The use of multilevel models as a method for synthesizing single-case experimental design results is receiving increased consideration. In this article we discuss the potential advantages and limitations of the multilevel modeling approach. We present a basic two-level model where observations are nested within cases, and then discuss extensions of the basic model to accommodate trends, moderators of the intervention effect, non-continuous outcomes, heterogeneity, autocorrelation, the nesting of cases within studies, and more complex single-case design types. We then consider methods for standardizing the effect estimates and alternative approaches to estimating the models. These modeling and analysis options are followed by an illustrative example.