I have been exploring the xxM package and am excited by its capabilities.
One capability that I noted in descriptions and that particularly important to me is the ability to capture multiple group membership. However, I could not find a reference to how to implement it in tutorials.
A brief description of my data and what I am trying to accomplish:
It is a 3-level dataset, level 1 being employees, level 2 being team leaders, and level 3 being executives. Employees are nested within teams/team leaders. Team leaders are/ can be connected to executives. Some have one connection/affiliation, some have multiple, some have none. This is where the multiple membership issue comes in.
In terms of model, what I am trying to do is to predict an outcome at the employee level by an attribute of the executives. In principle, this is very similar to the example 8 in the tutorial (school resources -> teacher intercept -> student outcomes), with the difference being that my level 2 units are neither nested nor fully crossed, but have between 0 and x affiliations/connections with the level 3 units.
I would be very grateful for any guidance you can provide or any material you can point me to that has an example of how to implement the multiple membership structure.