Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Open Science in the Developing World: A Collection of Practical Guides for Researchers in Developing Countries

Over the past decade, the open-science movement has transformed the research landscape, although its impact has largely been confined to developed countries. Recently, researchers from developing countries have called for a redesign of open science to better align with their unique contexts. However, raising awareness alone is insufficient—practical actions are required to drive meaningful and inclusive change. In this work, we analyze the opportunities offered by the open-science movement and explore the macro- and micro-level barriers researchers in developing countries face when engaging with these practices. Drawing on these insights and aiming to inspire researchers in developing regions or other resource-constrained contexts to embrace open-science practices, we offer a four-level guide for gradual engagement: (a) foundation, using open resources to build a solid foundation for rigorous research; (b) growth, adopting low-cost, easily implementable practices; (c) community, contributing to open-science communities through actionable steps; and (d) leadership, taking on leadership roles or forming local communities to foster cultural change. We further discuss potential pitfalls of the current open-science practices and call for readaptation of these practices in developing countries’ settings. We conclude by outlining concrete recommendations for future action.</p

Open Science in the Developing World: A Collection of Practical Guides for Researchers in Developing Countries

Over the past decade, the open-science movement has transformed the research landscape, although its impact has largely been confined to developed countries. Recently, researchers from developing countries have called for a redesign of open science to better align with their unique contexts. However, raising awareness alone is insufficient—practical actions are required to drive meaningful and inclusive change. In this work, we analyze the opportunities offered by the open-science movement and explore the macro- and micro-level barriers researchers in developing countries face when engaging with these practices. Drawing on these insights and aiming to inspire researchers in developing regions or other resource-constrained contexts to embrace open-science practices, we offer a four-level guide for gradual engagement: (a) foundation, using open resources to build a solid foundation for rigorous research; (b) growth, adopting low-cost, easily implementable practices; (c) community, contributing to open-science communities through actionable steps; and (d) leadership, taking on leadership roles or forming local communities to foster cultural change. We further discuss potential pitfalls of the current open-science practices and call for readaptation of these practices in developing countries’ settings. We conclude by outlining concrete recommendations for future action

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

Crowdsourcing hypothesis tests: making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams rendered statistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim