Applying the Generalized Logistic Model in Single Case Designs: Modeling Treatment-Induced Shifts

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Many analytical approaches to single-case data assume either linear effects (regression-based methods) or instant effects (mean-based methods). Neither assumption is realistic; therefore, these approaches’ assumptions are often violated. In this article, we propose modeling curvilinear effects to appropriately parametrize the characteristics of singe-case data. Specifically, we introduce the generalized logistic function as adequate function for this situation. The merits of the proposed procedure are demonstrated using data previously used in single case research that represent typical single case data. We provide the function with auxiliary graphical options to demonstrate the model parameters. The function is freely available in the R package “userfriendlyscience.” The proposed procedure is a new way to analyze single case data, which may provide applied single case researchers with a new tool to better understand their data and avoid applying methods with violated assumptions.
Original languageEnglish
Pages (from-to)27-48
Number of pages22
JournalBehavior Modification
Issue number1
Early online date5 Aug 2018
Publication statusPublished - 1 Jan 2020



  • single-case designs
  • effect size
  • logistic function
  • software
  • data analysis

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