Simulating multilevel dataset
On Fri, 2013-07-05 at 17:25 +0000, Thompson,Paul wrote:
Well, that's how it would be analyzed. But you have means and variability measures to generate the data. Do you have any notion of the dependencies/conditionalities inherent in the data?
The variance-covariances at the individual level are
3.308523 1.896117
1.896117 5.694732
at the school level they are
0.75137 0.59118
0.59118 0.93246
Are conditionalities additive? Are you considering some variance effects?
Not sure what you mean here. I'm not assuming there are any cross-level interactions, if that's what you're referring to.
Prior to modeling conditionality, you need parameters to model in the direction of.
Again, I apologize for my ignorance, but I don't understand this. The only parameters in the model are the individual means, variances and covariances; and the school means, variances and covariances. Do I need something else?
-----Original Message----- From: Stuart Luppescu [mailto:slu at ccsr.uchicago.edu] Sent: Friday, July 05, 2013 12:19 PM To: Thompson,Paul Cc: r-sig-mixed-models Subject: RE: [R-sig-ME] Simulating multilevel dataset On Fri, 2013-07-05 at 16:53 +0000, Thompson,Paul wrote:
You indicate that you have teacher level data and the school level
data. You do not mention data about conditionality. How can you model it if you do not have some relationship to model? In addition, you do not specify the manner in which error is incorporated into the model. The model would look something like this (in lmer notation): meas ~ var1.indic + var2.indic + (var1.indic|school) + (var2.indic|school) Does this address your questions?
Stuart Luppescu <slu at ccsr.uchicago.edu> University of Chicago