn this channel was a gold nugget among all the stats videos I've been looking through to understand LMMs. It doesn't particularly interest me so your enthusiasm and humours definitely keep my attention, not to mention that explanations are great. Thank you!

[sergiochavezlazo]

Amazing video.!!! This channel deserves much more views and subscriptors

[alessandrachuquipiondo]

this is gold, thank you SO MUCH. My thesis is alive bcs of you.

[fishfeelpain]

The background music reminds me of The Sims. So nostalgic.

[rickpack]

Love the Stevie Wonder at the minute mark

[AlceaRose]

Hello ! I have been binge-watching all of your videos, as you're the only one who somehow managed to make me understand something to mixed models (so first, thank you for this !) However, when I use model.comparison, the function doesn't display any p.value nor does it show r_squared_change. Do you know why is that ? Did you remove these from the lastest versions of the package ?

[maxduvall]

I am not sure if ill get a response to this (in time if at all) but I am finishing up my senior thesis right now and about a few days ago I found out that I messed up by trying to treat my repeated measures data (in long form) as normal data in simple linear regression. One of my hypotheses is that two variables (one a binary, and the other continuous) interact. So I expect the slopes to be different between the two groups (from the binary variable). however, I want to make sure I understand this correctly. Because I am testing for that effect I should NOT include the binary variable as a random effect because then I would NOT be able to test for the interaction right? Because the data is in long form, each participant has two measures, and therefor the only random effect I should include is the participants ID and its effect on the intercept right? If I include the random slope of the binary variable, the model accounts for it but it does not give me an estimate nor any information on significance. If anyone knows what I am talking about and can help please let me know! It would be greatly appreciated. Also let me know if you need more information.

ALSO ALSO I have been trying to use the estimates function in flex plot and I keep getting the following code "Error in str2lang(x) : <text>:1:10: unexpected ')' 1: RDM~1+(1|)" Its very confusing because no matter how hard I look and how many times I check I cannot find an extra parenthesis... I have tried adding and deleting different parantheses in various spots and nothing works... so let me know if you can answer that too.

[QueenShiva]

Subscribed within the first 10 seconds of the video 🤣 Thanks for making stats lighthearted! It should be fun!