- Disclosure (or transparency in design), which refers to the systematic reporting of data collection and analysis. This means to report all measures, manipulations and data exclusions as well as how they arrived at final sample sizes.
- Preregistration (or transparency in intentions), which means to state clearly and disclose parameters of the study and,
- Open Data and Materials (transparency in analysis), which provide the means for independent researchers to reproduce reported results, test alternative specifications on the data, identify misreported or fraudulent results and better understand the intervention, measure and context of the studies.
- Build consensus on key issues facing students, faculty, researchers, funders, journals, and other key partners to be more transparent in the social sciences;
- Improve our understanding of the problem and build evidence on solutions for increased transparency through long-term study of researcher practices;
- Increase supply of and access to tools and resources for research transparency, which is a necessary precursor for widespread adoption of best practices across the research community;
- Deliver coursework and change research practices at scale by harnessing the BITSS network of students, academic faculty, and researchers (a “push” mechanism); and
- Provide recognition and awards for the adoption of behaviors related to research transparency (a “pull” mechanism).
Boghossian, P. A.. (1994). The Transparency of Mental Content. Philosophical Perspectives, 8, 33–50. http://doi.org/10.2307/2214162
Social Studies of Science 36/3 (June 2006) 489–493. SSS. London, Thousand Oaks CA, New Delhi: SAGE Publications.