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Not for fixing the issue of multiple comparisons as bootstrapping will not help with that.

I would imagine if you don't want to do Bonferroni then you could try something like Benjamini & Hockberg's FDR which is less conservative (false discovery rate) and I believe could be used for most models. Don't think there is an estimation procedure related approach like tweaking IGLS here.

Hi, I think the fact level 2 is nested within level 3 is fine here as for MCMC nested is simply a special case of the more general cross classified model - just need to make sure that your IDs are unique i.e. if you have a reader 1 that this is the same person throughout and if say you have a reader...

Hi John, Thanks for the clarification. I'd therefore call the measures age 4 and age 5 rather than baseline and follow up. Then 3-levels might make more sense and you could include age as a predictor to control for age related differences perhaps with age related interactions which would pretty much...

Hi John, Presumably when you talk about a quadratic relationship you are talking globally as having only 2 measures per child would not allow anything more than a linear relationship at the child level. When someone uses terms like baseline and follow-up it suggests something happens in between thou...

Hi Cesarroga,
I can recommend my paper on this https://rss.onlinelibrary.wiley.com/doi ... 04.00365.x which looks at partitioning variation in a logistic model with overdispersion and has a similar example of counts for different categories.
Best wishes,
Bill.

Hi Cesarroga, You should be able to code logistic regression examples where the denominator is not 1 so that you can then model the 6 proportions as 6 couplets (y,n) one per row in the dataset and nest these within small areas. In MLwiN you would have as you say the same levels as level 1 and level ...

Hi John, With only 6 faculties you would be hard pressed to estimate random effects - I guess you may get enough departments to treat as random. If you decide to fit them as fixed effects then simply putting in department effects will saturate the model at that level and you will not be able to add ...

Hi, So there are various schools of thought here. Many people use weights that sum to 1 and I think in my MMMC paper (Browne, Goldstein and Rasbash, 2001) we did that. We have also argued (Tranmer, Steele and Browne, 2014) that it might make sense to make the squares of the weights sum to 1 as then ...

Hi JeanSebastien,
This seems more of a concept question rather than an MLwiN question. I think if you give more information about your data then it might be easier to answer and I am assuming the question is related to the earlier one you sent.
Best wishes,
Bill.