Visual analysis is the most commonly used method for interpreting data from singlecase designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach.

The purpose of our study was to examine the probability of observing false positives in nonsimulated data using the dual‐criteria methods. We extracted data from published studies to produce a series of 16,927 datasets and then assessed the proportion of false positives for various phase lengths. Our results indicate that collecting at least three data points in the first phase (Phase A) and at least five data points in the second phase (Phase B) is generally sufficient to produce acceptable levels of false positives.

The authors evaluated the effects of matched and unmatched stimuli on immediate and subsequent engagement in targeted vocal stereotypy (Experiment 1) and untargeted motor stereotypy (Experiment 2). Results of Experiment 1 showed that (a) matched stimulation decreased immediate engagement in vocal stereotypy for 8 of 11 participants and increased subsequent engagement in vocal stereotypy for only 1 of the 8 participants and (b) unmatched stimulation decreased immediate engagement in vocal stereotypy for only 1 of 10 participants and did not increase subsequent engagement in vocal stereotypy for this participant. Results of Experiment 2 showed that for 8 of 14 participants, untargeted stereotypy increased when the matched or unmatched stimulus was present, after it was removed, or both. The authors briefly discuss the potential clinical implications of using matched stimulation to decrease vocal stereotypy and limitations of the findings.

Vocal stereotypy is a common problem behavior in individuals with autism spectrum disorders that may interfere considerably with learning and social inclusion. To assist clinicians in treating the behavior and to guide researchers in identifying gaps in the research literature, the authors provide an overview of research on vocal stereotypy in individuals with autism spectrum disorders. Specifically, the authors review the research literature on behavioral interventions to reduce engagement in vocal stereotypy with an emphasis on the applicability of the procedures in the natural environment and discuss the clinical implications and limitations of research conducted to date. Researchers have shown that several antecedent-based and consequence-based interventions may be effective at reducing vocal stereotypy. However, the review suggests that more research is needed to assist clinicians in initially selecting interventions most likely to produce desirable changes in vocal stereotypy and collateral behavior in specific circumstances.

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