Wednesday, October 12, 2022

Bayesian analysis reporting guidelines now listed on The EQUATOR Network

The Bayesian analysis reporting guidelines (BARG) are now listed on The EQUATOR Network.

 Equator network

"The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines. It is the first coordinated attempt to tackle the problems of inadequate reporting systematically and on a global scale; it advances the work done by individual groups over the last 15 years." (Quoted from this page.)

"The [EQUATOR] Library for health research reporting provides an up-to-date collection of guidelines and policy documents related to health research reporting. These are aimed mainly at authors of research articles, journal editors, peer reviewers and reporting guideline developers." (Quoted from this page.)

The Bayesian analysis reporting guidelines (BARG) are described in an open-access article at Nature Human Behaviour. The BARG provide a thorough set of points to consider when reporting Bayesian data analyses. The points are thoroughly explained, and a summary table is provided.

The BARG are listed at The EQUATOR Network here.

Tuesday, February 15, 2022

A novel trend model made possible by Bayesian software

One reason I love Bayesian software (such as JAGS, etc.) is for its ability to express novel models that aren't prepackaged in canned stats packages. In some recent research, I had the opportunity to create a novel trend model and estimate its parameters in JAGS. 

We had data as graphed in Fig. 1, and I had to think of a model to describe the trends

Fig.1. Data to be modeled.

The variable on the vertical axis is a rating of emotion (such as sadness, happiness, etc.) in short stories. The horizontal axis is the retelling of the story, such that 0 is the original story, 1 is a retelling of the original, 2 is a retelling of the 1st retelling, and 3 is a retelling of the 2nd retelling. Retellings tend to lose a lot of information but nevertheless retain some info too. Do they retain emotions? Each curve in the graph corresponds to a different original story. I thought the trends in the data looked like the different original stories were converging toward (or diverging from) a common spine, as in Fig. 2:

Fig. 2. Model predictions.

So, I invented a simple trend model to express that idea, and I programmed it in JAGS. Because the ratings were on an ordinal scale, I used an ordered-probit response distribution on a latent scale that followed an underlying linear spine with exponential convergence, as suggested in Fig. 3:

Fig. 3. The model, with latent scale in left panel and rating scale in right panel.

You can read all the details of the model in the HTML document at https://osf.io/nbuxg/ (download the HTML document and then view it in a browser). The published article describing the research is titled Serial reproduction of narratives preserves emotional appraisals by Fritz Breithaupt, Binyan Li, and John K. Kruschke. It can be obtained from https://doi.org/10.1080/02699931.2022.2031906 and the final pre-publication manuscript is at https://osf.io/hwvza/.


Friday, February 11, 2022

Bayesian analysis reporting guidelines hit 10K downloads

The Bayesian analysis reporting guidelines (BARG) have now, in just under six months, had 10,000 downloads from the publisher's site:

I hope that people actually find the BARG to be useful!  You can read more about the BARG at the short blog post at Nature: