Applied Behavior Analysis (ABA)-based interventions for children with autism often produce substantial improvements across domains such as language development, social engagement, and reductions in severe problem behavior, as demonstrated by systematically collected data.These outcomes are frequently supported by high levels of stakeholder satisfaction. However, research suggests long-term outcomes—particularly in areas of social integration, employment, and the persistence of problem behavior—tend to be less favorable. One potential reason for this discrepancy is that the data typically collected during intervention may not accurately predict meaningful, sustained success beyond the treatment setting. This presentation will explore how even well-designed and rigorously implemented data collection systems can contribute to misleading conclusions about skill generalization and maintenance. These misinterpretations may inadvertently lead to suboptimal treatment decisions that hinder long-term progress. Systemic factors, including funder-driven contingencies, often reinforce these practices. Practical, research-based strategies will be offered to overcome this obstacle in educational, home, and clinical settings
Learning Objectives
- Describe the conditions that often lead to misleading data collected in practice.
- Describe 3 reasons why data collected during intervention may lead to misleading conclusions about maintenance.
- Describe how data collection procedures can be modified to produce better predictions about maintenance in real-world settings.
About the Presenter
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Course information
- Title: Achieving long-term successful outcomes for children with autism
- Presenter: Barry Morgenstern Ph. D., BCBA
- Date: Tuesday, October 7th, 2025
- CEUs: 2 Learning
- Time: 9:00 AM Pacific
- Duration: 1 hour and 40 minutes