New paper in Computational Brain and Behavior: Sample size determination in Bayesian Linear Mixed Models

This article is originally published at https://vasishth-statistics.blogspot.com/

We've just had a paper accepted in Computational Brain and Behavior, an open access journal of the Society for Mathematical Psychology. 

Even though I am not a psychologist, I feel an increasing affinity to this field compared to psycholinguistics proper. I will be submitting more of my papers to this journal and other open access journals (Glossa Psycholx, Open Mind in particular) in the future. 

Some things I liked about this journal:

- A fast and well-informed, intelligent, useful set of reviews. The reviewers actually understand what they are talking about! It's refreshing to find people out there who speak my language (and I don't mean English or Hindi). Also, the reviewers signed their reviews. This doesn't usually happen.

- Free availability of the paper after publication; I didn't have to do anything to make this happen. By contrast, I don't even have copies of my own articles published in APA journals. The same goes for Elsevier journals like the Journal of Memory and Language. Either I shell out $$$ to make the paper open access, or I learn to live with the arXiv version of my paper. 

-  The proofing was *excellent*. By contrast, the Journal of Memory and Language adds approximately 500 mistakes into my papers every time they publish it (then we have to correct them, if we catch them at all). E.g., in this paper we had to issue a correction about a German example; this error was added by the proofer! Another surprising example of JML actually destroying our paper's formatting is this one; here, the arXiv version has better formatting than the published paper, which cost several thousand Euros! 

- LaTeX is encouraged. By contrast, APA journals demand that papers be submitted in W**d. 

Here is the paper itself: here, we present an approach, adapted from the work of two statisticians (Wang and Gelfand), for determining approximate sample size needed for drawing meaningful inferences using Bayes factors in hierarchical models (aka linear mixed models). The example comes from a psycholinguistic study but the method is general. Code and data are of course available online.

The pdf: https://link.springer.com/article/10.1007/s42113-021-00125-y

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This article is originally published at https://vasishth-statistics.blogspot.com/
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