Applying the generalized logistic model in single case designs

Many analytical approaches to single-case data assume either linear effects (regression-based methods) or instant effects (mean-based methods). Neither assumption is realistic, and therefore these approaches’ assumptions are often violated. In the present paper, we propose modelling 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 analyse 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