 The overreliance on statistical goodness-of-fit and under-reliance on model validation in discrete choice models: A review of validation practices in the transportation academic literatureGiancarlos Parady, David Ory and Joan Walker Journal of choice modelling, 2021, vol. 38, issue C Abstract: An examination of model validation practices in the peer-reviewed transportation literature published between 2014 and 2018 reveals that 92% of studies reported goodness-of-fit statistics, and 64.6% reported some sort of policy-relevant inference analysis. However, only 18.1% reported validation performance measures, out of which 78% (14.2% of all studies) consisted of internal validation and 22% (4% of all studies) consisted of external validation. The proposition put forward in this paper is that the reliance on goodness-of-fit measures rather than validation performance is unwise, especially given the dependence of the transportation research field on observational (non-experimental) studies. Model validation should be a non-negotiable part of presenting a model for peer-review in academic journals. For that purpose, we propose a simple heuristic to select validation methods given the resources available to the researcher. Keywords: Validation; Generalizability; Transferability; Policy inference; Transportation; Discrete choice models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:38:y:2021:i:c:s1755534520300543 DOI: 10.1016/j.jocm.2020.100257 Access Statistics for this article Journal of choice modelling is currently edited by S. Hess and J.M. Rose More articles in Journal of choice modelling from Elsevier |
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