Chaining procedures, initially developed through the experimental analysis of behavior, are now regularly investigated and implemented in applied behavior analysis research and practice. This article takes a deep dive into the history of applied chaining, presenting a qualitative review of the literature on this subject. Topics covered include guidelines for procedural implementation, lists of terms with multiple meanings and multiple terms with the same meaning, and a history of chaining within behavior analysis. This history sheds light on our current practices and highlights the need for certain changes. The culmination of this review is the proposal of a new set of terms related to chaining, intended for use by researchers and practitioners alike. The purpose of these suggested terminological revisions to our field’s verbal behavior is to provide greater clarity and structure to the taxonomy of terms used when speaking and writing about applied chaining.
The recent practice of including citation diversity statements in scholarly writing represents a movement toward transparency in citation practices. Among other things, the statement includes name-based classifications of first- and last-authors' demographics, most commonly gender and ethnicity, which can be estimated using application programming interfaces (APIs). In this tutorial, we outline how to use the Clean Bib tool to generate a citation diversity statement for the references cited in scientific and academic publications. Clean Bib uses GenderAPI and EthnicolrAPI to classify whether first and last author names are most likely names of men, women, white authors, or authors of color. Although it is limited in that gender and ethnicity are reduced to binary variables and other important demographic characteristics are excluded, it is an accessible and scalable tool to begin to examine perceived author characteristics and reflect on citation practices.
Impulsive choice—preference for a smaller-sooner over a larger-later reward—is a transdiagnostic risk factor for behaviors that harm human health. Preventing a temporally extended pattern of impulsive choice should be a translational research priority. No existing intervention meets all the criteria outlined in this article of an ideal school-based prevention program: low cost, low teacher effort, fast-acting, and capable of producing lasting, generalizable reductions in impulsive choice. Here, we evaluate Pavlovian conditioning as a candidate approach. Our review is informed by evidence from nonhuman laboratory experiments and kindergarten classroom studies examining whether appetitive Pavlovian conditioning protocols can reduce impulsive choice and related behaviors. Laboratory experiments demonstrate that a brief Pavlovian conditioning protocol establishes a conditioned stimulus (CS) that may then be strategically used to reduce impulsive choice in rats by serving as an attractive antecedent and/or an appetitive delay-bridging stimulus. Studies conducted in kindergarten classrooms show that a class-wide protocol can establish an appetitive CS with minimal teacher training and classroom disruption. Presenting the CS during an in-situ waiting task reduced children's challenging behaviors and presenting it during academic-choice tasks increased children's willingness to work for larger rewards. This preliminary evidence supports the continued exploration of Pavlovian conditioning approaches to efficiently prevent patterns of impulsive choice that cumulate over time to negatively affect human health and wellbeing.
Case-specific effect sizes have been developed to supplement the visual analysis of line-graphed data in single-case design research by quantitatively summarizing intervention effectiveness. This article provides background on case-specific effect sizes and describes when they can effectively complement visual analysis. We discuss several distinct families of case-specific effect size metrics, including nonoverlap indices (e.g., nonoverlap of all pairs), standardized measures (e.g., standardized mean difference), response ratios (e.g., percentage change), and goal attainment (e.g., percent of goal obtained). Furthermore, we discuss the assumptions and limitations that hold for all families of case-specific effect sizes and the assumptions and limitations of each type of case-specific effect size. In conclusion, we provide guidance for determining when these metrics are appropriate for a single-case design study and present a decision framework to help researchers select the most suitable effect size measure(s), if any, for their data.
Type I errors, or false positives, occur when a researcher erroneously concludes that the independent variable caused a change in the dependent variable. Type I errors can result in resources being misused in both research and practice. Although Type I errors are most often associated with group design paradigms, researchers using single-case experimental designs are also susceptible to making Type I errors. In this article, we describe the research on Type I errors in single-case experimental designs across analytic methods, including visual analysis, machine learning, effect estimation, and randomization approaches. We also suggest ways that each analytic method can be used to mitigate Type I errors and highlight aspects of Type I error in single-case experimental designs that are understudied and in need of additional research.
