Using the ADDIE model of instructional design to create programming for comprehensive ABA treatment
Designed for BCBAs and clinical teams building comprehensive ABA programs, this post presents the ADDIE framework (Analyze, Design, Develop, Implement, Evaluate) as a repeatable planning process. It shows how to translate multi-source ABA data into a clear scope, mastery criteria, and actionable steps, reducing random targets and improving fidelity. The focus is on ethical, family-aligned decision-making and evaluating real-life impact to protect learner quality of life.
Comparing methods of evaluating sensitivity to common establishing operations and bias toward challenging behavior
This post targets behavior analysts, clinicians, and educators working with preschoolers, helping them translate ABA data into practical prevention and safety decisions. It compares symmetric versus asymmetrical reinforcement in brief functional analyses and shows how each design affects sensitivity to establishing operations and bias toward challenging behavior, with direct implications for planning and escalation risk. It offers ethical, data-driven guidance on turning assessment results into clear teaching priorities, reinforcement plans, and least-intrusive safety strategies.
Evaluating contributions of progressive ratio analysis to economic metrics of demand
This post helps clinicians and behavior analysts decide how to use progressive ratio data when designing reinforcer schedules for adults with disabilities. It asks whether Basis x PRA can match PFRA metrics like Pmax, and clarifies when it should not be used to set the “optimal” ratio. The main takeaway is that Basis x PRA rarely aligns with PFRA for precise demand values, but can aid in ranking reinforcers by relative strength and guiding quick comparisons. The piece emphasizes ethical practice, including choice, exit options, and PFRA-like sampling to inform schedule design.
Using the teach-back method to improve staff implementation of naturalistic environmental teaching
Designed for clinical supervisors, RBT trainers, and behavior analysts, this post explores whether the teach-back method can improve staff fidelity in naturalistic environmental teaching (NET) more quickly than traditional BST. It provides a practical guide to using teach-back as an early, data-informed check that surfaces understanding gaps and guides targeted modeling and brief coaching. By focusing on fidelity data and learner dignity, it helps clinicians turn ABA data into clear, ethical decisions about training and treatment delivery.