Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots.
Free web plots give you two quick numbers that confirm visual judgments and settle team disagreements.
01Research in Context
What this study did
Manolov et al. (2022) built free web tools that turn single-case graphs into modified Brinley plots.
The plots give two numbers: CONDAP tells how stable the baseline is, and CONEFF tells how big the jump is after treatment.
They tested the tools on many graph types to show the numbers stay reliable across designs.
What they found
The new indices give a quick, objective way to say “this pattern looks steady” or “this effect looks big.”
Because the math is simple, you can paste data into the web page and get results in seconds.
How this fits with other research
Kahng et al. (2010) already showed that trained BCBAs agree when they eye-ball graphs. Rumen’s tool turns that same agreement into a number you can report.
Wolfe et al. (2023) found that steep trend and high variability make raters disagree. Rumen’s CONDAP score directly flags those messy features, so the 2023 warning now has a built-in alarm.
Diller et al. (2016) and Wolfe et al. (2016) showed experts often disagree on multielement and multiple-baseline graphs. The new plots give you an objective backup when your team can’t reach consensus.
Why it matters
Next time you stare at a graph and wonder, “Is this change real?” run the Brinley tool. Paste your data, glance at the CONEFF number, and add one sentence to your report: “Effect size index = 0.85, meeting the consistency threshold.” You keep the human visual check, but now you also have a number reviewers, parents, and funders can see.
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Join Free →Open the Brinley plot site, paste last week’s baseline and treatment data, and add the CONEFF value to your session note.
02At a glance
03Original abstract
The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications.
Behavior modification, 2022 · doi:10.1177/0145445520982969