Chapter 12. Multilevel Analyses
In Chapter 10, Classification with k-Nearest Neighbors and Naïve Bayes, we discussed association with k-Nearest Neighbors and Naïve Bayes. In the previous chapter, we examined classification trees using notably C4.5, C50, CART, random forests, and conditional inference trees.
In this chapter, we will discuss:
- Nested data and the importance of dealing with them appropriately
- Multilevel regression including random intercepts and random slopes
- The comparison of multilevel models
- Prediction using multilevel modeling
If you have nested data, this chapter is essential for you! What is meant by nested data is that observations share a common context. The examples include:
- Consumers nested within shops
- Employees nested ...