Randomization procedures in single‐case intervention research contexts: (Some of) “the rest of the story”
Download the free Excel randomizer so your next single-case study meets current standards without extra math.
01Research in Context
What this study did
Levin et al. (2019) wrote a how-to paper about randomizing single-case designs. They give free Excel files that shuffle phases for you.
The authors show two kinds of randomization. One decides when phases start. The other decides how to analyze the data after you finish.
What they found
The paper does not report new data. Instead it gives ready-made tools so your next AB or multiple-baseline study meets today’s standards.
How this fits with other research
Ferron et al. (2023) took the same idea and built random rules for changing-criterion designs. Now you can randomize step sizes and still keep visual clarity.
Weaver et al. (2019) checked real alternating-treatment studies. They found randomization-test p-values match visual calls when you set alpha at .05. This backs up the tool Levin gives.
Jacobs (2019) explains why randomization tests beat t-tests for single-case data. Levin gives the Excel sheet that does the test Jacobs recommends.
Why it matters
Journal reviewers now ask for randomization. With these free sheets you can satisfy the request in five minutes. Plug in your planned sessions, click shuffle, and paste the output into your method section. No stats degree needed.
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Join Free →Open the Levin Excel file, enter your next client’s baseline and treatment lengths, and let it randomize the phase switch points.
02At a glance
03Original abstract
Following up on articles recently published in this journal, the present contribution tells (some of) "the rest of the story" about the value of randomization in single-case intervention research investigations. Invoking principles of internal, statistical-conclusion, and external validity, we begin by emphasizing the critical distinction between design randomization and analysis randomization, along with the necessary correspondence between the two. Four different types of single-case design-and-analysis randomization are then discussed. The persistent negative influence of serially dependent single-case outcome observations is highlighted, accompanied by examples of inappropriate applications of parametric and nonparametric tests that have appeared in the literature. We conclude by presenting valid applications of single-case randomization procedures in various single-case intervention contexts, with specific reference to a freely available Excel-based software package that can be accessed to incorporate the present randomization schemes into a wide variety of single-case intervention designs and analyses.
Journal of the Experimental Analysis of Behavior, 2019 · doi:10.1002/jeab.558