Professionals who provide services based on the principles of behavior are required to use evidence-based practices and to make data-based decisions regarding assessments and interventions. Doing so requires statistical literacy: competence in collecting, analyzing, interpreting and presenting aggregate quantitative data within or between subjects. Without statistical literacy, behavior analysts cannot take advantage of, or intelligently discuss, ongoing scientific developments around interventions for autism spectrum disorders (ASD). 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 behavior analysts will need to deliver and consume the larger scientific literature. The organization of the book follows the common sequence of decisions for collecting, describing and analyzing data associated with behavior analytic service delivery and research. By the end of the book, readers should be able to understand and accurately incorporate statistics in their assessment and intervention effects using statistics.
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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.
Dr. Jason C. Vladescu is the Founding Chair and Professor of the Applied Behavior Analysis Program at SUNY Downstate and a Founding Partner at The Capstone Center. He earned his Ph.D. from Central Michigan University and completed his pre- and post-doctoral training at the University of Nebraska Medical Center’s Munroe-Meyer Institute. Dr. Vladescu serves as the Editor-in-Chief of Behavior Analysis in Practice, has held editorial roles for multiple scientific journals, and received the APA Division 25 New Researcher Award in 2020. He has published over 90 peer-reviewed articles, chapters, and one book. His research focuses on behavioral interventions for individuals with autism, enhancing academic instruction, training staff and caregivers, and expanding the application of behavior analysis. Outside of his work in behavior analysis, Dr. Vladescu enjoys traveling, reading biographies and science fiction, playing pickleball, and indulging his love for pizza and coffee.
Professionals who provide applied behavior analysis (ABA) services are required to use evidence-based practices and to make data-based decisions regarding assessments and interventions. Taking a modern, scientific approach to derive knowledge and make decisions necessarily requires statistical literacy. Without statistical literacy, ABA practitioners cannot take advantage of, or intelligently discuss, ongoing scientific developments in the treatment of autism spectrum disorders (ASD). Statistics for Applied Behavior Analysis Practitioners 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.
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