We are in the process of searching and coding the entire history of lab research on human cooperation. Our goal is to establish an open access database where researchers can go and efficiently conduct their own meta-analyses on cooperation. We will also establish an institution where researchers can efficiently share their replications and null findings with the field (which are often never published). We envision that this database and institution will provide several outstanding benefits.
Detection of Type I Errors. The databank will allow scientists to better monitor the work of specific labs in relation to the work of other labs – and so a mechanism for a decentralized approach to the detection of Type I errors (i.e., an incorrect rejection of the null hypothesis).
Reducing Type II Errors: Estimating the True Effect Size for Power Analysis. Low statistical power is a pervasive problem in the social sciences. Statistical power is the probability that a researcher will correctly reject a false null hypothesis. In the behavioral sciences, on average power is less than 50 percent for a medium effect size (Cohen, 1962; Seldmeier & Gigerenzer, 1989). Not surprisingly, researchers are often failing to reject false null hypotheses (Type II errors). Power analysis will inform researchers how many participants are necessary to reach a specified level of power (recommended at .80, Cohen, 1992). However, a challenge for researchers when conducting power analyses is that they must estimate the true effect size at the level of the population. The databank can provide an estimate of the true effect size on several effects on cooperation allowing for a priori power analyses that can help to establish methodological standards in the field to increase a priori statistical power.
Promoting Replication. Researchers lack an incentive to replicate their own and other’s research findings, yet replication is the “backbone” of any scientific discipline. This institution will provide a platform for researchers to share their replications and null findings with the field and be recognized for that work. Researchers can cite this work as a peer-reviewed published finding and the results may be used in meta-analyses.
Reducing Publication Bias. Statistically significant findings are more likely to be published and this can have dramatic negative consequences for a field and the development of knowledge. For example, a few studies may be published in high impact journals which stimulate other labs to attempt to replicate (and extend) those findings, but when these replications are null results they tend not to be published. This institution will allow for scientists to share their null results with the field and so will address the long-standing problem of publication bias.
Stimulating Quantitative Reviews. Meta-analysis has become increasingly recognized as a key tool to advance science in a mature scientific discipline. The cooperation databank will enable researchers to more easily monitor the development of the field, and increase accuracy and reduce the time and cost of subsequent meta-analyses.
Chris Aberson, Humboldt State University
Athena Aktipis, Arizona State University
Nancy Buchan, University of South Carolina
Carsten de Dreu, Leiden University
Andrew Delton, Stony Brook University
Susann Fiedler, Max Planck Institute for Research on Collective Goods
Simon Gächter, University of Nottingham
Nir Halevy, Stanford University
Paul van Lange, Vrije Universiteit Amsterdam
Wolfgang Viechtbauer, Maastricht University
Junhui Wu, Beijing Normal University
Toshio Yamagishi, Hitotsubashi University
*We are currently searching for additional people to join our lab and contribute to developing the databank. Contact Daniel Balliet (d.p.balliet(at)vu.nl) if you are interested in joining this project.