Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

• Published in: Perspectives on behavior science 2024-03, Vol.47 (1), p.203-223
• Main Authors: Neely, Leslie, Oyama, Sakiko, Chen, Qian, Qutub, Amina, Chen, Chen
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Behavior modification; Behavioral Science and Psychology; Big Data; Clinical medicine; Computer science; Data collection; Data science; Machine learning; Psychology; Sensors; SI: Big Data and Behavior Science
• ISSN: 2520-8969; 2520-8977
• DOI: 10.1007/s40614-023-00376-z

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. We hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.