Hi Patrick, thank you for your video. It is the first video I have ever seen that explains the academic/research approach that starts with non-academic communication. This is what precisely new students need - explain things in their language, not at an academic level, if you try to support their academic journey. Making a difference deserves to be congregated and thanked. Thank you again as a newbie research student.
Thank you. This is helpful. I'm new in quantitative research and only learning about this concepts a PhD level. Learning them from the book or article can be confusing but this video is making it easy for me to understand
Wow. I've studied SEM so many times and you explanation of how true score (latent variable) and error "caused" the measures is the most clear one I've ever heard. Most people are surprised the arrow points the way they do so it is great you explain so clearly.
very clear and concise explanation. As many already mentioned, watching this short vdo can a better understanding than spending hours reading books on one's own. Thanks professor for making such an excellent vdo to share your knowledge.
Thank you so much for this INCREDIBLY helpful and well-explained video! I will watch all your videos. You are providing free education and spreading knowledge. Thank you!
thanks a lot for this informative video. It made my learning easier in SEM and this is gonna be helpful for my Ph.D. research. I'm looking forward to enhancing my understanding more on it. Grateful to u for this simplistic sharing of knowledge.
Would it be fine if in our path model we use the total of variables instead of each latent variable. (i.e. using Health_Behaviour_total = x10+x11+x12 instead of the measurement model with health behaviour as a latent variable, being affected by x10, x11, x12)
Great explanation regarding covariance based SEM. It would have been great to coin it as such (covariance based) and help novices to understand the difference between covariance and variance based approaches.
Thank you for the explanation! I have one question regarding the path diagram at 22:58 in the video: You are comparing this diagram with the multivariate regression and stating that X1 und X2 are independent. The last assumption is of course needed in the multivariate regression to avoid multicollinearity. But why is the diagram showing some relationship between the two variables X1 and X2 by the arrow? Isn't this introducing some kind of relationship between the explanatory variables? Looking forward to your response!
Prof. Patrick. I have a question. Do indicators refer to the items or not necessarily? Is dimension the same as indicators? Thank you for your guidance. Excellent video! From Peru, Claudia.
This series is so great! I learn more from merely ten-minutes of watching this than from 10-hours of literature reading.
Thats really the easiest way of learning SEM. Great lecture
Hi Patrick, thank you for your video. It is the first video I have ever seen that explains the academic/research approach that starts with non-academic communication. This is what precisely new students need - explain things in their language, not at an academic level, if you try to support their academic journey.
Making a difference deserves to be congregated and thanked. Thank you again as a newbie research student.
Thank you. This is helpful. I'm new in quantitative research and only learning about this concepts a PhD level. Learning them from the book or article can be confusing but this video is making it easy for me to understand
Wow. I've studied SEM so many times and you explanation of how true score (latent variable) and error "caused" the measures is the most clear one I've ever heard. Most people are surprised the arrow points the way they do so it is great you explain so clearly.
very clear and concise explanation. As many already mentioned, watching this short vdo can a better understanding than spending hours reading books on one's own. Thanks professor for making such an excellent vdo to share your knowledge.
Lovely! The pace and the language used inevitably lead to a good understanding of the topic in hand. Your efforts are highly appreciated.
Fantastic and clear explanation. The more I work with SEM the better these videoes become.
I am impressed with the simplicity of explanation /presentation
This is a great video. Loved how professor Sturgis lucidly explained and covered all the points. Thank you NCRM for this video.
I did not believe I can find such a good file. Really thank you for sharing...
Quite impressive, always thought it was difficult until I meet him teach it so lively
Thank you so much Professor Sturgis!This is fantastic indeed, i gained a lot from this presentation.
Incredibly straight to the point tutorial. Good job. :)
This is fantastic, not only understandable but also presented in a very interesting way. Thank you so much!
A wonderfully clear explanation of SEM. Each slide was a revelation.
Excellent introduction to SEM. Thanks !!
Wow, this is very simply explained and yet it's also rather comprehensive. Thank you so much for this content!
Best explanation about SEM
You are Super Human. super Man.. the true teacher .. Huge Respect
Thank you so much for this INCREDIBLY helpful and well-explained video! I will watch all your videos. You are providing free education and spreading knowledge. Thank you!
thanks a lot for this informative video. It made my learning easier in SEM and this is gonna be helpful for my Ph.D. research. I'm looking forward to enhancing my understanding more on it. Grateful to u for this simplistic sharing of knowledge.
Very clear and simple explained. Thank you so much!
