The Compound Multiple-Baseline Design

Lloveras et al. (2025) created a new single-case design called the compound multiple-baseline.

Instead of staggering baselines across only one dimension (like kids), you stagger across two (like kids AND classrooms).

The goal is to stop you from starting treatment on a baseline that is already drifting up or down.

The paper is a how-to guide, not a data report.

It shows step-by-step how to line up two sets of staggered baselines.

This gives stronger proof that the treatment, not chance, caused the change.

Barnes et al. (1990) first showed how staggered baselines can rescue studies when you cannot run a no-treatment phase.

Lloveras takes that same rescue idea and doubles it across two dimensions.

Joo et al. (2018) ran computer tests proving that extending a baseline until it looks flat does not hurt your results.

Lloveras offers a different fix: avoid the drifting baseline altogether by crossing two staggered sets.

Diller et al. (2016) found that BCBAs often disagree when they eyeball multielement data.

A compound multiple-baseline gives clearer visual proof, which may reduce that disagreement.

If you run single-case studies in schools or clinics, you can start using this design right away. Pick two dimensions you already track—say, three students across three play areas—and stagger the start times in both. You will get cleaner data and fewer arguments about whether the baseline was truly stable.