This is pretty general, but what are the pros and cons of including additional levels in multilevel model (linear mixed model)?

I have a data containing information on multilevel administrative division of the country and most of the levels are more or less of interest for me. Sample size is not a problem in here. On one hand, simpler models are in most cases better, on another, including additional levels would enable me to compare the variances on different levels. I found examples of 4-level models in the literature, but I haven't seen any practical advise on that. Could you provide any arguments and/or literature on that?

**Contents**hide

#### Best Answer

This is hard to answer without much context. But in general, parameters of additional levels will be harder to estimate. For each additional level you will need much more data, specially for the variance-covariance parameters of the higher levels. See here for a related discussion.

### Similar Posts:

- Solved – Is it possible to construct a discrete-time multilevel hazard model in R
- Solved – Number of level 2 units needed to carry out multilevel model
- Solved – Time varying predictors at higher aggregation levels in multilevel survival analysis
- Solved – Factor analysis with repeated measures
- Solved – How to compare changes in group means over time