Pre-analysis plans

Good in theory, but problems in implementation currently make them less useful than they should be. Note that the point of a plan is to show voluntary commitment to transparency as a way to distinguish oneself as credible—cf. separating equilibria. Features of plans that improve this “separating” function are preferred by those who want credible science.

I am going to focus on two issues: challenges to checking fidelity and lack of public vetting.

First it is too cumbersome at the moment to check papers against fidelity to the plan. This is partly because there are often so many hypothesis tests proposed. This is also because plans are formatted poorly such that we cannot quickly take in what is being proposed. And finally this is also because results are presented separately from what is specified in the plan. There are some exceptions to this dim assessment—eg, Beath et al. did a stellar job in the final report of their NSP study, but even in that case, the sheer volume of tests was quite dizzying. Similar for Casey et al. in their GoBifo study. In both cases though it would have been nice for formatting that permitted fidelity-checking in the main texts of the published papers.

Second is the lack of public vetting of plans. The standard now is to publicly register. But what is being registered? Mostly specifications and tests that the author thinks are persuasive. But the point isn’t to for authors to signal back to themselves. The point is to signal out to the academic community. This function would be enhanced if the academic community weighed in on the plan before it was finalized. The Comparative Political Studies pilot of results-free review was an awesome move toward improvement on this front. As are registered reports (a la Cortex journal). Let’s do more of this.

Improving on both of these fronts implies higher costs to plans, but of course that is quite the point (cf. separating equilibria).

(I will also continue here to push for plans to be just as much about specifying how to interpret results as about how to generate them. That is, plans are more meaningful when they show a theoretical model and they map the statistical estimates back to parameters in the model. But these are separate issues.)