Estimating intervention effects across different types of single-subject experimental designs: empirical illustration.

The purpose of this study is to illustrate the multilevel meta-analysis of results from single-subject experimental designs of different types, including AB phase designs, multiple-baseline designs, ABAB reversal designs, and alternating treatment designs. Current methodological work on the meta-analysis of single-subject experimental designs often focuses on combining simple AB phase designs or multiple-baseline designs. We discuss the estimation of the average intervention effect estimate across different types of single-subject experimental designs using several multilevel meta-analytic models. We illustrate the different models using a reanalysis of a meta-analysis of single-subject experimental designs (Heyvaert, Saenen, Maes, & Onghena, in press). The intervention effect estimates using univariate 3-level models differ from those obtained using a multivariate 3-level model that takes the dependence between effect sizes into account. Because different results are obtained and the multivariate model has multiple advantages, including more information and smaller standard errors, we recommend researchers to use the multivariate multilevel model to meta-analyze studies that utilize different single-subject designs.