Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant…

It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…

The Daily Report Card (DRC) is a commonly employed behavioral intervention for treating Attention Deficit Hyperactivity Disorder (ADHD) in schools. Much of the support for the DRC comes from single-case studies, which have traditionally received less attention than group studies. This lack of attention to single-case studies results in an…

The purpose of this project is to demonstrate the practical methods developed to utilize a dataset consisting of both multivariate and multilevel effect size data. The context for this project is a large-scale meta-analytic review of the predictors of academic achievement. This project is guided by three primary research questions: (1) How do we…

This meta-analysis used hierarchical linear modeling to examine 115 single-case studies with 343 participants that examined the effectiveness of social skills interventions for individuals with autism spectrum disorder (ASD). The average effect size of the included studies was 1.40 (SD = 0.43, 95% CL = 1.32-1.48, N = 115). In the further, several…