This Season 5 Premier explores the current state and future possibilities of visual analysis in behavior analysis, with particular focus on how artificial intelligence may enhance these practices. Dr. Kubina discusses limitations in current visual analysis practices, including inconsistent application of analysis techniques, lack of standardization in graph construction, and reliability issues in interpretation. The discussion extends to how AI tools might support more comprehensive and consistent visual analysis while maintaining the essential role of human judgment. The conversation includes practical considerations for implementing AI tools in clinical practice while maintaining ethical standards and professional competence.
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Show Notes
References:
Datchuk, S. M., & Kubina, R. M. (2011). Communicating experimental findings in single case design research: How to use celeration values and celeration multipliers to measure direction, magnitude, and change of slope. Journal of Precision Teaching & Celeration, 27, 3-17.
Kahng, S., Chung, K.-M., Gutshall, K., Pitts, S. C., Kao, J., & Girolami, K. (2010). Consistent visual analyses of intrasubject data. Journal of Applied Behavior Analysis, 43(1), 35–45.
Kubina, R. M., Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A Critical Review of Line Graphs in Behavior Analytic Journals. Educational Psychology Review, 29, 583-598.
Vanselow, N. R., Thompson, R., & Karsina, A. (2011). Data-based decision making: The impact of data variability, training, and context. Journal of Applied Behavior Analysis, 44(4), 767-780.
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