Vanhove, Jan 2021 Once they have learnt about the effects of collinearity on the output of multiple regression models, researchers may unduly worry about these and resort to (sometimes dubious) modelling techniques to mitigate them. I argue that, to the extent that problems occur in the presence of collinearity, they are not caused by it but rather by common mental shortcuts that researchers take when interpreting statistical models and that can also lead them astray in the absence of collinearity. Moreover, I illustrate that common strategies for dealing with collinearity only sidestep the perceived problem by biasing parameter estimates, reformulating the model in such a way that it maps onto different research questions, or both. I conclude that collinearity in itself is not a problem and that researchers should be aware of what their approaches for addressing it actually achieve. application/pdf https://folia.unifr.ch/global/documents/309361 https://folia.unifr.ch/documents/309361/files/2548-manuscriptinpdf-7605-1-10-20210412.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.15626/MP.2021.2548 info:eu-repo/semantics/openAccess License undefined Meta-Psychology. - 2021, vol. 5, p. 1-11 statistics regression assumptions multiple regression interpreting regression models info:eu-repo/classification/udc/81 Collinearity isn't a disease that needs curing http://purl.org/coar/resource_type/c_6501