These videos are extremely insightful and meaningful - - for explaining the power of SEM methods and value of testing alternate explanations for cause and effect. Thank You
Kia ora from the bottom of the world! (Aotearoa, New Zealand) - Your video's are incredible! Thanks so much, they are really helping introduce me to SEM which I think will work very well in my project.
Thanks for the very nice words. New Zealand is one of my very favorite places in the world -- you gotta love a place that has more sheep than people. I'm really glad you found the video helpful. If you're a true glutton for punishment, Dan Bauer and I have a full 3-day workshop on the SEM that is completely free -- see centerstat.org/introduction-to-structural-equation-modeling-async/ Good luck with your work -- Patrick
@@centerstat Thanks so much! it is very generous of you to off the course for free, I will have a look.... glad you enjoyed your time here! ... we do have the odd sheep, also lots of need for your type of research & LOTS of jobs if you ever wanted to join us! :)
Dr Curran, thank you. this was extremely helpful. I couldn't visualize what I was learning in MPLUS SEM textbook in class until I came across your videos. You have a gift!
Thanks for your very kind note -- Dan and I hope these videos might be of some use as you pursue your own research projects. Good luck with your work....patrick
I am currently applying for PhD programs and this video series of SEM is helping me a lot on understanding my potential advisors' work. Thank you for sharing this content!
Hi Shuoyu -- thanks for your nice note. We also have a full 3-day workshop on introduction to SEM that is completely free-of-charge: see centerstat.org for details. Good luck with your grad applications!
Thank you so much for democratizing such rich information for us on the outside of research looking in with wide eyes. I’m in marketing science and have been leaning very heavily on your work to inform how I help clients understand how our models are analogous to the real world in some regards. I’m the hugest fan of your work on quantitude as well and have listened to every episode❤
thanks so much for your kind words -- Dan and I have lots of fun doing these things, and it beats the heck out of working at our day jobs. Good luck with your work -- patrick
As someone currently majoring in psychological methods & data science this is endlessly fascinating to me!!! Great video! Even without the knowledge you presented in the first episode I was able to follow it without a problem.
Hi Julie Anne -- thanks for the very kind words. If you're truly a glutton for punishment, Dan and I have a full 3-day workshop on the SEM that is completely free-of-charge (the beauty of both being tenured). See centerstat.org if you're interested. Good luck with your work -- Patrick
@@centerstat I also look forward to attending! In the meanwhile I would appreciate a clarification. At about 5:32 you assume that the exogenous variables are correlated. Can you explain how this would happen considering they all point a collider? I thought the path was closed and the variables couldn't be associated...
@@domenicoscarpino3715 Thanks for the comment -- we always allow exogenous variables to freely correlate, either in the SEM or in any form of the GLM, because this allows the regression coefficients to be partialed for all other predictors (that is, the relation between one predictor and the outcome above and beyond all other predictors). Substantively why we do this is to represent the shared causes that might exist from things outside of our model that led to the predictors being correlated in the first place. I hope this helps -- patrick
@@centerstat please correct me if I'm wrong. If we don't include correlated exogenous variables in the model we would also incur in a confounding case scenario.
Thank you so much, I'm a student and wanted to study sem this quarantine. Your videos are truly helping me. Glad i found it. I'm watching all your playlists. Thanks again 😊
Hi NJ -- thanks for the kind words. Glad you're finding these videos of some use. Dan and I hope to use quarantine to make some new ones, so hopefully we can do that sometime soon. Take care -- patrick
Hi Larissa -- thanks for the note. At its core, SEM assumes independence -- no two residuals are any more or less related than any other two residuals. But as you note, more and more designs involve nested structures -- siblings nested in families, patients nested within physician, etc. There are two ways of handling this in the SEM. The first is to ignore nesting in the analysis itself, but then "adjust" the standard errors and test statistics for violations of independence. The second is to model the dependence directly, and this is often referred to as a multilevel SEM -- these models are challenging to estimate and procedures continue to be developed and perfected. Finally, some prefer to side-step the SEM entirely and bolt together tests of mediation directly within the multilevel model (that is naturally built for nesting). A few exemplar cites are below, but there is much more wonderful work on this topic out on the intertube. Good luck with your work -- Patrick McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological methods, 22(1), 114. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological methods, 15(3), 209. Zhang, Z., Zyphur, M. J., & Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695-719. Zigler, C. K., & Ye, F. (2019). A comparison of multilevel mediation modeling methods: recommendations for applied researchers. Multivariate Behavioral Research, 54(3), 338-359.
