Matt Harrington - masteringaba.com

Using the ADDIE model of instructional design to create programming for comprehensive ABA treatment

Comparison of enhanced and standard data sheets on treatment fidelity and data collection for tact training

This post asks whether an enhanced data sheet improves accuracy and fidelity in tact training compared with a standard form, with practical implications for busy ABA supervisors. It offers ethics-focused, clinician-friendly guidance on using pre-set trial order and prompts to support onboarding and data-driven program decisions. It also cautions about the study’s limits, and emphasizes routine accuracy checks and a standardized, low-cost template approach.

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What Most People Get Wrong About Career Pathways & Professional Growth

Hiring and Retention

Designed for ABA clinic leaders, supervisors, clinicians, and HR partners, this post highlights the top mistakes in career pathways and professional growth and offers practical, ethics-first fixes. It grounds guidance in real-world data and examples, with clear steps to avoid common pitfalls. By turning growth metrics into defensible decisions, it helps you align individual development with pathway design—using quick self-checks and implementation-ready actions.

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Using the ADDIE model of instructional design to create programming for comprehensive ABA treatment

Research Article Summaries

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.

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Comparing methods of evaluating sensitivity to common establishing operations and bias toward challenging behavior

Research Article Summaries

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.

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When to Rethink Your Approach to ABA Software & Tools

Technology and AI

This guide is for BCBAs and ABA clinic leaders who manage practice software and data. It offers a practical, ethics-first framework to rethink your ABA tools—diagnostics, quick audits, checklists, and a decision tree for scheduling, billing, data collection, reporting, and compliance. By turning ABA data into clear, defensible decisions, you can improve workflows and outcomes without compromising privacy or professional judgment.

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Evaluating contributions of progressive ratio analysis to economic metrics of demand

Research Article Summaries

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.

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How to Know If Ethics & Compliance for Businesses Is Actually Working

Business

Designed for ABA clinic leaders and clinical teams, this post shows how to judge whether ethics and compliance efforts are truly working in daily practice. It translates the program into concrete ABA clinic data signals—policies, training, reporting, investigations, and culture—and offers a simple scorecard to drive ethical improvement. Using the Inputs → Behaviors → Outcomes framework, it helps you turn ABA data into safe, fair, and trust-building decisions, with practical steps you can start this month.

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Using the teach-back method to improve staff implementation of naturalistic environmental teaching

Research Article Summaries

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.

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How to Know If Data Visualization & Analytics Is Actually Working

Technology and AI

Designed for practicing BCBAs, clinic owners, directors, and ABA leaders, this guide helps you move beyond pretty dashboards to actually better decisions. It explains what “effective” data visualization means and offers a simple four-level scorecard to assess clarity, accuracy, decisions, and follow-through—within a privacy-first, ethically sound workflow. Learn to spot common failures, select the right charts, and translate visuals into concrete clinical actions with human oversight at the center.

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How to Know If Behavior Reduction Is Actually Working

Clinical Practice

This practical guide is for BCBAs, clinical supervisors, RBTs, and clinically informed caregivers who want to know when a behavior-reduction plan is actually working, not just when the graph goes down. It helps you turn ABA data into clear, ethical decisions by aligning reduction with safety, dignity, participation, and quality of life, using function-first analysis and replacement skills. You’ll find concrete steps for defining the target, selecting measures, checking treatment integrity, monitoring side effects, and applying decision rules to keep, change, fade, or stop a plan.

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