How to check Likert scale summaries for plausibility

Suppose you are reading a paper that uses Likert scale responses. The paper reports the mean, standard deviation, and number of responses. If we are -- for some reason -- suspicious of a paper, we might ask, "Are these summary statistics possible for this number of responses, for this Likert scale?" Someone asked me this recently, so I wrote some simple code to help check. In this blog post, I outline how the code works.

Reviewers and open science: why PRO?

As of yesterday, our paper outlining the PRO Initiative for open science was accepted for publication in the journal Royal Society Open Science. It marks the end of many tweaks to the basic idea, and hopefully the beginning of a new era in peer reviewing: the empowered peer reviewer. The basic idea behind the PRO Initiative is that the peer relationship is fundamental in science, and it is this relationship that should drive cultural change. Open science is necessary, possible, and overdue. As reviewers, we can make it happen.

Habits and open data: Helping students develop a theory of scientific mind

This post is related to my open science talk with Candice Morey at Psychonomics 2015 in Chicago; also read Candice's new post on the pragmatics: "A visit from the Ghost of Research Past". In this post, we suggest three ideas that can be implemented in a lab setting to improve scientific practices, and encourage habits that make openness easier. These ideas are designed to be minimally effortful for the adviser, but to have a big impact on practice:

* Data partners: young scientists have a partner in another lab, with whom they swap data. The goal is to see if their data documentation is good enough that their partner can reproduce their main analysis with minimal interaction.
* Five year plan: When a project is part-way through, students must give a brief report that details what they have done to insure that the data and analyses will be comprehensible to members of the lab in five-year's time, after they have left.
* Submission check: At first submission of an article based on the project, advisors should discuss with their advisees the pros and cons of opening their data, and how the data will be promoted online, if it will be open.

On radical manuscript openness

One of my papers that has attracted a lot of attention lately is "The Fallacy of Placing Confidence in Confidence Intervals," in which we describe some of the fallacies held by the proponents and users of confidence intervals. This paper has been discussed on twitter, reddit, on blogs (eg, here and here), and via email with people who found the paper in various places.  A person unknown to me has used the article as the basis for edits to the Wikipedia article on confidence intervals. I have been told that several papers currently under review cite it. Perhaps this is a small sign that traditional publishers should be worried: this paper has not been "officially" published yet.

On making a Bayesian omelet

My colleagues Eric-Jan Wagenmakers and Jeff Rouder and I have a new manuscript in which we respond to Hoijtink, van Kooten, and Hulsker's in press manuscript Why Bayesian Psychologists Should Change the Way they Use the Bayes Factor. They suggest a method for "calibrating" Bayes factor using error rates. We show that this method is fatally flawed, but also along the way we describe how we think about the subjective properties of the priors we use in our Bayes factors:

"...a particular researcher's subjective prior is of limited use in the context of a public scientific discussion. Statistical analysis is often used as part of an argument. Wielding a fully personal, subjective prior and concluding 'If you were me, you would believe this' might be useful in some contexts, but in others it is less useful. In the context of a scientific argument, it is much more useful to have priors that approximate what a reasonable, but somewhat-removed researcher would have in the situation. One could call this a 'consensus prior' approach. The need for broadly applicable arguments is not a unique property of statistics; it applies to all scientific arguments. We do not argue to convince ourselves; we should therefore make use of statistical arguments that are not pegged to our own beliefs...

It should now be obvious how we make our 'Bayesian omelet'; we break the eggs and cook the omelet for others in the hopes that it is something like what they would choose for themselves. With the right choice of ingredients, we think our Bayesian omelet can satisfy most people; others are free to make their own, and we would be happy to help them if we can. "

Our completely open, reproducible manuscript --- “Calibrated” Bayes factors should not be used: a reply to Hoijtink, van Kooten, and Hulsker --- along with a supplement and R code, is available on github (with DOI!).

Embedding RData files in Rmarkdown files for more reproducible analyses

For those of us interested in reproducible analysis, Rmarkdown is a great way of communicating our code to other researchers. Rstudio, in particular, makes it very easy to create attractive HTML document containing text, code, and figures, which can then be sent to colleagues or put on the internet for anyone to see. If you aren't using Rmarkdown for your statistical analyses, I recommend you start; you'll never go back to simple script files again (and your colleagues won't want you to).

In this post, I describe how to improve your Rmarkdown by embedding data that can be downloaded by anyone viewing the document in a modern browser with javascript enabled. For a quick look, see the example Rmd file and resulting HTML file.