Thanks professor for a very clear explanation, loved it.
Thank you so much Prof ... though the lecture given was 4 years ago. Well said lecture and good lecture.
Thank you very much Prof Sturgis! Greetings from Germany
Well explained, such a helpfull video! Great prof! Thank you!
i am literally weeping with joy at this!!!
Amazing series Professor Sturgis. I came here first to learn SEM, and I am glad I did!!
The lecture is amazing! Clear and concise. Thanks!
Excellent explanations, hope to see some practical examples in future tutorials.
I thank you so much Professor for your helpful Lecture.
Thank you Sir. It is very helpful for me. Wish you great success Professor Patrick.
This is very good Presentation. Every body who conduct social science research must watch this.
Well.. starting my proposal.. so I need this now. Thank you.
Very nice, clear and useful talk. thank you very much Prof. Sturgis!
Very informative and helpful
Great explanation. Thank you for developing this video!
This was super helpful. So well explained! Thank you very much.
Great lecture - tremendous effect on my understanding of SEM
Excellent explanation!!
thank you very much Professor Sturgis
Would it be fine if in our path model we use the total of variables instead of each latent variable. (i.e. using Health_Behaviour_total = x10+x11+x12 instead of the measurement model with health behaviour as a latent variable, being affected by x10, x11, x12)
very interesting and valuable
Great explanation regarding covariance based SEM. It would have been great to coin it as such (covariance based) and help novices to understand the difference between covariance and variance based approaches.
I'd love to watch a clip about that too Henk
Excellent quality and perfectly structured. Many thanks!
Beyond imagination .. a seriously fantastic explanation.
thanks, professor the lesson is very helpful.
Absolutely superb. Easy to comprehend and explained so lucidly. Thank you
Well explained, Thanks so much.
brilliantly explained tutorial - Many thanks to professor
Such a great video!
Thank you so much. It is impressively well explained!
Amazing. I am able to understand everything. Love you all!
Great structure on the lesson (no pun inteded), brilliantly put together. Looking forward to the other two.
Thanks for clear explanation
this was very concise and helpful! Thanks!
Excellent and clear presentation. Great!
amazingly explained
Super! Thanks Professor, really easy to understand your videos.
you are an absolute hero
I found this video useful! 🙂
this is really good! thanks for being straight to the point :')
Very clearly explained. Thank you!
What’s interesting is that we use these same models and methods in Kinesiology for aptitude testing for latent potential as well lol
Prof. thanks for such an excellent lecture. best wishes.
Dr. Bilal
Very useful 👍🏻
The one stats/math (or whatever you call it) lecture that makes me want to eat pizza while watching.
Very easily and well described. Thanks for posting this..
Excellent explanation. Love this ❤️
Thanks professor! very good and clear explanation
Hello, is there a difference between PLS PM and PLS SEM or is it the same thing?
you did great! So clear and understandable!
GREAT
Thank you very much! Very clear, easy to follow and so informative! Super !!
good tutorial, it can make me more understand. thanks for sharing
Thank you very much
What is 1 on the path between D and Y? why it is 1? thank you!
This is phenomenal! Thank you!
very interesting Prof. Sturgis!
Thank you for the explanation! I have one question regarding the path diagram at 22:58 in the video: You are comparing this diagram with the multivariate regression and stating that X1 und X2 are independent. The last assumption is of course needed in the multivariate regression to avoid multicollinearity. But why is the diagram showing some relationship between the two variables X1 and X2 by the arrow? Isn't this introducing some kind of relationship between the explanatory variables? Looking forward to your response!
Thanks for this video
thank you
This video is tremendously helpful! thank you so much!
Awesome video, thank you prof
Great video, very useful! Thank you for sharing with us!
very clear!!!
Excellent presentation, thanks!
Why is there no error term for X1? 24:21
There was a time I thought I saw SEM for the last time... I was wrong. Thank you for this series, it's very useful. Bring on the tears😅
Prof. Patrick. I have a question. Do indicators refer to the items or not necessarily? Is dimension the same as indicators? Thank you for your guidance. Excellent video! From Peru, Claudia.
Awesome, thanks for sharing, prof..
could you please say somthing about the diagram in your last slide ?how can we read the relation between the cited variables
Great. Very helpful. Thanks
Thank you professor for explaining SEM with neat presentation.
Great video. Thank you
wooo... very clear explanations
Thank u prof. jazakallah
Thanks these videos have been particularly helpful.
Thanks Professor for a very excellent lecture :)