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Hi Michael, Thanks for the query. Always good to hear people using MLPowSim even if they aren't having much success. I can't really diagnose the issue as you have only sent some of the files through and I suspect setup.txt is the one to look at. I wonder whether the issue is the number of state cate...

My email address is on the CMM website team page.
Best wishes,
Bill.

Hi Gesine, The point is that they don't need to be recognized for the model to fit correctly - again you should have unique IDs so don't have classT1 = 1 in school 1 and classT1 = 1 in school 2 or they'll be treated as the same class. The MCMC algorithm unlike the IGLS algorithm doesn't rely on nest...

Hi Gerine, If you are using MCMC for your cross-classified model then the software doesn't need to know nestings as nested classifications are just a special case of the more general crossed classification. The only thing you have to be careful of is with regard how you label your units so things wi...

Hi Gerine, This is an interesting email and sounds like you may have specific features of your dataset that are causing these convergence difficulties. I'd happily take a look if it helps? A 3-level model isn't what I'd define as 'relatively simple' and it may be that you do not have much if any var...

Hi Jillian, I think you have got yourself completely confused here due to the use of the term 'effective sample size' for two totally unrelated concepts. In MCMC estimation, being a simulation-based procedure, the user has to run the technique for a large number of iterations to get estimates that a...

Hi Paris, This is a quite common phenomenon and easy to explain. Imagine for example growth curves between individuals measured yearly for 10 years. In a model without age we would have lots of variation within an individual as their heights at age 4 say will be very different from their heights at ...

Hi, You should always build up your model step by step starting with random intercepts and pressing more when you go to random slopes instead of start to help potential convergence. Convergence is more of an issue if you smaller numbers of level 2 units and particularly if there is little variation ...

Hi Jessica, In answer to your questions: a) Yes at higher levels you can check the normality assumption via residual plots - the residuals at level 1 should not be normal b) Not sure what you want to do here - you can plot the transformed fitted line to your data for example or you could test a quad...

No worries Ron,
Glad to be of help - basically the larger the clustering variance the harder it is to justify fitting a multilevel model rather than simply collapsing the data by aggregating up a level.
Best wishes,
Bill.