Toward a Data-based Approach to the Selection of Applied Behavior Analysis Program Characteristics.

There is a paucity of data that describe how program characteristics relate to program outcome goals. This gap limits the use of data to guide and support decisions concerning the selection of applied behavior analysis (ABA) program characteristics. Therefore, the purpose of the present study was to describe a methodology for the evaluation of the relationships between program characteristics and program outcome goals in the context of identifying the ideal program characteristics to propose for a new master of science in ABA program at Franciscan Missionaries of Our Lady University (FranU).

Predicting Substance Use Treatment Failure with Transfer Learning.

Transfer learning, which involves repurposing a trained model on a related task, may allow for better predictions with substance use data than models that are trained using the target data alone. This approach may also be useful for small clinical datasets. The current study examined a method of classifying substance use treatment success using transfer learning.

Tutorial: Artificial neural networks to analyze single-case experimental designs.

Since the start of the 21st century, few advances have had as far-reaching impact in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and psychology. In research methods, one promising area for the adoption of artificial neural networks involves the analysis of single-case experimental designs. Given that these types of networks are not generally part of training in the psychological sciences, the purpose of our article is to provide a step-by-step introduction to using artificial neural networks to analyze single-case designs.

Machine Learning for Supplementing Behavioral Assessment.

The Questions About Behavioral Function (QABF) has a high degree of convergent validity, but there is still a lack of agreement between the results of the assessment and the results of experimental function analysis. Machine learning (ML) may improve the validity of assessments by using data to build a mathematical model for more accurate predictions. We used published QABF and subsequent functional analyses to train ML models to identify the function of behavior.