Abstract

Several authors have recommended adopting the receiver operator characteristic (ROC) area under the curve (AUC) or mean ridit as an effect size, arguing that it measures an important and interpretable type of effect that conventional effect-size measures do not. It is base-rate insensitive, robust to outliers, and invariant under order-preserving transformations. However, applications have been limited to group comparisons, and usually just two groups, in line with the popular interpretation of the AUC as measuring the probability that a randomly chosen case from one group will score higher on the dependent variable than a randomly chosen case from another group. This tutorial article shows that the AUC can be used as an effect size for both categorical and continuous predictors in a wide variety of general linear models, whose dependent variables may be ordinal, interval, or ratio level. Thus, the AUC is a general effect-size measure. Demonstrations in this article include linear regression, ordinal logistic regression, gamma regression, and beta regression. The online supplemental materials to this tutorial provide a survey of currently available software resources in R for the AUC and ridits, along with the code and access to the data used in the examples. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.