Scientific research is an attempt to identify a working truth about the world that is as independent of ideology as possible. As we appear to be entering a time of heightened skepticism about the value of scientific information, we feel it is important to emphasize and foster research practices that enhance the integrity of scientific data and thus scientific information. We have therefore created a list of better research practices that we believe, if followed, would enhance the reproducibility and reliability of psychological science. The proposed methodological practices are applicable for exploratory or confirmatory research, and for observational or experimental methods.
- If testing a specific hypothesis, pre-register your research, so others can know that the forthcoming tests are informative. Report the planned analyses as confirmatory, and report any other analyses or any deviations from the planned analyses as exploratory.
- If conducting exploratory research, present it as exploratory. Then, document the research by posting materials, such as measures, procedures, and analytical code so future researchers can benefit from them. Also, make research expectations and plans in advance of analyses—little, if any, research is truly exploratory. State the goals and parameters of your study as clearly as possible before beginning data analysis.
- Consider data sharing options prior to data collection (e.g., complete a data management plan; include necessary language in the consent form), and make data and associated meta-data needed to reproduce results available to others, preferably in a trusted and stable repository. Note that this does not imply full public disclosure of all data. If there are reasons why data can’t be made available (e.g., containing clinically sensitive information), clarify that up-front and delineate the path available for others to acquire your data in order to reproduce your analyses.
- If some form of hypothesis testing is being used or an attempt is being made to accurately estimate an effect size, use power analysis to plan research before conducting it so that it is maximally informative.
- To the best of your ability maximize the power of your research to reach the power necessary to test the smallest effect size you are interested in testing (e.g., increase sample size, use within-subjects designs, use better, more precise measures, use stronger manipulations, etc.). Also, in order to increase the power of your research, consider collaborating with other labs, for example via StudySwap (https://osf.io/view/studyswap/). Be open to sharing existing data with other labs in order to pool data for a more robust study.
- If you find a result that you believe to be informative, make sure the result is robust. For smaller lab studies this means directly replicating your own work or, even better, having another lab replicate your finding, again via something like StudySwap. For larger studies, this may mean finding highly similar data, archival or otherwise, to replicate results. When other large studies are known in advance, seek to pool data before analysis. If the samples are large enough, consider employing cross-validation techniques, such as splitting samples into random halves, to confirm results. For unique studies, checking robustness may mean testing multiple alternative models and/or statistical controls to see if the effect is robust to multiple alternative hypotheses, confounds, and analytical approaches.
- Avoid performing conceptual replications of your own research in the absence of evidence that the original result is robust and/or without pre-registering the study. A pre-registered direct replication is the best evidence that an original result is robust.
- Once some level of evidence has been achieved that the effect is robust (e.g., a successful direct replication), by all means do conceptual replications, as conceptual replications can provide important evidence for the generalizability of a finding and the robustness of a theory.
- To the extent possible, report null findings. In science, null news from reasonably powered studies is informative news.
- To the extent possible, report small effects. Given the uncertainty about the robustness of results across psychological science, we do not have a clear understanding of when effect sizes are “too small” to matter. As many effects previously thought to be large are small, be open to finding evidence of effects of many sizes, particularly under conditions of large N and sound measurement.
- When others are interested in replicating your work be cooperative if they ask for input. Of course, one of the benefits of pre-registration is that there may be less of a need to interact with those interested in replicating your work.
- If researchers fail to replicate your work continue to be cooperative. Even in an ideal world where all studies are appropriately powered, there will still be failures to replicate because of sampling variance alone. If the failed replication was done well and had high power to detect the effect, at least consider the possibility that your original result could be a false positive. Given this inevitability, and the possibility of true moderators of an effect, aspire to work with researchers who fail to find your effect so as to provide more data and information to the larger scientific community that is heavily invested in knowing what is true or not about your findings.
We should note that these proposed practices are complementary to other statements of commitment, such as the commitment to research transparency (http://www.researchtransparency.org/). We would also note that the proposed practices are aspirational. Ideally, our field will adopt many, of not all of these practices. But, we also understand that change is difficult and takes time. In the interim, it would be ideal to reward any movement toward better research practices.
Brent W. Roberts
Rolf A. Zwaan
 van ’t Veer, A. E., & Giner-Sorolla, R. (2016). Pre-registration in social psychology—A discussion and suggested template. Journal of Experimental Social Psychology, 67, 2–12. doi:10.1016/j.jesp.2016.03.004
“We provide (and follow) the “21-word solution” in every empirical publication: “We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study.”1 If necessary, this statement is adjusted to ensure that it is accurate.”
I think that one should be among the 12 most important things to aspire to.