Structural Equation Modeling: what is it and what can we use it for? (part 1 of 6)

[@aliothrosen9242]

This series is so great! I learn more from merely ten-minutes of watching this than from 10-hours of literature reading.

[@Kerem-hm2xl]

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.

[@lihlendlovu6062]

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

[@lingfeitang8213]

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.

[@baagh3646]

I did not believe I can find such a good file. Really thank you for sharing...

[@martinandersen4280]

Fantastic and clear explanation. The more I work with SEM the better these videoes become.

[@englishstudies1320]

Lovely! The pace and the language used inevitably lead to a good understanding of the topic in hand. Your efforts are highly appreciated.

[@kriangkraicherngwiwatkit3]

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.

[@bobbyyankey5967]

I am impressed with the simplicity of explanation /presentation

[@reanalytics1863]

Quite impressive, always thought it was difficult until I meet him teach it so lively

[@rohitekka2674]

This is a great video. Loved how professor Sturgis lucidly explained and covered all the points. Thank you NCRM for this video.

[@martin-luthertopico7844]

Incredibly straight to the point tutorial. Good job. :)

[@Spiray]

This is fantastic, not only understandable but also presented in a very interesting way. Thank you so much!

[@makhosimadimabe68]

Thank you so much Professor Sturgis!This is fantastic indeed, i gained a lot from this presentation.

[@sarojadhikari2562]

Thats really the easiest way of learning SEM. Great lecture

[@davidjenkins1347]

A wonderfully clear explanation of SEM. Each slide was a revelation.

[@sskshats6453]

You are Super Human. super Man.. the true teacher .. Huge Respect

[@sarathchandran7418]

Thanks professor for a very clear explanation, loved it.

[@jedidiahsingco6319]

Wow, this is very simply explained and yet it's also rather comprehensive. Thank you so much for this content!

[@MBC999able]

Best explanation about SEM

[@FJL260]

Amazing series Professor Sturgis. I came here first to learn SEM, and I am glad I did!!

[@shiningcherries]

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!

[@ttina3216]

Well explained, such a helpfull video! Great prof! Thank you!

[@reggierosario4708]

Excellent introduction to SEM. Thanks !!

[@vladostapovets7209]

The lecture is amazing! Clear and concise. Thanks!

[@QuadirAliblaize]

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.

[@asankasenevirathneousl5973]

This is very good Presentation. Every body who conduct social science research must watch this.

[@chukwuemekaonwuchekwa730]

i am literally weeping with joy at this!!!

[@liwenzhang6603]

Very clear and simple explained. Thank you so much!

[@DisasterEqualPeace]

Thank you very much Prof Sturgis! Greetings from Germany

[@gsiyoum]

I thank you so much Professor for your helpful Lecture.

[@mzr786786]

Thank you so much Prof ... though the lecture given was 4 years ago. Well said lecture and good lecture.

[@reyrey4920]

Thank you Sir. It is very helpful for me. Wish you great success Professor Patrick.

[@exit47]

Great explanation. Thank you for developing this video!

[@naida7750]

This was super helpful. So well explained! Thank you very much.

[@juliapatrickengkasan96]

Great lecture - tremendous effect on my understanding of SEM

[@vivianaalarcon-s4569]

Very nice, clear and useful talk. thank you very much Prof. Sturgis!

[@Atleastyoushould]

Excellent quality and perfectly structured. Many thanks!

[@omarfaruqehamim8205]

Excellent explanations, hope to see some practical examples in future tutorials.

[@selwynstanley9066]

Absolutely superb. Easy to comprehend and explained so lucidly. Thank you

[@alialshebami8408]

Beyond imagination .. a seriously fantastic explanation.

[@henkjansen7528]

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.

[@lihlendlovu6062]

I'd love to watch a clip about that too Henk

[@doubledoctorateteluguammayi]

Well.. starting my proposal.. so I need this now. Thank you.

