Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way. For example, it is quite common to have data in which we have repeated measurements for the units of observation, or in which the units of observation are otherwise clustered (e.g. students within school, cities within geographic region). While there are different ways to approach such a situation, mixed models are a very common and powerful tool to do so.  In addition, they have ties to other statistical approaches that further expand their applicability.

The goal of this workshop is primarily to provide a sense of when one would use mixed models and how to incorporate a variety of standard techniques.  It is very applied in nature, and only assumes a basic understanding of standard regression models (and use of R for such models).

Link: https://m-clark.github.io/mixed-models-with-R/