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Model Flexibility Analysis does not measure the persuasiveness of a fit
journal contribution
posted on 2023-05-19, 00:30 authored by Evans, NJ, Howard, ZL, Heathcote, A, Brown, SDRecently, Veksler, Myers, and Gluck (2015) proposed model flexibility analysis as a method that "aids model evaluation by providing a metric for gauging the persuasiveness of a given fit" (p. 755) Model flexibility analysis measures the complexity of a model in terms of the proportion of all possible data patterns it can predict. We show that this measure does not provide a reliable way to gauge complexity, which prevents model flexibility analysis from fulfilling either of the 2 aims outlined by Veksler et al. (2015): absolute and relative model evaluation. We also show that model flexibility analysis can even fail to correctly quantify complexity in the most clear cut case, with nested models. We advocate for the use of well-established techniques with these characteristics, such as Bayes factors, normalized maximum likelihood, or cross-validation, and against the use of model flexibility analysis. In the discussion, we explore 2 issues relevant to the area of model evaluation: the completeness of current model selection methods and the philosophical debate of absolute versus relative model evaluation.
History
Publication title
Psychological ReviewVolume
124Pagination
339-345ISSN
0033-295XDepartment/School
School of Psychological SciencesPublisher
Amer Psychological AssocPlace of publication
United StatesRights statement
© 2017 American Psychological AssociationRepository Status
- Restricted