[@PrasannaA1971]

brilliantly explained tutorial - Many thanks to professor

[@gerwaynemaechua2797]

Amazing. I am able to understand everything. Love you all!

[@murtalamuhammadshaaban4058]

Very informative and helpful

[@yavarfadavi1234]

Thank you so much. It is impressively well explained!

[@clenchneerspoor]

this is really good! thanks for being straight to the point :')

[@M2m154]

Great structure on the lesson (no pun inteded), brilliantly put together. Looking forward to the other two.

[@adityaputu2107]

thank you very much Professor Sturgis

[@normalrender]

this was very concise and helpful! Thanks!

[@Suresh8848m]

Excellent and clear presentation. Great!

[@mulugetaguangul1706]

very interesting and valuable

[@pnjwck]

Super! Thanks Professor, really easy to understand your videos.

[@infinitesum]

Excellent explanation!!

[@spef7396]

you are an absolute hero

[@DrBilalMirza]

Prof. thanks for such an excellent lecture. best wishes. Dr. Bilal

[@sidasun6254]

This video is tremendously helpful! thank you so much!

[@mehmetkaya4330]

Thank you very much! Very clear, easy to follow and so informative! Super !!

[@maggiey9688]

Very clearly explained. Thank you!

[@mebatube766]

thanks, professor the lesson is very helpful.

[@harmanpreet1564]

Very easily and well described. Thanks for posting this..

[@joaolucashanafrade7899]

Such a great video!

[@ruthrephuck3473]

This is phenomenal! Thank you!

[@jorgemoreyra9029]

Thanks professor! very good and clear explanation

[@atisafarkhah5923]

you did great! So clear and understandable!

[@khasromiah4802]

Excellent explanation. Love this ❤️

[@richardtobii6369]

Well explained, Thanks so much.

[@DarkAng3lyka1]

Great video, very useful! Thank you for sharing with us!

[@rukevweolughor4712]

Thanks these videos have been particularly helpful.

[@btrjoe9812]

good tutorial, it can make me more understand. thanks for sharing

[@esisimp123456]

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)

[@karthiksrinivasan7687]

Thanks for clear explanation

[@rathidevi5664]

Thank you professor for explaining SEM with neat presentation.

[@jeromekiley7750]

Excellent presentation, thanks!

[@siribuppa.u723]

Great professor!

[@melastoma9767]

Thanks Professor for a very excellent lecture :)

[@nakshatraclasses706]

amazingly explained

[@philipppackmohr1997]

very interesting Prof. Sturgis!

[@TinaHelen]

Thank you for your effort and sharing, this is very understandable and helpful! :-)

[@yodhikawidyaa622]

Awesome, thanks for sharing, prof..

[@guanglongpang6463]

The one stats/math (or whatever you call it) lecture that makes me want to eat pizza while watching.

[@ken-xedu7577]

Awesome video, thank you prof

[@micheller2336]

This is so helpful! Thank you :)

[@nanaboateng3330]

Excellent stuff...thanks Prof

[@ariyaninafiah1316]

Great Professor, thank you

[@MarketingToolkit1]

I found this video useful! 🙂

[@claudiavallejos5500]

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.

[@cuachanhdong]

Thanks from Vietnam.

[@nurulalamification]

Thank u prof. jazakallah

[@soroswei]

wooo... very clear explanations

[@emmacoleman4644]

Thank you, that was extremely useful :)

[@eyadha1]

Great. Very helpful. Thanks

[@eyadha1]

Great video. Thank you

[@maximiliann.5410]

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!

[@robertalia9060]

Well presented, thanks.

[@Rave779]

What’s interesting is that we use these same models and methods in Kinesiology for aptitude testing for latent potential as well lol

[@heizehua5661]

What is 1 on the path between D and Y? why it is 1? thank you!

[@yuhaolu6595]

Great video. Just one question: in the "indirect" effect, x1 and x2 are not correlated?

[@shaliniprakash479]

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.