Identify the Relative Strengths of Single-Case Experimental Designs and Group Designs
If you’re a practicing BCBA, clinic director, or senior supervisor, you’ve likely faced this question: Should I use a single-case design or a group design to test whether this intervention actually works? The answer isn’t one-size-fits-all—and choosing the wrong one can lead to misleading conclusions that affect your clients’ care.
Single-case experimental designs (SCEDs) and group designs are both rigorous, but they answer fundamentally different questions. SCEDs show whether an intervention caused change for this individual, in this setting, right now. Group designs show whether an intervention caused an average change across many people. Understanding when to use each—and what their real limitations are—is essential to making ethical, evidence-informed decisions.
This post breaks down the core strengths of each design so you can choose wisely and defend your choice to supervisors, funders, and families.
What Single-Case Experimental Designs Are
A single-case experimental design measures one unit—a student, a client, a classroom, or even one behavior—repeatedly over time, using that unit as its own control. The design compares a baseline phase (no intervention) to one or more intervention phases (treatment in place). If behavior changes when the intervention starts and stays changed while it’s active, you have evidence of a functional relation.
The heart of SCED is replication. You don’t rely on a single observation that the intervention “worked.” You demonstrate the effect at least two or three times—by returning to baseline and reintroducing the intervention, by rolling it out across different settings or behaviors, or by showing the same pattern across multiple individuals. That repetition transforms a lucky coincidence into credible evidence.
Common SCED types include reversal designs (where you withdraw the intervention to see if behavior returns to baseline), multiple baseline designs (where you introduce the intervention at staggered times), and alternating treatments designs (where you rapidly switch between two interventions). Each has its own logic, but all rely on the same core principle: frequent measurement, clear phase changes, and demonstrated replication.
What Group Designs Are
A group design, like a randomized controlled trial (RCT), takes a different path. You recruit a larger group of participants, randomly assign them to treatment or control, and measure outcomes for both groups after a set time. The goal is to compare the average outcome for the treatment group against the control group. If the treatment group’s average is significantly better, you conclude the intervention likely caused the difference—and that this effect would probably show up in similar populations.
Group designs rely on randomization and sample size to build their case. Random assignment helps ensure groups are roughly equivalent at the start, so differences at the end are more likely due to the intervention. Larger sample sizes give you more statistical power, meaning you’re more likely to detect real effects if they exist. With a diverse, representative sample, you can say something meaningful about how the intervention would work beyond your study.
How They Answer Different Questions
This is the crucial distinction.
If your question is “Did this intervention cause this client’s behavior to change, and can I use it in my clinic?” an SCED is the right tool. It gives you tight, within-person evidence you can act on immediately. You see the data every day, adjust as needed, and know whether the intervention is working for this specific person right now.
If your question is “Does this intervention work on average for the population we serve, and should we adopt it as policy?” a group design is better suited. It tells you about typical effects, trade-offs, and generalizability in a way that single-case data alone cannot.
The unit of analysis differs too. In SCEDs, you’re analyzing change within one individual or a small cluster. In group designs, you’re analyzing the mean across all participants. That’s not a weakness of either—it reflects what question you’re trying to answer.
Key Strengths of Single-Case Experimental Designs
Sensitivity to individual change. SCEDs are extraordinarily sensitive to the person in front of you. Because you’re collecting data frequently—often daily—you catch even small shifts in behavior, motivation, or environmental factors. You see not just whether behavior changed, but how quickly, how stable the change is, and whether it’s holding up over time. That detail is invaluable when fine-tuning an intervention or troubleshooting why something isn’t working.
Demonstration of experimental control. In an SCED, you’re not relying on statistics or population averages. You’re showing, through repeated phases and replication, that when you apply the intervention, behavior changes. That’s direct, visual evidence of a causal link. A supervisor or skeptical family member can look at the data and see the functional relation themselves.
Replication without large samples. You don’t need 60 participants to prove something works. You might test an intervention with one client across three settings, or with three clients individually. If the pattern holds each time, you have credible evidence. That makes SCEDs the only practical option when working with rare conditions, highly individualized interventions, or small caseloads.
Ethical flexibility. Multiple baseline designs let you demonstrate an effect without ever withdrawing a treatment that’s working. If stopping an intervention would be harmful, you can stagger the start across settings or behaviors instead. The staggered pattern itself becomes your proof of control.
Speed and responsiveness. Because you’re measuring frequently and analyzing visually, you can adjust course quickly. If data shows the intervention isn’t working, you change it. If it’s working, you stick with it. You don’t have to wait months for a study to finish—you’re making real-time decisions based on real data.
Key Strengths of Group Designs
Generalizability to populations. A well-designed group study with a large, diverse sample can tell you something true about an entire population. If you randomly assign 100 schools to a new curriculum and 100 to standard practice, and the new curriculum produces higher gains on average, you have evidence it probably works for schools like yours. That’s policy-level information, hard to get from single-case work alone.
Statistical power and confidence. Group designs use inferential statistics to estimate how confident you can be that a difference is real. With adequate sample sizes, you can say something like, “We’re 95% confident the true average effect falls within this range.” That quantified certainty is valuable for funding decisions, adoption arguments, and publication.
