What are Multilevel Models?
Multilevel Models (also known as mixed effects, nested or hierarchical data models) are used to find the effects of levels on a predictor variable. These hierarchical levels can be geographical, such as cities within counties, or organizational, products within stores. This method is useful to determine how factors affect the variation between a unit and the group. Multilevel models are gaining popularity in the areas of organizational psychology, education research, and the geographical sciences. 1. Data - First, as its alternative names suggest, data needs to be nested. There is no specific number for a data sample; however, the smaller the dataset, the more prone to errors such as bias. For example, in my dissertation I used a two-level model for a dataset including 327 units (municipalities) nested within 12 geographical divisions: Once you have made sure your data is nested, you can run a single level regression using your units to choose the variables t...