Effect size estimation for combined single-case experimental designs

Abstract

The methodology of single-case experimental designs (SCED) has been expanding its efforts toward rigorous design tactics to address a variety of research questions related to intervention effectiveness. Effect size indicators appropriate to quantify the magnitude and the direction of interventions have been recommended and intensively studied for the major SCED design tactics, such as reversal designs, multiple-baseline designs across participants, and alternating treatment designs. In order to address complex and more sophisticated research questions, two or more different single-case design tactics can be merged (i.e., “combined SCEDs”). The two most common combined SCEDs are (a) a combination of a multiple-baseline design across participants with an embedded ABAB reversal design, and (b) a combination of a multiple-baseline design across participants with an embedded alternating treatment design. While these combined designs have the potential to address complex research questions and demonstrate functional relations, the development and use of proper effect size indicators lag behind and remain unexplored. Therefore, this study probes into the quantitative analysis of combined SCEDs using regression-based effect size estimates and two-level hierarchical linear modeling. This study is the first demonstration of effect size estimation for combined designs.

Keywords:

• Combined designs
• effect size
• hierarchical linear modeling
• regression models
• single-case experimental design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Institute of Education Sciences, U.S. Department of Education, through grants [R305D150007 and R305D190022]. The content is solely the responsibility of the author and does not necessarily represent the official views of the Institute of Education Sciences, or the U.S. Department of Education.