Mixed effects modeling – alternatively called hierarchical or multilevel modeling is a straightforward extension of (generalized) linear modeling as discussed in the previous chapter. A common characterization of mixed-effects modeling is that it accounts for situations where observations are clustered or come in groups. In corpus linguistics, there could be clusters of observations defined by individual speakers, registers, genres, modes, lemmas, etc. Instead of estimating coeffcients for each level of such a grouping factor (so-called fixed effects), in a mixed model they can be modeled as a normally distributed random variable (a so-called random effect) with predictions being made for each group. This chapter introduces readers to the situations where mixed effects modeling is useful or necessary. Thee proper specification of models is discussed, as well as some model diagnostics and ways of interpreting the output. Readers are assumed to be familiar with the concepts covered in the previous chapter on (Generalized) Linear Models.