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.
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.
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!
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.
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)
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.
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!
Thank you very much sir, You explained so well. I want to analyze an accumulative data of 10 countries. Do i need to constrain country dummy variables with 'regression weight =1" for fixed effects? or i don't even need to add them in path diagram? Thanks in anticipation
Really too good and helpful. Although I have some questions related to my research work. Is it necessary that the dependent variable to have indicators to measure it. I have several factors to measure the impact of independent variable on the dependent variable (performance ). Please let me know.
Good day Prof Patrick. Your videos are very helpful i am new to SEM. i am really confused why are constructs drawn using ellipse while making the measurement model may become rectangles and squares while making the structural model. please help me.
This series is so great! I learn more from merely ten-minutes of watching this than from 10-hours of literature reading.
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.
Fantastic and clear explanation. The more I work with SEM the better these videoes become.
I am impressed with the simplicity of explanation /presentation
Lovely! The pace and the language used inevitably lead to a good understanding of the topic in hand. Your efforts are highly appreciated.
Quite impressive, always thought it was difficult until I meet him teach it so lively
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...
Thats really the easiest way of learning SEM. Great lecture
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!
Thank you so much Professor Sturgis!This is fantastic indeed, i gained a lot from this presentation.
Excellent introduction to SEM. Thanks !!
A wonderfully clear explanation of SEM. Each slide was a revelation.
You are Super Human. super Man.. the true teacher .. Huge Respect
Thank you so much Prof ... though the lecture given was 4 years ago. Well said lecture and good lecture.
Best explanation about SEM
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.
Thanks professor for a very clear explanation, loved it.
Thank you Sir. It is very helpful for me. Wish you great success Professor Patrick.
Wow, this is very simply explained and yet it's also rather comprehensive. Thank you so much for this content!
Well.. starting my proposal.. so I need this now. Thank you.
I thank you so much Professor for your helpful Lecture.
Very clear and simple explained. Thank you so much!
Thank you very much Prof Sturgis! Greetings from Germany
Excellent explanations, hope to see some practical examples in future tutorials.
Great lecture - tremendous effect on my understanding of SEM
This is very good Presentation. Every body who conduct social science research must watch this.
i am literally weeping with joy at this!!!
Very nice, clear and useful talk. thank you very much Prof. Sturgis!
Beyond imagination .. a seriously fantastic explanation.
The lecture is amazing! Clear and concise. Thanks!
Well explained, such a helpfull video! Great prof! Thank you!
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!
thank you very much Professor Sturgis
Great explanation. Thank you for developing this video!
Amazing series Professor Sturgis. I came here first to learn SEM, and I am glad I did!!
Very informative and helpful
Excellent quality and perfectly structured. Many thanks!
brilliantly explained tutorial - Many thanks to professor
This was super helpful. So well explained! Thank you very much.
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
Amazing. I am able to understand everything. Love you all!
very interesting and valuable
Thank you so much. It is impressively well explained!
Excellent and clear presentation. Great!
Super! Thanks Professor, really easy to understand your videos.
Absolutely superb. Easy to comprehend and explained so lucidly. Thank you
Excellent explanation!!
this was very concise and helpful! Thanks!
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)
you are an absolute hero
thanks, professor the lesson is very helpful.
good tutorial, it can make me more understand. thanks for sharing
Very clearly explained. Thank you!
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.
Thank you professor for explaining SEM with neat presentation.
Well explained, Thanks so much.
Excellent explanation. Love this ❤️
very interesting Prof. Sturgis!
Thanks for clear explanation
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 professor! very good and clear explanation
Great structure on the lesson (no pun inteded), brilliantly put together. Looking forward to the other two.
Prof. thanks for such an excellent lecture. best wishes.
Dr. Bilal
Very easily and well described. Thanks for posting this..
Thank you very much sir, You explained so well. I want to analyze an accumulative data of 10 countries. Do i need to constrain country dummy variables with 'regression weight =1" for fixed effects? or i don't even need to add them in path diagram? Thanks in anticipation
Really too good and helpful. Although I have some questions related to my research work. Is it necessary that the dependent variable to have indicators to measure it. I have several factors to measure the impact of independent variable on the dependent variable (performance ). Please let me know.
What’s interesting is that we use these same models and methods in Kinesiology for aptitude testing for latent potential as well lol
amazingly explained
Excellent presentation, thanks!
Such a great video!
The one stats/math (or whatever you call it) lecture that makes me want to eat pizza while watching.
Enriching video!
you did great! So clear and understandable!
Awesome video, thank you prof
this is really good! thanks for being straight to the point :')
Great video, very useful! Thank you for sharing with us!
Excellent stuff...thanks Prof
Thank you very much! Very clear, easy to follow and so informative! Super !!
Awesome, thanks for sharing, prof..
Great. Very helpful. Thanks
This is phenomenal! Thank you!
Great video. Just one question:
in the "indirect" effect, x1 and x2 are not correlated?
Great video. Thank you
wooo... very clear explanations
Thanks Professor for a very excellent lecture :)
Well presented, thanks.
Very useful 👍🏻
Good day Prof Patrick. Your videos are very helpful
i am new to SEM. i am really confused why are constructs drawn using ellipse while making the measurement model may become rectangles and squares while making the structural model. please help me.
This video is tremendously helpful! thank you so much!
Hello, is there a difference between PLS PM and PLS SEM or is it the same thing?
Great Professor, thank you
Great professor!
Thank you, that was extremely useful :)
I found this video useful! 🙂
Is it possible to use SEM for secondary data particularly in field of finance?