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Conceptualizing Applied Behavior Analysis |
Friday, September 2, 2022 |
10:30 AM–10:55 AM |
Meeting Level 2; Wicklow Hall 2B |
Area: PCH |
Chair: Kimberly Vannest (University of Vermont) |
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Applied Behavior Analysis: A Discussion of Implementation Barriers |
Domain: Service Delivery |
ANGELIKA ANDERSON (The University of Waikato), Monica Carr (Melbourne Graduate School of Education, The University of Melbourne, Australia ) |
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Abstract: A brief review of the current evidence base for EIBI (Early Intensive Behavioural Intervention) will be followed by a discussion of implementation barriers: Why, in spite of strong supporting evidence, does the up-take of these procedures remains poor. We will outline and discuss key issues that contribute to controversy and division among researchers, clinicians, and within the autism community, including common misunderstandings and misconceptions about Applied Behavior Analysis (ABA). In order to promote the availability of evidence-based, effective, and affordable EIBI, we need to reconcile these differences, develop mutual understandings between these communities, and work together on research focused on promoting implementation, and to influence policy. |
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Meta-Analysis of Single Case Experimental Designs: Why, When, How |
Domain: Theory |
KIMBERLY VANNEST (University of Vermont), Mary Rose Sallese (University of Alabama at Birmingham), Justin Garwood (University of Vermont) |
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Abstract: Meta-analysis of single-case experimental designs are an opportunity to summarize research literature, identify evidence-based treatments, and consider moderators for theory building. There are an expanding number of examples in the research literature with variations in methods, analysis, and graphic representation of outcomes. The use of meta-analysis is not without controversy, however. This session will engage the participant in three learning objectives; a solid conceptual framework and the limitations of the practice, step-by-step how-to's and illustrations from published literature including an illustrated list of graphic representations of data. |
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