{"title":"ABkPowerCalculator: An App to Compute Power for Balanced (AB)<sup>k</sup> Single Case Experimental Designs.","authors":"Prathiba Batley, Madhav Thamaran, Larry V Hedges","doi":"10.1080/00273171.2023.2261229","DOIUrl":null,"url":null,"abstract":"<p><p>Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)<sup>k</sup> designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)<sup>k</sup> power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)<sup>k</sup> designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.</p>","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"406-410"},"PeriodicalIF":5.3000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multivariate Behavioral Research","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/00273171.2023.2261229","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.
单例实验设计是行为学和医学研究中的一个重要研究设计。尽管What Works Clearinghouse为单一案例实验设计规定了设计标准,但这些标准不包括统计推导的功率计算。最近,我们导出了(AB)k设计的计算能力方程。然而,应用研究人员可能无法访问这些计算和R中的软件代码,因为他们最有可能想为自己的研究计算能力。因此,我们开发了一款(AB)k功率计算器Shiny应用程序(https://abkpowercalculator.shinyapps.io/ABkpowercalculator/)研究人员可以在没有软件培训的情况下使用。这些功率计算假设研究人员有兴趣用自相关拟合多级模型或进行类似的分析。本软件贡献的目的是简要解释平衡(AB)k设计的功率是如何获得的,并详细说明如何使用Shiny应用程序。该应用程序不仅在电脑上运行良好,而且在不安装R程序的情况下在手机上也运行良好。我们相信,对于从业者和应用研究人员来说,这是一个有价值的工具,他们希望用足够的力量来规划他们的单一案例研究,以检测适当的影响大小。
期刊介绍:
Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.