Multilevel synthesis of single-case experimental data: further developments and empirical validation.

In single-case or single-subject designs (SSED), individual cases are measured repeatedly under different conditions, in order to assess the effect of the condition. Recently, multilevel models were proposed to combine the results of SSED studies, resulting in more general or detailed conclusions. The purpose of the proposed research is to empirically investigate the multilevel approach, using both real data and simulation studies. The research will entail several studies designed to address major complications encountered when syntesizing results from SSED research. A fist set is designed for synthesizin results from SSED studies with outcomes in the same metric. The initial study will focus on the basis three-level model for the meta-analysis of SSED studies with the same outcome. A series of additional studies will target complications encountered in the synthesis of SSED studies' results, including: autocorrelation, count data outcomes, designs with multiple outcomes and/or settings, models with nonlinear growth trajectories, and the use of bootstrapping estimation techniques. The second set will assess synthesis of standardized raw data and effect sizes necessary for meta-analyzing results from SSED studies employing outcomes on different metrics.

Keywords:SSED, Standardization, Bootstrapping, Autocorrelation, Multilevel, Single-subject, Single-case

Disciplines:Education curriculum, Education systems, General pedagogical and educational sciences, Specialist studies in education, Other pedagogical and educational sciences