Blocked AB Design — Blocked AB Single-Subject Experimental Design
Blocked AB Single-Subject Experimental Design · Also known as: blocked AB single-case design, randomized block AB design, AB design with blocking, blocked baseline-treatment design
The Blocked AB Design applies the logic of randomized block experimental design to the classic single-subject AB framework. Observation sessions are organized into blocks — matched sets of time points or contextual units — and the assignment of baseline (A) and treatment (B) phases is randomized within each block. This controls for nuisance time-based variability while preserving the interpretive simplicity of the fundamental two-phase single-case structure.
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When to use it
Use the Blocked AB Design when you are studying a single participant (or a small-N series of replications) and have reason to believe that systematic time-varying or context-varying nuisance factors will inflate within-phase variability. It is especially appropriate in applied behavioural, clinical, or educational research where sessions are organised in natural recurring cycles (school weeks, therapy sessions per fortnight). The design requires repeated measurement across sufficient sessions to populate blocks adequately — a minimum of two or three blocks with at least two sessions each in both A and B is needed for meaningful analysis. Do not use this design when (a) the target behaviour is highly unlikely to recover to baseline after treatment (i.e., irreversible effects), making within-block phase alternation implausible; (b) the number of available sessions is too small to form meaningful blocks; or (c) carryover between adjacent A and B sessions within a block is a serious concern, in which case a multiple-baseline or ABAB design is preferable.
Strengths & limitations
- Controls for time-varying nuisance variability (e.g., day-of-week effects, fatigue cycles) that the standard AB design cannot address.
- Randomization within blocks enables legitimate randomization-based statistical inference without requiring large group samples.
- Preserves the interpretive clarity of the two-phase (baseline vs. treatment) single-case framework.
- Suitable for applied settings where individual-level data are the primary unit of analysis and group assignment is impractical.
- Requires careful a priori identification of an appropriate blocking variable; an incorrectly chosen block can fail to reduce nuisance variability.
- With very few observation sessions, blocks may be too small to allow adequate randomization or reliable statistical inference.
- Carryover effects between adjacent A and B sessions within a block can contaminate phase estimates, particularly with fast-acting or sensitizing interventions.
- Findings describe a single participant's response and cannot be directly generalized to a population without systematic replication across individuals.
Frequently asked
How is the Blocked AB Design different from the standard AB Design?
In a standard AB design, all baseline (A) sessions precede all treatment (B) sessions in a fixed temporal order. In the Blocked AB Design, sessions are grouped into blocks and the assignment of A versus B sessions within each block is randomized. This blocking and randomization controls for time-varying nuisance factors and enables randomization-based statistical inference — two capabilities the simple AB design does not provide.
How many blocks and sessions do I need?
There is no single rule, but a practical minimum is three to five blocks with at least two sessions per phase per block (i.e., four sessions per block). Fewer blocks reduce statistical power markedly. The exact number of possible random assignments — and thus the precision of the randomization test p-value — depends on the block size and number of blocks; Edgington and Onghena (2007) provide tables and guidelines.
What statistical test should I use for a Blocked AB Design?
A randomization test that accounts for the blocked structure is the most principled choice — it respects the randomization scheme used to assign sessions and requires no distributional assumptions. Visual analysis of the time-series is a necessary complement. Non-overlap effect-size statistics such as Tau-U can also be adapted to blocked data. Standard t-tests or ANOVA ignoring the block structure are inappropriate because they assume independence that the design does not provide.
Can I use the Blocked AB Design for more than one participant?
Yes — single-case designs gain external validity through systematic replication. Running the Blocked AB Design across multiple participants (each with their own independently randomized block schedule) and observing consistent effects strengthens causal inference considerably. This is analogous to direct replication in single-case methodology.
What should I do if the treatment effect is irreversible?
The Blocked AB Design assumes that the target behaviour can potentially return toward baseline during A-phase sessions within each block. If the treatment effect is expected to be irreversible (e.g., a learned skill that cannot be unlearned), the design loses its internal logic. In such cases, a multiple-baseline design — which does not require withdrawal of treatment — is the more appropriate single-case approach.
Sources
- Edgington, E., & Onghena, P. (2007). Randomization Tests (4th ed.). Chapman and Hall/CRC. ISBN: 978-1584885894
- 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). Blocked AB Single-Subject Experimental Design. ScholarGate. https://scholargate.app/en/experimental-design/blocked-ab-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
- Blocked Randomized Controlled TrialExperimental design↔ compare
- Multiple Baseline DesignExperimental design↔ compare
- Single-Subject Experimental DesignExperimental design↔ compare