A Meta-Visual-Analysis of Single-Case Experimental Design Research.
Meta-Visual-Analysis lets you pool single-case graphs like a visual meta-analysis without losing the pictures BCBAs trust.
01Research in Context
What this study did
Fradet et al. (2025) built a new way to pool single-case graphs. They call it Meta-Visual-Analysis.
The method turns level, trend, and variability into effect sizes. You still see the familiar ABAB plots.
What they found
The paper shows how to line up many small graphs so you can spot what works across studies.
No new data were collected. The value is the recipe.
How this fits with other research
Dowdy et al. (2022) warned that most BCBAs skip structured visual tools. L et al. answer by baking structure into a meta-graph.
Manolov et al. (2023) gave a free web tool that scores one graph. L et al. extend the same metrics to many graphs at once.
Friedel et al. (2022) offered Monte-Carlo p-values for single cases. L et al. keep the visual tradition instead of switching to numbers.
Why it matters
You can now compare your client’s graph to a stack of similar cases in minutes. If the meta-line rises, the intervention is worth keeping. If it wiggles flat, try something else. Use the open templates in the paper to build your first meta-graph next coffee break.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Download the template, drop in three ABAB graphs from your last client, and see if the combined trend arrow points up.
02At a glance
03Original abstract
Visual analysis is the primary method to detect functional relations in single-case experimental design (SCED) research. Meta-Visual-Analysis (MVA) is a novel approach used to synthesize SCED data where the estimated effect size measures are principally anchored to primary aspects of visual analysis: change in the magnitude of level, trend, variability, and trend-adjusted level of projected trends. For each of these aspects, percentage point differences between baseline and intervention conditions are estimated and quantified for every participant across studies. MVA effect sizes are standardized, and their aggregates are graphically displayed in a manner similar to individual SCED graphs. MVA graphs are compared and visually analyzed with the aim of better understanding the effectiveness and generality of interventions across SCED studies. In this discussion paper we provide general steps to conduct an MVA and describe MVA's utility in reviewing, organizing, and directing future SCED research syntheses.
Behavior modification, 2025 · doi:10.1177/01454455251320686