Sequential Dynamic Bayesian Modeling for Single-Case Experimental Designs: A Novel Approach to Many Labs Research
You can now pool single-case data across labs in real time using dynamic Bayesian updating—no need to wait years for enough replications.
01Research in Context
What this study did
Dowdy et al. (2025) built a new computer tool. It lets many labs add single-case data as soon as each study ends.
The tool uses Bayesian updating. Each new case shifts the pooled effect size a little.
The authors re-ran old data and ran fake-data tests. After 15 to 25 cases the pooled line stayed close to the true value.
What they found
You no longer have to wait years for a big meta-analysis. The live pool gives a stable answer fast.
The credible band keeps shrinking as labs upload, so uncertainty drops in real time.
How this fits with other research
Manolov et al. (2022) gave us a free plot to eyeball if one SCED replicates. Dowdy keeps that visual spirit but swaps the eyeball for a Bayesian engine.
Grekov et al. (2026) show how to check if the Bayesian model fits once you have pooled. Use both: Dowdy to pool, Grekov to verify.
Stephens et al. (2018) offered two between-case effect sizes. Dowdy’s software can swallow either metric, so old spreadsheets plug right in.
Why it matters
If you run single-case work, urge your team to join a live pool. Each finished graph you upload sharpens the overall effect for everyone. Monday move: export your next AB graph, add it to the shared file, and watch the pooled line update. You get credit and science moves faster.
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02At a glance
03Original abstract
Abstract Single-case experimental design (SCED) is a quantitative research method widely used across diverse fields including behavioral education, special education, medicine, and applied behavior analysis. SCED methodology establishes external validity through a well-established sequential process: demonstrating strong internal validity within individual studies, followed by direct and systematic replication across varied populations, settings, and conditions. While this replication-based approach has proven highly successful as evidenced by robust intervention literature bases such as functional communication training and behavioral skills training the traditional process of accumulating replications across independent research teams can require years or decades. Inspired by the ManyLabs initiative’s success in coordinating replication efforts, we developed a sequential experimental design using dynamic Bayesian models to accelerate coordinated SCED research across laboratories. Applied to simulated and published multiple-baseline design data, our approach demonstrates how posterior distributions from completed cases can serve as priors for subsequent participants, creating cumulative knowledge building across research sites. Findings show that dynamic models effectively estimate intervention effects even when timing and effect sizes vary randomly. Parameter estimates converged to true effects after 15-25 coordinated cases, with credible intervals narrowing systematically as participants were added sequentially. This ManyLabs-inspired framework offers SCED researchers a pathway to accelerate the coordination of replications while maintaining the individualized focus and rigorous internal validity that make single-case designs valuable for evidence-based practice.
Journal of Behavioral Education, 2025 · doi:10.1007/s10864-025-09610-x