Applying the Generalized Logistic Model in Single Case Designs

Modeling Treatment-Induced Shifts

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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
Number of pages22
JournalBehavior Modification
DOIs
Publication statusE-pub ahead of print - 5 Aug 2018

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Logistic Models
Linear Models
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Keywords

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

Cite this

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title = "Applying the Generalized Logistic Model in Single Case Designs: Modeling Treatment-Induced Shifts",
abstract = "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.",
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author = "P. Verboon and G.J. Peters",
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Applying the Generalized Logistic Model in Single Case Designs : Modeling Treatment-Induced Shifts. / Verboon, P.; Peters, G.J.

In: Behavior Modification, 05.08.2018.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Applying the Generalized Logistic Model in Single Case Designs

T2 - Modeling Treatment-Induced Shifts

AU - Verboon, P.

AU - Peters, G.J.

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N2 - 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.

AB - 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.

KW - single-case designs

KW - effect size

KW - logistic function

KW - software

KW - data analysis

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