Further Analysis of Advanced Quantitative Methods and Supplemental Interpretative Aids with Single-Case Experimental Designs.

Abstract

Reliable and accurate visual analysis of graphically depicted behavioral data acquired using single-case experimental designs (SCEDs) is integral to behavior-analytic research and practice. Researchers have developed a range of techniques to increase reliable and objective visual inspection of SCED data including visual interpretive guides, statistical techniques, and nonstatistical quantitative methods to objectify the visual-analytic interpretation of data to guide clinicians, and ensure a replicable data interpretation process in research. These structured data analytic practices are now more frequently used by behavior analysts and the subject of considerable research within the field of quantitative methods and behavior analysis. First, there are contemporaneous analytic methods that have preliminary support with simulated datasets, but have not been thoroughly examined with nonsimulated clinical datasets. There are a number of relatively new techniques that have preliminary support (e.g., fail-safe k), but require additional research. Other analytic methods (e.g., dual-criteria and conservative dual criteria) have more extensive support, but have infrequently been compared against other analytic methods. Across three studies, we examine how these methods corresponded to clinical outcomes (and one another) for the purpose of replicating and extending extant literature in this area. Implications and recommendations for practitioners and researchers are discussed.