A variety of measures have been developed to quantify intervention effects for single-case experimental design studies. Within the family of non-overlap indices, the Tau-U measure is one of the most popular indices. There are several Tau-U variants, each one calculated differently. The appropriateness of each Tau-U variant depends upon the data…

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…

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect…

This study introduces an innovative meta-analytic approach, two-stage multilevel meta-analysis that considers the hierarchical structure of single-case experimental design (SCED) data. This approach is unique as it is suitable to include moderators at the intervention level, participant level, and study level, and is therefore especially…

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a…