Introduction to the Special Section: Translating Advanced Quantitative Techniques for Single-Case Experimental Design Data

Barnard-Brak et al. (2022) wrote the opening piece for a special section.

The section gathers new ways to crunch single-case data so BCBAs can read results faster and speak the same language as other scientists.

No new numbers were collected; the paper maps the road ahead.

The editors found a gap: most BCBAs still eyeball graphs, while modern stats sit unused in journals.

They lined up authors who turn heavy formulas into clear steps any practitioner can follow.

Young (2019) set the stage with a free Monte Carlo app that gives p-values for single-case graphs. Barnard-Brak widens the menu to include many tools, not just one.

Manolov et al. (2022) deliver a ready-made flowchart that helps you pick an effect measure before the first data point. The editorial shouts "do this"; the flowchart shows exactly how.

Ninci (2023) hands clinicians a visual-analysis checklist. Together the three papers move from "why we need better stats" to "here are three ways to do it today.

You no longer need a stats degree to strengthen your single-case reports. Grab the Monte Carlo app, run the flowchart, and use the visual checklist. Your next graph will speak to doctors, teachers, and funders in one clear voice.