Thank you for these excellent lectures, Patrick. (And thank you (and Greg) for Quantitude. I never miss an episode!) My question concerns your explanation of multicollinearity around the 4 minute mark. If I understand correctly, you say that by allowing for multicollinearity any given coefficient estimate controls for the other predictors in the model. But suppose you simulate data so that your predictors are perfectly uncorrelated with one another. Doesn't this same interpretation of the estimates still hold? Thank you in advance, Andrew.
Thanks for your kind words. Yes...if the predictors are correlated, then the regression coefficients are the partial effect of each predictor above-and-beyond all other predictors. If the predictors are uncorrelated (or "orthogonal") then no statistical control is necessary -- the coefficient is simply the relation between the predictor and the outcome. However, even if you simulate data to be uncorrelated, you may still get small chance correlations among predictors and they will rarely if ever be truly uncorrelated unless you have a massively large sample of data. Hope that helps - - good luck with your work.
Thank you, dear professor, super video I have a question : why estimate the variance of endogenous variables? Because the objective of a path analysis model is the estimates of three types of parameters: The paths, The covariances between the exogenous variables, and the variances of the exogenous variables. To determine the direct, indirect, and total effects between the variables. To avoid the Heywood cases, it is better to fix the variance of the endogenous variable to its empirical variance. And thus the parameters (Psi) variance of disturbance is constrained parameters not free?
Hi Ahmed -- thanks for your question and your kind words. We actually estimate all variances for all variables, whether they be exogenous or endogenous. If they are exogenous, the estimates simply take on their sample values (assuming there are no missing data); if they are endogenous, the variances are conditional given the effects of the predictors and are thus disturbances (or residuals). These are extremely important parameters in the model and should always be estimated. If you obtain a negative variance (or Heywood case), then that is a reflection of a problem elsewhere in the model that needs to be diagnosed and fixed. Citations for a couple of papers that might be of interest are below. Good luck with your work -- patrick Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological methods & research, 29(4), 468-508. Cooperman, A. W., & Waller, N. G. (2021). Heywood you go away! Examining causes, effects, and treatments for Heywood cases in exploratory factor analysis. Psychological Methods. Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification?. Sociological Methods & Research, 41(1), 124-167.
Thanks so much for the fantastic work. Even though I have some experience with the SEM, this video still teaches me a lot more. I have one small question, could you please share the name or link of Colin's book when you mentioned the attenuation effect from unreliability in the model? Maybe I missed when you talked about the book but I could not find it in google scholar... Thanks!
Thanks, Patrick! Again, I can't help let you know how helpful these videos are. Gonna share them to friends who want to learn SEM. Wish you more good work from you! Cheers!
Hi Patrick, thanks for your very helpful videos on SEM. One thing I didn’t fully understand in this video is that does the error terms already accounted for the unreliability of the manifest variables? Why do we still need to address that by making them latent with additional manifest variables and errors?
