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Guest post by Raphael Calel, Ciriacy-Wantrup Postdoctoral Fellow at the Department of Agricultural and Resource Economics at the University of California, Berkeley.
One of the most important tools for enhancing the credibility of research is the pre-analysis plan, or the PAP. Simply put, we feel more confident in someone’s inferences if we can verify that they weren’t data mining, engaging in motivated reasoning, or otherwise manipulating their results, knowingly or unknowingly. By publishing a PAP before collecting data, and then closely following that plan, researchers can credibly demonstrate to us skeptics that their analyses were not manipulated in light of the data they collected.
Still, PAPs are credible only when the researcher can anticipate and wait for the collection of new data. The vast majority of social science research, however, does not satisfy these conditions. For instance, while it is perfectly reasonable to test new hypotheses about the causes of the recent financial crisis, it is unreasonable to expect researchers to have pre-specified their analyses before the crisis hit. To give another example, no one analysing a time series of more than a couple of years can reasonably be expected to publish a PAP and then wait for years or decades before implementing the study. Most observational studies face this problem in one form or another.
Guest post by Olivia D’Aoust, Ph.D. in Economics from Université libre de Bruxelles, and former Fulbright Visiting Ph.D. student at the University of California, Berkeley.
As a Fulbright PhD student in development economics from Brussels, my experience this past year on the Berkeley campus has been eye opening. In particular, I discovered a new movement toward improving the standards of openness and integrity in economics, political science, psychology, and related disciplines lead by the Berkeley Initiative for Transparency in the Social Sciences (BITSS).
When I first discovered BITSS, it struck me how little I knew about research on research in the social sciences, the pervasiveness of fraud in science in general (from data cleaning and specification searching to faking data altogether), and the basic lack of consensus on what is the right and wrong way to do research. These issues are essential, yet too often they are left by the wayside. Transparency, reproducibility, replicability, and integrity are the building blocks of scientific research.