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

Professor Patrick Sturgis, NCRM director, in the first (of three) part of the Structural Equiation Modeling NCRM online course.
This video is part of the online learning resources from the National Centre for Research Methods (NCRM). To access the supporting materials (presentation slides, datasets, recommended reading, links to related publications and resources) visit www.ncrm.ac.uk/resources/onlin...

[@aliothrosen9242]

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

[@englishstudies1320]

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

[@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!

[@jedidiahsingco6319]

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

[@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!

[@davidjenkins1347]

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

[@baagh3646]

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

[@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.

[@vladostapovets7209]

The lecture is amazing! Clear and concise. Thanks!

[@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.

[@martinandersen4280]

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

[@bobbyyankey5967]

I am impressed with the simplicity of explanation /presentation

[@selwynstanley9066]

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

[@M2m154]

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

[@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.

[@makhosimadimabe68]

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

[@ttina3216]

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

[@Atleastyoushould]

Excellent quality and perfectly structured. Many thanks!

[@chukwuemekaonwuchekwa730]

i am literally weeping with joy at this!!!

[@vivianaalarcon-s4569]

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

[@clenchneerspoor]

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

[@sarathchandran7418]

Thanks professor for a very clear explanation, loved it.

[@reanalytics1863]

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

[@FJL260]

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

[@sidasun6254]

This video is tremendously helpful! thank you so much!

[@exit47]

Great explanation. Thank you for developing this video!

[@naida7750]

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

[@omarfaruqehamim8205]

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

[@reggierosario4708]

Excellent introduction to SEM. Thanks !!

[@harmanpreet1564]

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

[@mehmetkaya4330]

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

[@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.

[@DisasterEqualPeace]

Thank you very much Prof Sturgis! Greetings from Germany

[@DarkAng3lyka1]

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

[@gerwaynemaechua2797]

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

[@juliapatrickengkasan96]

Great lecture - tremendous effect on my understanding of SEM

[@yodhikawidyaa622]

Awesome, thanks for sharing, prof..

[@MBC999able]

Best explanation about SEM

[@rukevweolughor4712]

Thanks these videos have been particularly helpful.

[@Suresh8848m]

Excellent and clear presentation. Great!

[@siribuppa.u723]

Great professor!

[@PrasannaA1971]

brilliantly explained tutorial - Many thanks to professor

[@melastoma9767]

Thanks Professor for a very excellent lecture :)

[@ruthrephuck3473]

This is phenomenal! Thank you!

[@DrBilalMirza]

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

[@pnjwck]

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

[@jeromekiley7750]

Excellent presentation, thanks!

[@jorgemoreyra9029]

Thanks professor! very good and clear explanation

[@mzr786786]

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

[@alialshebami8408]

Beyond imagination .. a seriously fantastic explanation.

[@ariyaninafiah1316]

Great Professor, thank you

[@micheller2336]

This is so helpful! Thank you :)

[@khasromiah4802]

Excellent explanation. Love this ❤️

[@nanaboateng3330]

Excellent stuff...thanks Prof

[@philipppackmohr1997]

very interesting Prof. Sturgis!

[@maggiey9688]

Very clearly explained. Thank you!

[@sskshats6453]

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

[@asankasenevirathneousl5973]

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

[@TinaHelen]

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

[@cuachanhdong]

Thanks from Vietnam.

[@adityaputu2107]

thank you very much Professor Sturgis

[@yuhaolu6595]

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

[@gsiyoum]

I thank you so much Professor for your helpful Lecture.

[@normalrender]

this was very concise and helpful! Thanks!

[@yavarfadavi1234]

Thank you so much. It is impressively well explained!

[@atisafarkhah5923]

you did great! So clear and understandable!

[@joaolucashanafrade7899]

Such a great video!

[@amjed6579]

awesome job, thank you

[@spef7396]

you are an absolute hero

[@rathidevi5664]

Thank you professor for explaining SEM with neat presentation.

[@movinsq07]

Really helpful. Thank you

[@DavidRocaUAB]

Great video!

[@nurulalamification]

Thank u prof. jazakallah

[@btrjoe9812]

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

[@doubledoctorateteluguammayi]

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

[@ken-xedu7577]

Awesome video, thank you prof

[@infinitesum]

Excellent explanation!!

[@soroswei]

wooo... very clear explanations

[@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.

[@sameeullahkhan3254]

Terrific lecture

[@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!

[@reyrey4920]

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

[@karthiksrinivasan7687]

Thanks for clear explanation

[@eyadha1]

Great video. Thank you

[@richardtobii6369]

Well explained, Thanks so much.

[@edwinadrianta9455]

many thanks for tutorial

[@nursaadahabdmajid9353]

Thank you, Professor. very understandable video.

[@robertalia9060]

Well presented, thanks.

[@eyadha1]

Great. Very helpful. Thanks

[@nakshatraclasses706]

amazingly explained

[@javeda]

Is your centre offering research visits/collaborations for the doctoral students especially from EU

[@radd-e-nasibiyat]

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

[@mebatube766]

thanks, professor the lesson is very helpful.

[@bezawitgirma3231]

THANK YOU PRO.

[@murtalamuhammadshaaban4058]

Very informative and helpful

[@shyamdrury8986]

very clear!!!

[@jannethalcantara5742]

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.