Hi Wajdy -- thanks for the comment. This is a tricky question because it depends on your theoretical question, the development of the instrument, and the purpose of your study. That said, if prior validity studies have been conducted on your instrument, you typically do not need to re-do this for your own study. however, if this is a brand new instrument that you've developed, it's important to establish that it is measuring what it purports to measure in a valid and reliable way. I hope this helps -- good luck with your project -- patrick
Thanks for the nice words. Briefly, the parameters are what we hypothesize relates the variables to one another in the population; that is, we are trying to recreate the multivariate distribution of observed measures based on our hypothesized model structure, and the parameters create this structure. But we never have the actual parameters themselves as these only exist in the population. Instead, we get sample estimates of the parameters. Different types of parameters include regression coefficients, intercepts, residual variances, among others. I hope this helps -- patrick
@@centerstat Thank you so so much. this is very helpful. Could you please explain the differences between (free, fixed, and constrained)? why do we choose one over another? what conditions for each one? Thank you
@@USF-e3k Good morning -- A free parameter takes on the best estimate from the sample data, and a fixed or constrained parameter is set to a given value or set to be a function of some other parameter. If you're interested, we actually have a completely free 3-day online workshop in structural equation modeling. Go to centerstat.org and you can register for video lectures and PDFs of lecture notes and computer demonstrations. Hope this helps -- patrick
Most typically, in a CFA you set a single factor loading to a value of 1.0 to set the scale of the latent factor. However, this is not required. You can instead freely estimate all factor loadings but fix the variance of the latent factor to 1.0. Either way, you need to impose one restriction to set the scale of the latent variable.
I can't believe I am learning this knowledge for free!
These videos are extremely insightful and meaningful - - for explaining the power of SEM methods and value of testing alternate explanations for cause and effect. Thank You
I don't think i have ever listened to a clearer explanation!! Thank you Dr Curran :)
Kia ora from the bottom of the world! (Aotearoa, New Zealand) - Your video's are incredible! Thanks so much, they are really helping introduce me to SEM which I think will work very well in my project.
Thanks for the very nice words. New Zealand is one of my very favorite places in the world -- you gotta love a place that has more sheep than people. I'm really glad you found the video helpful. If you're a true glutton for punishment, Dan Bauer and I have a full 3-day workshop on the SEM that is completely free -- see
centerstat.org/introduction-to-structural-equation-modeling-async/
Good luck with your work -- Patrick
@@centerstat Thanks so much! it is very generous of you to off the course for free, I will have a look.... glad you enjoyed your time here! ... we do have the odd sheep, also lots of need for your type of research & LOTS of jobs if you ever wanted to join us! :)
Dr Curran, thank you. this was extremely helpful. I couldn't visualize what I was learning in MPLUS SEM textbook in class until I came across your videos. You have a gift!
Thanks for your very kind note -- Dan and I hope these videos might be of some use as you pursue your own research projects. Good luck with your work....patrick
@@centerstat I will share with my classmates
I am currently applying for PhD programs and this video series of SEM is helping me a lot on understanding my potential advisors' work. Thank you for sharing this content!
Hi Shuoyu -- thanks for your nice note. We also have a full 3-day workshop on introduction to SEM that is completely free-of-charge: see centerstat.org for details. Good luck with your grad applications!
Thank you so much for democratizing such rich information for us on the outside of research looking in with wide eyes. I’m in marketing science and have been leaning very heavily on your work to inform how I help clients understand how our models are analogous to the real world in some regards. I’m the hugest fan of your work on quantitude as well and have listened to every episode❤
thanks so much for your kind words -- Dan and I have lots of fun doing these things, and it beats the heck out of working at our day jobs. Good luck with your work -- patrick
Thank you for sharing! it really helps me a lot!
Thank you Dr. Curran for sharing this. Very well articulated.
As someone currently majoring in psychological methods & data science this is endlessly fascinating to me!!! Great video! Even without the knowledge you presented in the first episode I was able to follow it without a problem.
Hi Julie Anne -- thanks for the very kind words. If you're truly a glutton for punishment, Dan and I have a full 3-day workshop on the SEM that is completely free-of-charge (the beauty of both being tenured). See centerstat.org if you're interested. Good luck with your work -- Patrick
@@centerstat yes I already found it on your website! I’m very excited for it haha
@@centerstat I also look forward to attending! In the meanwhile I would appreciate a clarification. At about 5:32 you assume that the exogenous variables are correlated. Can you explain how this would happen considering they all point a collider? I thought the path was closed and the variables couldn't be associated...
