Single-Case Data Analysis: A Practitioner Guide for Accurate and Reliable Decisions.
Run internal-validity checks every time you eyeball a single-case graph.
01Research in Context
What this study did
Ninci (2023) wrote a how-to paper for BCBAs who read single-case graphs.
The guide lists internal-validity checks you should run before you trust your eyes.
No new data were collected; the paper is a checklist for everyday visual analysis.
What they found
The author shows that even skilled viewers can be fooled by trend, overlap, or autocorrelation.
Following the safeguard steps cuts the risk of calling a change real when it is not.
How this fits with other research
Young (2019) gave us a free Monte Carlo app that spits out p-values for the same graphs. Use both: let the app flag chance patterns and Jennifer’s checklist catch design flaws.
Manolov et al. (2022) tell us to pick an effect measure before we see the data. Ninci (2023) adds: run the visual safeguard steps before you open any measure. The two papers form a pre-analysis routine.
Tanious et al. (2020) created CONDAP and CONEFF indices for A-B-A-B consistency. Their numbers back up what your eyes see after Jennifer’s steps are met.
Why it matters
Next time you judge a client’s progress from a graph, run the safeguard list first. It takes two minutes and shields you from bad clinical calls. Your supervisor, the parent, and the insurance reviewer will all see the same clear story.
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02At a glance
03Original abstract
Practitioners frequently use single-case data for decision-making related to behavioral programming and progress monitoring. Visual analysis is an important and primary tool for reporting results of graphed single-case data because it provides immediate, contextualized information. Criticisms exist concerning the objectivity and reliability of the visual analysis process. When practitioners are equipped with knowledge about single-case designs, including threats and safeguards to internal validity, they can make technically accurate conclusions and reliable data-based decisions with relative ease. This paper summarizes single-case experimental design and considerations for professionals to improve the accuracy and reliability of judgments made from single-case data. This paper can also help practitioners to appropriately incorporate single-case research design applications in their practice.
Behavior modification, 2023 · doi:10.1177/0145445519867054