The article discusses the importance of assessing the consistency of effects when applying multilevel models to single-case data. Single-case experimental designs (SCEDs) are used to study the effects of interventions on individuals over time. Replication is crucial for demonstrating experimental control and establishing the generalizability of intervention effects. The "replicability crisis" highlights the need for assessing consistency in replications. The article proposes using multilevel models, also known as hierarchical linear models or mixed effects models, to assess the consistency of effects. This involves checking whether the confidence intervals for the random effects include zero, indicating whether the fixed effect estimate is a plausible value for each individual effect. This approach is more informative than checking whether the confidence interval for the individual treatment effect excludes zero. The article discusses two types of replication: direct replication and systematic replication. Direct replication involves replicating the basic effect within a study, while systematic replication involves replicating effects across studies. The article also discusses the importance of consistency in replication, which can be assessed using quantifications such as the coefficient of variation. The article provides examples of how to assess consistency using multilevel models, including the Lambert et al. (2006) and Sherer and Schreibman (2005) data sets. It also discusses the importance of considering trends and changes in trends when assessing consistency. The article concludes that multilevel models provide a promising approach for assessing consistency in single-case data, and that this assessment can be performed using free user-friendly software.The article discusses the importance of assessing the consistency of effects when applying multilevel models to single-case data. Single-case experimental designs (SCEDs) are used to study the effects of interventions on individuals over time. Replication is crucial for demonstrating experimental control and establishing the generalizability of intervention effects. The "replicability crisis" highlights the need for assessing consistency in replications. The article proposes using multilevel models, also known as hierarchical linear models or mixed effects models, to assess the consistency of effects. This involves checking whether the confidence intervals for the random effects include zero, indicating whether the fixed effect estimate is a plausible value for each individual effect. This approach is more informative than checking whether the confidence interval for the individual treatment effect excludes zero. The article discusses two types of replication: direct replication and systematic replication. Direct replication involves replicating the basic effect within a study, while systematic replication involves replicating effects across studies. The article also discusses the importance of consistency in replication, which can be assessed using quantifications such as the coefficient of variation. The article provides examples of how to assess consistency using multilevel models, including the Lambert et al. (2006) and Sherer and Schreibman (2005) data sets. It also discusses the importance of considering trends and changes in trends when assessing consistency. The article concludes that multilevel models provide a promising approach for assessing consistency in single-case data, and that this assessment can be performed using free user-friendly software.