Single-Subject Experimental Designs Terminology Guide

Introduction to Single-Subject Designs in ABA

When working in Applied Behavior Analysis (ABA), grasping single-subject experimental designs terminology is key for Board Certified Behavior Analysts (BCBAs) to show functional relations ethically and effectively. These designs drive data-based choices, letting you assess interventions for one person at a time—no need for group studies. As a BCBA, this knowledge helps you ace the certification exam and craft solid behavior plans that hold up under review.

This guide acts as your BCBA glossary, pulling from trusted ABA sources to explain main ideas. You'll cover SSD basics, terms like baseline and variables, how to achieve experimental control via prediction, verification, and replication, key design types such as AB, ABA, and multiple baseline, plus documentation needs. By the end, you'll gain practical tips to use these in your daily work.

Key Takeaways

• Single-subject designs focus on individuals, using repeated measures to prove intervention effects without group data.
• Core terms like baseline and variables build the foundation for clear, reliable analysis in ABA.
• Experimental control relies on prediction, verification, and replication to confirm functional relations.
• Documentation is vital for ethical reporting, including phases, graphs, and fidelity checks under BACB guidelines.
• Choose designs like multiple baseline for ethical scenarios where withdrawal isn't feasible.

Single-subject experimental designs, or SSDs, use within-subject comparisons where each person is their own control. They're perfect for ABA since BCBAs can pinpoint intervention impacts on specific behaviors, tailoring treatments. Unlike group methods, SSDs track repeated measures over time to spot patterns, fitting well in clinics where ethics rule out wide generalizations.

The Behavior Analyst Certification Board (BACB) Task List ties SSDs to Section D on experimental design. BCBAs must pick and explain these for evidence-based work. This ensures interventions get thorough checks, not just rollout. Steady-state responding—stable behavior before shifts—sets up trustworthy data reads.

SSDs boost internal validity by cutting risks like maturation or outside factors through phase controls. In your BCBA role, knowing these aids ethical compliance, like the BACB Professional and Ethical Compliance Code. They let you create functional relations that spark real behavior shifts.

Core Terminology: Baseline, Treatment Phase, and Variables

Single-subject experimental designs terminology starts with basic terms that shape data collection and review. The baseline phase, called "A," is the starting point without intervention. Here, you measure the target behavior often to find a steady pattern. It gives a benchmark so BCBAs can guess what happens without treatment.

The treatment phase, marked "B," brings in the intervention, changing from baseline. You keep measuring to spot behavior shifts against the baseline, catching possible effects. Resources from the American Speech-Language-Hearing Association (ASHA) stress enough data points per phase for stability.

Variables sharpen the setup. The dependent variable is the behavior you track, like on-task frequency, shown on a graph's y-axis over time. The independent variable is what you change, such as a token system, to test its influence. From behavior analysis guidelines, these allow exact definitions that boost measurement trust.

Achieving Experimental Control in ABA: Prediction, Verification, and Replication

Experimental control ABA means proving an intervention reliably changes behavior, using baseline logic: prediction, verification, and replication. Prediction guesses the behavior path from baseline data if nothing changes, prepping the test. It helps BCBAs foresee results, as academic single-case overviews note.

Verification checks baseline steadiness by holding back the intervention, weeding out outside effects on the dependent variable. Without it, shifts might come from elsewhere, not the independent variable. Say aggression stays level in phase A— that builds a strong case for intervention impact.

Replication locks in control by reapplying the intervention across phases or people, repeating the change. Key to internal validity, it fights issues like history or testing biases. ABA study materials from Behavior Analyst Study say replication in ABAB designs gives solid proof of functional ties, helping BCBAs tweak interventions with confidence.

SSDs' internal validity checks if the design truly pins down the intervention's effect. Threats like procedural drift—slips in delivery—can weaken it, so track fidelity. For more on this, see our guide on internal validity versus procedural fidelity.

Key Types of Single-Subject Experimental Designs

SSDs come in types that fit clinical needs, each strong in showing experimental control. The A-B design is basic: baseline (A) then intervention (B). It makes a first comparison but skips replication, so it's iffy on causality—best for early exploration.

