Further analysis of subtypes of automatically reinforced SIB: A replication and quantitative analysis of published datasets

Hagopian, Rooker, and Zarcone (2015) evaluated a model for subtyping automatically reinforced self‐injurious behavior (SIB) based on its sensitivity to changes in functional analysis conditions and the presence of self‐restraint. The current study tested the generality of the model by applying it to all datasets of automatically reinforced SIB published from 1982 to 2015. We identified 49 datasets that included sufficient data to permit subtyping. Similar to the original study, Subtype‐1 SIB was generally amenable to treatment using reinforcement alone, whereas Subtype‐2 SIB was not. Conclusions could not be drawn about Subtype‐3 SIB due to the small number of datasets. Nevertheless, the findings support the generality of the model and suggest that sensitivity of SIB to disruption by alternative reinforcement is an important dimension of automatically reinforced SIB. Findings also suggest that automatically reinforced SIB should no longer be considered a single category and that additional research is needed to better understand and treat Subtype‐2 SIB.     

Evaluating sources of baseline data using dual-criteria and conservative dual-criteria methods: A quantitative analysis

Scheithauer et al. (2020) recently demonstrated that differences in the source of baseline data extracted from a functional analysis (FA) may not affect subsequent clinical decision-making in comparison to a standard baseline. These outcomes warrant additional quantitative examination, as correspondence of visual analysis has sometimes been reported to be unreliable. In the current study, we quantified the occurrence of false positives within a dataset of FA and baseline data using the dual-criteria (DC) and conservative dual-criteria (CDC) methods. Results of this quantitative analysis suggest that false positives were more likely when using FA data (rather than original baseline data) as the initial treatment baseline. However, both sources of baseline data may have acceptably low levels of false positives for practical use. Overall, the findings provide preliminary quantitative support for the conclusion that determinations of effective treatment may be easily obtained using different sources of baseline data.     

Initial outcomes of an augmented competing stimulus assessment

Competing stimulus assessments (CSAs) are designed to identify stimuli that, when made freely available, reduce problem behavior. Although CSAs have demonstrated utility, identifying competing stimuli can be difficult for some individuals. The current study describes outcomes from an augmented CSA (A-CSA) for 6 consecutively encountered cases with treatment-resistant subtypes of automatically maintained problem behavior. When test stimuli were made freely available, only between 0 and 1 effective competing stimuli were identified for each case. Prompting and response blocking were temporarily employed in succession to promote engagement with stimuli and disrupt problem behavior. When those procedures were withdrawn and stimuli made freely available, the number of effective competing stimuli increased in all 6 cases. Findings suggest that procedures designed to promote engagement and disrupt problem behavior may allow the A-CSA to be a platform not only for identifying competing stimuli, but also for actively establishing competing stimuli.     

Identifying predictive behavioral markers: A demonstration using automatically reinforced self‐injurious behavior

Predictive biomarkers (PBioMs) are objective biological measures that predict response to medical treatments for diseases. The current study translates methods used in the field of precision medicine to identify PBioMs to identify parallel predictive behavioral markers (PBMs), defined as objective behavioral measures that predict response to treatment. We demonstrate the utility of this approach by examining the accuracy of two PBMs for automatically reinforced self‐injurious behavior (ASIB). Results of the analysis indicated both functioned as good to excellent PBMs. We discuss the compatibility of this approach with applied behavior analysis, describe methods to identify additional PBMs, and posit that variables related to the mechanisms of problem behavior and putative mechanism of treatment action hold the most promise as potential PBMs. We discuss how this technology could guide individualized treatment selection, inform our understanding of problem behavior and mechanisms of treatment action, and help determine the conditional effectiveness of clinical procedures.     

Injury characteristics across functional classes of self-injurious behavior

Self-injurious behavior (SIB) is inherently problematic because it can lead to injuries, including those that are quite severe and may result in loss of function or permanent disfigurement. The current study replicated and extended Rooker et al. (2018) by classifying the physical characteristics of injuries across groups of individuals with automatically maintained SIB (ASIB Subtypes 2 and 3) and socially maintained SIB. Individuals with Subtype 2 ASIB had the most frequent and severe injuries. Further, an inverse relation was found between the level of differentiation in the functional analysis and the number of injuries across groups. Studying the response products of SIB (the injuries) documents the risks associated with SIB, justifies the need for research and the intensive intervention, and advances knowledge of SIB. Additional research is needed to replicate these findings, and determine the variables that produce different characteristics of injury secondary to SIB.     

Authorship trends in the Journal of Applied Behavior Analysis: An update

The Journal of Applied Behavior Analysis (JABA) is considered the flagship journal for the discipline of applied behavior analysis. Thus, popular research topics and other publication trends within JABA reflect the current cultural and scientific contingencies governing the field of behavior analysis. Researchers have previously quantified a number of authorship trends in JABA (and other behavior-analytic journals) across a number of variables, such as gender identity and sex of author, country of origin, or seniority within the field (Dunlap et al., 1998) to examine demographic and organizational factors associated with successful publication in JABA. These analyses ought to be conducted continuously to monitor trends and detect any potential biases (e.g., sexism). Accordingly, the purpose of the present investigation was to replicate previous research in this area (e.g., Dymond et al., 2000) and provide an update of current publication trends within JABA. Implications for future research and publishing practices are discussed.     

Using dual-criteria methods to supplement visual inspection: Replication and extension

The dual-criteria and conservative dual-criteria methods effectively supplement visual analysis with both simulated and published datasets. However, extant research evaluating the probability of observing false positive outcomes with published data may be affected by case selection bias and publication bias. Thus, the probability of obtaining false positive outcomes using these methods with data collected in the course of clinical care is unknown. We extracted baseline data from clinical datasets using a consecutive controlled case-series design and calculated the proportion of false positive outcomes for baseline phases of various lengths. Results replicated previous findings from Lanovaz, Huxley, and Dufour (2017), as the proportion of false positive outcomes generally decreased as the number of points in Phase B (but not Phase A) increased using both methods. Extending these findings, results also revealed differences in the rate of false positive outcomes across different types of baselines.