A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research

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This article formulates a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data that would obviate the need for small sample corrections. The authors illustrate this within-subject effect size using real data for an ABAB design and provide codes for practitioners who may want to compute BRR.

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Batley, Pathiba Natesan; Mehta, Smita S. & Hitchcock, John H. June 19, 2020.

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This article formulates a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data that would obviate the need for small sample corrections. The authors illustrate this within-subject effect size using real data for an ABAB design and provide codes for practitioners who may want to compute BRR.

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40 p.

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Abstract: Single case experimental design (SCED) is an indispensable methodology when evaluating intervention efficacy. Despite long-standing success with using visual analyses to evaluate SCED data, this method has limited utility for conducting meta-analyses. This is critical because meta-analyses should drive practice and policy in behavioral disorders more than evidence derived from individual SCEDs. Even when analyzing data from individual studies, there is merit to using multiple analytic methods since statistical analyses in SCED can be challenging given small sample sizes and autocorrelated data. These complexities are exacerbated when using count data, which are common in SCEDs. Bayesian methods can be used to develop new statistical procedures that may address these challenges. The purpose of the present study was to formulate a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data that would obviate the need for small sample corrections. This effect size is the first step toward building a between-subject rate ratio that can be used for meta-analyses. We illustrate this within-subject effect size using real data for an ABAB design and provide codes for practitioners who may want to compute BRR.

Natesan Batley P., Shukla Mehta S., Hitchcock J.H., A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research, Behavioral Disorders 46(4) pp. 226-237. © 2020 Hammill Institute on Disabilities. DOI: 10.1177/0198742920930704. This is the accepted manuscript version of the published article.

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  • Behavioral Disorders, 46(4), Sage Publishing, June 19, 2020, pp. 1-40

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  • Publication Title: Behavioral Disorders
  • Volume: 46
  • Issue: 4
  • Page Start: 226
  • Page End: 237
  • Peer Reviewed: Yes

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  • June 19, 2020

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  • Oct. 21, 2021, 11:08 a.m.

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Batley, Pathiba Natesan; Mehta, Smita S. & Hitchcock, John H. A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research, article, June 19, 2020; (https://digital.library.unt.edu/ark:/67531/metadc1852150/: accessed May 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Education.

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