Double-Blind Single-Subject Experimental Design
Also known as: double-blind SCED, double-blind single-case experimental design, masked single-subject design, double-blind N-of-1 design
A double-blind single-subject experimental design applies systematic masking — concealing treatment assignment from both the participant and the outcome assessor — within a within-person repeated-measures framework. It is used when researchers need strong causal inference about an intervention's effect on a single individual while guarding against placebo responses and observer bias. Particularly prominent in pharmacological, behavioral, and clinical rehabilitation research.
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When to use it
Use this design when causal evidence is needed for a single individual and both demand characteristics (participant expectation effects) and observer bias are plausible threats to validity — most commonly in pharmacological or pain management research, rehabilitation science, or behavior analysis where the treatment and control can be credibly masked. It is also appropriate when population-level RCTs are infeasible due to rare conditions or highly idiosyncratic treatment responses. Do NOT use it when masking is impossible or unethical (e.g., overt behavioral interventions such as physical therapy exercises that cannot be disguised), when the research question concerns group averages rather than individual response, or when the outcome requires participant self-report that cannot be meaningfully blinded.
Strengths & limitations
- Combines within-person causal logic of single-subject designs with bias protection of double-blind masking, yielding strong internal validity for the individual.
- Eliminates placebo effects and observer expectancy bias simultaneously, which neither a standard SCED nor an unmasked trial achieves alone.
- Highly efficient for studying rare conditions or highly heterogeneous populations where enrolling large groups is impractical.
- Repeated measurement enables detection of rapid, delayed, or fluctuating treatment effects that between-group designs with single post-test measurements would miss.
- Findings are directly relevant to clinical decision-making for the specific individual studied (personalized evidence).
- Masking is only feasible when treatment and control conditions can be made perceptually indistinguishable — many behavioral, educational, and psychosocial interventions cannot be blinded.
- Findings describe one individual; external generalizability requires systematic replication across multiple single-subject studies.
- Carryover effects between treatment phases can confound withdrawal designs; appropriate washout periods must be planned and may lengthen the study considerably.
- Maintaining the blind across repeated contacts with the participant over many sessions is logistically demanding and susceptible to unintentional unblinding.
- Requires substantial expertise in both single-subject methodology and blinding protocol design — errors in either component undermine validity.
Frequently asked
Is a double-blind single-subject design the same as an N-of-1 RCT?
Largely yes: an N-of-1 RCT is a specific, often crossover, implementation of a double-blind single-subject design used in clinical medicine. The broader category of double-blind single-subject designs also includes ABAB withdrawal and multiple baseline structures that may not use the crossover randomization typical of N-of-1 RCTs. The N-of-1 RCT label is common in clinical and pharmacological contexts; the single-subject design label is more common in behavioral and educational research.
How do I test whether the blind was successfully maintained?
At the end of the study, ask participants and assessors to guess which phases were active treatment and which were control, then compare their accuracy to chance (50% in a two-condition design) using a binomial test. Accuracy significantly above chance indicates the blind was partially broken, which should be reported as a limitation.
Can I use visual analysis if the design is double-blind?
Yes. You can maintain the blind during data collection and analysis by labeling phases with neutral codes (Phase 1, Phase 2) rather than treatment labels, and only revealing the allocation after visual and statistical analysis is complete. This is analogous to keeping the analyst blind in a between-group RCT.
How many phases or conditions do I need?
At minimum, an AB design provides pre-post evidence but lacks the replication needed to demonstrate experimental control. ABAB or alternating-treatments designs provide the within-person replication (at least three demonstrations of effect) that supports a causal inference. Multiple baseline designs are preferred when the behavior cannot be ethically or practically reversed.
What statistical methods are appropriate?
Visual analysis of level, trend, variability, and overlap remains the primary method in the single-subject tradition. Supplementary statistics include the Percentage of Non-Overlapping Data (PND), Tau-U (which controls for baseline trends), and randomization tests. Effect sizes such as the standardized mean difference between phases can be reported for meta-analytic purposes.
Sources
- Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881
- Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson. ISBN: 978-0205474554
How to cite this page
ScholarGate. (2026, June 3). Double-Blind Single-Subject Experimental Design. ScholarGate. https://scholargate.app/en/experimental-design/double-blind-single-subject-experimental-design
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- AB DesignExperimental design↔ compare
- ABA DesignExperimental design↔ compare
- ABAB designExperimental design↔ compare
- Multiple Baseline DesignExperimental design↔ compare
- Single-Subject Experimental DesignExperimental design↔ compare