<p>Multilevel modeling is a promising approach that can be applied to evaluate intervention effectiveness and explain variability in intervention effectiveness in single-case experimental design research. This approach is recommended as it accounts for the nested data structure and allows to model complexities such as autocorrelation, nonlinear time trends and the inclusion of participant characteristics as moderators to account for variability in intervention effectiveness. Therefore, choices need to be made to select the most appropriate multilevel model given the research question and the need to model (some or all) complexities. This brief commentary provides criteria that can be used to inform appropriate model selection and ends with a recommendation for best practices for model building. Our hope is to further enhance the understanding of the appropriateness and applicability of the multilevel modeling approach to analyze single-case experimental data.</p>