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By Garret Christensen (BITSS)
BITSS is proud to announce the publication of the Transparency and Openness Promotion Guidelines in Science. The Guidelines are a set of standards in eight areas of research publication:
- Citation Standards
- Data Transparency
- Analytic Methods (Code) Transparency
- Research Materials Transparency
- Design and Analysis Transparency
- Preregistration of Sudies
- Preregistration of Analysis Plans
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.
By Garret Christensen (BITSS)
What are the tools you use to make your research more transparent and reproducible? A lot of my time at BITSS has been spent working on a manual of best practices, and that has required me to familiarize myself with computing tools and resources that make transparent work easier. I’ll be sharing a draft of the manual at the BITSS Annual Meeting, but for now here are a few of the resources I’ve found most useful. If you’d like to learn more about these tools, there are a ton of helpful resources on the respective websites, or for a hands-on learning experience you can sign up for a collaborative training (December 11, 9.00 AM – 12.00 PM) BITSS is organizing with the D-Lab.
1. Learn to code in some language. Any language.
Strasser begins her list urging students to learn a programming language. As the limitations of statistical packages including STATA, SAS and SPSS become increasingly apparent, empirical social scientists are beginning to learn languages such as MATLAB, R and Python. Strasser comments:
Growing amounts and diversity of data, more interdisciplinary collaborators, and increasing complexity of analyses mean that no longer can black-box models, software, and applications be used in research.
Start learning to code now so you are not behind the curve later!
2. Stop using Excel. Or at least stop ONLY using Excel.
In Excel modifying data is done without a trace. This makes documenting changes made to a dataset more difficult and prevents researchers using Excel from producing fully replicable research. Read “Potentially Problematic Excel Features” to learn more about the pitfalls of Excel.