Randomization Procedures for Changing Criterion Designs.
You can now roll dice to pick criterion shifts in a changing-criterion study and still keep clean visual analysis.
01Research in Context
What this study did
Ferron et al. (2023) wrote a how-to paper. They show ways to add coin-flip style random steps to a changing-criterion design.
You can lock in each new goal level before the study starts. You can also add surprise jumps later while the graph stays easy to read.
What they found
The paper gives rules and worked examples. It does not test real clients or report outcomes.
How this fits with other research
Lewis et al. (1976) invented the changing-criterion design. Ferron et al. (2023) simply bolt randomization onto that old frame.
Levin et al. (2019) already showed how to randomize other single-case graphs. The new paper tailors those same tricks for the step-wise kind.
Weaver et al. (2019) tested randomization with fast-flip conditions. Their p-values lined up with visual reads. John’s rules should give the same comfort for slower, stair-step graphs.
Why it matters
If you run changing-criterion studies, you can now flip a coin to set each new goal. This blocks bias and still lets you eyeball the graph. Reviewers like numbers? Add a randomization test and you get a p-value to back up your visual call.
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02At a glance
03Original abstract
To strengthen the scientific credibility arguments for single-case intervention studies, randomization design-and-analysis methods have been developed for the multiple-baseline, ABAB, and alternating treatment designs, including options for preplanned designs, wherein the series and phase lengths are established prior to gathering data, as well as options for response-guided designs, wherein ongoing visual analyses guide decisions about when to intervene. Our purpose here is to develop randomization methods for another class of single-case design, the changing criterion design. We first illustrate randomization design-and-analysis methods for preplanned changing criterion designs and then develop and illustrate methods for response-guided changing criterion designs. We discuss the limitations associated with the randomization methods and the validity of the corresponding intervention-effect inferences.
Behavior modification, 2023 · doi:10.1177/0145445519847627