Control of confounds through randomization. Random assignment levels the playing field. Known and unknown differences between people balance out across groups. If the treatment group improves more, it’s harder for a skeptic to argue that improvement was due to which clients happened to receive the intervention.
Efficiency at scale. If you’re testing whether a curriculum should be adopted district-wide, or whether a new medication should become standard practice, a group design answers that question more efficiently than dozens of single-case studies. You measure, analyze, and have your answer.
When to Choose Each Design
Choose a single-case design when:
- You need to show experimental control for an individual client and make real-time treatment decisions
- You’re working with a small or rare population
- Withdrawing or delaying an effective intervention would be unethical
- Your intervention is highly tailored to one person
- You want to detect and respond to small changes in real time
Choose a group design when:
- Your goal is to estimate average effects and decide whether to adopt a program broadly
- You have the resources to conduct a rigorous randomized trial
- Generalizability to a wider population is essential
- You’re answering a policy question, not a question about one client’s care
In practice, many organizations use both. A clinic might run SCEDs to show a new intervention works for individual clients, then run a group study to test whether it’s worth rolling out everywhere. A school might test a behavior program with single-case designs for a few students, then use a group design to evaluate whole-school adoption.
Common Mistakes That Lead to Wrong Conclusions
Assuming group averages apply to everyone. A study shows that on average, intervention X reduces problem behavior by 30%. Useful information—but it doesn’t mean it reduced problem behavior by 30% in every participant. Some may have improved more, others less, and a few might not have improved at all. If you adopt the intervention site-wide based on the group average, you might unknowingly implement it for clients who won’t benefit.
Overgeneralizing a single-case result. You test an intervention with one client and it works beautifully. You then assume it’ll work the same way for the next five clients. It might—but it might not. That’s why replication across people, settings, or behaviors matters. A robust single-case study includes multiple demonstrations, not just one success story.
Confusing descriptive case studies with experimental designs. A detailed description of a client’s progress is valuable, but it’s not a single-case experimental design unless you’ve deliberately manipulated phases, measured repeatedly, and shown replication. Without those elements, you can’t claim a functional relation—you’ve just documented what happened.
Ignoring variability and measurement error. A visual trend in an SCED can look convincing, but if measurement is unreliable or behavior is naturally variable, that trend might not mean much. Group designs can be underpowered if sample sizes are too small. Quality measurement matters for both.
Ethical Safeguards in Design Selection
Design choice is not just methodological—it’s ethical.
If your design requires withdrawing a treatment that’s helping a client, you need clear stopping rules and informed consent. You need a plan for what happens if withdrawal causes regression or harm. Many ethics boards now prefer multiple baseline or other non-withdrawal designs precisely because they avoid this risk.
Informed consent is essential. Families and clients need to understand what the design means: Will treatment be delayed? Will it be withdrawn? Will there be a period testing two interventions at once? Transparency builds trust and respects autonomy.
Measurement reliability is a hidden ethical issue. Unreliable data—low interobserver agreement, unclear definitions, inconsistent timing—can lead to wrong conclusions and poor decisions. That’s why interobserver agreement checks aren’t optional. They’re part of responsible practice.
Examples: How This Works in Real Clinics
Single-case example: A BCBA develops a new communication prompt for a nonspeaking student and tests it with a multiple baseline across three settings: classroom, speech therapy, and home. The intervention is introduced at staggered times—week 1 in the classroom, week 3 in speech therapy, week 5 at home. Data show requesting increases in each setting right after the intervention begins, and the effect holds steady. No reversal needed; the staggered pattern demonstrates control.
Group-design example: A large ABA organization wants to know whether a new intake procedure reduces no-shows. They randomly assign new families to the standard procedure or the new one, measure no-show rates over six months, and analyze whether the treatment group has significantly better attendance. Results show a 15% reduction for the treatment group. Based on this, leadership adopts the new procedure across all clinics.
Practical Questions to Guide Your Choice
Ask yourself: Do I need to show experimental control for one specific person, or estimate an average effect across a population? The first points to SCED; the second, to a group design.
How much time and resources do I have? SCEDs require ongoing measurement but fewer participants. Group designs require larger samples and longer study periods.
Is withdrawal of the intervention ethical? If yes, reversal designs are an option. If no, multiple baseline or other non-withdrawal SCEDs protect the client while still building evidence.
Am I trying to make a decision for this client right now, or inform policy for many clients? Real-time decisions favor SCEDs. Policy decisions favor group designs.
Key Takeaways
Single-case experimental designs excel at showing whether an intervention caused change for a specific individual through repeated measurement, phase manipulation, and replication. They’re fast, ethical with proper safeguards, and sensitive to real-world detail.
Group designs excel at estimating average effects and answering population-level questions that drive policy and funding decisions. They give you statistical confidence and generalizability.
Neither design is universally better. The best choice depends on your research question, sample size, timeline, resources, and ethical constraints. Both require solid measurement practices. Both can be misused if you overgeneralize or ignore individual variability.
Your job is to choose the design that answers your actual question while respecting your clients’ welfare. When you do that well, you build evidence you can trust and decisions you can defend.
