Disciplined decision making in an interdisciplinary environment: some implications for clinical applications of statistical process control.

Lyons (1995) wrote a think-piece, not an experiment.

The paper asks: can factory-style control charts help ABA teams decide when to change a program?

It maps out rule-based lines you draw on a graph: if data cross the line, the team acts.

The author argues that visual analysis alone lets daily ups and downs steer the bus.

Control charts add fixed, contingency-controlled rules so teams act only on real trends.

Johnson et al. (1994) set the stage one year earlier. They pulled W. Edwards Deming’s Plan-Do-Check-Act cycle into ABA and showed BCBAs already think like quality engineers.

Powell et al. (2020) pick up the baton twenty-five years later. They bolt modern PDSA cycles and control charts onto all seven dimensions of ABA, turning the 1995 idea into a full practice model.

Bacon et al. (1998) seem to push back. They say inferential statistics—control charts included—are overkill; your eyes are enough. The clash is only skin-deep: L et al. target p-values in single-case research, while Lyons (1995) targets daily service decisions where rules protect against human bias.

If you run team meetings, you can stop arguing over every blip. Plot your key data on a simple control chart, agree on the action lines, and let the rules call the next move. You save time, reduce escalation of commitment, and give doctors, teachers, and parents one clear picture.