Prior sensitivity in theory testing: An apologia for the Bayes factor

A commonly voiced concern with the Bayes factor is that unlike many other Bayesian and non-Bayesian quantitative measures of model evaluation it is highly sensitive to the parameter prior This paper argues that when dealing with psychological models that are quantitatively instantiated theories being sensitive to the prior is an attractive feature of a model evaluation measure This assertion follows from the observation that in psychological models parameters are not completely unknown but correspond to psychological variables about which theory often exists This theory can be formally captured in the prior range and prior distribution of the parameters indicating which parameter values are allowed likely unlikely and forbidden Because the prior is a vehicle for expressing psychological theory it should like the model equation be considered as an integral part of the model It is argued that the combined practice of building models using informative priors and evaluating models using prior sensitive measures advances knowledge (C) 2010 Elsevier Inc All rights reserved

Publication year:2010

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:3

CSS-citation score:3

Authors from:Higher Education

Accessibility:Open