@@domenicoscarpino3715 Thanks for the comment -- we always allow exogenous variables to freely correlate, either in the SEM or in any form of the GLM, because this allows the regression coefficients to be partialed for all other predictors (that is, the relation between one predictor and the outcome above and beyond all other predictors). Substantively why we do this is to represent the shared causes that might exist from things outside of our model that led to the predictors being correlated in the first place. I hope this helps -- patrick
@@centerstat please correct me if I'm wrong. If we don't include correlated exogenous variables in the model we would also incur in a confounding case scenario.
For a complete newbie this was very helpful.
Thank you so much, I'm a student and wanted to study sem this quarantine. Your videos are truly helping me. Glad i found it. I'm watching all your playlists. Thanks again 😊
Hi NJ -- thanks for the kind words. Glad you're finding these videos of some use. Dan and I hope to use quarantine to make some new ones, so hopefully we can do that sometime soon. Take care -- patrick
Fantastic! But how do we model within-subjects design? How do we design a mediation model for within-subjects design?
Hi Larissa -- thanks for the note. At its core, SEM assumes independence -- no two residuals are any more or less related than any other two residuals. But as you note, more and more designs involve nested structures -- siblings nested in families, patients nested within physician, etc. There are two ways of handling this in the SEM. The first is to ignore nesting in the analysis itself, but then "adjust" the standard errors and test statistics for violations of independence. The second is to model the dependence directly, and this is often referred to as a multilevel SEM -- these models are challenging to estimate and procedures continue to be developed and perfected. Finally, some prefer to side-step the SEM entirely and bolt together tests of mediation directly within the multilevel model (that is naturally built for nesting). A few exemplar cites are below, but there is much more wonderful work on this topic out on the intertube.
Good luck with your work -- Patrick
McNeish, D., Stapleton, L. M., & Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological methods, 22(1), 114.
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological methods, 15(3), 209.
Zhang, Z., Zyphur, M. J., & Preacher, K. J. (2009). Testing multilevel mediation using hierarchical linear models: Problems and solutions. Organizational Research Methods, 12(4), 695-719.
Zigler, C. K., & Ye, F. (2019). A comparison of multilevel mediation modeling methods: recommendations for applied researchers. Multivariate Behavioral Research, 54(3), 338-359.
Very clear explanation. Thank you very much!
Thank you very much for sharing this content. I'm finally learning about SEM :). You explain things so well it's just amazing!
Rodrigo -- thanks for your nice comment. Dan and I hope these are of some use to you in your work. Take care -- patrick
this is such a great explanation of SEM. Thank you so much for posting this.
Hi, thank you for your explanation, but I didn't understand what are the two measures of association the predictors with the outcome?
Thank you for these excellent lectures, Patrick. (And thank you (and Greg) for Quantitude. I never miss an episode!) My question concerns your explanation of multicollinearity around the 4 minute mark. If I understand correctly, you say that by allowing for multicollinearity any given coefficient estimate controls for the other predictors in the model. But suppose you simulate data so that your predictors are perfectly uncorrelated with one another. Doesn't this same interpretation of the estimates still hold? Thank you in advance, Andrew.
Thanks for your kind words. Yes...if the predictors are correlated, then the regression coefficients are the partial effect of each predictor above-and-beyond all other predictors. If the predictors are uncorrelated (or "orthogonal") then no statistical control is necessary -- the coefficient is simply the relation between the predictor and the outcome. However, even if you simulate data to be uncorrelated, you may still get small chance correlations among predictors and they will rarely if ever be truly uncorrelated unless you have a massively large sample of data. Hope that helps - - good luck with your work.
@@centerstat Ahhhh! I get it now. Thank you, Patrick.
Super clear, very easy to understand! Thank you very muchhh for the great videos!
Thank you, dear professor, super video
I have a question :
why estimate the variance of endogenous variables?
Because the objective of a path analysis model is the estimates of three types of parameters: The paths, The covariances between the exogenous variables, and the variances of the exogenous variables. To determine the direct, indirect, and total effects between the variables.
To avoid the Heywood cases, it is better to fix the variance of the endogenous variable to its empirical variance.
And thus the parameters (Psi) variance of disturbance is constrained parameters not free?
