Message/Author |
|
|
Dear Linda and Bengt, I am using BSEM to estimate a model with latent interactions. It seems the built-in method for latent interactions (LMS) described in the Mplus manual (Example 5.13) will not work with ESTIMATOR= Bayes. Please correct me if I'm wrong but I get the following error: *** ERROR in MODEL command Interaction variables are not allowed with ESTIMATOR=BAYES. Can you recommend another way to do latent interactions in BSEM? Would you recommend the unconstrained method by Marsh, Wen & Hau (2004, 2006)? |
|
|
Yes, we haven't gotten to XWITH for Bayes yet. I am not in a position to make a recommendation on alternatives. |
|
|
Hello Bengt & Linda, I tries an approach to estimating a latent interaction using Bayesian estimation. This approach requires specifying a single non-linear constraint, following this example (code snippet is from Coenders, Batista & Saris 2008): model: eta1 by y1@1; eta1 by y2* (p1); eta2 by y3@1; eta2 by y4* (p2); eta3 by y1y3@1; eta3 by y2y4* (p3); model constraint: p3=p2*p1; I get the following error: *** FATAL ERROR THIS MODEL CONSTRAINT IS NOT AVAILABLE WITH BAYES ESTIMATION. However, I was able to do the following in Bayesian estimation to test an indirect effect following an example in the manual: NEW(indirect1 indirect2); indirect1 = a*b1; indirect2 = a*b2; Am I doing something wrong? or are non-linear constraints not supported with Bayesian estimation? If not, is there another approach? Another posts mentions phantom variables but I don't know what that is and can't find anything about this term in the manual. Thanks fore your help, 'Alim |
|
|
Only NEW parameters are available with BAYES. |
|
|
Thanks. can you explain about phantom variables please or suggest where to read about this? mentioned here: http://www.statmodel.com/discussion/messages/11/7736.html?1310007137 |
|
|
Google a Psychometrika article by Rindskopf. |
|
Tibor Zin posted on Thursday, December 06, 2018 - 4:19 am
|
|
|
Dear Dr. Muthen, I would like to ask a question about how to inspect the effect of IV on DV on different levels of moderating variable(Johnson-Neyman technique) using Bayesian estimator. Would it be correct approach to consider the value -1 as a minimum, 0 as a medium, and 1 as a maximum value? Thank you! |
|
|
Typically, you use 1 SD below and 1 SD above the mean for the moderator. Or, the 20th and 80th percentiles. |
|
Tibor Zin posted on Saturday, December 08, 2018 - 11:04 pm
|
|
|
Thank you for the answer, Dr. Muthen. Please, could you tell me how to obtain SD and mean of a latent variable using Bayesian estimator or 20th and 80th percentiles? I know that the question may be trivial but I do not know how to proceed. |
|
|
TECH4 gives you the mean and variance of a latent variable. The latent variables are assume to be normally distributed, so you can get the percentiles from a standard normal distribution table (subtracting the mean and dividing by the SD). |
|
Tibor Zin posted on Monday, December 10, 2018 - 1:20 am
|
|
|
Thank you for the advice. The problem is that when I use ML estimator, the model is not correctly estimated. When I use Bayesian estimator, TECH4 output is unavailable. My goal was to estimate an interaction between two latent variables, type = random and algorithm = integration. Please, do you know what could be a problem or whether there is another way how to approach this problem? |
|
|
Send your ML and Bayes output that you have questions about to Mplus Support along with your license number. |
|
|
Dear Dr Muthen, I am having some difficulty using BAYES estimator in a latent moderation analysis. Of course I cannot use the DEFINE command to create a latent interaction of the X and W factors... Could you please point me in the right direction to specify such a model... Many thanks in advance |
|
|
Bayes XWITH was introduced in Mplus Version 8.2 last November. |
|
|
Hello, I am trying to use BAYES XWITH but I am unsure how to define the interaction because the intearction variable e.g., inter1| is not availabe. Thanks in advance, |
|
|
If you say f1f2 | f1 XWITH f2; you can then regress a variable on f1f2: y on f1 f2 f1f2; |
|
Back to top |