Double-blind Multiple Baseline Design
Double-blind Multiple Baseline Single-Subject Experimental Design · Also known as: DB-MBD, blinded multiple baseline design, masked multiple baseline design, double-blind MBD
The double-blind multiple baseline design is a single-subject experimental design in which an intervention is introduced sequentially across two or more independent baselines — behaviors, individuals, or settings — while outcome assessors (and ideally participants) remain unaware of which baseline is currently in the intervention phase. The double-blind procedural overlay reduces measurement bias and demand characteristics, strengthening causal inference beyond what a standard multiple baseline design offers.
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When to use it
Use this design when you need to demonstrate a causal effect of an intervention for a single individual (or a small number of individuals) and withdrawal of the intervention is ethically or practically impossible — making an ABAB design unsuitable. It is appropriate when two or more independent behaviors, settings, or individuals can serve as baselines, when the outcome can be assessed by an independent masked rater, and when treatment cannot generalize immediately across baselines. It is especially valuable in clinical behavioral research and applied behavior analysis where performance or symptom ratings are susceptible to observer expectancy bias. Do not use it if baselines are not functionally independent (cross-baseline generalization will confound the staggered logic), if fewer than two baselines are available, if an intervention effect is expected to be reversible and an ABAB design is feasible, or if the logistical burden of maintaining rater masking cannot be met.
Strengths & limitations
- Demonstrates causality through replication logic without requiring withdrawal of a potentially beneficial intervention.
- Double-blind assessment dramatically reduces observer bias and demand characteristics that can inflate single-subject effect estimates.
- Applicable when randomized group designs are infeasible due to small population size or ethical constraints.
- Flexible: baselines can be defined across behaviors, individuals, or settings to match the research question.
- Inter-rater reliability data collected during blinded assessment provides an independent validity check on the outcome measure.
- Requires genuine independence between baselines; if treatment effects generalize across baselines immediately, the staggered logic breaks down.
- Maintaining rater masking is logistically demanding and may fail in practice, especially in tightly knit clinical settings.
- The design produces evidence for a small number of individuals and is not statistically generalizable to a population without replication across studies.
- Extended baseline phases needed to establish stability can be burdensome and may raise ethical concerns about withholding treatment.
Frequently asked
How is this different from a standard multiple baseline design?
The core replication logic is identical: the intervention is introduced sequentially across independent baselines to demonstrate a causal effect. The difference is that in the double-blind variant, outcome assessors are kept unaware of which baseline is currently receiving the intervention, eliminating observer expectancy bias from the measurements. The double-blind procedure strengthens measurement validity without altering the design's causal logic.
How many baselines are needed?
A minimum of two baselines is required, but three or four are recommended. Each additional baseline provides an additional replication of the treatment effect, strengthening causal inference. With only two baselines the evidence for causality is weaker — a chance coincidence cannot be ruled out as confidently as with three or more replications.
What counts as an independent baseline?
Baselines are independent when a change in one does not cause a change in another. In practice, this means selecting behaviors that are functionally separate (not in the same response class), individuals who do not share the same immediate environment, or settings that do not share common antecedents. Pilot observation or logical analysis of the behaviors/settings should confirm independence before the study begins.
Can the person delivering the intervention also rate outcomes?
No — this is precisely what the double-blind procedure prevents. Treatment administrators must be separate from outcome assessors. If this separation is impossible logistically, the design loses its double-blind property and reverts to a standard multiple baseline design, which should be reported accurately.
What statistical analyses are appropriate?
Visual analysis of graphed time-series data is the primary method in the single-subject tradition. Supplementary effect-size statistics designed for single-case data — such as Tau-U, nonoverlap of all pairs (NAP), or the percentage of nonoverlapping data (PND) — can quantify effect magnitude and support meta-analytic aggregation across studies, but they do not replace visual analysis.
Sources
- Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97. DOI: 10.1901/jaba.1968.1-91 ↗
- Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881
How to cite this page
ScholarGate. (2026, June 3). Double-blind Multiple Baseline Single-Subject Experimental Design. ScholarGate. https://scholargate.app/en/experimental-design/double-blind-multiple-baseline-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.
- ABA DesignExperimental design↔ compare
- ABAB designExperimental design↔ compare
- Crossover Randomized Controlled TrialExperimental design↔ compare
- Multiple Baseline DesignExperimental design↔ compare
- Single-Subject Experimental DesignExperimental design↔ compare