Purpose: Research in communication sciences and disorders frequently involves the collection of clusters of observations, such as a series of scores for each individual receiving treatment over the course of an intervention study. However, little discipline-specific guidance is currently available on the subject of building and interpreting multilevel models. This article offers a tutorial on multilevel models, using notation from the R statistical software, and discusses their implications for research in communication sciences and disorders. Method: This tutorial introduces multilevel models and contrasts them with other methods to analyze repeated measures data, such as repeated measures analysis of variance or standard linear regression. It also provides guidance on interpreting the components of a multilevel model and selecting the best-fitting model. Finally, these models are illustrated through an analysis of real data from a study of speech production training in second-language speakers of English. Conclusions: As a flexible method that can increase the rigor of modeling for clustered data, multilevel modeling represents an important tool for research in communication disorders. Given their increasingly prominent role in the analysis of experimental data in communication sciences, it is important for researchers to be familiar with the basics of building and interpreting these models.