I have been asked to run a model in which individuals are nested within divisions of an organisation, and divisions are nested within departments.
I am not sure how to treat the upper levels of the hierarchy. I currently have received data from about 96% of the divisions (about 250 in total), which amounts to a full set of divisional data from over 90% of departments (about 50 in total).
The remaining data may or may not be eventually supplied.
If I do not receive any more data, I wonder if I can classify divisions and departments as random effects (even though the population of divisions is barely greater than the sample of divisions, and likewise for departments); and hence construct a multilevel model with individuals at level 1, divisions level 2 and departments level 3. I have reason to believe that the 96% of divisions who have supplied data so far is not a random sample of divisions: I could almost have completely correctly predicted the 4% I was not going to get.
If the assumption that divisions and departments are random factors is not justified, or I do subsequently receive a complete set of data, both divisions and departments will be fixed effects. I don't want to use 249 indicator variables to model the divisions and another 49 to model the departments, so I wondered what would be the implications of treating them as levels in the hierarchy, even though they would not be random effects  or whether there was an alternative appropriate treatment.
Many thanks in advance for any advice that can be provided.
John
Nested models with fixed  sort of  categories

 Posts: 12
 Joined: Tue Oct 16, 2018 1:24 pm
Re: Nested models with fixed  sort of  categories
Hi John,
It is quite common for people to fit random effects to a set of units even if the sampled units are in fact the population. Arguments are made that for example the data is a snapshot in time and thus there would be random uncertainty. A good example is voting data where the dataset is often all constituencies.
Does that help?
Best wishes,
Bill.
It is quite common for people to fit random effects to a set of units even if the sampled units are in fact the population. Arguments are made that for example the data is a snapshot in time and thus there would be random uncertainty. A good example is voting data where the dataset is often all constituencies.
Does that help?
Best wishes,
Bill.

 Posts: 12
 Joined: Tue Oct 16, 2018 1:24 pm
Re: Nested models with fixed  sort of  categories
That does indeed help Bill  it's exactly what I needed to know. Many thanks.