Single-case or single-subject experimental designs (SSED) are used to evaluate the effect of one or more treatments on a single case. Although SSED studies are growing in popularity, the results are in theory case-specific. One systematic and statistical approach for combining single-case data within and across studies is multilevel modeling. The purpose of the study is to investigate the applicability of the multilevel approach for analyzing the data from a study by Lambert, Cartledge, Heward, and Yo (2006), which is being used as a common data set in this symposium to explore the applicability of a range of different approaches to analyzing single-case data. Although the multilevel modeling approach and its flexibility are appealing, there is much about SSED data and the functioning of multilevel modeling with this type of data that is not fully understood. It is concluded that multilevel models are flexible enough to handle many of the complexities that arise in analyzing single-case data, and thus we expect them to be of general interest to single-case researchers.