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In 2:03:19, he said "I am just showing you the real world", I died.

Thanks for the informative video. For time-varying Cox model (2:32:50), do we still need to check PH assumption for the time-varying covariate? Thanks!

Can you please suggest how to perform survival analysis for paired data for longitudinal studies.

Great explanation. Has anyone ever thought of using these ideas for a language model? It could have continuous learning built in, due to the Bayesian Approach.

Excellent. Do you have a video for latent variable means comparison please?

Thanks for your very educative talks. I found very helpful.

General comment Prof. , @9.07, the real part id the natural frequency and wi is the growth rate of the eigenvalue problem. Thats the general convention.

Excellent teaching, thank you very much

Provide simulation with macine learning matlab etc

The first half was really great as an introduction to the topic. The second half is absolutely useless. Filling your slides with formulas and switching back and forth between slides is no way to teach a topic.

Definitely, the best R tutorial I have ever come across on TH-cam! Good stuff!

Thank you for such a detailed introduction to SEM. I have a question - at 46:18 the model is called a "saturated model" because the df = 0. However I have been reading that a "saturated model" occurs when there are the same number of parameters as there are data points. In this case, the model has 5 parameters and 500 data points. Is it still a saturated model then?

very insightful, I would wish to know how to use these commands after doing missing imputation with MICE

I have learned so much, gracias!

how do we get the value of the latent ? not the variance but the value

Thanks a lot! Greetings from University of Adelaide.

Fabulous Lecture

YOUR THE BEST, YOU JUST SIMPLIFIED EVERYTHING THANKYOU SO MUCH PROFESSOR

great video! Thanks. What do you think of using the estimation method DWLS instead of ML for ordinal items (such as those in the video "strongly disagree to strongly agree")? I have just read a paper (Reimann et al. 2024) where they used DWLS in a 2-factor CFA and got a great RMSEA (0.01). Their rationale was that the responses are ordinal and not continuous. Interestingly, I could run the same data set with ML and got an RMSEA = 0.13. Obviously a big difference. In papers, authors often do not even mention their estimation method.

Wow, this is outstanding! Will it be possible to do similar in R program

Wow..what a tutorial Thank you so much❤❤

For a complete novice to Mplus this was a great introductory tutorial. Thank you.

I am in an intermediate statistics and research course for my doctoral program. I began reading Hayes and found myself dissociating with glazed stares. This workshop has provided an informative path to at least begin to consume the material with some understanding. Your assistance with downloading the PROCESS macro was also very helpful. I hope to find moderation and conditional process analysis workshops from this source as well. Thank you very much!!

Matlab? Mathematica? Maple? Python? R? Thanks.

Once I save the folium html, after two days, the interactive visualization stops to display. What could be the problem?

Thank you so much. Very helpful!

Great video thank you!

Thanks for the great tutorial. At 1:51:35, is the wt.loss also by the time of beginning the study (treatment), like the age variable? Thanks.

My rmse is more than 0.8

My cfi is 1 and rmse NA

Thank you very much for an excellent lecture on CFA. Just a small comment/correction on the very final exercise: the Test statistic for the User Model, is 554.191. In your solution, it is 562.790. and the Degree of Freedom is 20, not 21. By putting these numbers in the formula, we get the correct CFI, which is 0.871 (rounded).

The 'li' syntax didn't work for me. It returned errors

Still can't believe this is free to watch for everyone. Thank you so much.

So much rambling in this presentation. The content gets lost in it.

Great video! Great delivery and insights!

This is the resource I needed for my Dissertation. As someone with no strong stat and R background, this really helped me. Thank you very much Dr. Lin!

I swear to god, this is the least technical intro I've ever seen in a stat course. It speaks volumes of the teachin style. Amazing job.

this is so bad!

this is the worst tutorial ever, this person cannot even speak English properly. why UCLA does not ask someone to teach this material who can actually speak English? waste of resources and time!

Hello, Author. Could you tell me how to get the residual vairances of a MSE by lavaan()? Thanks

The HR for wt.loss in the lung data has a p value in excess of 0.05. Thus it is not statistically significant.

I am not really seeing the difference between informative and formative censoring. I am also struggling with the comparison with NAs. NAs missing at random is easy to spot but not so sure about the censoring.

This is a very informative presentation and clearly laid out. However, in terms of earthquakes I don't think it is safe to say that the hazard is constant. Hazard along the San Andreas fault should clearly vary depending on the region next to the fault (rock types vary, last time there was earthquake and so on).

Excellent , thanks for sharing -- Best Regards

Great workshop, Can I found any advance level course of the instructor?

very good analysis. well done

Animated plots from 1:01:40

Recipes start at 47:52

Dr. Lin, is there any video on your seminar on EFA?

These videos are fantastic. Thank you!