Mediation in two-level using bootstrap PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
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 Roger Brown posted on Thursday, November 20, 2014 - 10:58 am
While I am aware that Mplus currently doesn't allow bootstrapping of two-level models with indirect effects. Is there a solution to this? I have discovered a pieced together approach by Lau and Cheung (2012) using Mplus in conjuction with LISREL/PRELIS, but wow lots of work here. Is Mplus planning this in the near future? Thanks.

Roger

Ref:

Lau, R. S., and Cheung, G. W. (2012) Estimating and comparing specific mediation effects in complex latent variable models. Organizational Research Methods, 15(1), 3-16.
 Bengt O. Muthen posted on Thursday, November 20, 2014 - 3:23 pm
It's high on our list. In the meanwhile, I recommend Bayesian analysis which like bootstrap allows non-symmetric confidence intervals.
 Dan R. posted on Thursday, June 11, 2015 - 7:36 pm
Hello, Dr. Muthen. Are there any other means of obtaining asymmetrical confidence intervals in Mplus with two-level models with indirect effects? Do you know of any literature (or resources) describing how this is accomplished in Mplus, using either Bayesian analysis or other means? Thanks!
 Bengt O. Muthen posted on Friday, June 12, 2015 - 7:42 am
Bayes is a good choice here. I would use the 2-level mediation literature (using ML) and use Bayes together with Model Constraint to express the effects. I can't think of 2-level Bayes mediation literature.
 Ansylla Payne posted on Saturday, December 17, 2016 - 12:46 pm
Hi,
I have a 2 level moderated mediation model for testing. I have opted to assess the indirect effect using Confidence Interval, but face major limitations with the inability to bootstrap. I tried Estimator = Bayes, only to realize that this feature cannot be used in conjunction with Model Indirect.

Is there any other suggestion please?
 Linda K. Muthen posted on Sunday, December 18, 2016 - 12:37 pm
As mentioned above, you can specify indirect effects using MODEL CONSTRAINT when you use BAYES.
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