Hi Ahmed -- thanks for your question and your kind words. We actually estimate all variances for all variables, whether they be exogenous or endogenous. If they are exogenous, the estimates simply take on their sample values (assuming there are no missing data); if they are endogenous, the variances are conditional given the effects of the predictors and are thus disturbances (or residuals). These are extremely important parameters in the model and should always be estimated. If you obtain a negative variance (or Heywood case), then that is a reflection of a problem elsewhere in the model that needs to be diagnosed and fixed. Citations for a couple of papers that might be of interest are below. Good luck with your work -- patrick
Chen, F., Bollen, K. A., Paxton, P., Curran, P. J., & Kirby, J. B. (2001). Improper solutions in structural equation models: Causes, consequences, and strategies. Sociological methods & research, 29(4), 468-508.
Cooperman, A. W., & Waller, N. G. (2021). Heywood you go away! Examining causes, effects, and treatments for Heywood cases in exploratory factor analysis. Psychological Methods.
Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification?. Sociological Methods & Research, 41(1), 124-167.
17:30 if you used a big enough sample size, wouldn't the idiosyncratic bits of the data cancel out and render a data driven approach better?
Thanks so much for the fantastic work. Even though I have some experience with the SEM, this video still teaches me a lot more.
I have one small question, could you please share the name or link of Colin's book when you mentioned the attenuation effect from unreliability in the model? Maybe I missed when you talked about the book but I could not find it in google scholar... Thanks!
Thanks, Patrick! Again, I can't help let you know how helpful these videos are. Gonna share them to friends who want to learn SEM. Wish you more good work from you! Cheers!
Bollen, K. A. (1989). Structural equations with latent variables. New York : Wiley.
Hi Patrick, thanks for your very helpful videos on SEM. One thing I didn’t fully understand in this video is that does the error terms already accounted for the unreliability of the manifest variables? Why do we still need to address that by making them latent with additional manifest variables and errors?
Dear Patrick, thanks so much for your explanation, that was very clear!
Very articulated! Great job explaining!
please prof. Should I use Convergent Validity and Discriminant Validity before I perform path analysis or the alpha Cronbach is enough?
Hi Wajdy -- thanks for the comment. This is a tricky question because it depends on your theoretical question, the development of the instrument, and the purpose of your study. That said, if prior validity studies have been conducted on your instrument, you typically do not need to re-do this for your own study. however, if this is a brand new instrument that you've developed, it's important to establish that it is measuring what it purports to measure in a valid and reliable way. I hope this helps -- good luck with your project -- patrick
This is very helpful. Could you please explain more what is the parameters and what is different types of parameters?
Thanks for the nice words. Briefly, the parameters are what we hypothesize relates the variables to one another in the population; that is, we are trying to recreate the multivariate distribution of observed measures based on our hypothesized model structure, and the parameters create this structure. But we never have the actual parameters themselves as these only exist in the population. Instead, we get sample estimates of the parameters. Different types of parameters include regression coefficients, intercepts, residual variances, among others. I hope this helps -- patrick
@@centerstat Thank you so so much. this is very helpful. Could you please explain the differences between (free, fixed, and constrained)? why do we choose one over another? what conditions for each one? Thank you
@@USF-e3k Good morning -- A free parameter takes on the best estimate from the sample data, and a fixed or constrained parameter is set to a given value or set to be a function of some other parameter. If you're interested, we actually have a completely free 3-day online workshop in structural equation modeling. Go to centerstat.org and you can register for video lectures and PDFs of lecture notes and computer demonstrations. Hope this helps -- patrick
@@centerstat you are so nice thank you so so much. Definitely I will take that course. Appreciate it
Thank you Dr Curran
Why set weights to 1 in confirmatory factor analysis?When?
Most typically, in a CFA you set a single factor loading to a value of 1.0 to set the scale of the latent factor. However, this is not required. You can instead freely estimate all factor loadings but fix the variance of the latent factor to 1.0. Either way, you need to impose one restriction to set the scale of the latent variable.
Thank you so much for this! It was so useful :)
Great explanation! Thanks a lot!
Very helpful. Thank you!
Really helpful thanks!
Thank you so much this saved me
Gracias