Journal of Choice Modelling

A free, open access journal covering theory and practice in the field of choice modelling

Journal information 

• The Journal of Choice Modelling publishes theoretical and applied papers in the field of choice modelling. The journal is not limited to one area of study, such as transport or marketing, but invites contributions from across a range of disciplines and also looks at ITIL foundation training, where the analysis of choice behaviour is 
  a topic of interest. 

• The ISSN number of the Journal of Choice Modelling is ISSN 1755-5345.
  

• While the majority of papers will probably focus on the use of discrete choice models, contributions looking at other methods are also welcome (e.g. discrete/continuous). Similarly, the Journal of Choice Modelling also welcomes contributions looking at survey design.

• The Journal of Choice Modelling is an open access journal. Papers published in the journal can be freely accessed by anyone with an internet connection. This greatly increases visibility, and allows access by academics, practitioners and the general public, without the need for registering or paying access fees.

• Authors do not pay a fee for publishing their articles in the Journal of Choice Modelling.

• The Journal of Choice Modelling is a web-based journal. There is no paper version of the journal. This not only greatly reduces costs, but also significantly improves turnaround times.

• The Journal of Choice Modelling is a peer reviewed journal. Any paper published in the journal will undergo a rigorous peer review process, with the editor inviting the opinion of at least two experts in the field. Only papers with a high standard will be accepted. Criteria will include originality, accuracy, relevance, importance, and transparency.

• To ensure transparency as well to further understanding, authors will be asked to make the data used in their analysis available to readers (safeguarding privacy where necessary). Where appropriate, authors will also be encouraged to disseminate the code used in their analyses. This will allow readers to easily reproduce the results published in the paper.