Assumptions cross-classification models

Welcome to the forum for MLwiN users. Feel free to post your question about MLwiN software here. The Centre for Multilevel Modelling take no responsibility for the accuracy of these posts, we are unable to monitor them closely. Do go ahead and post your question and thank you in advance if you find the time to post any answers!

Remember to check out our extensive software FAQs which may answer your question: ... port-faqs/
Post Reply
Posts: 2
Joined: Tue Feb 28, 2017 4:36 pm

Assumptions cross-classification models

Post by grietvwb » Fri Apr 14, 2017 1:57 pm

Dear forum

I have a question regarding assumptions of cross-classification models. Do rules or assumptions exist about the minimum required crossing between classifications?
I’ll specify my question using an example: I’m using cross-classified models to investigate the effects of teachers, so I have a model of students within first grade and second grade teachers. There is no hierarchy between the first and second grade teachers, but there is also not so much crossing, since students mostly stayed in the same school and went on, with their classmates, to a second grade teacher. Comparing to other situations in which cross-classified models are used (f.e. students in neighbourhoods and schools), there is little crossing. Can this be a problem? Is a minimum amount of crossing required to have a good model? Or is it required that every student from a certain classification can be randomly assigned to the other classification? Or is this not at all a problem?

Many thanks in advance for your answer!


Posts: 93
Joined: Fri May 21, 2010 1:21 pm

Re: Assumptions cross-classification models

Post by billb » Wed Apr 19, 2017 9:45 am

Hi Griet,
In theory a nested structure is a special case of the more general crossed-structure with no crossing !! So yes there is no restriction here and if you tried selecting cross-classified for a nested structure you should get the same answers.

Post Reply