On the Need to Address Serial Dependency When Dealing with Data from Single-Case Experimental Designs

Abstract The use of single-case experimental designs (SCEDs) for assessing intervention effectiveness in educational sciences and related fields has risen sharply in recent years. With it, the number of available visual and statistical analysis methods has spiked as well. The application and interpretation of results obtained via these methods might, however, be affected in different degrees by the presence of serial dependency in SCED data. The effect that serial dependency has on the application of statistical methods to SCED data might vary depending on the exact method used. In addition, the amount of serial dependency in SCED data might depend on design and measurement choices. However, to date, no concise answer has been found about the extent of serial dependency and its moderators in SCED data. In the present commentary, we give an overview of the state of the art on serial dependency research in SCEDs, argue that serial dependency should be routinely assessed when reporting the results of SCEDs, and sketch an agenda for serial dependency research. Addressing serial dependency can be facilitated by the inclusion of serial dependency as a data feature in standard reporting tools, which will ultimately lead to improved open science practices in the SCED community.