Assessing Growth of BACB Certificants (1999-2019).

Geographic distribution patterns of board certified behavior analysts may be useful in analyzing the growth of the field. First, we present an international snapshot of Behavior Analyst Certification Board (BACB) certificants, then analyze relative growth rates between countries from 1999 to 2019. This is followed by an in depth review of certificant distribution patterns in the United States and Canada, as well as the ratios of experienced behavior analysts to new certificants. These data highlight regions with a potential deficit of qualified supervisors. There are factors that influence different dispersal patterns, and without drilling deeper into the data we may be unable to effectively identify or influence them in order meet the specific needs of a geographic region. Supplementary Information: The online version contains supplementary material available at 10.1007/s40614-023-00370-5.

Building a Social Justice Framework for Cultural and Linguistic Diversity in ABA.

ABA may be uniquely positioned to have broader impacts with culturally and linguistically diverse (CLD) client populations, not only due to its growth and increased social acceptability, but also because a segment of the field practices within the education system. Decades of misinformation have potentially reduced the widespread reliance on learning pedagogies derived from behavior analytic research. Disseminating ABA technologies depends on an advocacy-based approach to close gaps from research-to-practice. Social justice is both an approach and a framework that can be integrated into our strategic planning for the field. This article describes how to apply social justice guidelines when working with CLD students and families. There are culturally relevant considerations that can be included in our research, training, and service delivery, in particular if we want the field to grow in a sustainable fashion. Behavior analysts must consider it an ongoing long-term objective to engage in culturally informed assessment, culturally and linguistically relevant intervention, culturally focused advocacy and collaboration with families, and use self-assessment of their cultural competence.

Quantification of behavioral data with effect sizes and statistical significance tests.

This article describes the use of statistical significance tests and distance-based effect sizes with behavioral data from single case experimental designs (SCEDs). Such data often are interpreted only with visual analysis. However, a growing movement in the field is to quantify results to improve decision-making and communication across studies and sciences. The goal of the present study was to assess the agreement between visual analysis and various statistical tests. We recruited visual analysts to judge 160 pairwise data sets from published articles and compared these analyses to significance tests and effect sizes. One-tailed significance testing of Tau z and the percentage of pairwise differences in the predicted direction (PWD) generally agreed with each other, and complemented the effect sizes of Ratio of Distances (RD) and g. Visual analysis was somewhat unreliable and should be combined with statistical complements to maximize decision accuracy.

Reconsideration of the type I error rate for psychological science in the era of replication.

Careful consideration of the tradeoff between Type I and Type II error rates when designing experiments is critical for maximizing statistical decision accuracy. Typically, Type I error rates (e.g., .05) are significantly lower than Type II error rates (e.g., .20 for .80 power) in psychological science. Further, positive findings (true effects and Type I errors) are more likely to be the focus of replication. This conventional approach leads to very high rates of Type II error. Analyses show that increasing the Type I error rate to .10, thereby increasing power and decreasing the Type II error rate for each test, leads to higher overall rates of correct statistical decisions. This increase of Type I error rate is consistent with, and most beneficial in the context of, the replication and "New Statistics" movements in psychology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Statistical Decision-Making Accuracies for Some Overlap- and Distance-based Measures for Single-Case Experimental Designs.

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.

Development of a Distance-Based Effect Size Metric for Single-Case Research: Ratio of Distances.

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.

Behavioral Research with Planaria.

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.

Recording an Excel(®) Macro to Specify Date Ranges for Clinical Data.

The individuals served by behavior analysts are often funded by Medicaid, insurance companies, or private pay. The first two options usually require progress notes detailing graphically and quantitatively the behavioral outcomes. These progress notes usually come in the form of a written account of milestones achieved or barriers faced, graphical displays of behavioral data, and summary tables. The graphical displays are monthly, quarterly, and annual reports for the individuals that they serve. Microsoft Excel® is one of the most accessible tools by which to accomplish this task; however, presenting the required date ranges can be a time-consuming task. A task analysis is outlined to automate this process and reduce the time taken to accomplish indirect service hours to the clients served.

Phase-change lines, scale breaks, and trend lines using Excel 2013.

The development of graphing skills for behavior analysts is an ongoing process. Specialized graphing software is often expensive, is not widely disseminated, and may require specific training. Dixon et al. (2009) provided an updated task analysis for graph making in the widely used platform Excel 2007. Vanselow and Bourret (2012) provided online tutorials that outline some alternate methods also using Office 2007. This article serves as an update to those task analyses and includes some alternative and underutilized methods in Excel 2013. To examine the utility of our recommendations, 12 psychology graduate students were presented with the task analyses, and the experimenters evaluated their performance and noted feedback. The task analyses were rated favorably.