Confirmatory Factor Analysis; Patrick Sturgis (part 3 of 6)

[@dongxu2257]

As a professor myself, I truly appreciate the way Prof. Sturgis present the course - focusing on intuition rather than mathematics, which in my view is of great importance for non-statistical students.

[@mbonezakabanda7971]

I enjoy the way the Professor explains SEM so that even beginners can understand. Great job, Professor.

[@MCshaneization]

As a stats instructor myself I am very impressed by the clarity and concision of your presentation. Great job!

[@annabellegadabu7566]

i like the way you explain things slowly and at ease. for someone unfamiliar with statistical analysis at all it's been helpful in understanding SEM. Great job Professor

[@tenkolew]

The best learning resource I’ve found so far. On point. Clear. Your style calms any anxiety

[@afpierson]

This guy is a great speaker and very articulate. Thank you, Professor Sturgis. You took a very complicated topic and made it simple by breaking it down in three separate videos.

[@dr-rer-nat-jonathan]

he's a very bad speaker loads of ehs, for instance and it's way too slow to stay interested

[@joseannknight8820]

The explanations here are simple yet insightful. A great job Professor!

[@baburajesh1000]

Respected professor, Thank you so much for providing more insights on SEM and many of the researchers will get benefited from your teachings.

[@Deborah28277]

I’ve watched your videos over and over, each time my SEM understanding increases .. this time around the AHA moment occurred within the Mean Structure discussion , specifically reflexive vs formative indicators when measuring constructs like social capital and imposter syndrome.

[@mafaldacampos799]

Been watching these videos to help me with my master's thesis and it has been a great help. Thank you!

[@wjingzhou1967]

Thank you so much for your concise and clear explaination of SEM in such a slow and elegant way! Gorgeous!

[@bugendijoseph1313]

I really enjoy alot listening to professor Patrick sessions on SEM

[@kiwanukajoseph6668]

IAM really impressed by the clarity and concision of your presentation. Great job prof !

[@kevinkelly1960]

Fantastic 3 lectures. Brilliant. Thank you for sharing, Kevin

[@texla-kh9qx]

Could someone explain what is the definition of degrees of freedom here? Naively, I thought degrees of freedom are free model parameters, but this is in contradictory to the use of the term in the lecture. If we apply constraints, the number of free parameters decreases and hence the model becomes less flexible (less capable). How do I understand the fact that the less flexibility implies the larger number of degrees of freedom?

[@dr.vincentkibambila3003]

Nice work Professor, I like your style of teaching. May God Lord Protect you

[@priyankagupta2813]

sir add a line from n2 to x3 @ 18:32.......Your explanation is really v good, easy to understand

[@KarthikSrinivasanTucson]

Enjoyed all three talks. Very well made. Thank you

[@Moe4572]

Seriously saving me right now :) Can't thank you enough!

[@oindrilamukherjee6030]

Thank you so much Professor, it has been so insightful and helpful learning from you!

[@kritiarora7585]

Hi! Thank you for this great explanation of the CFA. I wanted to ask what is the difference between item parcelling and the higher order factor model? Is item parceling a special case of higher order factor model?

[@wansitihajar5558]

Thank you Prof, it is informative and came in hand for my research. I have a question regarding the high order factors structure. Why is that the errrors are no longer respective to their observed indicators? such that error 3 points to indicator 1 etc?

[@students4life821]

I need to like this video twice. What a great explanation!

[@mbonezakabanda7971]

Me too

[@michaelpaulse1]

Thank you for great lectures! How do I calculate apriori sample size for CFA - I have seven 1st order and one 2nd order latent variables. Observable items 120 questions predominantly Likert type. Could you please assist?

[@simplyram2676]

Hi Dr., how if discriminant validity is NOT met after CFA? But model fit and convergent validity is met. What should we do? Can I do EFA?

[@pulkit21aug]

Prof Patrick Sturgis - could you please confirm apart from psychology can SEM be used in other domain e.g. econometrics , finance , sentiment analysis ?

