Mixed-effect models, multilevel models, hierarchical linear models – all refer to a class of statistical models used to analyze correlated data. By “correlated” we mean data that is not independent. Such data include repeated measurements on the same subjects, or subjects observed in clusters, such as students in classrooms or plants in different plots. In this workshop we introduce the basics of linear mixed-effect modeling with an emphasis on implementation and interpretation. Examples will be given in R using the lme4 package.

Previous experience with R and linear regression will be helpful but not required.