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Hi Shane, Saw your question and on the list. I might suggest asking MPlus why they interpret things differently here? Let's suppose one didn't fit level 2 in the model then the model is a straightforward logistic regression and level 2 variables are just standard fixed effects like level 1 variables...

Hi Shane, Interesting question. I guess one could look at the correlation between the various predictors prior to putting in the model as if you are trying to explain the contextual effect as being in reality compositional factors then there would be strong correlations. Even in a normal model If yo...

Hi Maelsu2012, If you allow a level 2 covariate to be random at level 1 you are essentially saying that the within level 2 variability is different for different values of the level 2 predictor. This is perfectly acceptable and for example is a route for allowing a different level 1 variance for boy...

Hi SCEinShef, This is a misunderstanding of the Interval and Tests window. It will only give you a chi-squared test statistic and not a P value which you will need the tail areas window to calculate. The number +/- 95% sep is simply 1/2 the interval width i.e. in the case of Gender you have coeff 0....

Hi Manny, Apologies half asleep when I wrote the earlier reply and read your code too quickly. The u terms in your model are simply higher level terms and so they are fine and clustering here is fine. The question is really whether one can construct a correlation between the 2 responses at level 1 w...

Hi MannyGomez, There is a reason why we haven't implemented bivariate mixtures in the MLwiN -> BUGS interface. Basically BUGS struggles with covariance matrices that have constraints in them - in the case of the model you have one variance is constrained to 1 and what you have done in this part of y...

Hi Nils, Lots of questions so here are some pointers: 1) Orthogonal reparameterization is in theory only of use when MLwiN has to use Metropolis Hastings i.e. when we don't have normal responses and it improves mixing as it gets around the correlations between the various fixed effects. If MLwiN can...

Hi, I am not sure what is happening here but I'd be happy to take a look at your spreadsheet. I am presuming that your grouping is not a level 5 variable as this would cause issues? Otherwise if you have coded your dummies correctly it shouldn't matter which is used and they should have the same DIC...

Hi Nils, Have been discussing your emails with Chris and we will look at why the starting values cause a crash and get back to you. It may be that your starting values are generating an initial position for the variance function which implies a negative variance for certain observations which would ...

Depends what you mean here. Are you using ordered or unordered categories? Would suggest you read the manual chapters on ordered/unordered models in the MLwiN manual and see if this answers your question. The fact that
your data is repeated measures is probably irrelevant here. Bill.