Multilevel path analysis for binary o... PreviousNext
Mplus Discussion > Multilevel Data/Complex Sample >
 john posted on Sunday, December 05, 2004 - 6:55 am

is it possible to conduct
multilevel path analysis for binary outcome variables with Mplus?

 Linda K. Muthen posted on Sunday, December 05, 2004 - 8:03 am
 EKim posted on Sunday, September 28, 2014 - 1:12 am

Would Model Indirect command in Mplus produce an accurate estimate of the indirect effect for a 2-level path analysis using multiply imputed datasets (from Norm)?
 Linda K. Muthen posted on Monday, September 29, 2014 - 10:05 am
MODEL INDIRECT is not available with TYPE=IMPUTATION. You would need to use MODEL CONSTRAINT.
 Liu Yue posted on Wednesday, May 09, 2018 - 6:03 pm
I have a binary data nested within person and item, so i use cross-classified modeling. Then i add some covarites for person and item respectively. How can i compute the % variance explained by each of the covarites? (if type= random)
Thank you!
 Tihomir Asparouhov posted on Friday, May 11, 2018 - 2:49 pm
The percentage variance explained doesn't come out one covariate at a time (unless they are independent) it is for the whole set. The quickest way to do this is to get the factor score for the person then use a single level analysis model run where you use the same model as on the between person level, fix all the parameter estimates to those from the cross classified run and use "output:stand;" to get the R2.
 Liu Yue posted on Sunday, May 13, 2018 - 9:24 pm
I have a binary data nested within person and item, so i use cross-classified modeling. Then i add some covarites for person and item respectively.Then, i want to compute the effect size for each of the covarites? How can i do that?

Thank you!
 Tihomir Asparouhov posted on Monday, May 14, 2018 - 6:14 pm
You would have to use plausible values for the random parameters and then compute it as in regular IRT. See
and see User's guide example 11.7 for how to get the Bayes factor scores/plausible values and page 838 from the User's Guide.
 Joseph Harris posted on Friday, September 14, 2018 - 1:40 pm
Could you go into more detail on how to compute effect sizes for parameters in a cross-classified model?

 Bengt O. Muthen posted on Friday, September 14, 2018 - 2:02 pm
The only change I can think of is to get the full SD of the outcome going into the denominator of the effect. With cross-classified you need to add up the variances on within, between subject, and between time. That is straightforward if it is a random intercept only model.
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