[@DrMichael_Psychology]

That's great, very appreciative. Small note: at minute 18 there are only 5 single-headed arrows going from the latent variables to the indicators, not 6.

[@MohsinUlAminKhan]

That's only for n2, not n1. I guess that is just an unintentional mistake.

[@areejalmehdar8467]

amazing lectures! Thank you very much indeed

[@niklaskonig4278]

Great lecture. Thank you! Just noticed, on the first slide "Two Factor, Six Item EFA" there is an one-headed-arrow missing between eta2 and x3. But anyway, great lecture!

[@artimalik1126]

Sir, my topic is to measure impact of work-life balance on job satisfaction. I measured these two through different constructs.Items were taken from existing questionnaires of different authors. I combined few constructs and I added and deleted some items form constructs. What should I use CFA or EFA and why. Sir, please guide

[@pauljoseph9276]

Great lecture Prof. you make it very simple to understand. How can i analyse path analysis with categorical data?

[@javeda]

Can our observed exogenous and endogenous variables be continuous and categorical (dichotomous,ordinal, binary) simultaneoulsy, if yes which software is most appropriate for conducting CFA and SEM then alongside moderation and mediation analyses.

[@kunmunmishra1555]

It can be done in AMOS

[@Tram-cuoc-doi]

Hi professor Can we use 5 levels scale as bellows: -2 -1 0 1 2 for EFA, CFA, SEM analysis?

[@syafiqsidqisaidi6337]

thanks Prof on the lecture ! really helpful may God Bless you

[@isaackwao608]

please prof what is NNI in one factor CFA ?

[@wissamkheir]

Thank you so much for this ! Very clear and concise !

[@Josefk40]

This is an awesome lecture. Thank you

[@laxmanpokhrel2091]

Remarkably crafted presentation and contents ...Great learning experience...Thank you. ..

[@sintakristanti6108]

Thank you so much, very very helpful!!! Please keep making video ...

[@yingc1233]

Great series, thank you!

[@Goliad2016]

dear professor if you can please show how to calculate sample size in structural equation model !!!

[@Flower-62]

Thanks prof. I suggest to give numerical example and clarify each step. Only theory would not be understandable for everyone. Thus if could prepare a numerical example to explain the steps friendly.

[@kswill4514]

Better than I've learnt from school

[@AngeloYeo]

Thank you for such a nice lecture :)

[@mikojavier2435]

thank you,very clear explanation

[@l.3890]

Thank you! finally I could understand SEM!

[@MayadaMAref]

thank you for these very informative lectures

[@nazdezigns31]

25:00

[@MBC999able]

Brilliant lecture

[@fernandojackson7207]

I also enjoyed and understood well, but I am really craving an actual example

[@shobhitchaturvedi4866]

Without an apt example...all this has no real flavour

[@madaraogot5322]

Great information, thanks.

[@yosefsol8262]

I still don't see why it is required that students have to do SEM thesis, or a thesis at least

[@sethjchandler]

Perhaps it is just me, but I find it very hard to fully grasp the material without worked examples. Some code or pseudo-code that made the procedures more precise would have been very useful. Everyone has their own learning style, but the one used here does not work for me.

[@adilzia]

i like your explanation #CFA

[@isaackosi2889]

Thanks very much for the insight

[@barbarabomfim7408]

Very informative!

[@mugomuiruri2313]

good

[@dr-rer-nat-jonathan]

eh eh eh eh eh eh eh eh eh

[@shawnmiller6147]

Get a life! If you are so bothered by the eh's then don't watch! Better yet, if you're so damn great, make your own video! I watch the video to learn about SEM, NOT to worry about speech patterns.

[@DonJonston]

thank you for ruining a very, very helpful video. Be better. Fuck you

[@laxmanpokhrel2091]

Remarkably crafted presentation and contents ...Great learning experience...Thank you. ..