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<p>Alternating treatment design (ATD) and changing criterion design (CCD) are representative single-case experimental designs (SCED). Bayesian methods have recently received increasing attention for SCED data analysis. de Vries and Morey (Vries and Morey, Psychol Methods 18:165–185, 2013) proposed a method to examine intervention effects using the Bayes factor. However, their method can only be applied to the AB design—the simplest method with limited applicability. Yamada and Okada (Yamada and Okada, Behaviormetrika 51:277–286, 2024) extended their method to ensure its application to the ABAB design—a frequently used design among SCED. We propose a method for calculating the Bayes factors (BFs) for ATD and CCD data. We then present an example of applying the proposed method to actual data. The ability to calculate BFs for ATD and CCD provides a new analytical tool for these designs. It shows new findings that can be applied to the meta-analysis of single-case experiments using the common metric of BFs.</p>

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Bayes factor for major single-case experimental designs: case for alternating treatment design and changing criterion design

  • 期刊论文

  • 发表日期：

    2025年5月3日

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  • Tsuyoshi Yamada,
  • Kensuke Okada

摘要

Alternating treatment design (ATD) and changing criterion design (CCD) are representative single-case experimental designs (SCED). Bayesian methods have recently received increasing attention for SCED data analysis. de Vries and Morey (Vries and Morey, Psychol Methods 18:165–185, 2013) proposed a method to examine intervention effects using the Bayes factor. However, their method can only be applied to the AB design—the simplest method with limited applicability. Yamada and Okada (Yamada and Okada, Behaviormetrika 51:277–286, 2024) extended their method to ensure its application to the ABAB design—a frequently used design among SCED. We propose a method for calculating the Bayes factors (BFs) for ATD and CCD data. We then present an example of applying the proposed method to actual data. The ability to calculate BFs for ATD and CCD provides a new analytical tool for these designs. It shows new findings that can be applied to the meta-analysis of single-case experiments using the common metric of BFs.