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The Mystery Motivator intervention has been shown to increase prosocial behavior among school-age children by capitalizing on the anticipation afforded by reward concealment or randomization. Although numerous evaluations have supported the effectiveness of the Mystery Motivator, the overall status of this intervention is still unknown. The purpose of the current meta-analysis was to quantify the effects of the Mystery Motivator for improving behavior outcomes in school-age children and determine if participant, setting, and intervention components moderate intervention effects. A systematic review of academic databases was conducted that yielded a total of 24 studies. These studies were evaluated for methodological rigor and analyzed for effects using Tau-U. Results indicate a strong overall effect for this intervention (Tau-U = .77 (CI 95 [.73 to .80]). The results discussed and practical issues are considered for future research on the use of the Mystery Motivator to promote the academic and social-emotional development of children and adolescents.

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AbstractA recent article (Joslyn, P. R., & Morris, S. L. in Perspectives on Behavior Science, 47(1), 167–196, 2024) advocates the use of risk ratios, or relative risk, in behavior analysis. The authors present a strong case for the use of risk ratios and how they might improve the science and application of behavior analysis. Unfortunately, their computation of the risk ratio is incorrect and their examples gloss over important nuances in how risk ratios should be used. The present article corrects the calculations, describes how to determine whether a particular risk ratio differs from a reference group, comments on the importance of stability of the data entering the calculation, and demonstrates approaches to presenting them visually, such as Forest plots.

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Abstract Science, understood to be the behavior of scientists, falls within the purview of behavior analysis. All scientists use scientific instruments to study a natural phenomenon, and for the behavior analyst, perhaps no tool is more important than the graph used to show changes in level, trend, and variability, and upon which behavior analysts make data-based decisions. Modern behaviorism as we know it dates back to the development of the cumulative recorder first developed in the 1930s. Though revolutionary to the science of behavior, two-dimensional graphs may be limited in application for analyzing complex human behavior. In the current article, we conceptualize verbal behavior as a multidimensional field of environmental relations, and introduce the use of multi-axial radar charts for its visual and quantitative analysis. From there, we survey the use of radar charts toward advancing a behavior-analytic understanding of human language and cognition. We demonstrate the use of radar charts for calculating simple shape descriptors as a quantitative measure of dynamic interactants, and show how they can be used to measure change over time.