I've hit a wall with my multilevel models, which I was hoping someone could help me with. It's almost certainly because I'm using MLWin wrong, so with any luck, someone will be able to point out my obvious mistake.
I have monthly inflation indices for a series of categories of goods for the 12 Government Office Regions and nations of the UK. So my data is structured as 12 GORS > 13 Categories > 84 monthly measurements.
The descriptives suggest that the monthly measurements vary by both category and GOR, so I expect MLWin to tell me something like - month accounts for most of the variance, category most of the remaining variance, GOR for a small but noticeable amount.
If I run a 2-level model with GOR as level 2 and month as level 1, I get roughly that - GOR accounts for about 5% of the variance, month for about 95%.
However, if I make the model 3-level, with GOR as level 3, Category as level 2 and month as level 1, the variance due to GOR disappears. It all goes to the Category, along with some of the variance previously due to month. GOR is given a variance of 0.0000 with a standard error of 0.0000.
Unless I'm interpreting the results incorrectly, I'm being given an answer that makes no sense. I think my data is sorted correctly, and the hierarchy viewer shows what I expect it to.
Is there something extra I should be taking account of in the 3-level model that I wouldn't in the 2-level model? Does my data need to be structured differently?
Any help would be greatly appreciated.