Shawn -- thanks for your nice words. If you are a total glutton for punishment, Dan and I have a full 3-day online workshop in SEM that is freely available -- see centerstat.org for details. Good luck with your work -- patrick
I had Mark Appelbaum when I was in UNC psych grad program. He was good, but you make things so exceptionally clear. What a great teacher and how lucky the students are to have someone who is such a good instructor!
Paul -- thanks for your incredibly kind message. I really appreciate it. You were so fortunate to have Mark in class -- he is one of my heroes. I don't know if you saw, but he tragically passed away earlier this spring -- what a loss for the field. Take care -- patrick
I have a question about modification indices and correlated residuals. In my model, it looks like if I allow two error variances (across latent variables) to freely covary, my model will substantially improve. I remember hearing that you should only consider allowing residuals to freely covary within the same LV. The two observed variables in question could be viewed as theoretically similar (one is emotion dysregulation - impulsivity, the other is conflict engagement). What is recommended to address this issue? I noticed that if I drop one of the variables completely, the model improves but not sure if this is ethical.
Hi Alexandra -- thanks for the note. That's a tricky question. Although correlated residuals can be used to quickly improve model fit when there is not strong theoretical support for their inclusion, at the same time a correlated residual might make perfect theoretical sense and you would do well to include this in your model. I personally don't think it is an issue of ethics as long as you clearly communicate what you are doing to the reader. If you include a correlated residual based on an MI, simply articulate this to the reader and justify why this was included. Hope this helps -- patrick
Hi -- thanks for your note. Usually if a CFI and TLI are equal to 1.0 and the RMSEA is equal to 0, that means that the model chi-square is smaller than the model degrees-of-freedom. This is perfectly fine -- it's not a problem at all -- it simply reflects that the model fits extremely well. This sometimes happens when you have a small sample size (because the model chi-square is directly a function of sample size), but by and large it simply reflects a well fitting model. Good luck with your work -- patrick
@@centerstat Thanks for your answer. It's very helpfull! I have a large sample, and it's a very simple model. I would have liked to make it more complex but when I add variables then my indices decrease... It is thus better simple than complex!
@@vutungkhachhang3162 Look into modification indices. Consider respecifying your model if there are theoretically justifiable parameters that modification indices suggest are contributing to poor model fit.
Wish I had watched these videos ages ago. This entire series does a way better job of explaining SEM than most resources.
Shawn -- thanks for your nice words. If you are a total glutton for punishment, Dan and I have a full 3-day online workshop in SEM that is freely available -- see centerstat.org for details. Good luck with your work -- patrick
@@centerstat Thanks ill check it out
I had Mark Appelbaum when I was in UNC psych grad program. He was good, but you make things so exceptionally clear. What a great teacher and how lucky the students are to have someone who is such a good instructor!
Paul -- thanks for your incredibly kind message. I really appreciate it. You were so fortunate to have Mark in class -- he is one of my heroes. I don't know if you saw, but he tragically passed away earlier this spring -- what a loss for the field. Take care -- patrick
This is excellent. Thank you so much for creating and posting these. Very helpful!
This is such a good series, thank you for making this.
Thanks so much, Pedro -- that's very kind of you -- patrick
Thanks so much for this video. I found it very helpful.
Helping data analysis/econometrics students worldwide, thanks for the videos!
Simply explained model-SEM
Sir... Love you for the explanation provided & Simplifying the "Model fit Indices" topic...
Thanks for your generosity & kind-heartedness, Sir. Bye...
Very good explanation.. Many thanks
Thanks a lot. I found it very helpful
I have a question about modification indices and correlated residuals. In my model, it looks like if I allow two error variances (across latent variables) to freely covary, my model will substantially improve. I remember hearing that you should only consider allowing residuals to freely covary within the same LV. The two observed variables in question could be viewed as theoretically similar (one is emotion dysregulation - impulsivity, the other is conflict engagement). What is recommended to address this issue? I noticed that if I drop one of the variables completely, the model improves but not sure if this is ethical.
Hi Alexandra -- thanks for the note. That's a tricky question. Although correlated residuals can be used to quickly improve model fit when there is not strong theoretical support for their inclusion, at the same time a correlated residual might make perfect theoretical sense and you would do well to include this in your model. I personally don't think it is an issue of ethics as long as you clearly communicate what you are doing to the reader. If you include a correlated residual based on an MI, simply articulate this to the reader and justify why this was included. Hope this helps -- patrick
@@centerstat This is very helpful, thank you for your response!
Really good explanation thank you
great professor
HI thanks ! What can explain a CFI and TLI of 1.0 and an RMSEA of 0.0 being insignificant?
Hi -- thanks for your note. Usually if a CFI and TLI are equal to 1.0 and the RMSEA is equal to 0, that means that the model chi-square is smaller than the model degrees-of-freedom. This is perfectly fine -- it's not a problem at all -- it simply reflects that the model fits extremely well. This sometimes happens when you have a small sample size (because the model chi-square is directly a function of sample size), but by and large it simply reflects a well fitting model. Good luck with your work -- patrick
@@centerstat Thanks for your answer. It's very helpfull! I have a large sample, and it's a very simple model. I would have liked to make it more complex but when I add variables then my indices decrease... It is thus better simple than complex!
Thanks for the explanation! I have a question, what should I do if the RMSEA value is higher than 0.05, e.g. 0.06?
Have u had an answer? I get the same problem and dont know how to fix it :'(
@@vutungkhachhang3162 Look into modification indices. Consider respecifying your model if there are theoretically justifiable parameters that modification indices suggest are contributing to poor model fit.
Super vidio
Thank you. And this time, you do not say " as always, thanks for your time"
I was so confused until 20:30