Statistics for Applied Behavior Analysis Practitioners and Researchers (eBook)

David J. Cox, Ph.D., M.S.B., BCBA-D currently works as the VP of Data Science at RethinkFirst and is faculty at the Institute for Applied Behavioral Science at Endicott College. Dr. Cox has earned a M.S. in Bioethics from Union Graduate College; a PhD in behavior analysis from the University of Florida; a post-doctoral fellowship at the Behavioral Pharmacology Research Unit of Johns Hopkins University School of Medicine; and a post-doctoral fellowship at Insight! Data Science. Since 2014, Dr. Cox's research and applied work has focused on how to effectively leverage technology, quantitative modeling, and artificial intelligence to ethically optimize behavioral health outcomes and clinical decision-making. Based on his individual and collaborative work, he has published 50+ peer-reviewed articles, three books, and 150+ presentations at scientific conferences.

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Statistics for Applied Behavior Analysis Practitioners and Researchers provides practical and useful content for individuals who work directly with, or supervise those who work directly with, individuals with ASD. This book introduces core concepts and principles of modern statistical analysis that practitioners will need to deliver ABA services. The organization of the book works through the flow of behavior analytic service provision, aiming to help practitioners read through research, evaluate intervention options, incorporate statistics in their analysis of time-series intervention and assessment data, and effectively communicate assessment and intervention effects using statistics. As professionals who provide applied behavior analysis (ABA) services are required to use evidence-based practices and make data-based decisions regarding assessments and interventions, this book will help them take a modern, scientific approach to derive knowledge and make decisions based on statistical literacy. Describes the logic behind the many variations to statistically describe human behaviorExplains the conditions under which variations in statistical description are most appropriateHighlights common methods for quantitively determine the effectiveness of an interventionDiscusses the unique challenges of the time-series data and reviews current solutionsCovers current standards in writing and presenting statistical effects of interventionsReviews the application of statistical description for both single-case and between-group experimental designs