www.cmm.bristol.ac.uk/forum

Hi Pia,
It depends on what model you are fitting. If you look at the MLwiN manuals for MV models you will see that the data is in wide format and then when you specific the columns for the different responses the software constructs the long format in the background.
Best wishes,
Bill.

Hi Pia, MLwiN has some functionality for multilevel factor analysis (using MCMC) and I suspect it could do cross-classified/multiple membership as well though I have never tested it so I wouldn't vouch for it. I think if you construct factor scores elsewhere and want to fit a more standard multivari...

Morning Johannes, I am assuming looking at the syntax you are using runmlwin in Stata? It is probably worth reading a little bit more about how MCMC works e.g. my MCMC in MLwiN book as unlike the IGLS algorithm for ML estimation which converges to ML estimates, MCMC constructs via simulation depende...

Hi Johannes,
The VPC and ICC have the same formulae for all random intercept models (including your null model). It is only in random slopes models where the variability at higher levels depends on predictor variables that the 2 concepts diverge.
Best wishes,
Bill.

Hi Johannes, My suggestion is to fit your model as you are doing and then extract the different variances for each level along with pi^2/3 for the binomial variation at level 1 and sum these together to get total variance. Then the ICC for any particular classification is it's variance / total varia...

Hi Chenr392, That all sounds quite complicated. Perhaps a starting point would be to fit separate univariate models for the type A and B responses you could then look at the estimates produced. I would then be tempted to fit a univariate model but with indicator functions to identify A and B so that...

Hi John, Good question. I think probably the answer is to not worry overly about the levels when choosing your blocking though often your blocks might naturally group into levels anyway. The bigger challenge is when you move on to random slopes and deciding which variables to allow to vary at higher...

Hi Megan,
As you are fitting a binary outcome the model is a Binomial model and so is there no level 1 variance i.e. for the binomial distribution the variance is a function of the mean so therefore the box disappears. Hope this makes sense.
Bill.

Hi John, It is quite standard to construct compositional variables in a multilevel model and add them to the model as contextual effects on top of the individual effects. If you think about our example datasets we talk about gender and school gender and one would use the gender variable to construct...

Hi Rina,
Presumably you can do a likelihood ratio test between the models with and without heterogeneity and keep those predictors that make a significant difference in the random part?
Best wishes,
Bill.