The A-B-A design, or reversal, returns to baseline after treatment for effect checks. Behavior should go back in the second A, showing no change without intervention. But ethics flag withdrawing helpful treatments if it hurts clients, like with self-injury.

The multiple baseline design delays interventions across behaviors, settings, or people, keeping baselines steady. It repeats effects sans withdrawal, upping internal validity. The National Center for Biotechnology Information (NCBI) says it needs at least three baselines for strong credibility, fitting ABA skill building.

Check our BCBA experimental design study guide for Domain D exam prep. Variants like alternating treatments test multiple interventions fast but need distinct results.

Documentation and Reporting Requirements for BCBAs in Single-Subject Designs

Single subject design documentation is vital for BCBAs to back interventions, matching BACB ethics on data decisions. The BACB Task List (5th edition) skips specific graph formats but demands phase, variable, and visual analysis records under Section D. Include dependent variable definitions, independent variable fidelity, and enough phase data points for clear reads.

Reports highlight phase shifts and design choice reasons, like multiple baseline for ethics. Graphs plot time on x-axis, noting changes for level, trend, and variability views. Professional ABA standards call for interobserver agreement (IOA) data to check measurement reliability, targeting at least 80% agreement (The Ins and Outs of Interobserver Agreement).

BCBAs document to show experimental control, highlighting prediction, verification, and replication. This aids stakeholder updates and HIPAA-secure storage. See our piece on BCBA visual analysis prep for tips. Ethical reports skip broad claims, sticking to individual relations.

Frequently Asked Questions

What are the main differences between the ABA and ABAB designs?

• ABA: Baseline (A), intervention (B), return to baseline (A)—gives basic replication but ends without retreatment.
• ABAB: Adds another B phase for fuller reversal and replication, strengthening effect checks. The Single-Subject Experimental Design for Evidence-Based Practice notes ABAB boosts experimental control but can raise ethics flags if withdrawal harms, so ABA fits some clinical spots better.

How does the multiple-baseline design differ from the reversal design?

• Multiple-baseline: Staggers interventions over baselines (behaviors, settings) without pullback, dodging reversal ethics woes.
• Reversal (like ABA): Repeats baseline-intervention on one line for straight comparisons. The ASHA Single-Subject Experimental Design Overview says multiple-baseline uses staggered replication for control, ideal for ABA skill work where withdrawal doesn't work.

What is baseline logic in single-subject experimental designs?

Baseline logic uses prediction (baseline carryover forecast), verification (stability test), and replication (effect repeats) to link intervention to change. It anchors all SSDs for internal validity. Behavior Analyst Study resources call it key for BCBAs critiquing designs per BACB Task List D-6.

How do you determine if experimental control has been established in a single-subject study?

Control shows when behavior shifts tie reliably to independent variable changes, backed by prediction, verification, and replication. Look for steady baselines versus sharp intervention jumps in visuals. The Pass the Big ABA Exam glossary says three-plus replications with low variability confirm it for reports.

What are the advantages of using single-subject experimental designs in ABA?

• Individual focus: Tailors analysis without group hurdles.
• Ethical ease: Adapts fast in clinics via repeated measures.
• Direct proof: Shows functional relations clearly. UConn Research Basics points to their fit for small studies, helping BCBAs with evidence-based work.

How do dependent and independent variables function in single-subject designs?

• Dependent: The tracked behavior, like response rate, for time-based changes.
• Independent: The changed intervention, like prompts, to test effects. Sage Publishing's single-subject guide stresses operational definitions for reliable graphing of ties.

Wrapping up, single-subject experimental designs terminology gives BCBAs the tools for experimental control and better client results in ABA. From baselines to replications, these ideas make interventions effective and ethical, backed by BACB and peer sources. Link it to our functional relation in ABA guide for prediction, verification, and replication practice.

To use this, audit a behavior plan: Define variables, pick an SSD like multiple baseline, graph sample data with Excel or ABA tools, and check BACB for fidelity. This simplifies your notes at Praxis Notes for compliant, strong sessions.