Shige posted on Saturday, August 20, 2005 - 12:44 pm
Dear Linda and Bengt,
Is it possible to estimate two-level model with cross-classified random effects? For example, in educational research, kids are nested within primary school and secondary school, but primary and secondary schools are not nested within each other but cross-classified. Some references on this topic can be found in Leyland and Goldstein (2001), chapter 7, or Raudenbush and Bryk (2002), chapter 12.
Leyland, A. H., and Harvey Goldstein. 2001. Multilevel Modelling of Health Statistics. Chichester ; New York: Wiley.
Raudenbush, Stephen W., and Anthony S. Bryk. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks: Sage Publications.
bmuthen posted on Saturday, August 20, 2005 - 2:29 pm
Crossed random effects are not implemented in the current Mplus version, so unless one can think of some latent variable trick, this cannot be done yet.
If you don't have too many categories and have a lot of data, you could conduct a multigroup analysis and then constrain parameters across groups to test for the effects of the classifications.
Jonathan posted on Sunday, July 06, 2014 - 9:23 am
It looks like it's possible to do CCREMs in Mplus now, but I'm having some difficulty replicating a particular model.
I'm looking to run a cross-classified model which has no predictors but has two random intercepts. So using the terminology of the Mplus manual and the example Michael posted above: -->There would be no "within" model -->The only things influencing y would be level2a (primary school) and level2b (secondary school), which would vary randomly
Is this possible in the new MPlus framework? If so, how would I code it?
Tao Yang posted on Friday, June 05, 2015 - 3:36 pm
Hello, my data had encounter nested within day nested within participant while encounter was also nested within patient. So it is three level plus a cross-classified cluster of patient. Is this type of data structure implemented in Mplus at this time? If I understand correctly, Mplus can handle level-1 variables nested within the cross-classification of level-2a and level-2b. Not sure how to handle three-level with a crossed cluster.