Multilevel model with many groups, and half the sample = individuals (groups of 1) I have a project predicting sentencing outcomes based on several predictors. Database of about 800 people. Some people share the offence that their sentencing is based on (i.e., co-accused), so they need to be grouped (i.e., doing a multilevel model). There are 127 groups (many of which have 2 people, up to 11 people). The rest of the sample (about half) do not share offences with others and so have unique values/no nesting or hierarchy to account for. Is is okay to use shared offence as a level 2 variable in a multilevel model even if half the sample isn't part of a group? I am using SPSS.

I can't speak for SPSS specifically, but in general this should work. While classical multilevel ANOVA is not good at handling unbalanced designs, modern multilevel models fitted by maximum likelihood (ML) or restricted maximum likelihood (REML) should be fine. While there are limits (e.g. I probably wouldn't try to use this to fit a data set where 99% of the individuals were singletons and only a few were in groups), your case sounds like it should be OK.
Typically in multilevel modeling you don't have to specify at which levels the fixed effects vary, you can just include them in the model and let the software sort it out. The only caveat is that it won't work to include a random slope term in the model for a covariate that doesn't vary within the appropriate levels. For example, if subjects are measured at multiple times, but each subject is measured repeatedly at a single temperature, you can't estimate the variation of temperature effects across individuals.
