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Selecting a quantitative measure to guide decision making in single-case experimental designs (SCEDs) is complicated. Many measures exist and all have been rightly criticized. The two general classes of measure are overlap-based (e.g., percentage nonoverlapping data) and distance-based (e.g., Cohen’s d). We compare several measures from each category for Type I error rate and power across a range of designs using equal numbers of observations (i.e., 3–10) in each phase. Results showed that Tau and the distance-based measures (i.e., RD and g) provided the highest decision accuracies. Other overlap-based measures (e.g., PND, dual-criterion method) did not perform as well. It is recommended that Tau be used to guide decision making about the presence/absence of a treatment effect, and RD or g be used to quantify the magnitude of the treatment effect. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40614-021-00317-8.

This article serves as a brief primer on planaria for behavior scientists. In the 1950s and 1960s, McConnell’s planarian laboratory posited that conditioned behavior could transfer after regeneration, and through cannibalization of trained planaria. These studies, the responses, and replications have been collectively referred to as the “planarian controversy.” Successful behavioral assays still require refinement with this organism, but they could add valuable insight into our conceptualization of memory and learning. We discuss how the planarian’s distinctive biology enables an examination of biobehavioral interaction models, and what behavior scientists must consider if they are to advance behavioral research with this organism. Suggestions for academics interested in building planaria learning laboratories are offered. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40614-018-00176-w) contains supplementary material, which is available to authorized users.

This article describes the development of an effect size measure called Ratio of Distances (RD). The goal was to develop a measure of level change for single case experimental research that met several practical requirements: (a) the measure is adaptable to designs with varying numbers of observations per, and across, phases; (b) the measure is adaptable to situations in which slope does and does not exist; (c) the measure has no ceiling, as is the limitation with commonly used overlap-based measures of effect size; and (d) the measure is computationally transparent and easily computed using widely available analysis tools (e.g., Microsoft Excel). The measure is applicable to single cases and meta-analyses.

The present research determined if articles describing attempts to improve the behavior of people with autism reported whether or not participants were receiving medication and, if so, whether an interaction between the intervention and the medication was mentioned. Fifty-one articles published from 2004 through 2008 were examined. Information regarding medication was provided in 18% of these articles, none of which mentioned an interaction. Very little is known regarding interactions between nonpharmacological interventions and psychotropic (or other) medications, but some studies demonstrate that interactions can occur. Given this possibility, and the fact that information regarding medications received by participants is easy to obtain and report, it appears worthwhile to include this information in published articles.