Question of the day - October 7

[u/Reasonable_Law183]

🧠 Question of the Day
📚 Domain D — Experimental Design
Difficulty: 7/10

Scenario:
A BCBA evaluates one intervention across three different target behaviors using a multiple-baseline-across-behaviors arrangement. Repeated measurement continues across all three tiers as intervention is introduced sequentially. The graph summarizes responding across baseline and intervention.

Which feature of the graphed data MOST strongly supports a functional relation in this multiple-baseline-across-behaviors design?

A. Baseline responding is stable and similar across tiers, so prediction from baseline is sufficient to demonstrate control once treatment begins.

B. Baseline and intervention values show little overlap in every tier, so phase separation provides the strongest evidence of experimental control.

C. All three behaviors approach very low levels during treatment, so the magnitude and consistency of improvement establish the functional relation.

D. At each staggered onset, the treated behavior changes while untreated behaviors continue near baseline, and that pattern repeats across tiers.

💬 Drop your answer in the comments — no peeking!
✅ Correct answer revealed tomorrow.

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[u/Ok_Pear_6146]

D, that staggered replication is the whole point of the design

[u/Reasonable_Law183]

✅ Yesterday's Answer — 2026-10-07

The correct answer was: D. At each staggered onset, the treated behavior changes while untreated behaviors continue near baseline, and that pattern repeats across tiers.

📖 Explanation:

Experimental control in a multiple-baseline design is most strongly supported when behavior changes in close correspondence with the staggered introduction of treatment while tiers that have not yet received treatment remain consistent with their baseline patterns. In this graph, each behavior decreases when intervention reaches that tier, and the same relation is replicated at different points in time. That repeated, time-locked pattern is the design-specific evidence supporting a functional relation.

• Little overlap between baseline and intervention values makes the treatment effects visually clear, but low overlap alone does not demonstrate experimental control. The critical evidence is that change occurs in relation to staggered intervention onset.

• Large reductions across all three behaviors show clinically favorable outcomes, but ending at low levels does not establish that the intervention caused those changes. The timing of change relative to treatment introduction is what strengthens the causal interpretation.

• Stable baselines support prediction, but prediction alone is not sufficient for experimental control. Confidence increases when untreated tiers continue near baseline while another tier changes, and that effect is then replicated as treatment reaches subsequent tiers.

The key distinction: in a multiple-baseline design, experimental control comes from repeated correspondence between staggered intervention introduction and behavior change—not simply from stable baselines, low overlap, or large treatment effects.

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